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Convolutional neural networks (CNNs) are currently among the most widely-used deep neural network (DNN) architectures available and achieve state-of-the-art performance for many problems. Originally applied to computer vision tasks, CNNs work well with any data with a spatial relationship, besides images, and have been applied to different fields. However, recent works have highlighted numerical stability challenges in DNNs, which also relates to their known sensitivity to noise injection. These challenges can jeopardise their performance and reliability. This paper investigates DeepGOPlus, a CNN that predicts protein function. DeepGOPlus has achieved state-of-the-art performance and can successfully take advantage and annotate the abounding protein sequences emerging in proteomics. We determine the numerical stability of the model's inference stage by quantifying the numerical uncertainty resulting from perturbations of the underlying floating-point data. In addition, we explore the opportunity to use reduced-precision floating point formats for DeepGOPlus inference, to reduce memory consumption and latency. This is achieved by instrumenting DeepGOPlus' execution using Monte Carlo Arithmetic, a technique that experimentally quantifies floating point operation errors and VPREC, a tool that emulates results with customizable floating point precision formats. Focus is placed on the inference stage as it is the primary deliverable of the DeepGOPlus model, widely applicable across different environments. All in all, our results show that although the DeepGOPlus CNN is very stable numerically, it can only be selectively implemented with lower-precision floating-point formats. We conclude that predictions obtained from the pre-trained DeepGOPlus model are very reliable numerically, and use existing floating-point formats efficiently. [ABSTRACT FROM AUTHOR]

Protein hotspot residues are key sites that mediate protein-protein interactions. Accurate identification of these residues is essential for understanding the mechanism from protein to function and for designing drug targets. Current research has mostly focused on using machine learning methods to predict hot spots from known interface residues, which artificially extract the corresponding features of amino acid residues from sequence, structure, evolution, energy, and other information to train and test machine learning models. The process is cumbersome, time-consuming and laborious to some extent. This paper proposes a novel idea that develops a pre-trained protein sequence embedding model combined with a one-dimensional convolutional neural network, called Embed-1dCNN, to predict protein hotspot residues. In order to obtain large data samples, this work integrates and extracts data from the datasets of ASEdb, BID, SKEMPI and dbMPIKT to generate a new dataset, and adopts the SMOTE algorithm to expand positive samples to form the training set. The experimental results show that the method achieves an F1 score of 0.82 on the test set. Compared with other hot spot prediction methods, our model achieved better prediction performance. [ABSTRACT FROM AUTHOR]

Introduction: Emerging tick-borne viruses of medical and veterinary importance are increasingly being reported globally. This resurgence emphasizes the need for sustained surveillance to provide insights into tick-borne viral diversity and associated potential public health risks. We report on a virus tentatively designated Kinna virus (KIV) in the family Phenuiviridae and genus Bandavirus. The virus was isolated from a pool of Amblyomma gemma ticks from Kinna in Isiolo County, Kenya. High throughput sequencing of the virus isolate revealed close relatedness to the Guertu virus. The virus genome is consistent with the described genomes of other members of the genus Bandavirus, with nucleotides lengths of 6403, 3332 and 1752 in the Large (L), Medium (M) and Small (S) segments respectively. Phylogenetic analysis showed that the virus clustered with Guertu virus although it formed a distinct and well supported branch. The RdRp amino acid sequence had a 93.3% identity to that of Guertu virus, an indication that the virus is possibly novel. Neutralizing antibodies were detected in 125 (38.6%, 95% CI 33.3–44.1%) of the human sera from the communities in this region. In vivo experiments showed that the virus was lethal to mice with death occurring 6–9 days post-infection. The virus infected mammalian cells (Vero cells) but had reduced infectivity in the mosquito cell line (C636) tested. Conclusion: Isolation of this novel virus with the potential to cause disease in human and animal populations necessitates the need to evaluate its public health significance and contribution to disease burden in the affected regions. This also points to the need for continuous monitoring of vector and human populations in high-risk ecosystems to update pathogen diversity. [ABSTRACT FROM AUTHOR]

Drug discovery relies on predicting drug-target interaction (DTI), which is an important challenging task. The purpose of DTI is to identify the interaction between drug chemical compounds and protein targets. Traditional wet lab experiments are time-consuming and expensive, that's why in recent years, the use of computational methods based on machine learning has attracted the attention of many researchers. Actually, a dry lab environment focusing more on computational methods of interaction prediction can be helpful in limiting search space for wet lab experiments. In this paper, a novel multi-stage approach for DTI is proposed that called SRX-DTI. In the first stage, combination of various descriptors from protein sequences, and a FP2 fingerprint that is encoded from drug are extracted as feature vectors. A major challenge in this application is the imbalanced data due to the lack of known interactions, in this regard, in the second stage, the One-SVM-US technique is proposed to deal with this problem. Next, the FFS-RF algorithm, a forward feature selection algorithm, coupled with a random forest (RF) classifier is developed to maximize the predictive performance. This feature selection algorithm removes irrelevant features to obtain optimal features. Finally, balanced dataset with optimal features is given to the XGBoost classifier to identify DTIs. The experimental results demonstrate that our proposed approach SRX-DTI achieves higher performance than other existing methods in predicting DTIs. The datasets and source code are available at: https://github.com/Khojasteh-hb/SRX-DTI. [ABSTRACT FROM AUTHOR]

With the growing interest in using phages to combat antimicrobial resistance, computational methods for predicting phage-host interactions have been explored to help shortlist candidate phages. Most existing models consider entire proteomes and rely on manual feature engineering, which poses difficulty in selecting the most informative sequence properties to serve as input to the model. In this paper, we framed phage-host interaction prediction as a multiclass classification problem that takes as input the embeddings of a phage's receptor-binding proteins, which are known to be the key machinery for host recognition, and predicts the host genus. We explored different protein language models to automatically encode these protein sequences into dense embeddings without the need for additional alignment or structural information. We show that the use of embeddings of receptor-binding proteins presents improvements over handcrafted genomic and protein sequence features. The highest performance was obtained using the transformer-based protein language model ProtT5, resulting in a 3% to 4% increase in weighted F1 and recall scores across different prediction confidence thresholds, compared to using selected handcrafted sequence features. [ABSTRACT FROM AUTHOR]

Protein secondary structure prediction is extremely important for determining the spatial structure and function of proteins. In this paper, we apply an optimized convolutional neural network and long short-term memory neural network models to protein secondary structure prediction, which is called OCLSTM. We use an optimized convolutional neural network to extract local features between amino acid residues. Then use the bidirectional long short-term memory neural network to extract the remote interactions between the internal residues of the protein sequence to predict the protein structure. Experiments are performed on CASP10, CASP11, CASP12, CB513, and 25PDB datasets, and the good performance of 84.68%, 82.36%, 82.91%, 84.21% and 85.08% is achieved respectively. Experimental results show that the model can achieve better results. [ABSTRACT FROM AUTHOR]

Viral-host protein protein interaction (PPI) analysis is essential to decode the molecular mechanism of viral pathogen and host immunity processes which eventually help to control viral diseases and optimize therapeutics. The state-of-the-art viral-host PPI predictor leverages unsupervised embedding learning technique (doc2vec) to generate statistical representations of viral-host protein sequences and a Random Forest classifier for interaction prediction. However, doc2vec approach generates the statistical representations of viral-host protein sequences by merely modelling the local context of residues which only partially captures residue semantics. The paper in hand proposes a novel technique for generating better statistical representations of viral and host protein sequences based on the infusion of comprehensive local and global contextual information of the residues. While local residue context aware encoding captures semantic relatedness and short range dependencies of residues. Global residue context aware encoding captures comprehensive long-range residues dependencies, positional invariance of residues, and unique residue combination distribution important for interaction prediction. Using concatenated rich statistical representations of viral and host protein sequences, a robust machine learning framework "LGCA-VHPPI" is developed which makes use of a deep forest model to effectively model complex non-linearity of viral-host PPI sequences. An in-depth performance comparison of the proposed LGCA-VHPPI framework with existing diverse sequence encoding schemes based viral-host PPI predictors reveals that LGCA-VHPPI outperforms state-of-the-art predictor by 6%, 2%, and 2% in terms of matthews correlation coefficient over 3 different benchmark viral-host PPI prediction datasets. [ABSTRACT FROM AUTHOR]

Mammalian protein arginine methyltransferase 7 (PRMT7) has been shown to target substrates with motifs containing two arginine residues separated by one other residue (RXR motifs). In particular, the repression domain of human histone H2B (29-RKRSR-33) has been a key substrate in determining PRMT7 activity. We show that incubating human PRMT7 and [3H]-AdoMet with full-length Xenopus laevis histone H2B, containing the substitutions K30R and R31K (RKRSR to RRKSR), results in greatly reduced methylation activity. Using synthetic peptides, we have now focused on the enzymology behind this specificity. We show for the human and Xenopus peptide sequences 23–37 the difference in activity results from changes in the Vmax rather than the apparent binding affinity of the enzyme for the substrates. We then characterized six additional peptides containing a single arginine or a pair of arginine residues flanked by glycine and lysine residues. We have corroborated previous findings that peptides with an RXR motif have much higher activity than peptides that contain only one Arg residue. We show that these peptides have similar apparent km values but significant differences in their Vmax values. Finally, we have examined the effect of ionic strength on these peptides. We found the inclusion of salt had little effect on the Vmax value but a considerable increase in the apparent km value, suggesting that the inhibitory effect of ionic strength on PRMT7 activity occurs largely by decreasing apparent substrate-enzyme binding affinity. In summary, we find that even subtle substitutions in the RXR recognition motif can dramatically affect PRMT7 catalysis. [ABSTRACT FROM AUTHOR]

In this paper, we use network approaches to analyze the relations between protein sequence features for the top hierarchical classes of CATH and SCOP. We use fundamental connectivity measures such as correlation (CR), normalized mutual information rate (nMIR), and transfer entropy (TE) to analyze the pairwise-relationships between the protein sequence features, and use centrality measures to analyze weighted networks constructed from the relationship matrices. In the centrality analysis, we find both commonalities and differences between the different protein 3D structural classes. Results show that all top hierarchical classes of CATH and SCOP present strong non-deterministic interactions for the composition and arrangement features of Cystine (C), Methionine (M), Tryptophan (W), and also for the arrangement features of Histidine (H). The different protein 3D structural classes present different preferences in terms of their centrality distributions and significant features. [ABSTRACT FROM AUTHOR]

Approximate string matching with k-differences has a number of practical applications, ranging from pattern recognition to computational biology. This paper proposes an efficient memory-access algorithm for parallel approximate string matching with k-differences on Graphics Processing Units (GPUs). In the proposed algorithm, all threads in the same GPUs warp share data using warp-shuffle operation instead of accessing the shared memory. Moreover, we implement the proposed algorithm by exploiting the memory structure of GPUs to optimize its performance. Experiment results for real DNA packages revealed that the performance of the proposed algorithm and its implementation archived up to 122.64 and 1.53 times compared to that of sequential algorithm on CPU and previous parallel approximate string matching algorithm on GPUs, respectively. [ABSTRACT FROM AUTHOR]

Background: Identifying conserved regions in protein sequences is a fundamental operation, occurring in numerous sequence-driven analysis pipelines. It is used as a way to decode domain-rich regions within proteins, to compute protein clusters, to annotate sequence function, and to compute evolutionary relationships among protein sequences. A number of approaches exist for identifying and characterizing protein families based on their domains, and because domains represent conserved portions of a protein sequence, the primary computation involved in protein family characterization is identification of such conserved regions. However, identifying conserved regions from large collections (millions) of protein sequences presents significant challenges. Methods: In this paper we present a new, alignment-free method for detecting conserved regions in protein sequences called NADDA (No-Alignment Domain Detection Algorithm). Our method exploits the abundance of exact matching short subsequences (k-mers) to quickly detect conserved regions, and the power of machine learning is used to improve the prediction accuracy of detection. We present a parallel implementation of NADDA using the MapReduce framework and show that our method is highly scalable. Results: We have compared NADDA with Pfam and InterPro databases. For known domains annotated by Pfam, accuracy is 83%, sensitivity 96%, and specificity 44%. For sequences with new domains not present in the training set an average accuracy of 63% is achieved when compared to Pfam. A boost in results in comparison with InterPro demonstrates the ability of NADDA to capture conserved regions beyond those present in Pfam. We have also compared NADDA with ADDA and MKDOM2, assuming Pfam as ground-truth. On average NADDA shows comparable accuracy, more balanced sensitivity and specificity, and being alignment-free, is significantly faster. Excluding the one-time cost of training, runtimes on a single processor were 49s, 10,566s, and 456s for NADDA, ADDA, and MKDOM2, respectively, for a data set comprised of approximately 2500 sequences. [ABSTRACT FROM AUTHOR]

The American manatee (Trichechus manatus), experiencing population declines due to various threats, is the focus of conservation efforts that include the capture, rehabilitation, and release of orphaned calves when their mothers are unable to care for them. These efforts are compromised by the use of commercially available milk substitutes that lack essential components found in natural manatee breast milk, particularly immunoglobulin A (IgA). IgA plays a crucial role in nurturing the immune mucosal system and fostering a healthy microbiota. However, research on IgA in non-maternally fed manatees is limited due to the lack of species-specific reagents. To address this gap, our study employs immuno-informatics analysis to compare IgA sequences from manatees with those from other species, aiming to explore epitope similarity and sharing. We compared the protein sequence of manatee IgA with available IgA sequences, assessing similarity at the sequence, 3D structures, and epitope levels. Our findings reveal that human IgA exhibits the highest similarity in terms of sequence and 3D structure. Additionally, epitope analysis shows high conservation, identity, and similarity of predicted epitopes compared to human IgA. Future studies should focus on functional analysis using human IgA polyclonal reagents to detect manatee IgA in breast milk. Our findings highlight the potential of comparative analysis in advancing the understanding of immunology in non-human animals and overcoming challenges associated with the scarcity of species-specific reagents. [ABSTRACT FROM AUTHOR]

Glutathione transferase P1 (GSTP1) is a multi-functional protein that protects cells from electrophiles by catalyzing their conjugation with glutathione, and contributes to the regulation of cell proliferation, apoptosis, and signalling. GSTP1, usually described as a cytosolic enzyme, can localize to other cell compartments and we have reported its strong association with the plasma membrane. In the current study, the hypothesis that GSTP1 is palmitoylated and this modification facilitates its dynamic localization and function was investigated. Palmitoylation is the reversible post-translational addition of a 16-C saturated fatty acid to proteins, most commonly on Cys residues through a thioester bond. GSTP1 in MCF7 cells was modified by palmitate, however, GSTP1 Cys to Ser mutants (individual and Cys-less) retained palmitoylation. Treatment of palmitoylated GSTP1 with 0.1 N NaOH, which cleaves ester bonds, did not remove palmitate. Purified GSTP1 was spontaneously palmitoylated in vitro and peptide sequencing revealed that Cys48 and Cys102 undergo S-palmitoylation, while Lys103 undergoes the rare N-palmitoylation. N-palmitoylation occurs via a stable NaOH-resistant amide bond. Analysis of subcellular fractions of MCF7-GSTP1 cells and a modified proximity ligation assay revealed that palmitoylated GSTP1 was present not only in the membrane fraction but also in the cytosol. GSTP1 isolated from E. coli, and MCF7 cells (grown under fatty acid free or regular conditions), associated with plasma membrane-enriched fractions and this association was not altered by palmitoyl CoA. Overall, GSTP1 is modified by palmitate, at multiple sites, including at least one non-Cys residue. These modifications could contribute to regulating the diverse functions of GSTP1. [ABSTRACT FROM AUTHOR]

The IUCN Red List of Threatened Species contains 175 Brazilian bat species that are threatened by extinction in some degree. From this perspective, it is essential to expand the knowledge about the genetic diversity of vulnerable bats. Genomic sequencing can be useful to generate robust and informative genetic references, increasing resolution when analyzing relationships among populations, species, or higher taxonomic levels. In this study, we sequenced and characterized in detail the first complete mitochondrial genomes of Furipterus horrens, Lonchorhina aurita, and Natalus macrourus, and investigated their phylogenetic position based on amino acid sequences of protein-coding genes (PCGs). The mitogenomes of these species are 16,516, 16,697, and 16,668 bp in length, respectively, and each comprises 13 PCGs, 22 tRNA genes, two rRNA genes, and a putative control region (CR). In the three species, genes were arranged similarly to all other previously described bat mitogenomes, and nucleotide composition was also consistent with the reported range. The length and arrangement of rrnS and rrnL were also consistent with those of other bat species, showing a positive AT-skew and a negative GC-skew. Except for trnS1, for which we did not observe the DHU arm, all other tRNAs showed the cloverleaf secondary structure in the three species. In addition, the mitogenomes showed minor differences in start and stop codons, and in all PCGs, codons ending in adenine were more common compared to those ending in guanine. We found that PCGs of the three species use multiple codons to encode each amino acid, following the previously documented pattern. Furthermore, all PCGs are under purifying selection, with atp8 experiencing the most relaxed purifying selection. Considering the phylogenetic reconstruction, F. horrens was recovered as sister to Noctilio leporinus, L. aurita and Tonatia bidens shared a node within Phyllostomidae, and N. macrourus appeared as sister to Molossidae and Vespertilionidae. [ABSTRACT FROM AUTHOR]

Celiac disease (CD) is an inflammatory disorder of the upper small intestine caused by the ingestion of storage proteins (prolamins and glutelins) from wheat, barley, rye, and, in rare cases, oats. CD patients need to follow a gluten-free diet by consuming gluten-free products with gluten contents of less than 20 mg/kg. Currently, the recommended method for the quantitative determination of gluten is an enzyme-linked immunosorbent assay (ELISA) based on the R5 monoclonal antibody. Because the R5 ELISA mostly detects the prolamin fraction of gluten, a new independent method is required to detect prolamins as well as glutelins. This paper presents the development of a method to quantitate 16 wheat marker peptides derived from all wheat gluten protein types by liquid chromatography tandem mass spectrometry (LC-MS/MS) in the multiple reaction monitoring mode. The quantitation of each marker peptide in the chymotryptic digest of a defined amount of the respective reference wheat protein type resulted in peptide-specific yields. This enabled the conversion of peptide into protein type concentrations. Gluten contents were expressed as sum of all determined protein type concentrations. This new method was applied to quantitate gluten in wheat starches and compared to R5 ELISA and gel-permeation high-performance liquid chromatography with fluorescence detection (GP-HPLC-FLD), which resulted in a strong correlation between LC-MS/MS and the other two methods. [ABSTRACT FROM AUTHOR]

DNA-binding proteins play pivotal roles in alternative splicing, RNA editing, methylating and many other biological functions for both eukaryotic and prokaryotic proteomes. Predicting the functions of these proteins from primary amino acids sequences is becoming one of the major challenges in functional annotations of genomes. Traditional prediction methods often devote themselves to extracting physiochemical features from sequences but ignoring motif information and location information between motifs. Meanwhile, the small scale of data volumes and large noises in training data result in lower accuracy and reliability of predictions. In this paper, we propose a deep learning based method to identify DNA-binding proteins from primary sequences alone. It utilizes two stages of convolutional neutral network to detect the function domains of protein sequences, and the long short-term memory neural network to identify their long term dependencies, an binary cross entropy to evaluate the quality of the neural networks. When the proposed method is tested with a realistic DNA binding protein dataset, it achieves a prediction accuracy of 94.2% at the Matthew’s correlation coefficient of 0.961. Compared with the LibSVM on the arabidopsis and yeast datasets via independent tests, the accuracy raises by 9% and 4% respectively. Comparative experiments using different feature extraction methods show that our model performs similar accuracy with the best of others, but its values of sensitivity, specificity and AUC increase by 27.83%, 1.31% and 16.21% respectively. Those results suggest that our method is a promising tool for identifying DNA-binding proteins. [ABSTRACT FROM AUTHOR]

This study presents the complete genome sequence of a novel nege-like virus identified in whiteflies (Bemisia tabaci MEAM1), provisionally designated as whitefly negevirus 1 (WfNgV1). The virus possesses a single-stranded RNA genome comprising 11,848 nucleotides, organized into four open reading frames (ORFs). These ORFs encode the putative RNA-dependent-RNA-polymerase (RdRp, ORF 1), a glycoprotein (ORF 2), a structural protein with homology to those in the SP24 family, (ORF 3), and a protein of unknown function (ORF 4). Phylogenetic analysis focusing on RdRp and SP24 amino acid sequences revealed a close relationship between WfNgV1 and Bemisia tabaci negevirus 1, a negevirus sequence recently discovered in whiteflies from Israel. Both viruses form a clade sharing a most recent common ancestor with the proposed nelorpivirus and centivirus taxa. The putative glycoprotein from ORF 2 and SP24 (ORF 3) of WfNgV1 exhibit the characteristic topologies previously reported for negevirus counterparts. This marks the first reported negevirus-like sequence from whiteflies in the Americas. [ABSTRACT FROM AUTHOR]

Since the importance of DNA-binding proteins in multiple biomolecular functions has been recognized, an increasing number of researchers are attempting to identify DNA-binding proteins. In recent years, the machine learning methods have become more and more compelling in the case of protein sequence data soaring, because of their favorable speed and accuracy. In this paper, we extract three features from the protein sequence, namely NMBAC (Normalized Moreau-Broto Autocorrelation), PSSM-DWT (Position-specific scoring matrix—Discrete Wavelet Transform), and PSSM-DCT (Position-specific scoring matrix—Discrete Cosine Transform). We also employ feature selection algorithm on these feature vectors. Then, these features are fed into the training SVM (support vector machine) model as classifier to predict DNA-binding proteins. Our method applys three datasets, namely PDB1075, PDB594 and PDB186, to evaluate the performance of our approach. The PDB1075 and PDB594 datasets are employed for Jackknife test and the PDB186 dataset is used for the independent test. Our method achieves the best accuracy in the Jacknife test, from 79.20% to 86.23% and 80.5% to 86.20% on PDB1075 and PDB594 datasets, respectively. In the independent test, the accuracy of our method comes to 76.3%. The performance of independent test also shows that our method has a certain ability to be effectively used for DNA-binding protein prediction. The data and source code are at . [ABSTRACT FROM AUTHOR]

Protein classification is one of the critical problems in bioinformatics. Early studies used geometric distances and polygenetic-tree to classify proteins. These methods use binary trees to present protein classification. In this paper, we propose a new protein classification method, whereby theories of information and networks are used to classify the multivariate relationships of proteins. In this study, protein universe is modeled as an undirected network, where proteins are classified according to their connections. Our method is unsupervised, multivariate, and alignment-free. It can be applied to the classification of both protein sequences and structures. Nine examples are used to demonstrate the efficiency of our new method. [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. [ABSTRACT FROM AUTHOR]

The red abalone (Haliotis rufescens) represents North America's most important aquaculture species. Its hepatopancreas is rich in cellulases and other polysaccharide-degrading enzymes, which provide it the remarkable ability to digest cellulose-rich macroalgae; nevertheless, its cellulolytic systems are poorly explored. This manuscript describes some functional and structural properties of an endogenous trimeric glycosylated endoglucanase from H. rufescens. The purified enzyme showed a molecular mass of 23.4 kDa determined by MALDI-TOF mass spectrometry, which behaved as a homotrimer in gel filtration chromatography and zymograms. According to the periodic acid-Schiff reagent staining, detecting sugar moieties in SDS-PAGE gel confirmed that abalone cellulase is a glycoprotein. Hydrolysis of cello-oligosaccharides and p-nitrophenyl-β-D-glucopyranosides confirmed its endo/exoactivity. A maximum enzyme activity toward 0.5% (w/v) carboxymethylcellulose of 53.9 ± 1.0 U/mg was achieved at 45°C and pH 6.0. We elucidated the abalone cellulase primary structure using proteases and mass spectrometry methods. Based on these results and using a bioinformatic approach, we identified the gene encoding this enzyme and deduced its full-length amino acid sequence; the mature protein comprised 177 residues with a calculated molecular mass of 19.1 kDa and, according to sequence similarity, it was classified into the glycosyl-hydrolase family 45 subfamily B. An AlphaFold theoretical model and docking simulations with cellopentaose confirmed that abalone cellulase is a β-sheet rich protein, as also observed by circular dichroism experiments, with conserved catalytic residues: Asp26, Asn109, and Asp134. Interestingly, the AlphaFold-Multimer analysis indicated a trimeric assembly for abalone cellulase, which supported our experimental findings. The discovery and characterization of these enzymes may contribute to developing efficient cellulose bioconversion processes for biofuels and sustainable bioproducts. [ABSTRACT FROM AUTHOR]

Monogenic diabetes is characterized as a group of diseases caused by rare variants in single genes. Like for other rare diseases, multiple genes have been linked to monogenic diabetes with different measures of pathogenicity, but the information on the genes and variants is not unified among different resources, making it challenging to process them informatically. We have developed an automated pipeline for collecting and harmonizing data on genetic variants linked to monogenic diabetes. Furthermore, we have translated variant genetic sequences into protein sequences accounting for all protein isoforms and their variants. This allows researchers to consolidate information on variant genes and proteins linked to monogenic diabetes and facilitates their study using proteomics or structural biology. Our open and flexible implementation using Jupyter notebooks enables tailoring and modifying the pipeline and its application to other rare diseases. [ABSTRACT FROM AUTHOR]

The black soldier fly (BSF), Hermetia illucens, has the potential to serve as a valuable resource for waste bioconversion due to the ability of the larvae to thrive in a microbial-rich environment. Being an ecological decomposer, the survival of BSF larvae (BSFL) relies on developing an efficient defense system. Cathepsin L (CTSL) is a cysteine protease that plays roles in physiological and pathological processes. In this study, the full-length of CTSL was obtained from BSF. The 1,020-bp open reading frame encoded a preprotein of 339 amino acids with a predicted molecular weight of 32 kDa. The pro-domain contained the conserved ERFNIN, GNYD, and GCNGG motifs, which are all characteristic of CTSL. Homology revealed that the deduced amino acid sequence of BSF CTSL shared 74.22–72.99% identity with Diptera flies. Immunohistochemical (IHC) analysis showed the CTSL was predominantly localized in the gut, especially in the midgut. The mRNA expression of CTSL in different larval stages was analyzed by quantitative real-time PCR (RT-qPCR), which revealed that CTSL was expressed in the second to sixth instar, with the highest expression in the fifth instar. Following an immune challenge in vivo using Escherichia coli (E. coli), CTSL mRNA was significantly up-regulated at 6 h post-stimulation. The Z-Phe-Arg-AMC was gradually cleaved by the BSFL extract after 3 h post-stimulation. These results shed light on the potential role of CTSL in the defense mechanism that helps BSFL to survive against pathogens in a microbial-rich environment. [ABSTRACT FROM AUTHOR]

Analbuminemia is characterized by the near absence of albumin in the plasma. Different methods are available for measuring albumin levels, but they do not necessarily agree with one another. It is a concern that analbuminemic samples could be falsely characterized due to the incorrect estimation of albumin. The objective of the work was to evaluate the performance of different assays in detecting analbuminemia. Albumin knockout (Alb-/-) mouse plasma was used to test the suitability of different albumin assays for their ability to properly characterize extreme albumin deficiency. Bromocresol green (BCG), bromocresol purple (BCP), enzyme-linked immunosorbent assay (ELISA), liquid chromatography-tandem mass spectrometry (LC-MS/MS), and gel electrophoresis were tested. The LC-MS/MS assay exhibited broad coverage of the amino acid sequence of albumin and indicated 8,400-fold lower (P<0.0001) albumin expression in Alb-/- than wildtype (WT), demonstrating its suitability for identifying extreme albumin deficiency. ELISA estimated albumin at 1.5±0.1 g/dL in WT and was below the detection limit in all Alb-/- samples. Gel electrophoresis yielded consistent results with LC-MS/MS and ELISA. The BCG assay overestimated albumin with apparently appreciable albumin concentrations in Alb-/- mice, yet the assay still indicated a significant difference between genotypes (Alb-/-, 1.2±0.05 g/dL, WT, 3.7±0.1 g/dL, P<0.0001). BCP drastically overestimated albumin and could not successfully identify the known analbuminemic phenotype of Alb-/- mice. By using Alb-/- plasma as a reference material and LC-MS/MS as a reference method, ELISA and gel electrophoresis appear appropriate for identifying analbuminemia, while BCG and BCP are not suitable. It is concluded that dye-binding assays should be avoided when extreme hypoalbuminemia or analbuminemia is suspected. [ABSTRACT FROM AUTHOR]

FASTA and FASTQ are basic and ubiquitous formats for storing nucleotide and protein sequences. Common manipulations of FASTA/Q file include converting, searching, filtering, deduplication, splitting, shuffling, and sampling. Existing tools only implement some of these manipulations, and not particularly efficiently, and some are only available for certain operating systems. Furthermore, the complicated installation process of required packages and running environments can render these programs less user friendly. This paper describes a cross-platform ultrafast comprehensive toolkit for FASTA/Q processing. SeqKit provides executable binary files for all major operating systems, including Windows, Linux, and Mac OSX, and can be directly used without any dependencies or pre-configurations. SeqKit demonstrates competitive performance in execution time and memory usage compared to similar tools. The efficiency and usability of SeqKit enable researchers to rapidly accomplish common FASTA/Q file manipulations. SeqKit is open source and available on Github at . [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]

We introduce a new representation and feature extraction method for biological sequences. Named bio-vectors (BioVec) to refer to biological sequences in general with protein-vectors (ProtVec) for proteins (amino-acid sequences) and gene-vectors (GeneVec) for gene sequences, this representation can be widely used in applications of deep learning in proteomics and genomics. In the present paper, we focus on protein-vectors that can be utilized in a wide array of bioinformatics investigations such as family classification, protein visualization, structure prediction, disordered protein identification, and protein-protein interaction prediction. In this method, we adopt artificial neural network approaches and represent a protein sequence with a single dense n-dimensional vector. To evaluate this method, we apply it in classification of 324,018 protein sequences obtained from Swiss-Prot belonging to 7,027 protein families, where an average family classification accuracy of 93%±0.06% is obtained, outperforming existing family classification methods. In addition, we use ProtVec representation to predict disordered proteins from structured proteins. Two databases of disordered sequences are used: the DisProt database as well as a database featuring the disordered regions of nucleoporins rich with phenylalanine-glycine repeats (FG-Nups). Using support vector machine classifiers, FG-Nup sequences are distinguished from structured protein sequences found in Protein Data Bank (PDB) with a 99.8% accuracy, and unstructured DisProt sequences are differentiated from structured DisProt sequences with 100.0% accuracy. These results indicate that by only providing sequence data for various proteins into this model, accurate information about protein structure can be determined. Importantly, this model needs to be trained only once and can then be applied to extract a comprehensive set of information regarding proteins of interest. Moreover, this representation can be considered as pre-training for various applications of deep learning in bioinformatics. The related data is available at Life Language Processing Website: and Harvard Dataverse: . [ABSTRACT FROM AUTHOR]

Knowledge-based potentials are energy functions derived from the analysis of databases of protein structures and sequences. They can be divided into two classes. Potentials from the first class are based on a direct conversion of the distributions of some geometric properties observed in native protein structures into energy values, while potentials from the second class are trained to mimic quantitatively the geometric differences between incorrectly folded models and native structures. In this paper, we focus on the relationship between energy and geometry when training the second class of knowledge-based potentials. We assume that the difference in energy between a decoy structure and the corresponding native structure is linearly related to the distance between the two structures. We trained two distance-based knowledge-based potentials accordingly, one based on all inter-residue distances (PPD), while the other had the set of all distances filtered to reflect consistency in an ensemble of decoys (PPE). We tested four types of metric to characterize the distance between the decoy and the native structure, two based on extrinsic geometry (RMSD and GTD-TS*), and two based on intrinsic geometry (Q* and MT). The corresponding eight potentials were tested on a large collection of decoy sets. We found that it is usually better to train a potential using an intrinsic distance measure. We also found that PPE outperforms PPD, emphasizing the benefits of capturing consistent information in an ensemble. The relevance of these results for the design of knowledge-based potentials is discussed. [ABSTRACT FROM AUTHOR]

Phylogenetic analysis of protein sequences provides a powerful means of identifying novel protein functions and subfamilies, and for identifying and resolving annotation errors. However, automation of functional clustering based on phylogenetic trees has been challenging and most of it is done manually. Clustering phylogenetic trees usually requires the delineation of tree-based thresholds (e.g., distances), leading to an ad hoc problem. We propose a new phylogenetic clustering approach that identifies clusters without using ad hoc distances or other pre-defined values. Our workflow combines uniform manifold approximation and projection (UMAP) with Gaussian mixture models as a k-means like procedure to automatically group sequences into clusters. We then apply a "second pass" clade identification algorithm to resolve non-monophyletic groups. We tested our approach with several well-curated protein families (outer membrane porins, acyltransferase, and nuclear receptors) and showed our automated methods recapitulated known subfamilies. We also applied our methods to a broad range of different protein families from multiple databases, including Pfam, PANTHER, and UniProt, and to alignments of RNA viral genomes. Our results showed that AutoPhy rapidly generated monophyletic clusters (subfamilies) within phylogenetic trees evolving at very different rates both within and among phylogenies. The phylogenetic clusters generated by AutoPhy resolved misannotations and identified new protein functional groups and novel viral strains. [ABSTRACT FROM AUTHOR]

Spinocerebellar ataxia 2 (SCA2) is a neurodegenerative disorder caused by the expansion of the poly-glutamine (polyQ) tract of Ataxin-2 (ATXN2). Other polyQ-containing proteins such as ATXN7 and huntingtin are associated with the development of neurodegenerative diseases when their N-terminal polyQ domains are expanded. Furthermore, they undergo proteolytic processing events that produce N-terminal fragments that include the polyQ stretch, which are implicated in pathogenesis. Interestingly, N-terminal ATXN2 fragments were reported in a brain extract from a SCA2 patient, but it is currently unknown whether an expanded polyQ domain contributes to ATXN2 proteolytic susceptibility. Here, we used transient expression in HEK293 cells to determine whether ATXN2 is a target for specific N-terminal proteolysis. We found that ATXN2 proteins with either normal or expanded polyQ stretches undergo proteolytic cleavage releasing an N-terminal polyQ-containing fragment. We identified a short amino acid sequence downstream of the polyQ domain that is necessary for N-terminal cleavage of full-length ATXN2 and sufficient to induce proteolysis of a heterologous protein. However, this sequence is not required for cleavage of a short ATXN2 isoform produced from an alternative start codon located just upstream of the CAG repeats encoding the polyQ domain. Our study extends our understanding of ATXN2 posttranslational regulation by revealing that this protein can be the target of specific proteolytic cleavage events releasing polyQ-containing products that are modulated by the N-terminal domain of ATXN2. N-terminal ATXN2 proteolysis of expanded polyQ domains might contribute to SCA2 pathology, as observed in other neurodegenerative disorders caused by polyQ domain expansion. [ABSTRACT FROM AUTHOR]

Proteins are fundamental components of diverse cellular systems and play crucial roles in a variety of disease processes. Consequently, it is crucial to comprehend their structure, function, and intricate interconnections. Classifying proteins into families or groups with comparable structural and functional characteristics is a crucial aspect of this comprehension. This classification is crucial for evolutionary research, predicting protein function, and identifying potential therapeutic targets. Sequence alignment and structure-based alignment are frequently ineffective techniques for identifying protein families.This study addresses the need for a more efficient and accurate technique for feature extraction and protein classification. The research proposes a novel method that integrates bispectrum characteristics, deep learning techniques, and machine learning algorithms to overcome the limitations of conventional methods. The proposed method uses numbers to represent protein sequences, utilizes bispectrum analysis, uses different topologies for convolutional neural networks to pull out features, and chooses robust features to classify protein families. The goal is to outperform existing methods for identifying protein families, thereby enhancing classification metrics. The materials consist of numerous protein datasets, whereas the methods incorporate bispectrum characteristics and deep learning strategies. The results of this study demonstrate that the proposed method for identifying protein families is superior to conventional approaches. Significantly enhanced quality metrics demonstrated the efficacy of the combined bispectrum and deep learning approaches. These findings have the potential to advance the field of protein biology and facilitate pharmaceutical innovation. In conclusion, this study presents a novel method that employs bispectrum characteristics and deep learning techniques to improve the precision and efficiency of protein family identification. The demonstrated advancements in classification metrics demonstrate this method's applicability to numerous scientific disciplines. This furthers our understanding of protein function and its implications for disease and treatment. [ABSTRACT FROM AUTHOR]

Microtubules are polymeric filaments, constructed of α-β tubulin heterodimers that underlie critical subcellular structures in eukaryotic organisms. Four homologous proteins (γ-, δ-, ε- and ζ-tubulin) additionally contribute to specialized microtubule functions. Although there is an immense volume of publicly available data pertaining to tubulins, it is difficult to assimilate all potentially relevant information across diverse organisms, isotypes, and categories of data. We previously assembled an extensive web-based catalogue of published missense mutations to tubulins with >1,500 entries that each document a specific substitution to a discrete tubulin, the species where the mutation was described and the associated phenotype with hyperlinks to the amino acid sequence and citation(s) for research. This report describes a significant update and expansion of our online resource (TubulinDB.bio.uci.edu) to nearly 18,000 entries. It now encompasses a cross-referenced catalog of post-translational modifications (PTMs) to tubulin drawn from public datasets, primary literature, and predictive algorithms. In addition, tubulin protein structures were used to define local interactions with bound ligands (GTP, GDP and diverse microtubule-targeting agents) and amino acids at the intradimer interface, within the microtubule lattice and with associated proteins. To effectively cross-reference these datasets, we established a universal tubulin numbering system to map entries into a common framework that accommodates specific insertions and deletions to tubulins. Indexing and cross-referencing permitted us to discern previously unappreciated patterns. We describe previously unlinked observations of loss of PTM sites in the context of cancer cells and tubulinopathies. Similarly, we expanded the set of clinical substitutions that may compromise MAP or microtubule-motor interactions by collecting tubulin missense mutations that alter amino acids at the interface with dynein and doublecortin. By expanding the database as a curated resource, we hope to relate model organism data to clinical findings of pathogenic tubulin variants. Ultimately, we aim to aid researchers in hypothesis generation and design of studies to dissect tubulin function. [ABSTRACT FROM AUTHOR]

With the numerous genomes sequenced today, it has been revealed that a noteworthy percentage of genes in a given taxon of organisms in the phylogenetic tree of life do not have orthologous sequences in other taxa. These sequences are commonly referred to as "orphans" or "ORFans" if found as single occurrences in a single species or as "taxonomically restricted genes" (TRGs) when found at higher taxonomic levels. Quantitative and collective studies of these genes are necessary for understanding their biological origins. However, the current software for identifying orphan genes is limited in its functionality, database search range, and very complex algorithmically. Thus, researchers studying orphan genes must harvest their data from many disparate sources. ORFanID is a graphical web-based search engine that facilitates the efficient identification of both orphan genes and TRGs at all taxonomic levels, from DNA or amino acid sequences in the NCBI database cluster and other large bioinformatics repositories. The software allows users to identify genes that are unique to any taxonomic rank, from species to domain, using NCBI systematic classifiers. It provides control over NCBI database search parameters, and the results are presented in a spreadsheet as well as a graphical display. The tables in the software are sortable, and results can be filtered using the fuzzy search functionality. The visual presentation can be expanded and collapsed by the taxonomic tree to its various branches. Example results from searches on five species and gene expression data from specific orphan genes are provided in the Supplementary Information. [ABSTRACT FROM AUTHOR]

Aelia fieberi Scott, 1874 is a pest of crops. The mitogenome of A. fieberi (OL631608) was decoded by next-generation sequencing. The mitogenome, with 41.89% A, 31.70% T, 15.44% C and 10.97% G, is 15,471 bp in size. The phylogenetic tree showed that Asopinae and Phyllocephalinae were monophyletic; however, Pentatominae and Podopinae were not monophyletic, suggesting that the phylogenetic relationships of Pentatomoidae are complex and need revaluation and revision. Phytophagous bugs had a ~20-nucleotide longer in nad2 than predatory bugs. There were differences in amino acid sequence at six sites between phytophagous bugs and predatory bugs. The codon usage analysis indicated that frequently used codons used either A or T at the third position of the codon. The analysis of amino acid usage showed that leucine, isoleucine, serine, methionine, and phenylalanine were the most abundant in 53 species of Pentatomoidae. Thirteen protein-coding genes were evolving under purifying selection, cox1, and atp8 had the strongest and weakest purifying selection stress, respectively. Phytophagous bugs and predatory bugs had different evolutionary rates for eight genes. The mitogenomic information of A. fieberi could fill the knowledge gap for this important crop pest. The differences between phytophagous bugs and predatory bugs deepen our understanding of the effect of feeding habit on mitogenome. [ABSTRACT FROM AUTHOR]

One of the first steps in protein sequence analysis is comparing sequences to look for similarities. We propose an information theoretical distance to compare cellular automata representing protein sequences, and determine similarities. Our approach relies in a stationary Hamming distance for the evolution of the automata according to a properly chosen rule, and to build a pairwise similarity matrix and determine common ancestors among different species in a simpler and less computationally demanding computer codes when compared to other methods. [ABSTRACT FROM AUTHOR]

Nuclear receptors (NRs) are important transcriptional modulators in metazoans. Typical NRs possess a conserved DNA binding domain (DBD) and a ligand binding domain (LBD). Since we discovered a type of novel NRs each of them has two DBDs and single LBD (2DBD-NRs) more than decade ago, there has been very few studies about 2DBD-NRs. Recently, 2DBD-NRs have been only reported in Platyhelminths and Mollusca and are thought to be specific NRs to lophotrochozoan. In this study, we searched different databases and identified 2DBD-NRs in different animals from both protostomes and deuterostomes. Phylogenetic analysis shows that at least two ancient 2DBD-NR genes were present in the urbilaterian, a common ancestor of protostomes and deuterostomes. 2DBD-NRs underwent gene duplication and loss after the split of different animal phyla, most of them in a certain animal phylum are paralogues, rather than orthologues, like in other animal phyla. Amino acid sequence analysis shows that the conserved motifs in typical NRs are also present in 2DBD-NRs and they are gene specific. From our phylogenetic analysis of 2DBD-NRs and following the rule of Nomenclature System for the Nuclear Receptors, a nomenclature for 2DBD-NRs is proposed. [ABSTRACT FROM AUTHOR]

We report here the first occurrence of an adenosine deaminase-related growth factor (ADGF) that deaminates adenosine 5' monophosphate (AMP) in preference to adenosine. The ADGFs are a group of secreted deaminases found throughout the animal kingdom that affect the extracellular concentration of adenosine by converting it to inosine. The AMP deaminase studied here was first isolated and biochemically characterized from the roman snail Helix pomatia in 1983. Determination of the amino acid sequence of the AMP deaminase enabled sequence comparisons to protein databases and revealed it as a member of the ADGF family. Cloning and expression of its cDNA in Pichia pastoris allowed the comparison of the biochemical characteristics of the native and recombinant forms of the enzyme and confirmed they correspond to the previously reported activity. Uncharacteristically, the H. pomatia AMP deaminase was determined to be dissimilar to the AMP deaminase family by sequence comparison while demonstrating similarity to the ADGFs despite having AMP as its preferred substrate rather than adenosine. [ABSTRACT FROM AUTHOR]

The structure and sequence of proteins strongly influence their biological functions. New models and algorithms can help researchers in understanding how the evolution of sequences and structures is related to changes in functions. Recently, studies of SARS-CoV-2 Spike (S) protein structures have been performed to predict binding receptors and infection activity in COVID-19, hence the scientific interest in the effects of virus mutations due to sequence, structure and vaccination arises. However, there is the need for models and tools to study the links between the evolution of S protein sequence, structure and functions, and virus transmissibility and the effects of vaccination. As studies on S protein have been generated a large amount of relevant information, we propose in this work to use Protein Contact Networks (PCNs) to relate protein structures with biological properties by means of network topology properties. Topological properties are used to compare the structural changes with sequence changes. We find that both node centrality and community extraction analysis can be used to relate protein stability and functionality with sequence mutations. Starting from this we compare structural evolution to sequence changes and study mutations from a temporal perspective focusing on virus variants. Finally by applying our model to the Omicron variant we report a timeline correlation between Omicron and the vaccination campaign. [ABSTRACT FROM AUTHOR]

The organization of homologous protein sequences into multiple sequence alignments (MSAs) is a cornerstone of modern analysis of proteins. Recent focus on the importance of alternatively-spliced isoforms in disease and cell biology has highlighted the need for MSA software that can appropriately account for isoforms and the exon-length insertions or deletions that isoforms may have relative to each other. We previously developed Mirage, a software package for generating MSAs for isoforms spanning multiple species. Here, we present Mirage2, which retains the fundamental algorithms of the original Mirage implementation while providing substantially improved translated mapping and improving several aspects of usability. We demonstrate that Mirage2 is highly effective at mapping proteins to their encoding exons, and that these protein-genome mappings lead to extremely accurate intron-aware alignments. Additionally, Mirage2 implements a number of engineering improvements that simplify installation and use. [ABSTRACT FROM AUTHOR]

AlphaFold changed the field of structural biology by achieving three-dimensional (3D) structure prediction from protein sequence at experimental quality. The astounding success even led to claims that the protein folding problem is "solved". However, protein folding problem is more than just structure prediction from sequence. Presently, it is unknown if the AlphaFold-triggered revolution could help to solve other problems related to protein folding. Here we assay the ability of AlphaFold to predict the impact of single mutations on protein stability (ΔΔG) and function. To study the question we extracted the pLDDT and metrics from AlphaFold predictions before and after single mutation in a protein and correlated the predicted change with the experimentally known ΔΔG values. Additionally, we correlated the same AlphaFold pLDDT metrics with the impact of a single mutation on structure using a large scale dataset of single mutations in GFP with the experimentally assayed levels of fluorescence. We found a very weak or no correlation between AlphaFold output metrics and change of protein stability or fluorescence. Our results imply that AlphaFold may not be immediately applied to other problems or applications in protein folding. [ABSTRACT FROM AUTHOR]

Proteins are diverse with their sequences, structures and functions, it is important to study the relations between the sequences, structures and functions. In this paper, we conduct a study that surveying the relations between the protein sequences and their structures. In this study, we use the natural vector (NV) and the averaged property factor (APF) features to represent protein sequences into feature vectors, and use the multi-class MSE and the convex hull methods to separate proteins of different structural classes into different regions. We found that proteins from different structural classes are separable by hyper-planes and convex hulls in the natural vector feature space, where the feature vectors of different structural classes are separated into disjoint regions or convex hulls in the high dimensional feature spaces. The natural vector outperforms the averaged property factor method in identifying the structures, and the convex hull method outperforms the multi-class MSE in separating the feature points. These outcomes convince the strong connections between the protein sequences and their structures, and may imply that the amino acids composition and their sequence arrangements represented by the natural vectors have greater influences to the structures than the averaged physical property factors of the amino acids. [ABSTRACT FROM AUTHOR]

Spontaneous tumors are a major cause of death in cats. Treatment of human tumors has progressed dramatically in the past decade, partly due to the success of immunotherapies using immune checkpoint inhibitors, such as anti-programmed death 1 (PD-1) and anti-PD-ligand 1 (PD-L1) antibodies. However, little is known about the PD-1 pathway and its association with tumor disease in cats. This study investigated the applicability of anti-PD-1/PD-L1 therapy in feline tumors. We first determined the complete coding sequence of feline PD-L1 and PD-L2, and found that the deduced amino acid sequences of feline PD-L1/PD-L2 share high sequence identities (66–83%) with orthologs in other mammalian species. We prepared recombinant feline PD-1, PD-L1, and PD-L2 proteins and confirmed receptor–ligand binding between PD-1 and PD-L1/PD-L2 using flow cytometry. Next, we established an anti-feline PD-L1 monoclonal antibody (clone CL1Mab-7) to analyze the expression of PD-L1. Flow cytometry using CL1Mab-7 revealed the cell surface expression of PD-L1 in a feline macrophage (Fcwf-4) and five mammary adenocarcinoma cell lines (FKNp, FMCm, FYMp, FONp, and FONm), and showed that PD-L1 expression was upregulated by interferon-γ stimulation. Finally, immunohistochemistry using CL1Mab-7 also showed PD-L1 expression in feline squamous cell carcinoma (5/5, 100%), mammary adenocarcinoma (4/5, 80%), fibrosarcoma (5/5, 100%), and renal cell carcinoma (2/2, 100%) tissues. Our results strongly encourage further investigations of the PD-1/PD-L1 pathway as a potential therapeutic target for feline tumors. [ABSTRACT FROM AUTHOR]

Identification of peptides by analysis of data acquired by the two established methods for bottom-up proteomics, DDA and DIA, relies heavily on the fragment spectra. In DDA, peptide features detected in mass spectrometry data are identified by matching their fragment spectra with a peptide database. In DIA, a peptide's fragment spectra are targeted for extraction and matched with observed spectra. Although fragment ion matching is a central aspect in most peptide identification strategies, the precursor ion in the MS1 data reveals important characteristics as well, including charge state, intensity, monoisotopic m/z, and apex in retention time. Most importantly, the precursor's mass is essential in determining the potential chemical modification state of the underlying peptide sequence. In the timsTOF, with its additional dimension of collisional cross-section, the data representing the precursor ion also reveals the peptide's peak in ion mobility. However, the availability of tools to survey precursor ions with a wide range of abundance in timsTOF data across the full mass range is very limited. Here we present a de novo feature detector called three-dimensional intensity descent (3DID). 3DID can detect and extract peptide features down to a configurable intensity level, and finds many more features than several existing tools. 3DID is written in Python and is freely available with an open-source MIT license to facilitate experimentation and further improvement (DOI 10.5281/zenodo.6513126). The dataset used for validation of the algorithm is publicly available (ProteomeXchange identifier PXD030706). [ABSTRACT FROM AUTHOR]

The three-dimensional structure of the synthetic lung Surfactant Protein B Peptide Super Mini-B was determined using an integrative experimental approach, including mass spectrometry and isotope enhanced Fourier-transform infrared (FTIR) spectroscopy. Mass spectral analysis of the peptide, oxidized by solvent assisted region-specific disulfide formation, confirmed that the correct folding and disulfide pairing could be facilitated using two different oxidative structure-promoting solvent systems. Residue specific analysis by isotope enhanced FTIR indicated that the N-terminal and C-terminal domains have well defined α-helical amino acid sequences. Using these experimentally derived measures of distance constraints and disulfide connectivity, the ensemble was further refined with molecular dynamics to provide a medium resolution, residue-specific structure for the peptide construct in a simulated synthetic lung surfactant lipid multilayer environment. The disulfide connectivity combined with the α-helical elements stabilize the peptide conformationally to form a helical hairpin structure that resembles critical elements of the Saposin protein fold of the predicted full-length Surfactant Protein B structure. [ABSTRACT FROM AUTHOR]

Drug–target protein interaction (DTI) identification is fundamental for drug discovery and drug repositioning, because therapeutic drugs act on disease-causing proteins. However, the DTI identification process often requires expensive and time-consuming tasks, including biological experiments involving large numbers of candidate compounds. Thus, a variety of computation approaches have been developed. Of the many approaches available, chemo-genomics feature-based methods have attracted considerable attention. These methods compute the feature descriptors of drugs and proteins as the input data to train machine and deep learning models to enable accurate prediction of unknown DTIs. In addition, attention-based learning methods have been proposed to identify and interpret DTI mechanisms. However, improvements are needed for enhancing prediction performance and DTI mechanism elucidation. To address these problems, we developed an attention-based method designated the interpretable cross-attention network (ICAN), which predicts DTIs using the Simplified Molecular Input Line Entry System of drugs and amino acid sequences of target proteins. We optimized the attention mechanism architecture by exploring the cross-attention or self-attention, attention layer depth, and selection of the context matrixes from the attention mechanism. We found that a plain attention mechanism that decodes drug-related protein context features without any protein-related drug context features effectively achieved high performance. The ICAN outperformed state-of-the-art methods in several metrics on the DAVIS dataset and first revealed with statistical significance that some weighted sites in the cross-attention weight matrix represent experimental binding sites, thus demonstrating the high interpretability of the results. The program is freely available at https://github.com/kuratahiroyuki/ICAN. [ABSTRACT FROM AUTHOR]

Plasmodium falciparum is a parasitic protozoan that can cause malaria, which is a deadly disease. Therefore, the accurate identification of malaria parasite mitochondrial proteins is essential for understanding their functions and identifying novel drug targets. For classifying protein sequences, several adaptive statistical techniques have been devised. Despite significant gains, prediction performance is still constrained by the lack of appropriate feature descriptors and learning strategies in current systems. Moreover, good ground truth data is important for Artificial Intelligence (AI)-based models but there is a lack of that data in the literature. Therefore, in this work, we propose a novel hybrid network that combines 1D Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (BGRU) to classify the malaria parasite mitochondrial proteins. Furthermore, we curate a sequential data that are collected from National Center for Biotechnology Information (NCBI) and UniProtKB/Swiss-Prot proteins databanks to prepare a dataset that can be used by the research community for AI-based algorithms evaluation. We obtain 4204 cases after preprocessing of the collected data and denote this set of proteins as PF4204. Finally, we conduct an ablation study on several conventional and deep models using PF4204 and the benchmark PF2095 datasets. The proposed model 'CNN-BGRU' obtains the accuracy values of 0.9096 and 0.9857 on PF4204 and PF2095 datasets, respectively. In addition, the CNN-BGRU is compared with state-of-the-arts, where the results illustrate that it can extract robust features and identify proteins accurately. [ABSTRACT FROM AUTHOR]

The number of adverse drug events in the United States is critically high, with annual rates exceeding 1 million cases over the last nine years. One cause of adverse drug events is the underlying genetic variation that can alter drug responses. Pharmacogenomics is a growing field that seeks to better understand the relationship between a patient's genetics and drug efficacy. Currently, pharmacogenomics relies largely on human trials, as there is not a well-developed animal model for studying preventative measures and alternative treatments. Here, we analyzed pharmacogene expression at two developmental time points in zebrafish to demonstrate the potential of using this model organism for high-throughput pharmacogenomics research. We found that 76% of tiered human pharmacogenes have a zebrafish ortholog, and of these, many have highly conserved amino acid sequences. Additional gene ontology analysis was used to classify pharmacogenes and identify candidate pathways for future modeling in zebrafish. As precision medicine burgeons, adopting a high-throughput in vivo model such as the zebrafish could greatly increase our understanding of the molecular pathology underlying adverse drug events. [ABSTRACT FROM AUTHOR]

Based on the hypothesis that the neighbors of disease genes trend to cause similar diseases, network-based methods for disease prediction have received increasing attention. Taking full advantage of network structure, the performance of global distance measurements is generally superior to local distance measurements. However, some problems exist in the global distance measurements. For example, global distance measurements may mistake non-disease hub proteins that have dense interactions with known disease proteins for potential disease proteins. To find a new method to avoid the aforementioned problem, we analyzed the differences between disease proteins and other proteins by using essential proteins (proteins encoded by essential genes) as references. We find that disease proteins are not well connected with essential proteins in the protein interaction networks. Based on this new finding, we proposed a novel strategy for gene prioritization based on protein interaction networks. We allocated positive flow to disease genes and negative flow to essential genes, and adopted network propagation for gene prioritization. Experimental results on 110 diseases verified the effectiveness and potential of the proposed method. [ABSTRACT FROM AUTHOR]

Bacillus cereus is a food contaminant with widely varying enterotoxic potential due to its virulence proteins. In this article, phylogenetic analysis of the amino acid sequences from the whole-genomes of 41 strains, evolutionary distance calculation of the amino acid sequences of the virulence genes, and functional and structural predictions of the virulence proteins were performed to reveal the taxonomically diverse distribution of virulence factors. The genome evolution of the strains showed a clustering trend based on the protein-coding virulence genes. The strains of B. cereus have evolved into non-toxic risk and toxic risk clusters with medium-high- and medium-low-risk subclusters. The evolutionary transfer distances of incomplete virulence genes relative to housekeeping genes were greater than those of complete virulence genes, and the distance values of HblACD were higher than those of nheABC and CytK among the complete virulence genes. Cytoplasmic localization was impossible for all the virulence proteins, and NheB, NheC, Hbl-B, and Hbl-L1 were predicted to be extracellular. Nhe and Hbl proteins except CytK had similar spatial structures. The predicted structures of Nhe and Hbl mainly showed 'head' and 'tail' domains. The 'head' of NheA and Hbl-B, including two α-helices separated by β-tongue strands, might play a special role in the formation of Nhe trimers and Hbl trimers, respectively. The 'cap' of CytK, which includes two 'latches' with many β-sheets, formed a β-barrel structure with pores, and a 'rim' balanced the structure. The evolution of B. cereus strains showed a clustering tendency based on the protein-coding virulence genes, and the complete virulence-gene operon combination had higher relative genetic stability. The beta-tongue or latch associated with β-sheet folding might play an important role in the binding of virulence structures and pore-forming toxins in B. cereus. [ABSTRACT FROM AUTHOR]

Dickeya are plant pathogenic bacteria able to provoke disease on a wide range of plants. A type 2 secretion system (T2SS) named Out is necessary for Dickeya virulence. Previous studies showed that the D. dadantii T2SS secretes a wide range of plant cell wall degrading enzymes, including pectinases and a cellulase. However, the full repertoire of exoproteins it can secrete has probably not yet been identified. Secreted proteins possess a signal peptide and are first addressed to the periplasm before their recruitment by Out. T2SS-specific secretion signals remain unknown which prevents in silico identification of T2SS substrates. To identify new Out substrates, we analyzed D. dadantii transcriptome data obtained in plant infection condition and searched for genes strongly induced and encoding proteins with a signal sequence. We identified four new Out-secreted proteins: the expansin YoaJ, the putative virulence factor VirK and two proteins of the DUF 4879 family, SvfA and SvfB. We showed that SvfA and SvfB are required for full virulence of D. dadantii and that svf genes are present in a variable number of copies in other Pectobacteriaceae, up to three in D. fanghzongdai. This work opens the way to the study of the role of non-pectinolytic proteins secreted by the Out pathway in Pectobacteriaceae. [ABSTRACT FROM AUTHOR]