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Next-generation single-molecule protein sequencing technologies have the potential to significantly accelerate biomedical research. These technologies offer sensitivity and scalability for proteomic analysis. One auspicious method is fluorosequencing, which involves: cutting naturalized proteins into peptides, attaching fluorophores to specific amino acids, and observing variations in light intensity as one amino acid is removed at a time. The original peptide is classified from the sequence of light-intensity reads, and proteins can subsequently be recognized with this information. The amino acid step removal is achieved by attaching the peptides to a wall on the C-terminal and using a process called Edman Degradation to remove an amino acid from the N-Terminal. Even though a framework (Whatprot) has been proposed for the peptide classification task, processing times remain restrictive due to the massively parallel data acquisicion system. In this paper, we propose a new beam search decoder with a novel state formulation that obtains considerably lower processing times at the expense of only a slight accuracy drop compared to Whatprot. Furthermore, we explore how our novel state formulation may lead to even faster decoders in the future. Author summary: Proteomic analyses frequently rely on mass spectrometry, a method characterized by its limited dynamic range, potentially overlooking low-abundant proteins. To address this limitation, single-molecule protein sequencing methods offer a solution. Fluorosequencing is a cutting-edge single-molecule protein sequencing method, which can distinguish peptides or protein molecules massively parallelly. This method has attracted interest from investors, as evidenced by the recent funding of Erisyon, a company developing this technology. This technique contains a challenging classification task: determining the original peptide sequence from light-intensity observations obtained after several Edman cycles. A classifier based on a combination of k Nearest Neighbors (kNN) with Hidden Markov Models (HMM) had been shown to have close-to-optimal accuracy with tractable complexity. We propose in this paper a new algorithm that reduces computation time significantly at the expense of a slight reduction in accuracy compared to state-of-the-art method. [ABSTRACT FROM AUTHOR]

In proteomics, a crucial aspect is to identify peptide sequences. De novo sequencing methods have been widely employed to identify peptide sequences, and numerous tools have been proposed over the past two decades. Recently, deep learning approaches have been introduced for de novo sequencing. Previous methods focused on encoding tandem mass spectra and predicting peptide sequences from the first amino acid onwards. However, when predicting peptides using tandem mass spectra, the peptide sequence can be predicted not only from the first amino acid but also from the last amino acid due to the coexistence of b-ion (or a- or c-ion) and y-ion (or x- or z-ion) fragments in the tandem mass spectra. Therefore, it is essential to predict peptide sequences bidirectionally. Our approach, called NovoB, utilizes a Transformer model to predict peptide sequences bidirectionally, starting with both the first and last amino acids. In comparison to Casanovo, our method achieved an improvement of the average peptide-level accuracy rate of approximately 9.8% across all species. Author summary: Understanding the characteristics of data is very important in deep learning methods. When predicting sentences, the transformer model naturally predicts from the first word. For this reason, previous methods predicted peptide sequences from the first amino acid. However, in tandem mass spectra, it is possible to predict peptide sequences bidirectionally. This method shows better results than previous approaches because it can better encode tandem mass spectra. We have demonstrated that good results can be achieved simply by understanding the characteristics of such data and using the model appropriately. We hope that this paper will help various readers improve the performance capabilities of their models. Furthermore, given that bidirectional peptide sequence prediction is crucial in de novo peptide sequence analysis, we hope that this approach will be applied to both existing and future methods utilizing deep learning techniques. [ABSTRACT FROM AUTHOR]

With the increasing problem of antimicrobial drug resistance, the search for new antimicrobial agents has become a crucial task in the field of medicine. Antimicrobial peptides, as a class of naturally occurring antimicrobial agents, possess broad-spectrum antimicrobial activity and lower risk of resistance development. However, traditional screening methods for antimicrobial peptides are inefficient, necessitating the development of an efficient screening model. In this study, we aimed to develop an ensemble learning model for the identification of antimicrobial peptides, named E-CLEAP, based on the Multilayer Perceptron Classifier (MLP Classifier). By considering multiple features, including amino acid composition (AAC) and pseudo amino acid composition (PseAAC) of antimicrobial peptides, we aimed to improve the accuracy and generalization ability of the identification process. To validate the superiority of our model, we employed five-fold cross-validation and compared it with other commonly used methods for antimicrobial peptide identification. In the experimental results on an independent test set, E-CLEAP achieved accuracies of 97.33% and 84% for the AAC and PseAAC features, respectively. The results demonstrated that our model outperformed other methods in all evaluation metrics. The findings of this study highlight the potential of the E-CLEAP model in enhancing the efficiency and accuracy of antimicrobial peptide screening, which holds significant implications for drug development, disease treatment, and biotechnology advancement. Future research can further optimize the model by incorporating additional features and information, as well as validating its reliability on larger datasets and in real-world environments. The source code and all datasets are publicly available at https://github.com/Wangsicheng52/E-CLEAP. [ABSTRACT FROM AUTHOR]

The field of protein residue network (PRN) research has brought several useful methods and techniques for structural analysis of proteins and protein complexes. Many of these are ripe and ready to be used by the proteomics community outside of the PRN specialists. In this paper we present software which collects an ensemble of (network) methods tailored towards the analysis of protein-protein interactions (PPI) and/or interactions of proteins with ligands of other type, e.g. nucleic acids, oligosaccharides etc. In parallel, we propose the use of the network differential analysis as a method to identify residues mediating key interactions between proteins. We use a model system, to show that in combination with other, already published methods, also included in pyProGA, it can be used to make such predictions. Such extended repertoire of methods allows to cross-check predictions with other methods as well, as we show here. In addition, the possibility to construct PRN models from various kinds of input is so far a unique asset of our code. One can use structural data as defined in PDB files and/or from data on residue pair interaction energies, either from force-field parameters or fragment molecular orbital (FMO) calculations. pyProGA is a free open-source software available from https://gitlab.com/Vlado_S/pyproga., Competing Interests: The authors have declared that no competing interests exist.

A study evaluating apparent digestibilities of protein and amino acids and their corresponding digestion rates in four small intestinal sites in broiler chickens was completed to further investigate dietary optimisation via synchronised nutrient digestion and absorption. A total of 288 male Ross 308 broiler chickens were offered semi-purified diets with eight protein-rich feedstuffs, including; blood meal (BM), plasma protein meal (PPM), cold pressed (CCM) and expeller-pressed (ECM) canola meal, high (SBM HCP) and low (SBM LCP) crude protein soybean meals, lupins and peas. Diets were iso-caloric, iso-nitrogenous and the test ingredient was the sole source of dietary nitrogen. Each diet was offered to 6 bioassay cages with 6 birds per cage from day 21 to 28 post hatch. On day 28, all birds were euthanized and digesta samples were collected from the proximal jejunum, distal jejunum, proximal ileum and distal ileum to determine apparent protein and amino acids digestibility coefficients, digestion rates and potential digestible protein and amino acids. Dietary protein source significantly influenced energy utilisation, nitrogen retention, apparent protein (N) digestibilities, digestion rates and potential digestible protein along the small intestine. Diets containing BM and SBM LCP exhibited the highest protein digestion rate and potential digestible protein, respectively. Digestibility coefficients and disappearance rates of the majority of amino acids in four sections of the small intestine were influenced by dietary protein source (P < 0.01) and blood meal had the fastest protein digestion rate. In general, jejunal amino acid and protein digestibilities were more variable in comparison to ileal digestibilities, and the differences in protein and amino acid disappearance rates were more pronounced between types of feedstuffs than sources of similar feedstuffs., Competing Interests: The authors have read the journal's policy and the authors of this paper have the following competing interests: SYL received funding from AgriFutures Chicken-meat (https://www.agrifutures.com.au/) - PRJ-010216. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Translation elongation is regulated by a series of complicated mechanisms in both prokaryotes and eukaryotes. Although recent advance in ribosome profiling techniques has enabled one to capture the genome-wide ribosome footprints along transcripts at codon resolution, the regulatory codes of elongation dynamics are still not fully understood. Most of the existing computational approaches for modeling translation elongation from ribosome profiling data mainly focus on local contextual patterns, while ignoring the continuity of the elongation process and relations between ribosome densities of remote codons. Modeling the translation elongation process in full-length coding sequence (CDS) level has not been studied to the best of our knowledge. In this paper, we developed a deep learning based approach with a multi-input and multi-output framework, named RiboMIMO, for modeling the ribosome density distributions of full-length mRNA CDS regions. Through considering the underlying correlations in translation efficiency among neighboring and remote codons and extracting hidden features from the input full-length coding sequence, RiboMIMO can greatly outperform the state-of-the-art baseline approaches and accurately predict the ribosome density distributions along the whole mRNA CDS regions. In addition, RiboMIMO explores the contributions of individual input codons to the predictions of output ribosome densities, which thus can help reveal important biological factors influencing the translation elongation process. The analyses, based on our interpretable metric named codon impact score, not only identified several patterns consistent with the previously-published literatures, but also for the first time (to the best of our knowledge) revealed that the codons located at a long distance from the ribosomal A site may also have an association on the translation elongation rate. This finding of long-range impact on translation elongation velocity may shed new light on the regulatory mechanisms of protein synthesis. Overall, these results indicated that RiboMIMO can provide a useful tool for studying the regulation of translation elongation in the range of full-length CDS. Author summary: Translation elongation is a process in which amino acids are linked into proteins by ribosomes in cells. Translation elongation rates along the mRNAs are not constant, and are regulated by a series of mechanisms, such as codon rarity and mRNA stability. In this study, we modeled the translation elongation process at a full-length coding sequence level and developed a deep learning based approach to predict the translation elongation rates from mRNA sequences, through extracting the regulatory codes of elongation rates from the contextual sequences. The analyses, based on our interpretable metric named codon impact score, for the first time (to the best of our knowledge), revealed that in addition to the neighboring codons of the ribosomal A sites, the remote codons may also have an important impact on the translation elongation rates. This new finding may stimulate additional experiments and shed light on the regulatory mechanisms of protein synthesis. [ABSTRACT FROM AUTHOR]

Plants are constantly under the threat of various biotic and abiotic stress conditions and to overcome these stresses, they have evolved multiple mechanisms including systematic accumulation of different phytohormones, phytoalexins and pathogenesis related (PR) proteins. PR proteins are cluster of proteins with low molecular weight which get incited in plants under different stresses. In this paper, in-silico approaches are used to compare the physico-chemical properties of 6 PR proteins (PR1, PR2, PR5, PR9, PR10, PR12) of Arabidopsis thaliana and Oryza sativa. Topological analysis revealed the presence of transmembrane localization of PR2 and absence of transmembrane domain in PR10 of both model plants studied. Amino acid composition shows the dominance of small aliphatic amino acids i.e. alanine, glycine and serine in both plants studied. These results highlights the similarities and differences between PRs of both model plants, which provides clue towards their diversified roles in plants. [ABSTRACT FROM AUTHOR]

B cells develop high affinity receptors during the course of affinity maturation, a cyclic process of mutation and selection. At the end of affinity maturation, a number of cells sharing the same ancestor (i.e. in the same "clonal family") are released from the germinal center; their amino acid frequency profile reflects the allowed and disallowed substitutions at each position. These clonal-family-specific frequency profiles, called "substitution profiles", are useful for studying the course of affinity maturation as well as for antibody engineering purposes. However, most often only a single sequence is recovered from each clonal family in a sequencing experiment, making it impossible to construct a clonal-family-specific substitution profile. Given the public release of many high-quality large B cell receptor datasets, one may ask whether it is possible to use such data in a prediction model for clonal-family-specific substitution profiles. In this paper, we present the method "Substitution Profiles Using Related Families" (SPURF), a penalized tensor regression framework that integrates information from a rich assemblage of datasets to predict the clonal-family-specific substitution profile for any single input sequence. Using this framework, we show that substitution profiles from similar clonal families can be leveraged together with simulated substitution profiles and germline gene sequence information to improve prediction. We fit this model on a large public dataset and validate the robustness of our approach on two external datasets. Furthermore, we provide a command-line tool in an open-source software package (https://github.com/krdav/SPURF) implementing these ideas and providing easy prediction using our pre-fit models., Competing Interests: The authors have declared that no competing interests exist.

It is well known that proteins are built up from an alphabet of 20 different amino acid types. These suffice to enable the protein to fold into its operative form relevant to its required functional roles. For carrying out these allotted functions, there may in some cases be a need for post-translational modifications and it has been established that an additional three types of amino acid have at some point been recruited into this process. But it still remains the case that the 20 residue types referred to are the major building blocks in all terrestrial proteins, and probably "universally". Given this fact, it is surprising that no satisfactory answer has been given to the two questions: "why 20?" and "why just these 20?". Furthermore, a suggestion is made as to how these 20 map to the codon repertoire which in principle has the capacity to cater for 64 different residue types. Attempts are made in this paper to answer these questions by employing a combination of quantum chemical and chemoinformatic tools which are applied to the standard 20 amino acid types as well as 3 "non-standard" types found in nature, a set of fictitious but feasible analog structures designed to test the need for greater coverage of function space and the collection of candidate alternative structures found either on meteorites or in experiments designed to reconstruct pre-life scenarios., Competing Interests: The author has declared that no competing interests exist.

Computational protein design of an ensemble of conformations for one protein–i.e., multi-state design–determines the side chain identity by optimizing the energetic contributions of that side chain in each of the backbone conformations. Sampling the resulting large sequence-structure search space limits the number of conformations and the size of proteins in multi-state design algorithms. Here, we demonstrated that the REstrained CONvergence (RECON) algorithm can simultaneously evaluate the sequence of large proteins that undergo substantial conformational changes. Simultaneous optimization of side chain conformations across all conformations increased sequence conservation when compared to single-state designs in all cases. More importantly, the sequence space sampled by RECON MSD resembled the evolutionary sequence space of flexible proteins, particularly when confined to predicting the mutational preferences of limited common ancestral descent, such as in the case of influenza type A hemagglutinin. Additionally, we found that sequence positions which require substantial changes in their local environment across an ensemble of conformations are more likely to be conserved. These increased conservation rates are better captured by RECON MSD over multiple conformations and thus multiple local residue environments during design. To quantify this rewiring of contacts at a certain position in sequence and structure, we introduced a new metric designated 'contact proximity deviation' that enumerates contact map changes. This measure allows mapping of global conformational changes into local side chain proximity adjustments, a property not captured by traditional global similarity metrics such as RMSD or local similarity metrics such as changes in φ and ψ angles. Author summary: Multi-state design can be used to engineer proteins that need to exist in multiple conformations or that bind to multiple partner molecules. In essence, multi-state design selects a compromise of protein sequences that allow for an ensemble of protein conformations, or states, associated with a particular biological function. In this paper, we used the REstrained CONvergence (RECON) algorithm with Rosetta to show that multi-state design of flexible proteins predicts sequences optimal for conformational change, mimicking mutation preferences sampled in evolution. Modeling optimal local side chain physicochemical environments within an ensemble selected significantly more native-like sequences than selections performed when all conformations states are designed independently. This outcome was particularly true for amino acids whose local side chain environment change between conformations. To quantify such contact map changes, we introduced a novel metric to show that sequence conservation is dependent on protein flexibility, i.e., changes in local side chain environments between stated limit the space of tolerated mutations. Additionally, such positions in sequence and structure are more likely to be energetically frustrated, at least in some states. Importantly, we showed that multi-state design over an ensemble of conformations (space) can explore evolutionary tolerated sequence space (time), thus enabling RECON to not only design proteins that require multiple states for function but also predict mutations that might be tolerated in native proteins but have not yet been explored by evolution. The latter aspect can be important to anticipate escape mutations, for example in pathogens or oncoproteins. [ABSTRACT FROM AUTHOR]

Transporters mediate the movement of compounds across the membranes that separate the cell from its environment and across the inner membranes surrounding cellular compartments. It is estimated that one third of a proteome consists of membrane proteins, and many of these are transport proteins. Given the increase in the number of genomes being sequenced, there is a need for computational tools that predict the substrates that are transported by the transmembrane transport proteins. In this paper, we present TranCEP, a predictor of the type of substrate transported by a transmembrane transport protein. TranCEP combines the traditional use of the amino acid composition of the protein, with evolutionary information captured in a multiple sequence alignment (MSA), and restriction to important positions of the alignment that play a role in determining the specificity of the protein. Our experimental results show that TranCEP significantly outperforms the state-of-the-art predictors. The results quantify the contribution made by each type of information used. [ABSTRACT FROM AUTHOR]

A key step in replication of human cytomegalovirus (HCMV) in the host cell is the generation and packaging of unit-length genomes into preformed capsids. The enzymes involved in this process are the terminases. The HCMV terminase complex consists of two terminase subunits, the ATPase pUL56 and the nuclease pUL89. A potential third component pUL51 has been proposed. Even though the terminase subunit pUL89 has been shown to be essential for DNA packaging and interaction with pUL56, it is not known how pUL89 mechanistically achieves sequence-specific DNA binding and nicking. To identify essential domains and invariant amino acids vis-a-vis nuclease activity and DNA binding, alanine substitutions of predicted motifs were analyzed. The analyses indicated that aspartate 463 is an invariant amino acid for the nuclease activity, while argine 544 is an invariant aa for DNA binding. Structural analysis of recombinant protein using electron microscopy in conjunction with single particle analysis revealed a curvilinear monomer with two distinct domains connected by a thinner hinge-like region that agrees well with the predicted structure. These results allow us to model how the terminase subunit pUL89's structure may mediate its function. Author summary: HCMV is a member of the herpesvirus family and represents a major human pathogen causing severe disease in newborns and immunocompromised patients for which the development of new non-nucleosidic antiviral agents are highly important. This manuscript focuses on DNA packaging, which is a target for development of new antivirals. The terminase subunit pUL89 is involved in this process. The paper presents the identification of DNA binding and nuclease motifs with invariant amino acids and highlights its first 3-D surface structure at approx. 3 nm resolution. At this resolution, the calculated 3-D surface structure matches well with the predicted structure. In conjunction with earlier studies it was possible to define structure-function relationships for the HCMV terminase subunit pUL89. [ABSTRACT FROM AUTHOR]

A DNA cassette assembly method is described which utilizes inexpensive oligomers no longer than 40 nt in length. The five-segment cassettes have 20 nt overlaps which give an effective length of 80 nt, making it possible to code for peptides up to 20 amino acids long. The cassettes have three phosphate free nicks, which can be successfully inserted into plasmid DNA and used to transform E. coli. The nicks are repaired in vivo by an unknown mechanism. Insertions are not successful for cassettes with greater than three nicks. A procedure is provided for rapid turnaround from DNA design to peptides, which are easily isolated as C-terminal fusions with GFP. The technique generally gives the expected sequence, with errors which occur about 1% of the time. Several representative DNA inserts are described which illustrate the method, as well as chemical details on the new peptides coded for. The peptides can be readily mutated to make it possible to understand how polar and aromatic residues affect GFP-fusion solubility, and how histidine residues can be strategically placed in a peptide for good IMAC retention. The method can be used to explore a large number of new designed peptides as fusion products quickly and economically. [ABSTRACT FROM AUTHOR]

Co-evolution between pairs of residues in a multiple sequence alignment (MSA) of homologous proteins has long been proposed as an indicator of structural contacts. Recently, several methods, such as direct-coupling analysis (DCA) and MetaPSICOV, have been shown to achieve impressive rates of contact prediction by taking advantage of considerable sequence data. In this paper, we show that prediction success rates are highly sensitive to the structural definition of a contact, with more permissive definitions (i.e., those classifying more pairs as true contacts) naturally leading to higher positive predictive rates, but at the expense of the amount of structural information contributed by each contact. Thus, the remaining limitations of contact prediction algorithms are most noticeable in conjunction with geometrically restrictive contacts-precisely those that contribute more information in structure prediction. We suggest that to improve prediction rates for such "informative" contacts one could combine co-evolution scores with additional indicators of contact likelihood. Specifically, we find that when a pair of co-varying positions in an MSA is occupied by residue pairs with favorable statistical contact energies, that pair is more likely to represent a true contact. We show that combining a contact potential metric with DCA or MetaPSICOV performs considerably better than DCA or MetaPSICOV alone, respectively. This is true regardless of contact definition, but especially true for stricter and more informative contact definitions. In summary, this work outlines some remaining challenges to be addressed in contact prediction and proposes and validates a promising direction towards improvement., Competing Interests: The authors have declared that no competing interests exist.

The GC contents of 2670 prokaryotic genomes that belong to diverse phylogenetic lineages were analyzed in this paper. These genomes had GC contents that ranged from 13.5% to 74.9%. We analyzed the distance of base frequencies at the three codon positions, codon frequencies, and amino acid compositions across genomes with respect to the differences in the GC content of these prokaryotic species. We found that although the phylogenetic lineages were remote among some species, a similar genomic GC content forced them to adopt similar base usage patterns at the three codon positions, codon usage patterns, and amino acid usage patterns. Our work demonstrates that in prokaryotic genomes: a) base usage, codon usage, and amino acid usage change with GC content with a linear correlation; b) the distance of each usage has a linear correlation with the GC content difference; and c) GC content is more essential than phylogenetic lineage in determining base usage, codon usage, and amino acid usage. This work is exceptional in that we adopted intuitively graphic methods for all analyses, and we used these analyses to examine as many as 2670 prokaryotes. We hope that this work is helpful for understanding common features in the organization of microbial genomes. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed a novel alignment-free method for comparing the similarity of protein sequences. We first encode a protein sequence into a 440 dimensional feature vector consisting of a 400 dimensional Pseudo-Markov transition probability vector among the 20 amino acids, a 20 dimensional content ratio vector, and a 20 dimensional position ratio vector of the amino acids in the sequence. By evaluating the Euclidean distances among the representing vectors, we compare the similarity of protein sequences. We then apply this method into the ND5 dataset consisting of the ND5 protein sequences of 9 species, and the F10 and G11 datasets representing two of the xylanases containing glycoside hydrolase families, i.e., families 10 and 11. As a result, our method achieves a correlation coefficient of 0.962 with the canonical protein sequence aligner ClustalW in the ND5 dataset, much higher than those of other 5 popular alignment-free methods. In addition, we successfully separate the xylanases sequences in the F10 family and the G11 family and illustrate that the F10 family is more heat stable than the G11 family, consistent with a few previous studies. Moreover, we prove mathematically an identity equation involving the Pseudo-Markov transition probability vector and the amino acids content ratio vector., Competing Interests: The authors have declared that no competing interests exist.

Combining different metabolomics platforms can contribute significantly to the discovery of complementary processes expressed under different conditions. However, analysing the fused data might be hampered by the difference in their quality. In metabolomics data, one often observes that measurement errors increase with increasing measurement level and that different platforms have different measurement error variance. In this paper we compare three different approaches to correct for the measurement error heterogeneity, by transformation of the raw data, by weighted filtering before modelling and by a modelling approach using a weighted sum of residuals. For an illustration of these different approaches we analyse data from healthy obese and diabetic obese individuals, obtained from two metabolomics platforms. Concluding, the filtering and modelling approaches that both estimate a model of the measurement error did not outperform the data transformation approaches for this application. This is probably due to the limited difference in measurement error and the fact that estimation of measurement error models is unstable due to the small number of repeats available. A transformation of the data improves the classification of the two groups. [ABSTRACT FROM AUTHOR]

Post-translational modification refers to the biological mechanism involved in the enzymatic modification of proteins after being translated in the ribosome. This mechanism comprises a wide range of structural modifications, which bring dramatic variations to the biological function of proteins. One of the recently discovered modifications is succinylation. Although succinylation can be detected through mass spectrometry, its current experimental detection turns out to be a timely process unable to meet the exponential growth of sequenced proteins. Therefore, the implementation of fast and accurate computational methods has emerged as a feasible solution. This paper proposes a novel classification approach, which effectively incorporates the secondary structure and evolutionary information of proteins through profile bigrams for succinylation prediction. The proposed predictor, abbreviated as SSEvol-Suc, made use of the above features for training an AdaBoost classifier and consequently predicting succinylated lysine residues. When SSEvol-Suc was compared with four benchmark predictors, it outperformed them in metrics such as sensitivity (0.909), accuracy (0.875) and Matthews correlation coefficient (0.75). [ABSTRACT FROM AUTHOR]

Periplasmic Binding Proteins (PBPs) trap nutrients for their internalization into bacteria by ABC transporters. Ligand binding triggers PBP closure by bringing its two domains together like a Venus flytrap. The atomic determinants that control PBP opening and closure for nutrient capture and release are not known, although it is proposed that opening and ligand release occur while in contact with the ABC transporter for concurrent substrate translocation. In this paper we evaluated the effect of the isomerization of a conserved proline, located near the binding site, on the propensity of PBPs to open and close. ArgT/LAO from Salmonella typhimurium and HisJ from Escherichia coli were studied through molecular mechanics at two different temperatures: 300 and 323 K. Eight microseconds were simulated per protein to analyze protein opening and closure in the absence of the ABC transporter. We show that when the studied proline is in trans, closed empty LAO and HisJ can open. In contrast, with the proline in cis, opening transitions were much less frequent and characterized by smaller changes. The proline in trans also renders the open trap prone to close over a ligand. Our data suggest that the isomerization of this conserved proline modulates the PBP mechanism: the proline in trans allows the exploration of conformational space to produce trap opening and closure, while in cis it restricts PBP movement and could limit ligand release until in productive contact with the ABC transporter. This is the first time that a proline isomerization has been related to the control of a large conformational change like the PBP flytrap mechanism. [ABSTRACT FROM AUTHOR]

Several mathematical equations have been proposed to modeling nutrient-response curve for animal and human justified on the goodness of fit and/or on the biological mechanism. In this paper, a functional form of a generalized quantitative model based on Rayleigh distribution principle for description of nutrient-response phenomena is derived. The three parameters governing the curve a) has biological interpretation, b) may be used to calculate reliable estimates of nutrient response relationships, and c) provide the basis for deriving relationships between nutrient and physiological responses. The new function was successfully applied to fit the nutritional data obtained from 6 experiments including a wide range of nutrients and responses. An evaluation and comparison were also done based simulated data sets to check the suitability of new model and four-parameter logistic model for describing nutrient responses. This study indicates the usefulness and wide applicability of the new introduced, simple and flexible model when applied as a quantitative approach to characterizing nutrient-response curve. This new mathematical way to describe nutritional-response data, with some useful biological interpretations, has potential to be used as an alternative approach in modeling nutritional responses curve to estimate nutrient efficiency and requirements. [ABSTRACT FROM AUTHOR]

Type VI secretion systems (T6SS) function to deliver lethal payloads into target cells. Many studies have shown that protection against a single, lethal T6SS effector protein requires a cognate antidote immunity protein, both of which are often encoded together in a two-gene operon. The T6SS and an effector-immunity pair is sufficient for both killing and immunity. HereIn this paper we describe a T6SS effector operon that differs from conventional effector-immunity pairs in that eight genes are necessary for lethal effector function, yet can be countered by a single immunity protein. In this study, we investigated the role that the PefE T6SS immunity protein plays in recognition between two strains harboring nearly identical effector operons. Interestingly, despite containing seven of eight identical effector proteins, the less conserved immunity proteins only provided protection against their native effectors, suggesting that specificity and recognition could be dependent on variation within an immunity protein and one effector gene product. The variable effector gene product, PefD, is encoded upstream from pefE, and displays toxic activity that can be countered by PefE independent of T6SS-activity. Interestingly, while the entire pef operon was necessary to exert toxic activity via the T6SS in P. mirabilis, production of PefD and PefE alone was unable to exert this effector activity. Chimeric PefE proteins constructed from two P. mirabilis strains were used to localize immunity function to three amino acids. A promiscuous immunity protein was created using site-directed mutagenesis to change these residues from one variant to another. These findings support the notion that subtle differences between conserved effectors are sufficient for T6SS-mediated kin discrimination and that PefD requires additional factors to function as a T6SS-dependent effector. [ABSTRACT FROM AUTHOR]

Development of novel biodosimetry assays and medical countermeasures is needed to obtain a level of radiation preparedness in the event of malicious or accidental mass exposures to ionizing radiation (IR). For biodosimetry, metabolic profiling with mass spectrometry (MS) platforms has identified several small molecules in easily accessible biofluids that are promising for dose reconstruction. As our microbiome has profound effects on biofluid metabolite composition, it is of interest how variation in the host microbiome may affect metabolomics based biodosimetry. Here, we 'knocked out' the microbiome of male and female C57BL/6 mice (Abx mice) using antibiotics and then irradiated (0, 3, or 8 Gy) them to determine the role of the host microbiome on biofluid radiation signatures (1 and 3 d urine, 3 d serum). Biofluid metabolite levels were compared to a sham and irradiated group of mice with a normal microbiome (Abx-con mice). To compare post-irradiation effects in urine, we calculated the Spearman's correlation coefficients of metabolite levels with radiation dose. For selected metabolites of interest, we performed more detailed analyses using linear mixed effect models to determine the effects of radiation dose, time, and microbiome depletion. Serum metabolite levels were compared using an ANOVA. Several metabolites were affected after antibiotic administration in the tryptophan and amino acid pathways, sterol hormone, xenobiotic and bile acid pathways (urine) and lipid metabolism (serum), with a post-irradiation attenuative effect observed for Abx mice. In urine, dose×time interactions were supported for a defined radiation metabolite panel (carnitine, hexosamine-valine-isoleucine [Hex-V-I], creatine, citric acid, and Nε,Nε,Nε-trimethyllysine [TML]) and dose for N1-acetylspermidine, which also provided excellent (AUROC ≥ 0.90) to good (AUROC ≥ 0.80) sensitivity and specificity according to the area under the receiver operator characteristic curve (AUROC) analysis. In serum, a panel consisting of carnitine, citric acid, lysophosphatidylcholine (LysoPC) (14:0), LysoPC (20:3), and LysoPC (22:5) also gave excellent to good sensitivity and specificity for identifying post-irradiated individuals at 3 d. Although the microbiome affected the basal levels and/or post-irradiation levels of these metabolites, their utility in dose reconstruction irrespective of microbiome status is encouraging for the use of metabolomics as a novel biodosimetry assay. [ABSTRACT FROM AUTHOR]

Globally, the rapid aging of the population is predicted to become even more severe in the second half of the 21st century. Thus, it is expected to establish a growing expectation for innovative, non-invasive health indicators and diagnostic methods to support disease prevention, care, and health promotion efforts. In this study, we aimed to establish a new health index and disease diagnosis method by analyzing the minerals and free amino acid components contained in hair shaft. We first evaluated the range of these components in healthy humans and then conducted a comparative analysis of these components in subjects with diabetes, hypertension, androgenetic alopecia, major depressive disorder, Alzheimer's disease, and stroke. In the statistical analysis, we first used a student's t test to compare the hair components of healthy people and those of patients with various diseases. However, many minerals and free amino acids showed significant differences in all diseases, because the sample size of the healthy group was very large compared to the sample size of the disease group. Therefore, we attempted a comparative analysis based on effect size, which is not affected by differences in sample size. As a result, we were able to narrow down the minerals and free amino acids for all diseases compared to t test analysis. For diabetes, the t test narrowed down the minerals to 15, whereas the effect size measurement narrowed it down to 3 (Cr, Mn, and Hg). For free amino acids, the t test narrowed it down to 15 minerals. By measuring the effect size, we were able to narrow it down to 7 (Gly, His, Lys, Pro, Ser, Thr, and Val). It is also possible to narrow down the minerals and free amino acids in other diseases, and to identify potential health indicators and disease-related components by using effect size. [ABSTRACT FROM AUTHOR]

The effects of jogi (the fish Atlantic croaker, Micropogonias undulatus) on the production of physicochemical components, such as color, organic acids, and amino acids, in kimchi, a traditional fermented vegetable food of Korea, were determined. As fermentation progressed, the color change of jogi-added kimchi increased, but in comparison with that of the control group without jogi-added kimchi, was difficult to distinguish with the naked eye. Reducing sugar decreased in all experimental groups, and as fermentation progressed, kimchi with jogi showed a lower value. Acetic acid, citric acid, lactic acid, and ethanol, were highly produced in both types of kimchi, and above all, the jogi—baechu—kimchi group showed higher acetic acid and lactic acid contents than the control group. The increase and decrease of amino acids were similar in both types of kimchi. However, significantly, immediately after manufacture, the savory components aspartic acid and glutamic acid were detected higher than the control group. Subsequently, the fermentation tended to decrease as it progressed, but the content was higher than that of the control group. The above results show that jogi addition has a greater effect on the contents of amino acid, especially the savory component, than on the physicochemical components. [ABSTRACT FROM AUTHOR]

Compound specific stable isotope analysis of amino acids (CSIA-AA) is a powerful tool for determining dietary behaviors in complex environments and improving dietary reconstructions. Here, we conducted CSIA-AA on human (n = 32) and animal (n = 13) remains from two prehistoric archaeological sites (Mumun, Imdang) to assess in more detail the dietary sources consumed by prehistoric Korean populations. Results of estimated trophic position (TP) using Δ15NGlx-Phe show that the Imdang individuals consumed aquatic resources, as well as terrestrial resources. Principal component analysis (PCA) using δ13C and δ15N essential amino acid (EAA) values show that the Imdang humans closely cluster with game birds and terrestrial herbivores, whilst the Mumun humans closely cluster with C4 plants. Quantitative estimation by a Bayesian mixing model (MixSIAR) indicates that the Imdang humans derived a large proportion of their proteins from terrestrial animals and marine fish, whereas the main protein sources for the Mumun humans were C4 plants and terrestrial animals. Additionally, the comparison between the EAA and bulk isotope models shows that there is a tendency to overestimate the consumption of plant proteins when using bulk isotopic data. Our CSIA-AA approach reveals that in prehistoric Korea there were clear differences in human diets through time. This study adds to a growing body of literature that demonstrates the potential of CSIA-AA to provide more accurate estimations of protein consumption in mixed diets than previous bulk isotopic studies. [ABSTRACT FROM AUTHOR]

Antimicrobial peptides (AMPs) are at the interface of interactions between hosts and microbes and are therefore expected to be rapidly evolving in a coevolutionary arms race with pathogens. In contrast, previous work demonstrated that insect AMPs tend to evolve more slowly than the genome average. Metchikowin (Mtk) is a Drosophila AMP that has a single amino acid residue that segregates as either proline (P) or arginine (R) in populations of four different species, some of which diverged more than 10 million years ago. These results suggest that there is a distinct functional importance to each allele. The most likely hypotheses are driven by two main questions: does each allele have a different efficacy against different specific pathogens (specificity hypothesis)? Or, is one allele a more potent antimicrobial, but with a host fitness cost (autoimmune hypothesis)? To assess their functional differences, we created D. melanogaster lines with the P allele, R allele, or Mtk null mutation using CRISPR/Cas9 genome editing and performed a series of life history and infection assays to assess them. In males, testing of systemic immune responses to a repertoire of bacteria and fungi demonstrated that the R allele performs as well or better than the P and null alleles with most infections. Females show some results that contrast with males, with Mtk alleles either not contributing to survival or with the P allele outperforming the R allele. In addition, measurements of life history traits demonstrate that the R allele is more costly in the absence of infection for both sexes. These results are consistent with both the specificity hypothesis (either allele can perform better against certain pathogens depending on context), and the autoimmune hypothesis (the R allele is generally the more potent antimicrobial in males, and carries a fitness cost). These results provide strong in vivo evidence that differential fitness with or without infection and sex-based functional differences in alleles may be adaptive mechanisms of maintaining immune gene polymorphisms in contrast with expectations of rapid evolution. Therefore, a complex interplay of forces including pathogen species and host sex may lead to balancing selection for immune genotypes. Strikingly, this selection may act on even a single amino acid polymorphism in an AMP. Author summary: Antimicrobial peptides (AMPs) are small peptides produced by many living organisms as a frontline immune response to a variety of microbial pathogens. These peptides directly interface with pathogens to kill them shortly after infection. Importantly, multiple AMP alleles can be balanced within a population, but little is known about how this immune variation among individuals is maintained over time. Multiple species of Drosophila balance two specific alleles of the AMP, Metchnikowin (Mtk), a canonically antifungal and antibacterial peptide. Here, various life-history and infection assays assess the mechanism of Metchnikowin balancing selection using CRISPR-generated D. melanogaster with a null mutation or either of the two alleles, MtkR or MtkP. Findings demonstrate that the MtkR allele usually confers greater protection than the MtkP allele in males, but carries a fitness cost without infection. However, this pattern flips in females with certain infections, with the MtkP allele conferring greater protection than MtkR. This suggests that the two alleles are repeatedly maintained in fly populations due to differential fitness of hosts in the context of many variables including host genotype, sex, infection status, and pathogen species. It also suggests that maintenance of immune variation in a population can be based on just a single amino acid difference between individuals. [ABSTRACT FROM AUTHOR]

Protein-protein interfaces hold the key to understanding protein-protein interactions. In this paper we investigated local interaction network patterns beyond pair-wise contact sites by considering interfaces as contact networks among residues. A contact site was defined as any residue on the surface of one protein which was in contact with a residue on the surface of another protein. We labeled the sub-graphs of these contact networks by their amino acid types. The observed distributions of these labeled sub-graphs were compared with the corresponding background distributions and the results suggested that there were preferred chemical patterns of closely packed residues at the interface. These preferred patterns point to biological constraints on physical proximity between those residues on one protein which were involved in binding to residues which were close on the interacting partner. Interaction interfaces were far from random and contain information beyond pairs and triangles. To illustrate the possible application of the local network patterns observed, we introduced a signature method, called iScore, based on these local patterns to assess interface predictions. On our data sets iScore achieved 83.6% specificity with 82% sensitivity. [ABSTRACT FROM AUTHOR]

Background: Salt, protein, acid precursors, and fluid intake have been identified as factors that influence cyst growth in ADPKD. Unfortunately, the feasibility of following these dietary restrictions/enhancements from a patient's point-of-view has yet to be studied. The purpose of this study is to understand better the experiences of patients following a relatively complex dietary prescription targeting these factors., Methods: Twelve adults with ADPKD and kidney function >30ml/min/1.73m2 were recruited from the University of Kansas Medical Center Polycystic Kidney Disease clinic. In a qualitative design, semi-structured interviews of participants were conducted following a four week dietary intervention (experimental diet lower in sodium, protein, and acid precursors, and supplemented with water) either face-to-face or by telephone. All interviews were recorded, transcribed verbatim, and checked for accuracy. Transcripts were analyzed thematically for emerging themes., Results: Participants reported that eating less meat and more fruits and vegetables were the easiest components of the diet, whereas reaching the daily goal amount of fruits and vegetables and tracking the diet constantly were the most difficult components. Participants had little difficulty with fluid intake and reported the prescribed fluid goal as achievable. The tracking system for fruits and vegetables and protein was reported to be both helpful and intuitive, but tracking their intake on paper was tedious. Eating out was the most significant barrier to following the diet with some individuals avoiding restaurants in order to comply with the dietary prescription., Conclusion: Participants on the experimental diet heightened their awareness of the consumption of dietary salt, protein, acid precursors, and fluid intake. Additionally, most participants believed adherence to the prescribed diet was feasible. However, participants wanted less cumbersome ways to track and monitor the diet, especially given that the prescribed diet is designed for lifelong adherence. Future studies should focus on targeting these specific dietary factors in larger groups of more ethnically and culturally diverse populations to help inform clinicians and how best to help diverse populations adhere to the dietary intervention., Trial Registration: ClinicalTrials.gov NCT01810614.

Background: The A/H3N2 influenza viruses circulated in humans have been shown to undergo antigenic drift, a process in which amino acid mutations result from nucleotide substitutions. There are few reports regarding the charged amino acid mutations. The purpose of this paper is to explore the relations between charged amino acids, N-glycosylation and epitopes in hemagglutinin (HA) and neuraminidase (NA). Methods: A total of 700 HA genes (691 NA genes) of A/H3N2 viruses were chronologically analyzed for the mutational variants in amino acid features, N-glycosylation sites and epitopes since its emergence in 1968. Results: It was found that both the number of HA N-glycosylation sites and the electric charge of HA increased gradually up to 2016. The charges of HA and HA1 increased respectively 1.54-fold (+7.0 /+17.8) and 1.08-fold (+8.0/+16.6) and the number of NGS in nearly doubled (7/12). As great diversities occurred in 1990s, involving Epitope A, B and D mutations, the charged amino acids in Epitopes A, B, C and D in HA1 mutated at a high frequency in global circulating strains last decade. The charged amino acid mutations in Epitopes A (T135K) has shown high mutability in strains near years, resulting in a decrease of NGT135-135. Both K158N and K160T not only involved mutations charged in epitope B, but also caused a gain of NYT158-160. Epitope B and its adjacent N-glycosylation site NYT158-160 mutated more frequently, which might be under greater immune pressure than the rest. Conclusions: The charged amino acid mutations in A/H3N2 Influenza play a significant role in virus evolution, which might cause an important public health issue. Variability related to both the epitopes (A and B) and N-glycosylation is beneficial for understanding the evolutionary mechanisms, disease pathogenesis and vaccine research. [ABSTRACT FROM AUTHOR]

Prokaryotic proteins are regulated by pupylation, a type of post-translational modification that contributes to cellular function in bacterial organisms. In pupylation process, the prokaryotic ubiquitin-like protein (Pup) tagging is functionally analogous to ubiquitination in order to tag target proteins for proteasomal degradation. To date, several experimental methods have been developed to identify pupylated proteins and their pupylation sites, but these experimental methods are generally laborious and costly. Therefore, computational methods that can accurately predict potential pupylation sites based on protein sequence information are highly desirable. In this paper, a novel predictor termed as pbPUP has been developed for accurate prediction of pupylation sites. In particular, a sophisticated sequence encoding scheme [i.e. the profile-based composition of k-spaced amino acid pairs (pbCKSAAP)] is used to represent the sequence patterns and evolutionary information of the sequence fragments surrounding pupylation sites. Then, a Support Vector Machine (SVM) classifier is trained using the pbCKSAAP encoding scheme. The final pbPUP predictor achieves an AUC value of 0.849 in 10-fold cross-validation tests and outperforms other existing predictors on a comprehensive independent test dataset. The proposed method is anticipated to be a helpful computational resource for the prediction of pupylation sites. The web server and curated datasets in this study are freely available at http://protein.cau.edu.cn/pbPUP/.

Nilaparvata lugens harbors yeast-like symbionts (YLSs). In present paper, a genome-wide analysis found 115 genes from Ni. lugens and 90 genes from YLSs that were involved in the metabolic degradation of 20 proteinogenic amino acids. These 205 genes encoded for 77 enzymes. Accordingly, the degradation pathways for the 20 amino acids were manually constructed. It is postulated that Ni. lugens can independently degrade fourteen amino acids (threonine, alanine, glycine, serine, aspartate, asparagine, phenylalanine, tyrosine, glutamate, glutamine, proline, histidine, leucine and lysine). Ni. lugens and YLSs enzymes may work collaboratively to break down tryptophan, cysteine, arginine, isoleucine, methionine and valine. We cloned a lysine-ketoglutarate reductase/saccharopine dehydrogenase gene (Nllkr/sdh) that encoded a bifunctional enzyme catalyzing the first two steps of lysine catabolism. Nllkr/sdh is widely expressed in the first through fifth instar nymphs and adults, and is highly expressed in the fat body, ovary and gut in adults. Ingestion of dsNllkr/sdh by nymphs successfully knocked down the target gene, and caused nymphal/adult mortality, shortened nymphal development stage and reduced adult fresh weight. Moreover, Nllkr/sdh knockdown resulted in three defects: wings were shortened and thickened; cuticles were stretched and thinned; and old nymphal cuticles remained on the tips of legs and abdomen and were not completely shed. These data indicate that impaired lysine degradation negatively affects the survival and development of Ni. lugens.

Spinocerebellar ataxia (SCA) 3, the most common form of SCA, is a neurodegenerative rare disease characterized by polyglutamine tract expansion and self-assembly of Ataxin3 (At3) misfolded proteins into highly organized fibrillar aggregates. The At3 N-terminal Josephin Domain (JD) has been suggested as being responsible for mediating the initial phase of the At3 double-step fibrillogenesis. Several issues concerning the residues involved in the JD's aggregation and, more generally, the JD clumping mechanism have not been clarified yet. In this paper we present an investigation focusing on the JD protein-protein interaction by means of molecular modeling. Our results suggest possible aminoacids involved in JD contact together with local and non-local effects following JD dimerization. Surprisingly, JD conformational changes following the binding may involve ubiquitin binding sites and hairpin region even though they do not pertain to the JD interaction surfaces. Moreover, the JD binding event has been found to alter the hairpin open-like conformation toward a closed-like arrangement over the simulated timescale. Finally, our results suggest that the JD aggregation might be a multi-step process, with an initial fast JD-JD binding mainly driven by Arg101, followed by slower structural global rearrangements involving the exposure to the solvent of Leu84-Trp87, which might play a role in a second step of JD aggregation.

Playing crucial roles in various cellular processes, such as recognition of specific nucleotide sequences, regulation of transcription, and regulation of gene expression, DNA-binding proteins are essential ingredients for both eukaryotic and prokaryotic proteomes. With the avalanche of protein sequences generated in the postgenomic age, it is a critical challenge to develop automated methods for accurate and rapidly identifying DNA-binding proteins based on their sequence information alone. Here, a novel predictor, called "iDNA-Prot|dis", was established by incorporating the amino acid distance-pair coupling information and the amino acid reduced alphabet profile into the general pseudo amino acid composition (PseAAC) vector. The former can capture the characteristics of DNA-binding proteins so as to enhance its prediction quality, while the latter can reduce the dimension of PseAAC vector so as to speed up its prediction process. It was observed by the rigorous jackknife and independent dataset tests that the new predictor outperformed the existing predictors for the same purpose. As a user-friendly web-server, iDNA-Prot|dis is accessible to the public at http://bioinformatics.hitsz.edu.cn/iDNA-Prot&#95;dis/. Moreover, for the convenience of the vast majority of experimental scientists, a step-by-step protocol guide is provided on how to use the web-server to get their desired results without the need to follow the complicated mathematic equations that are presented in this paper just for the integrity of its developing process. It is anticipated that the iDNA-Prot|dis predictor may become a useful high throughput tool for large-scale analysis of DNA-binding proteins, or at the very least, play a complementary role to the existing predictors in this regard.

Prokaryotic chromosomes contain numerous small open reading frames (ORFs) of less than 200 bases. Since high-throughput proteomics methods often miss proteins containing fewer than 60 amino acids, it is difficult to decern if they encode proteins. Recent studies have revealed that many small proteins are membrane proteins with a single membrane-anchoring α-helix. As membrane anchoring or transmembrane motifs are accurately identifiable with high confidence using computational algorithms like Phobius and TMHMM, small membrane proteins (SMPS) can be predicted with high accuracy. This study employed a systematic approach, utilizing well-verified algorithms such as Orfipy, Phobius, and Blast to identify SMPs in prokaryotic organisms. Our main search parameters targeted candidate SMPs with an open reading frame between 60–180 nucleotides, a membrane-anchoring or transmembrane region 15 and 30 amino acids long, and sequence conservation among other microorganisms. Our findings indicate that each prokaryote possesses many SMPs, with some identified in the intergenic regions of currently annotated chromosomes. More extensively studied microorganisms, such as Escherichia coli and Bacillus subtilis, have more SMPs identified in their genomes compared to less studied microorganisms, suggesting the possibility of undiscovered SMPs in less studied microorganisms. In this study, we describe the common SMPs identified across various microorganisms and explore their biological roles. We have also developed a software pipeline and an accompanying online interface for discovering SMPs (http://cs.indstate.edu/pro-smp-finder). This resource aims to assist researchers in identifying new SMPs encoded in microbial genomes of interest. [ABSTRACT FROM AUTHOR]

Newcastle disease virus (NDV) has been extensively studied as a promising oncolytic virus for killing tumor cells in vitro and in vivo in clinical trials. However, the viral components that regulate the oncolytic activity of NDV remain incompletely understood. In this study, we systematically compared the replication ability of different NDV genotypes in various tumor cells and identified NP protein determines the oncolytic activity of NDV. On the one hand, NDV strains with phenylalanine (F) at the 450th amino acid position of the NP protein (450th-F-NP) exhibit a loss of oncolytic activity. This phenotype is predominantly associated with genotype VII NDVs. In contrast, the NP protein with a leucine amino acid at this site in other genotypes (450th-L-NP) can facilitate the loading of viral mRNA onto ribosomes more effectively than 450th-F-NP. On the other hand, the NP protein from NDV strains that exhibit strong oncogenicity interacts with eIF4A1 within its 366–489 amino acid region, leading to the inhibition of cellular mRNA translation with a complex 5' UTR structure. Our study provide mechanistic insights into how highly oncolytic NDV strains selectively promote the translation of viral mRNA and will also facilitate the screening of oncolytic strains for oncolytic therapy. Author summary: The oncolytic potential of NDV has gained significant attention in the context of clinical trials. Therefore, it is crucial to systematically compare the oncolytic activities of different NDV subtypes and understand their underlying mechanisms. Our previous investigations revealed that genotype VII NDVs, predominant during the fourth epidemic, exhibit heightened avian pathogenicity compared to other subtypes. Interestingly, in tumor cells, NDV strains of varying virulence and genotypes possess some oncolytic capacity. However, in several genotype VII strains, the NP protein fails to facilitate the efficient loading of viral mRNA onto ribosomes. This is primarily attributed to the presence of phenylalanine at the 450th amino acid position of the NP protein. Additionally, it loses its ability to inhibit the eIF4A1-relative cellular mRNA translation through interaction with eIF4A1, mainly because of differences in its non-conserved region (366-489aa) of NP protein compared to other genotypes. These significantly hinder its ability to establish effective infections in tumor cells. Our study provides valuable insights for oncolytic NDV strain screening and enhances understanding of the functional role of NP protein. [ABSTRACT FROM AUTHOR]

Influenza A virus (FLUAV) infects a wide range of hosts and human-to-swine spillover events are frequently reported. However, only a few of these human viruses have become established in pigs and the host barriers and molecular mechanisms driving adaptation to the swine host remain poorly understood. We previously found that infection of pigs with a 2:6 reassortant virus (hVIC/11) containing the hemagglutinin (HA) and neuraminidase (NA) gene segments from the human strain A/Victoria/361/2011 (H3N2) and internal gene segments of an endemic swine strain (sOH/04) resulted in a fixed amino acid substitution in the HA (A138S, mature H3 HA numbering). In silico analysis revealed that S138 became predominant among swine H3N2 virus sequences deposited in public databases, while 138A predominates in human isolates. To understand the role of the HA A138S substitution in the adaptation of a human-origin FLUAV HA to swine, we infected pigs with the hVIC/11A138S mutant and analyzed pathogenesis and transmission compared to hVIC/11 and sOH/04. Our results showed that the hVIC/11A138S virus had an intermediary pathogenesis between hVIC/11 and sOH/04. The hVIC/11A138S infected the upper respiratory tract, right caudal, and both cranial lobes while hVIC/11 was only detected in nose and trachea samples. Viruses induced a distinct expression pattern of various pro-inflammatory cytokines such as IL-8, TNF-α, and IFN-β. Flow cytometric analysis of lung samples revealed a significant reduction of porcine alveolar macrophages (PAMs) in hVIC/11A138S-infected pigs compared to sOH/04 while a MHCIIlowCD163neg population was increased. The hVIC/11A138S showed a higher affinity for PAMs than hVIC/11, noted as an increase of infected PAMs in bronchoalveolar lavage fluid (BALF), and showed no differences in the percentage of HA-positive PAMs compared to sOH/04. This increased infection of PAMs led to an overexpression of granulocyte-monocyte colony-stimulating factor (GM-CSF) stimulation but a reduced expression of peroxisome proliferator-activated receptor gamma (PPARγ) in the sOH/04-infected group. Analysis using the PAM cell line 3D4/21 revealed that the A138S substitution improved replication and apoptosis induction in this cell type compared to hVIC/11 but at lower levels than sOH/04. Overall, our study indicates that adaptation of human viruses to the swine host involves an increased affinity for the lower respiratory tract and alveolar macrophages. Author summary: Human-to-swine FLUAV spillover events are relatively common and have led to the establishment of multiple evolutionarily distinct virus lineages in the swine population worldwide, leading to important economic losses in the pork industry. This also increases the risk of reassortment and emergence of novel FLUAV that may retain the ability to infect and transmit in humans, an event exemplified by the H1N1 pandemic in 2009. However, the molecular mechanisms driving FLUAV adaptation to pigs following the introduction of a human virus are still not fully understood. In this study we found that a point mutation in a human-origin HA improved infection and transmissibility in pigs characterized by an enhanced lung replication and the disruption of lung-resident immune cell populations. This relationship between improved replication and infectivity of lung-resident cells provides insights into host barriers limiting human FLUAV adaptation to pigs and the complexities associated with the host immune response that influenza A viruses must overcome to become established in the swine population. [ABSTRACT FROM AUTHOR]

Breast tumours are embedded in a collagen I-rich extracellular matrix (ECM) network, where nutrients are scarce due to limited blood flow and elevated tumour growth. Metabolic adaptation is required for cancer cells to endure these conditions. Here, we demonstrated that the presence of ECM supported the growth of invasive breast cancer cells, but not non-transformed mammary epithelial cells, under amino acid starvation, through a mechanism that required macropinocytosis-dependent ECM uptake. Importantly, we showed that this behaviour was acquired during carcinoma progression. ECM internalisation, followed by lysosomal degradation, contributed to the up-regulation of the intracellular levels of several amino acids, most notably tyrosine and phenylalanine. This resulted in elevated tyrosine catabolism on ECM under starvation, leading to increased fumarate levels, potentially feeding into the tricarboxylic acid (TCA) cycle. Interestingly, this pathway was required for ECM-dependent cell growth and invasive cell migration under amino acid starvation, as the knockdown of p-hydroxyphenylpyruvate hydroxylase-like protein (HPDL), the third enzyme of the pathway, opposed cell growth and motility on ECM in both 2D and 3D systems, without affecting cell proliferation on plastic. Finally, high HPDL expression correlated with poor prognosis in breast cancer patients. Collectively, our results highlight that the ECM in the tumour microenvironment (TME) represents an alternative source of nutrients to support cancer cell growth by regulating phenylalanine and tyrosine metabolism. Breast tumors are embedded in a collagen-rich extracellular matrix (ECM) network, where nutrients are scarce due to limited blood flow and elevated tumor growth. This study shows that invasive breast cancer cells use macropinocytosis-dependent uptake and lysosomal degradation of ECM to promote their growth. [ABSTRACT FROM AUTHOR]

Kinks are a structural feature of alpha-helices and many are known to have functional roles. Kinks have previously tended to be defined in a binary fashion. In this paper we have deliberately moved towards defining them on a continuum, which given the unimodal distribution of kink angles is a better description. From this perspective, we examine the conservation of kinks in proteins. We find that kink angles are not generally a conserved property of homologs, pointing either to their not being functionally critical or to their function being related to conformational flexibility. In the latter case, the different structures of homologs are providing snapshots of different conformations. Sequence identity between homologous helices is informative in terms of kink conservation, but almost equally so is the sequence identity of residues in spatial proximity to the kink. In the specific case of proline, which is known to be prevalent in kinked helices, loss of a proline from a kinked helix often also results in the loss of a kink or reduction in its kink angle. We carried out a study of the seven transmembrane helices in the GPCR family and found that changes in kinks could be related both to subfamilies of GPCRs and also, in a particular subfamily, to the binding of agonists or antagonists. These results suggest conformational change upon receptor activation within the GPCR family. We also found correlation between kink angles in different helices, and the possibility of concerted motion could be investigated further by applying our method to molecular dynamics simulations. These observations reinforce the belief that helix kinks are key, functional, flexible points in structures. [ABSTRACT FROM AUTHOR]

The promise of portable diagnostic devices relies on three basic requirements: comparable sensitivity to established platforms, inexpensive manufacturing and cost of operations, and the ability to survive rugged field conditions. Solid state nanopores can meet all these requirements, but to achieve high manufacturing yields at low costs, assays must be tolerant to fabrication imperfections and to nanopore enlargement during operation. This paper presents a model for molecular engineering techniques that meets these goals with the aim of detecting target sequences within DNA. In contrast to methods that require precise geometries, we demonstrate detection using a range of pore geometries. As a result, our assay model tolerates any pore-forming method and in-situ pore enlargement. Using peptide nucleic acid (PNA) probes modified for conjugation with synthetic bulk-adding molecules, pores ranging 15-50 nm in diameter are shown to detect individual PNA-bound DNA. Detection of the CFTRΔF508 gene mutation, a codon deletion responsible for ∼66% of all cystic fibrosis chromosomes, is demonstrated with a 26-36 nm pore size range by using a size-enhanced PNA probe. A mathematical framework for assessing the statistical significance of detection is also presented. [ABSTRACT FROM AUTHOR]

Recent studies suggest that Sirt inhibition may have beneficial effects on several human diseases such as neurodegenerative diseases and cancer. Coffee is one of most popular beverages with several positive health effects. Therefore, in this paper, potential Sirt inhibitors were screened using coffee extract. First, HPLC was utilized to fractionate coffee extract, then screened using a Sirt1/2 inhibition assay. The screening led to the isolation of a potent Sirt1/2 inhibitor, whose structure was determined as javamide-II (N-caffeoyltryptophan) by NMR. For confirmation, the amide was chemically synthesized and its capacity of inhibiting Sirt1/2 was also compared with the isolated amide. Javamide-II inhibited Sirt2 (IC50; 8.7μM) better than Sirt1(IC50; 34μM). Since javamide-II is a stronger inhibitor for Sirt2 than Sirt1. The kinetic study was performed against Sirt2. The amide exhibited noncompetitive Sirt2 inhibition against the NAD+ (Ki = 9.8 μM) and showed competitive inhibition against the peptide substrate (Ki = 5.3 μM). Also, a docking simulation showed stronger binding pose of javamide-II to Sirt2 than AGK2. In cellular levels, javamide-II was able to increase the acetylation of total lysine, cortactin and histone H3 in neuronal NG108-15 cells. In the same cells, the amide also increased the acetylation of lysine (K382) in p53, but not (K305). This study suggests that Javamide-II found in coffee may be a potent Sirt1/2 inhibitor, probably with potential use in some conditions of human diseases. [ABSTRACT FROM AUTHOR]

Pectate lyases are known to play a key role in pectin degradation by catalyzing the random cleavage of internal polymer linkages (endo-pectinases). In this paper, four novel cDNAs, designated Hg-pel-3, Hg-pel-4, Hg-pel-6 and Hg-pel-7, that encode pectate lyases were cloned and characterized from the soybean cyst nematode, Heterodera glycines. The predicted protein sequences of HG-PEL-3, HG-PEL-4 and HG-PEL-6 differed significantly in both their amino acid sequences and their genomic structures from other pectate lyases of H. glycines (HG-PEL-1, HG-PEL-2 and HG-PEL-7). A phylogenetic study revealed that the pectate lyase proteins of H. glycines are clustered into distinct clades and have distinct numbers and positioning of introns, which suggests that the pectate lyase genes of H. glycines may have evolved from at least two ancestral genes. A Southern blot analysis revealed that multiple Hg-pel-6-like genes were present in the H. glycines genome. In situ hybridization showed that four novel pectate lyases (Hg-pel-3, Hg-pel-4, Hg-pel-6 and Hg-pel-7) were actively transcribed in the subventral esophageal gland cells. A semi-quantitative RT-PCR assay supported the finding that the expression of these genes was strong in the egg, pre-parasitic second-stage juvenile (J2) and early parasitic J2 stages and that it declined in further developmental stages of the nematode. This expression pattern suggests that these proteins play a role in the migratory phase of the nematode life cycle. Knocking down Hg-pel-6 using in vitro RNA interference resulted in a 46.9% reduction of the number of nematodes that invaded the plants and a 61.5% suppression of the development of H. glycines females within roots compared to the GFP-dsRNA control. Plant host-derived RNAi induced the silencing of the Hg-pel-6gene, which significantly reduced the nematode infection levels at 7 Days post inoculation (dpi). Similarly, this procedure reduced the number of female adults at 40 dpi, which suggests the important roles of this gene in the early stages of parasitism. Our combined data suggest that two types of pectate lyases are present in the H. glycines genome and may have different roles during infection. [ABSTRACT FROM AUTHOR]

Nitrotyrosine is one of the post-translational modifications (PTMs) in proteins that occurs when their tyrosine residue is nitrated. Compared with healthy people, a remarkably increased level of nitrotyrosine is detected in those suffering from rheumatoid arthritis, septic shock, and coeliac disease. Given an uncharacterized protein sequence that contains many tyrosine residues, which one of them can be nitrated and which one cannot? This is a challenging problem, not only directly related to in-depth understanding the PTM's mechanism but also to the nitrotyrosine-based drug development. Particularly, with the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop a high throughput tool in this regard. Here, a new predictor called "iNitro-Tyr" was developed by incorporating the position-specific dipeptide propensity into the general pseudo amino acid composition for discriminating the nitrotyrosine sites from non-nitrotyrosine sites in proteins. It was demonstrated via the rigorous jackknife tests that the new predictor not only can yield higher success rate but also is much more stable and less noisy. A web-server for iNitro-Tyr is accessible to the public at http://app.aporc.org/iNitro-Tyr/. For the convenience of most experimental scientists, we have further provided a protocol of step-by-step guide, by which users can easily get their desired results without the need to follow the complicated mathematics that were presented in this paper just for the integrity of its development process. It has not escaped our notice that the approach presented here can be also used to deal with the other PTM sites in proteins.

Protein-protein interactions are involved in nearly all regulatory processes in the cell and are considered one of the most important issues in molecular biology and pharmaceutical sciences but are still not fully understood. Structural and computational biology contributed greatly to the elucidation of the mechanism of protein interactions. In this paper, we present a collection of the physicochemical and structural characteristics that distinguish interface-forming residues (IFR) from free surface residues (FSR). We formulated a linear discriminative analysis (LDA) classifier to assess whether chosen descriptors from the BlueStar STING database (http://www.cbi.cnptia.embrapa.br/SMS/) are suitable for such a task. Receiver operating characteristic (ROC) analysis indicates that the particular physicochemical and structural descriptors used for building the linear classifier perform much better than a random classifier and in fact, successfully outperform some of the previously published procedures, whose performance indicators were recently compared by other research groups. The results presented here show that the selected set of descriptors can be utilized to predict IFRs, even when homologue proteins are missing (particularly important for orphan proteins where no homologue is available for comparative analysis/indication) or, when certain conformational changes accompany interface formation. The development of amino acid type specific classifiers is shown to increase IFR classification performance. Also, we found that the addition of an amino acid conservation attribute did not improve the classification prediction. This result indicates that the increase in predictive power associated with amino acid conservation is exhausted by adequate use of an extensive list of independent physicochemical and structural parameters that, by themselves, fully describe the nano-environment at protein-protein interfaces. The IFR classifier developed in this study is now integrated into the BlueStar STING suite of programs. Consequently, the prediction of protein-protein interfaces for all proteins available in the PDB is possible through STING&#95;interfaces module, accessible at the following website: (http://www.cbi.cnptia.embrapa.br/SMS/predictions/index.html).

Previous statistical analyses have shown that amino acid sites in a protein evolve in a correlated way instead of independently. Even though located distantly in the linear sequence, the coevolved amino acids could be spatially adjacent in the tertiary structure, and constitute specific protein sectors. Moreover, these protein sectors are independent of one another in structure, function, and even evolution. Thus, systematic studies on protein sectors inside a protein will contribute to the clarification of protein function. In this paper, we propose a new algorithm BIFANR (Bi-factor Analysis Based on Noise-reduction) for detecting protein sectors in amino acid sequences. After applying BIFANR on S1A family and PDZ family, we carried out internal correlation test, statistical independence test, evolutionary rate analysis, evolutionary independence analysis, and function analysis to assess the prediction. The results showed that the amino acids in certain predicted protein sector are closely correlated in structure, function, and evolution, while protein sectors are nearly statistically independent. The results also indicated that the protein sectors have distinct evolutionary directions. In addition, compared with other algorithms, BIFANR has higher accuracy and robustness under the influence of noise sites.

Depression is a type of complex psychiatric disorder with long-term, recurrent bouts, and its etiology remains largely unknown. Here, an integrated approach utilizing (1)H NMR and UPLC-Q-TOF/MS together was firstly used for a comprehensive urinary metabonomics study on chronic unpredictable mild stress (CUMS) treated rats. More than twenty-nine metabolic pathways were disturbed after CUMS treatment and thirty-six potential biomarkers were identified by using two complementary analytical technologies. Among the identified biomarkers, nineteen (10, 11, 16, 17, 21-25, and 27-36) were firstly reported as potential biomarkers of CUMS-induced depression. Obviously, this paper presented a comprehensive map of the metabolic pathways perturbed by CUMS and expanded on the multitude of potential biomarkers that have been previously reported in the CUMS model. Four metabolic pathways, including valine, leucine and isoleucine biosynthesis; phenylalanine, tyrosine and tryptophan biosynthesis; tryptophan metabolism; synthesis and degradation of ketone bodies had the deepest influence in the pathophysiologic process of depression. Fifteen potential biomarkers (1-2, 4-6, 15, 18, 20-23, 27, 32, 35-36) involved in the above four metabolic pathways might become the screening criteria in clinical diagnosis and predict the development of depression. Moreover, the results of Western blot analysis of aromatic L-amino acid decarboxylase (DDC) and indoleamine 2, 3-dioxygenase (IDO) in the hippocampus of CUMS-treated rats indicated that depletion of 5-HT and tryptophan, production of 5-MT and altered expression of DDC and IDO together played a key role in the initiation and progression of depression. In addition, none of the potential biomarkers were detected by NMR and LC-MS simultaneously which indicated the complementary of the two kinds of detection technologies. Therefore, the integration of (1)H NMR and UPLC-Q-TOF/MS in metabonomics study provided an approach to identify the comprehensive potential depression-related biomarkers and helpful in further understanding the underlying molecular mechanisms of depression through the disturbance of metabolic pathways.

Cholinium amino acid ionic liquids ([Ch][AA] ILs), which are wholly composed of renewable biomaterials, have recently been demonstrated to have very promising properties for applications in organic synthesis and biomass pretreatment. In this work, the toxicity of these ILs toward enzymes and bacteria was assessed, and the effect of the anion on these properties is discussed. The inhibitory potentials of this type of ILs to acetylcholinesterase were weaker approximately an order of magnitude than the traditional IL 1-butyl-3-methylimidazolium tetrafluoroborate. Additionally, the [Ch][AA] ILs displayed low toxicity toward the bacteria tested. Furthermore, the biodegradability of the [Ch][AA] ILs was evaluated via the closed bottle and CO(2) headspace tests using wastewater microorganisms. All the ILs were classified as 'readily biodegradable' based on their high levels of mineralization (62-87%). The presence of extra carboxyl or amide groups on the amino acid side chain rendered the ILs significantly more susceptible to microbial breakdown. In addition, for most of the [Ch][AA] ILs, low toxicity correlated with good biodegradability. The low toxicity and high biodegradability of these novel [Ch][AA] make them promising candidates for use as environmentally friendly solvents in large-scale applications.

The malaria disease has become a cause of poverty and a major hindrance to economic development. The culprit of the disease is the parasite, which secretes an array of proteins within the host erythrocyte to facilitate its own survival. Accordingly, the secretory proteins of malaria parasite have become a logical target for drug design against malaria. Unfortunately, with the increasing resistance to the drugs thus developed, the situation has become more complicated. To cope with the drug resistance problem, one strategy is to timely identify the secreted proteins by malaria parasite, which can serve as potential drug targets. However, it is both expensive and time-consuming to identify the secretory proteins of malaria parasite by experiments alone. To expedite the process for developing effective drugs against malaria, a computational predictor called "iSMP-Grey" was developed that can be used to identify the secretory proteins of malaria parasite based on the protein sequence information alone. During the prediction process a protein sample was formulated with a 60D (dimensional) feature vector formed by incorporating the sequence evolution information into the general form of PseAAC (pseudo amino acid composition) via a grey system model, which is particularly useful for solving complicated problems that are lack of sufficient information or need to process uncertain information. It was observed by the jackknife test that iSMP-Grey achieved an overall success rate of 94.8%, remarkably higher than those by the existing predictors in this area. As a user-friendly web-server, iSMP-Grey is freely accessible to the public at http://www.jci-bioinfo.cn/iSMP-Grey. Moreover, for the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated mathematical equations involved in this paper.

As one of the most widespread protein post-translational modifications, phosphorylation is involved in many biological processes such as cell cycle, apoptosis. Identification of phosphorylated substrates and their corresponding sites will facilitate the understanding of the molecular mechanism of phosphorylation. Comparing with the labor-intensive and time-consuming experiment approaches, computational prediction of phosphorylation sites is much desirable due to their convenience and fast speed. In this paper, a new bioinformatics tool named CKSAAP&#95;PhSite was developed that ignored the kinase information and only used the primary sequence information to predict protein phosphorylation sites. The highlight of CKSAAP&#95;PhSite was to utilize the composition of k-spaced amino acid pairs as the encoding scheme, and then the support vector machine was used as the predictor. The performance of CKSAAP&#95;PhSite was measured with a sensitivity of 84.81%, a specificity of 86.07% and an accuracy of 85.43% for serine, a sensitivity of 78.59%, a specificity of 82.26% and an accuracy of 80.31% for threonine as well as a sensitivity of 74.44%, a specificity of 78.03% and an accuracy of 76.21% for tyrosine. Experimental results obtained from cross validation and independent benchmark suggested that our method was very promising to predict phosphorylation sites and can be served as a useful supplement tool to the community. For public access, CKSAAP&#95;PhSite is available at http://59.73.198.144/cksaap&#95;phsite/.

With the rapid increase of protein sequences in the post-genomic age, it is challenging to develop accurate and automated methods for reliably and quickly predicting their subcellular localizations. Till now, many efforts have been tried, but most of which used only a single algorithm. In this paper, we proposed an ensemble classifier of KNN (k-nearest neighbor) and SVM (support vector machine) algorithms to predict the subcellular localization of eukaryotic proteins based on a voting system. The overall prediction accuracies by the one-versus-one strategy are 78.17%, 89.94% and 75.55% for three benchmark datasets of eukaryotic proteins. The improved prediction accuracies reveal that GO annotations and hydrophobicity of amino acids help to predict subcellular locations of eukaryotic proteins.

An empirical observation suggests that Giardia lamblia proteins have larger cysteine content than their counterparts in other organisms. As this parasite lacks conventional antioxidant stress systems, it is generally accepted that high cysteine content helps G. lamblia cope with oxygen toxicity, a strategy apparently shared by other organisms. Here, we question whether the high cysteine content in some organisms is genuine or just a simple assumption based on singular observations. To this end, we analyzed the cysteine content in 78 proteomes of organisms spanning the three domains of life. The results indicate that the cysteine content in eukaryota is approximately double that in archaea and bacteria, with G. lamblia among the highest. Atypical cysteine contents were found in a few organisms correlating with specific environmental conditions, supporting the evolutionary amino acid-level selection of amino acid composition. [ABSTRACT FROM AUTHOR]