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Although advancing the therapeutic alternatives for treating deadly cancers has gained much attention globally, still the primary methods such as chemotherapy have significant downsides and low specificity. Most recently, Anticancer peptides (ACPs) have emerged as a potential alternative to therapeutic alternatives with much fewer negative side-effects. However, the identification of ACPs through wet-lab experiments is expensive and time-consuming. Hence, computational methods have emerged as viable alternatives. During the past few years, several computational ACP identification techniques using hand-engineered features have been proposed to solve this problem. In this study, we propose a new multi headed deep convolutional neural network model called ACP-MHCNN, for extracting and combining discriminative features from different information sources in an interactive way. Our model extracts sequence, physicochemical, and evolutionary based features for ACP identification using different numerical peptide representations while restraining parameter overhead. It is evident through rigorous experiments using cross-validation and independent-dataset that ACP-MHCNN outperforms other models for anticancer peptide identification by a substantial margin on our employed benchmarks. ACP-MHCNN outperforms state-of-the-art model by 6.3%, 8.6%, 3.7%, 4.0%, and 0.20 in terms of accuracy, sensitivity, specificity, precision, and MCC respectively. ACP-MHCNN and its relevant codes and datasets are publicly available at: https://github.com/mrzResearchArena/Anticancer-Peptides-CNN. ACP-MHCNN is also publicly available as an online predictor at: https://anticancer.pythonanywhere.com/.

Dengue poses a global health threat, which will persist without therapeutic intervention. Immunity induced by exposure to one serotype does not confer long-term protection against secondary infection with other serotypes and is potentially capable of enhancing this infection. Although vaccination is believed to induce durable and protective responses against all the dengue virus (DENV) serotypes in order to reduce the burden posed by this virus, the development of a safe and efficacious vaccine remains a challenge. Immunoinformatics and computational vaccinology have been utilized in studies of infectious diseases to provide insight into the host–pathogen interactions thus justifying their use in vaccine development. Since vaccination is the best bet to reduce the burden posed by DENV, this study is aimed at developing a multi-epitope based vaccines for dengue control. Combined approaches of reverse vaccinology and immunoinformatics were utilized to design multi-epitope based vaccine from the sequence of DENV. Specifically, BCPreds and IEDB servers were used to predict the B-cell and T-cell epitopes, respectively. Molecular docking was carried out using Schrödinger, PATCHDOCK and FIREDOCK. Codon optimization and in silico cloning were done using JCAT and SnapGene respectively. Finally, the efficiency and stability of the designed vaccines were assessed by an in silico immune simulation and molecular dynamic simulation, respectively. The predicted epitopes were prioritized using in-house criteria. Four candidate vaccines (DV-1–4) were designed using suitable adjuvant and linkers in addition to the shortlisted epitopes. The binding interactions of these vaccines against the receptors TLR-2, TLR-4, MHC-1 and MHC-2 show that these candidate vaccines perfectly fit into the binding domains of the receptors. In addition, DV-1 has a better binding energies of − 60.07, − 63.40, − 69.89 kcal/mol against MHC-1, TLR-2, and TLR-4, with respect to the other vaccines. All the designed vaccines were highly antigenic, soluble, non-allergenic, non-toxic, flexible, and topologically assessable. The immune simulation analysis showed that DV-1 may elicit specific immune response against dengue virus. Moreover, codon optimization and in silico cloning validated the expressions of all the designed vaccines in E. coli. Finally, the molecular dynamic study shows that DV-1 is stable with minimum RMSF against TLR4. Immunoinformatics tools are now applied to screen genomes of interest for possible vaccine target. The designed vaccine candidates may be further experimentally investigated as potential vaccines capable of providing definitive preventive measure against dengue virus infection.

Acinetobacter baumannii (A. baumannii), an opportunistic, gram-negative pathogen, has evoked the interest of the medical community throughout the world because of its ability to cause nosocomial infections, majorly infecting those in intensive care units. It has also drawn the attention of researchers due to its evolving immune evasion strategies and increased drug resistance. The emergence of multi-drug-resistant-strains has urged the need to explore novel therapeutic options as an alternative to antibiotics. Due to the upsurge in antibiotic resistance mechanisms exhibited by A. baumannii, the current therapeutic strategies are rendered less effective. The aim of this study is to explore novel therapeutic alternatives against A. baumannii to control the ailed infection. In this study, a computational framework is employed involving, pan genomics, subtractive proteomics and reverse vaccinology strategies to identify core promiscuous vaccine candidates. Two chimeric vaccine constructs having B-cell derived T-cell epitopes from prioritized vaccine candidates; APN, AdeK and AdeI have been designed and checked for their possible interactions with host BCR, TLRs and HLA Class I and II Superfamily alleles. These vaccine candidates can be experimentally validated and thus contribute to vaccine development against A. baumannii infections.

Eukaryotic cells can expand their coding ability by using their splicing machinery, spliceosome, to process precursor mRNA (pre-mRNA) into mature messenger RNA. The mega-macromolecular spliceosome contains multiple subcomplexes, referred to as small nuclear ribonucleoproteins (snRNPs). Among these, U1 snRNP and its central component, U1-70K, are crucial for splice site recognition during early spliceosome assembly. The human U1-70K has been linked to several types of human autoimmune and neurodegenerative diseases. However, its phylogenetic relationship has been seldom reported. To this end, we carried out a systemic analysis of 95 animal U1-70K genes and compare these proteins to their yeast and plant counterparts. Analysis of their gene and protein structures, expression patterns and splicing conservation suggest that animal U1-70Ks are conserved in their molecular function, and may play essential role in cancers and juvenile development. In particular, animal U1-70Ks display unique characteristics of single copy number and a splicing isoform with truncated C-terminal, suggesting the specific role of these U1-70Ks in animal kingdom. In summary, our results provide phylogenetic overview of U1-70K gene family in vertebrates. In silico analyses conducted in this work will act as a reference for future functional studies of this crucial U1 splicing factor in animal kingdom.

The oleaginous yeast Yarrowia lipolytica is a potent cell factory as it is able to use a wide variety of carbon sources to convert waste materials into value-added products. Nonetheless, there are still gaps in our understanding of its central carbon metabolism. Here we present an in-depth study of Y. lipolytica hexokinase (YlHxk1), a structurally unique protein. The greatest peculiarity of YlHxk1 is a 37-amino acid loop region, a structure not found in any other known hexokinases. By combining bioinformatic and experimental methods we showed that the loop in YlHxk1 is essential for activity of this protein and through that on growth of Y. lipolytica on glucose and fructose. We further proved that the loop in YlHxk1 hinders binding with trehalose 6-phosphate (T6P), a glycolysis inhibitor, as hexokinase with partial deletion of this region is 4.7-fold less sensitive to this molecule. We also found that YlHxk1 devoid of the loop causes strong repressive effect on lipase-encoding genes LIP2 and LIP8 and that the hexokinase overexpression in Y. lipolytica changes glycerol over glucose preference when cultivated in media containing both substrates.

Enterovirus A71 (EV-A71), Coxsackievirus A16 (CV-A16) and CV-A10 are the major causative agents of hand, foot and mouth disease (HFMD). The conformational epitopes play a vital role in monitoring the antigenic evolution, predicting dominant strains and preparing vaccines. In this study, we employed a Bioinformatics-based algorithm to predict the conformational epitopes of EV-A71 and CV-A16 and compared with that of CV-A10. Prediction results revealed that the distribution patterns of conformational epitopes of EV-A71 and CV-A16 were similar to that of CV-A10 and their epitopes likewise consisted of three sites: site 1 (on the “north rim” of the canyon around the fivefold vertex), site 2 (on the “puff”) and site 3 (one part was in the “knob” and the other was near the threefold vertex). The reported epitopes highly overlapped with our predicted epitopes indicating the predicted results were reliable. These data suggested that three-site distribution pattern may be the basic distribution role of epitopes on the enteroviruses capsids. Our prediction results of EV-A71 and CV-A16 can provide essential information for monitoring the antigenic evolution of enterovirus.

Interestingly, some protein domains are intrinsically disordered (abbreviated as IDD), and the disorder degree of same domains may differ in different contexts. However, the evolutionary causes and biological significance of these phenomena are unclear. Here, we address these issues by genome-wide analyses of the evolutionary and functional features of IDDs in 1,870 species across the three superkingdoms. As the result, there is a significant positive correlation between the proportion of IDDs and organism complexity with some interesting exceptions. These phenomena may be due to the high disorder of clade-specific domains and the different disorder degrees of the domains shared in different clades. The functions of IDDs are clade-specific and the higher proportion of post-translational modification sites may contribute to their complex functions. Compared with metazoans, fungi have more IDDs with a consecutive disorder region but a low disorder ratio, which reflects their different functional requirements. As for disorder variation, it’s greater for domains among different proteins than those within the same proteins. Some clade-specific ‘no-variation’ or ‘high-variation’ domains are involved in clade-specific functions. In sum, intrinsic domain disorder is related to both the organism complexity and clade-specific functions. These results deepen the understanding of the evolution and function of IDDs.

Knowing protein function is crucial to advance molecular and medical biology, yet experimental function annotations through the Gene Ontology (GO) exist for fewer than 0.5% of all known proteins. Computational methods bridge this sequence-annotation gap typically through homology-based annotation transfer by identifying sequence-similar proteins with known function or through prediction methods using evolutionary information. Here, we propose predicting GO terms through annotation transfer based on proximity of proteins in the SeqVec embedding rather than in sequence space. These embeddings originate from deep learned language models (LMs) for protein sequences (SeqVec) transferring the knowledge gained from predicting the next amino acid in 33 million protein sequences. Replicating the conditions of CAFA3, our method reaches an Fmax of 37 ± 2%, 50 ± 3%, and 57 ± 2% for BPO, MFO, and CCO, respectively. Numerically, this appears close to the top ten CAFA3 methods. When restricting the annotation transfer to proteins with

Protein is the primary building block of living organisms. It interacts with other proteins and is then involved in various biological processes. Protein–protein interactions (PPIs) help in predicting and hence help in understanding the functionality of the proteins, causes and growth of diseases, and designing new drugs. However, there is a vast gap between the available protein sequences and the identification of protein–protein interactions. To bridge this gap, researchers proposed several computational methods to reveal the interactions between proteins. These methods merely depend on sequence-based information of proteins. With the advancement of technology, different types of information related to proteins are available such as 3D structure information. Nowadays, deep learning techniques are adopted successfully in various domains, including bioinformatics. So, current work focuses on the utilization of different modalities, such as 3D structures and sequence-based information of proteins, and deep learning algorithms to predict PPIs. The proposed approach is divided into several phases. We first get several illustrations of proteins using their 3D coordinates information, and three attributes, such as hydropathy index, isoelectric point, and charge of amino acids. Amino acids are the building blocks of proteins. A pre-trained ResNet50 model, a subclass of a convolutional neural network, is utilized to extract features from these representations of proteins. Autocovariance and conjoint triad are two widely used sequence-based methods to encode proteins, which are used here as another modality of protein sequences. A stacked autoencoder is utilized to get the compact form of sequence-based information. Finally, the features obtained from different modalities are concatenated in pairs and fed into the classifier to predict labels for protein pairs. We have experimented on the human PPIs dataset and Saccharomyces cerevisiae PPIs dataset and compared our results with the state-of-the-art deep-learning-based classifiers. The results achieved by the proposed method are superior to those obtained by the existing methods. Extensive experimentations on different datasets indicate that our approach to learning and combining features from two different modalities is useful in PPI prediction.

Serine phosphorylation is one type of protein post-translational modifications (PTMs), which plays an essential role in various cellular processes and disease pathogenesis. Numerous methods are used for the prediction of phosphorylation sites. However, the traditional wet-lab based experimental approaches are time-consuming, laborious, and expensive. In this work, a computational predictor was proposed to predict serine phosphorylation sites mapping onSchizosaccharomyces pombe(SP) by the fusion of three encoding schemes namely k-spaced amino acid pair composition (CKSAAP), binary and amino acid composition (AAC) with the random forest (RF) classifier. So far, the proposed method is firstly developed to predict serine phosphorylation sites forSP. Both the training and independent test performance scores were used to investigate the success of the proposed RF based fusion prediction model compared to others. We also investigated their performances by 5-fold cross-validation (CV). In all cases, it was observed that the recommended predictor achieves the largest scores of true positive rate (TPR), true negative rate (TNR), accuracy (ACC), Mathew coefficient of correlation (MCC), Area under the ROC curve (AUC) and pAUC (partial AUC) at false positive rate (FPR) = 0.20. Thus, the prediction performance as discussed in this paper indicates that the proposed approach may be a beneficial and motivating computational resource for predicting serine phosphorylation sites in the case of Fungi. The online interface of the software for the proposed prediction model is publicly available athttp://mollah-bioinformaticslab-stat.ru.ac.bd/PredSPS/.

In biological systems, Glutamic acid is a crucial amino acid which is used in protein biosynthesis. Carboxylation of glutamic acid is a significant post-translational modification which plays important role in blood coagulation by activating prothrombin to thrombin. Contrariwise, 4-carboxy-glutamate is also found to be involved in diseases including plaque atherosclerosis, osteoporosis, mineralized heart valves, bone resorption and serves as biomarker for onset of these diseases. Owing to the pathophysiological significance of 4-carboxyglutamate, its identification is important to better understand pathophysiological systems. The wet lab identification of prospective 4-carboxyglutamate sites is costly, laborious and time consuming due to inherent difficulties of in-vivo, ex-vivo and in vitro experiments. To supplement these experiments, we proposed, implemented, and evaluated a different approach to develop 4-carboxyglutamate site predictors using pseudo amino acid compositions (PseAAC) and deep neural networks (DNNs). Our approach does not require any feature extraction and employs deep neural networks to learn feature representation of peptide sequences and performing classification thereof. Proposed approach is validated using standard performance evaluation metrics. Among different deep neural networks, convolutional neural network-based predictor achieved best scores on independent dataset with accuracy of 94.7%, AuC score of 0.91 and F1-score of 0.874 which shows the promise of proposed approach. The iCarboxE-Deep server is deployed at https://share.streamlit.io/sheraz-n/carboxyglutamate/app.py.

The number of cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (COVID-19) has reached over 114,000. SARS-CoV-2 caused a pandemic in Wuhan, China, in December 2019 and is rapidly spreading globally. It has been reported that peptide-like anti-HIV-1 drugs are effective against SARS-CoV Main protease (Mpro). Due to the close phylogenetic relationship between SARS-CoV and SARS-CoV-2, their main proteases share many structural and functional features. Thus, these drugs are also regarded as potential drug candidates targeting SARS-CoV-2 Mpro. However, the mechanism of action of SARS-CoV-2 Mpro at the atomic-level is unknown. In the present study, we revealed key interactions between SARS-CoV-2 Mpro and three drug candidates by performing pharmacophore modeling and 1μs molecular dynamics (MD) simulations. His41, Gly143, and Glu166 formed interactions with the functional groups that were common among peptide-like inhibitors in all MD simulations. These interactions are important targets for potential drugs against SARS-CoV-2 Mpro.

MDM2 and MDMX are known as overexpressed oncoproteins in several wild-type p53 cancer cells. The development of potent and dual antagonist peptides for p53-MDM2/X is a continuous challenge. In this study, we intended to investigate the pivotal structural points respecting the development of potent and dual inhibitors of MDM2/X. Correspondingly, MD simulation was performed on the experimentally confirmed peptides, comprising p53, pDI, pDIQ, PMI, and computationally screened mutant pDI and pDIQ. A follow-up secondary structure analysis showed the last three C-terminal residues provide the helicity reservation of peptides bound to MDM2/X. Furthermore, a delicate residue-residue examination displayed Met 11 and Ser12 in the modified peptides contribute significantly to dual inhibition of MDM2/X. Additionally, the peptides_MDM2/X complexes’ ΔGbinding extracted by the umbrella sampling method were in agreement with the pattern of their experimental affinity values. It was concluded the screened pDI mutants were considered as suitable anti-MDM2/X peptides, and the data obtained could be exploited as the theoretical structure-based guide for rational peptide design. Taking account of results, the suitable C-terminal residues of p53-based peptides especially Met11, and Ser12, as well as higher umbrella sampling, generated ΔGbinding to MDM2/X would be considered as the positive structural markers of a promising anti-cancer agent.

Protein methylation plays a vital role in cell processing. Many novel methods try to predict methylation sites from protein sequence by sequence information or predicted structural information, but none of them use protein tertiary structure information in prediction. In particular, most of them do not build models for predicting methylation types (mono-, di-, tri-methylation). To address these problems, we propose a novel method, Met-predictor, to predict methylation sites and methylation types using a support vector machine-based network. Met-predictor combines a variety of sequence-based features that are derived from protein sequences with structure model-based features, which are geometric information extracted from predicted protein tertiary structure models, and are firstly used in methylation prediction. Met-predictor was tested on two independent test sets, where the addition of structure model-based features improved AUC from 0.611 and 0.520 to 0.655 and 0.566 for lysine and from 0.723 and 0.640 to 0.734 and 0.643 for arginine. When compared with other state-of-the-art methods, Met-predictor had 13.1% (3.9%) and 8.5% (16.4%) higher accuracy than the best of other methods for methyllysine and methylarginine prediction on the independent test set I (II). Furthermore, Met-predictor also attains excellent performance for predicting methylation types.

Cyanobacteria, a group of photosynthetic prokaryotes, are attractive hosts for biotechnological applications. It is envisaged that future biorefineries will deploy engineered cyanobacteria for the conversion of carbon dioxide to useful chemicals via light-driven, endergonic reactions. Fast-growing, genetically amenable, and stress-tolerant cyanobacteria are desirable as chassis for such applications. The recently reported strains such as Synechococcus elongatus UTEX 2973 and PCC 11801 hold promise, but additional strains may be needed for the ongoing efforts of metabolic engineering. Here, we report a novel, fast-growing, and naturally transformable cyanobacterium, S. elongatus PCC 11802, that shares 97% genome identity with its closest neighbor S. elongatus PCC 11801. The new isolate has a doubling time of 2.8 h at 1% CO2, 1000 µmole photons.m−2.s−1 and grows faster under high CO2 and temperature compared to PCC 11801 thus making it an attractive host for outdoor cultivations and eventual applications in the biorefinery. Furthermore, S. elongatus PCC 11802 shows higher levels of key intermediate metabolites suggesting that this strain might be better suited for achieving high metabolic flux in engineered pathways. Importantly, metabolite profiles suggest that the key enzymes of the Calvin cycle are not repressed under elevated CO2 in the new isolate, unlike its closest neighbor.

Identifying and evaluating the right target are the most important factors in early drug discovery phase. Most studies focus on one protein ignoring the multiple splice-variant or protein-isoforms, which might contribute to unexpected therapeutic activity or adverse side effects. Here, we present computational analysis of cancer drug-target interactions affected by alternative splicing. By integrating information from publicly available databases, we curated 883 FDA approved or investigational stage small molecule cancer drugs that target 1,434 different genes, with an average of 5.22 protein isoforms per gene. Of these, 618 genes have ≥5 annotated protein-isoforms. By analyzing the interactions with binding pocket information, we found that 76% of drugs either miss a potential target isoform or target other isoforms with varied expression in multiple normal tissues. We present sequence and structure level alignments at isoform-level and make this information publicly available for all the curated drugs. Structure-level analysis showed ligand binding pocket architectures differences in size, shape and electrostatic parameters between isoforms. Our results emphasize how potentially important isoform-level interactions could be missed by solely focusing on the canonical isoform, and suggest that on- and off-target effects at isoform-level should be investigated to enhance the productivity of drug-discovery research.

Patients with oral squamous cell carcinoma (OSCC) present significant alterations in their saliva proteome. We have used the shotgun Phage Display (PD) technology to identify candidate proteins that were upregulated in saliva of OSCC by selecting ligands to salivary proteins from a single-chain variable fragment (scFv) PD combinatorial library. After two selection cycles, the highly reactive clone scFv-D09 was able to distinguish saliva of OSCC patients from healthy subjects by enzyme-linked immunosorbent assay (ELISA) with sensitivity and specificity of 96.67%. Additionally, the scFv-D09 clone presented a positive immunostaining for invasive malignant epithelial cells in the connective tissue, keratin pearls in the OSCC, and ducts of salivary glands. We have further identified the target protein as the tropomyosin alpha-4 chain (TPM4) by two-dimensional polyacrylamide gel electrophoresis and mass spectrometry, and its binding to the scFV-D09 was demonstrated by bioinformatics. Briefly, we have identified TPM4 as upregulated salivary protein in patients with OSCC, which plays a central role in stabilizing cytoskeleton actin filaments, probably linked with tumor tissue remodeling. Long-term longitudinal studies are needed to validate TPM4 as a potential marker of a malignant process.

An amino acid substitution scoring matrix encapsulates the rates at which various amino acid residues in proteins are substituted by other amino acid residues, over time. Database search methods make use of substitution scoring matrices to identify sequences with homologous relationships. However, widely used substitution scoring matrices, such as BLOSUM series, have been developed using aligned blocks that are mostly devoid of disordered regions in proteins. Hence, these substitution-scoring matrices are mostly inappropriate for homology searches involving proteins enriched with disordered regions as the disordered regions have distinct amino acid compositional bias, and therefore expected to have undergone amino acid substitutions that are distinct from those in the ordered regions. We, therefore, developed a novel series of substitution scoring matrices referred to as EDSSMat by exclusively considering the substitution frequencies of amino acids in the disordered regions of the eukaryotic proteins. The newly developed matrices were tested for their ability to detect homologs of proteins enriched with disordered regions by means of SSEARCH tool. The results unequivocally demonstrate that EDSSMat matrices detect more number of homologs than the widely used BLOSUM, PAM and other standard matrices, indicating their utility value for homology searches of intrinsically disordered proteins.

Owing to their ability to maintain a thermodynamically stable fold at extremely high temperatures, thermophilic proteins (TTPs) play a critical role in basic research and a variety of applications in the food industry. As a result, the development of computation models for rapidly and accurately identifying novel TTPs from a large number of uncharacterized protein sequences is desirable. In spite of existing computational models that have already been developed for characterizing thermophilic proteins, their performance and interpretability remain unsatisfactory. We present a novel sequence-based thermophilic protein predictor, termed SCMTPP, for improving model predictability and interpretability. First, an up-to-date and high-quality dataset consisting of 1853 TPPs and 3233 non-TPPs was compiled from published literature. Second, the SCMTPP predictor was created by combining the scoring card method (SCM) with estimated propensity scores of g-gap dipeptides. Benchmarking experiments revealed that SCMTPP had a cross-validation accuracy of 0.883, which was comparable to that of a support vector machine-based predictor (0.906–0.910) and 2–17% higher than that of commonly used machine learning models. Furthermore, SCMTPP outperformed the state-of-the-art approach (ThermoPred) on the independent test dataset, with accuracy and MCC of 0.865 and 0.731, respectively. Finally, the SCMTPP-derived propensity scores were used to elucidate the critical physicochemical properties for protein thermostability enhancement. In terms of interpretability and generalizability, comparative results showed that SCMTPP was effective for identifying and characterizing TPPs. We had implemented the proposed predictor as a user-friendly online web server at http://pmlabstack.pythonanywhere.com/SCMTPP in order to allow easy access to the model. SCMTPP is expected to be a powerful tool for facilitating community-wide efforts to identify TPPs on a large scale and guiding experimental characterization of TPPs.

A novel and simple procedure (RaSPDB) for Protein Data Bank mining is described. 10 PDB subsets, each containing 7000 randomly selected protein chains, are built and used to make 10 estimations of the average value of a generic feature F – the length of the protein chain, the amino acid composition, the crystallographic resolution, and the secondary structure composition. These 10 estimations are then used to compute an average estimation of F together with its standard error. It is heuristically verified that the dimension of these 10 subsets –7000 protein chains – is sufficiently small to avoid redundancy within each subset and sufficiently large to guarantee stable estimations amongst different subsets. RaSPDB has two major advantages over classical procedures aimed to build a single, non-redundant PDB subset: a larger fraction of the information stored in the PDB is used and an estimation of the standard error of F is possible.

The mutation of SARS-CoV-2 influences viral function as residue replacements affect both physiochemical properties and folding conformations. Although a large amount of data on SARS-CoV-2 is available, the investigation of how viral functions change in response to mutations is hampered by a lack of effective structural analysis. Here, we exploit the advances of protein structure fingerprint technology to study the folding conformational changes induced by mutations. With integration of both protein sequences and folding conformations, the structures are aligned for SARS-CoV to SARS-CoV-2, including Alpha variant (lineage B.1.1.7) and Delta variant (lineage B.1.617.2). The results showed that the virus evolution with change in mutational positions and physicochemical properties increased the affinity between spike protein and ACE2, which plays a critical role in coronavirus entry into human cells. Additionally, these structural variations impact vaccine effectiveness and drug function over the course of SARS-CoV-2 evolution. The analysis of structural variations revealed how the coronavirus has gradually evolved in both structure and function and how the SARS-CoV-2 variants have contributed to more severe acute disease worldwide.

Genomic islands (GIs) are horizontally transferred elements that shape bacterial genomes and contributes to the adaptation to different environments. Some GIs encode an integrase and a recombination directionality factor (RDF), which are the molecular GI-encoded machinery that promotes the island excision from the chromosome, the first step for the spread of GIs by horizontal transfer. Although less studied, this process can also play a role in the virulence of bacterial pathogens. While the excision of GIs is thought to be similar to that observed in bacteriophages, this mechanism has been only studied in a few families of islands. Here, we aimed to gain a better understanding of the factors involved in the excision of ROD21 a pathogenicity island of the food-borne pathogen Salmonella enterica serovar Enteritidis and the most studied member of the recently described Enterobacteriaceae-associated ROD21-like family of GIs. Using bioinformatic and experimental approaches, we characterized the conserved gene SEN1998, showing that it encodes a protein with the features of an RDF that binds to the regulatory regions involved in the excision of ROD21. While deletion or overexpression of SEN1998 did not alter the expression of the integrase-encoding gene SEN1970, a slight but significant trend was observed in the excision of the island. Surprisingly, we found that the expression of both genes, SEN1998 and SEN1970, were negatively correlated to the excision of ROD21 which showed a growth phase-dependent pattern. Our findings contribute to the growing body of knowledge regarding the excision of GIs, providing insights about ROD21 and the recently described EARL family of genomic islands.

Cell-penetrating peptides have important therapeutic applications in drug delivery, but the variety of known cell-penetrating peptides is still limited. With a promise to accelerate peptide development, artificial intelligence (AI) techniques including deep generative models are currently in spotlight. Scientists, however, are often overwhelmed by an excessive number of unannotated sequences generated by AI and find it difficult to obtain insights to prioritize them for experimental validation. To avoid this pitfall, we leverage molecular dynamics (MD) simulations to obtain mechanistic information to prioritize and understand AI-generated peptides. A mechanistic score of permeability is computed from five steered MD simulations starting from different initial structures predicted by homology modelling. To compensate for variability of predicted structures, the score is computed with sample variance penalization so that a peptide with consistent behaviour is highly evaluated. Our computational pipeline involving deep learning, homology modelling, MD simulations and synthesizability assessment generated 24 novel peptide sequences. The top-scoring peptide showed a consistent pattern of conformational change in all simulations regardless of initial structures. As a result of wet-lab-experiments, our peptide showed better permeability and weaker toxicity in comparison to a clinically used peptide, TAT. Our result demonstrates how MD simulations can support de novo peptide design by providing mechanistic information supplementing statistical inference.

Fish has poor utilization capacity for glucose metabolism. The possible reasons are related to the core regulatory elements of glucose metabolism: transport proteins. Studies on the species and functions of Sglt1 in aquatic animals are scarce, therefore further studies are needed. In this study, the full length of blunt snout bream (Megalobrama amblycephala) sglt1 (Masglt1) was 2965 bp including 5′-UTR region of 168 bp and a 3′-UTR region of 820 bp. Masglt1 have a highest sequence homology in Cypriniformes fish. MaSglt1 protein was identified as a transmembrane protein with 14 α-helix structures locating plasma membrane by the methods of predicted tertiary structure and immunohistochemical staining. MaSglt1 protein has a hollow channel forms which could be specifically coupled with two Na+ ions to recognize glucose and carry out transmembrane transport. High sglt1 mRNA was found in the intestine and kidney. The mRNA levels of intestinal sglt1 had a positive correlation with dietary starch levels at 3 h after feeding, and the mRNA was significantly higher than that at 24 h, however, the mRNA levels of renal sglt1 presented results opposite to those of intestinal sglt1. The mRNA levels of renal sglt1 had a positive correlation with dietary starch levels at 24 h after feeding, and the expression was significantly higher than that at 3 h. These results confirmed that Masglt11 was mainly found in the intestine and kidney and was located in the cell membrane, playing a role in glucose homeostasis.

The recent outbreak of the coronavirus (SARS-CoV2) is an unprecedented threat to human health and society across the globe. In this context, development of suitable interventions is the need of the hour. The viral spike protein (S Protein) and the cognate host cell receptor ACE2 can be considered as effective and appropriate targets for interventions. It is evident from the present computational study, that catechin and curcumin, not only exhibit strong binding affinity to viral S Protein and host receptor ACE2 but also to their complex (receptor-binding domain (RBD) of the spike protein of SARS-CoV2 and ACE2; RBD/ACE2-complex). The binding affinity values of catechin and curcumin for the S protein, ACE2 and RBD/ACE2-complex are - 10.5 and - 7.9 kcal/mol; - 8.9 and - 7.8 kcal/mol; and - 9.1 and - 7.6 kcal/mol, respectively. Curcumin directly binds to the receptor binding domain (RBD) of viral S Protein. Molecular simulation study over a period of 100 ns further substantiates that such interaction within RBD site of S Protein occurs during 40-100 ns out of 100 ns simulation trajectory. Contrary to this, catechin binds with amino acid residues present near the RBD site of S Protein and causes fluctuation in the amino acid residues of the RBD and its near proximity. Both catechin and curcumin bind the interface of 'RBD/ACE2-complex' and intervene in causing fluctuation of the alpha helices and beta-strands of the protein complex. Protein-protein interaction studies in presence of curcumin or catechin also corroborate the above findings suggesting the efficacy of these two polyphenols in hindering the formation of S Protein-ACE2 complex. In conclusion, this computational study for the first time predicts the possibility of above two polyphenols for therapeutic strategy against SARS-CoV2.

The light chain (AL) amyloidosis is caused by the aggregation of light chain of antibodies into amyloid fibrils. There are plenty of computational resources available for the prediction of short aggregation-prone regions within proteins. However, it is still a challenging task to predict the amyloidogenic nature of the whole protein using sequence/structure information. In the case of antibody light chains, common architecture and known binding sites can provide vital information for the prediction of amyloidogenicity at physiological conditions. Here, in this work, we have compared classical sequence-based, aggregation-related features (such as hydrophobicity, presence of gatekeeper residues, disorderness, β-propensity, etc.) calculated for the CDR, FR or VL regions of amyloidogenic and non-amyloidogenic antibody light chains and implemented the insights gained in a machine learning-based webserver called “VLAmY-Pred” (https://web.iitm.ac.in/bioinfo2/vlamy-pred/). The model shows prediction accuracy of 79.7% (sensitivity: 78.7% and specificity: 79.9%) with a ROC value of 0.88 on a dataset of 1828 variable region sequences of the antibody light chains. This model will be helpful towards improved prognosis for patients that may likely suffer from diseases caused by light chain amyloidosis, understanding origins of aggregation in antibody-based biotherapeutics, large-scale in-silico analysis of antibody sequences generated by next generation sequencing, and finally towards rational engineering of aggregation resistant antibodies.

In this report, a new type of tridimensional (3D) biomacro-molecular descriptors for proteins are proposed. These descriptors make use of multi-linear algebra concepts based on the application of 3-linear forms (i.e., Canonical Trilinear (Tr), Trilinear Cubic (TrC), Trilinear-Quadratic-Bilinear (TrQB) and so on) as a specific case of the N-linear algebraic forms. The definition of the kth 3-tuple similarity-dissimilarity spatial matrices (Tensor’s Form) are used for the transformation and for the representation of the existing chemical information available in the relationships between three amino acids of a protein. Several metrics (Minkowski-type, wave-edge, etc) and multi-metrics (Triangle area, Bond-angle, etc) are proposed for the interaction information extraction, as well as probabilistic transformations (e.g., simple stochastic and mutual probability) to achieve matrix normalization. A generalized procedure considering amino acid level-based indices that can be fused together by using aggregator operators for descriptors calculations is proposed. The obtained results demonstrated that the new proposed 3D biomacro-molecular indices perform better than other approaches in the SCOP-based discrimination and the prediction of folding rate of proteins by using simple linear parametrical models. It can be concluded that the proposed method allows the definition of 3D biomacro-molecular descriptors that contain orthogonal information capable of providing better models for applications in protein science.

Proteins often work as oligomers or multimers in vivo. Therefore, elucidating their oligomeric or multimeric form (quaternary structure) is crucially important to ascertain their function. X-ray crystal structures of numerous proteins have been accumulated, providing information related to their biological units. Extracting information of biological units from protein crystal structures represents a meaningful task for modern biology. Nevertheless, although many methods have been proposed for identifying biological units appearing in protein crystal structures, it is difficult to distinguish biological protein–protein interfaces from crystallographic ones. Therefore, our simple but highly accurate classifier was developed to infer biological units in protein crystal structures using large amounts of protein sequence information and a modern contact prediction method to exploit covariation signals (CSs) in proteins. We demonstrate that our proposed method is promising even for weak signals of biological interfaces. We also discuss the relation between classification accuracy and conservation of biological units, and illustrate how the selection of sequences included in multiple sequence alignments as sources for obtaining CSs affects the results. With increased amounts of sequence data, the proposed method is expected to become increasingly useful.

Antigenic variations of influenza A viruses are induced by genomic mutation in their trans-membrane protein HA1, eliciting viral escape from neutralization by antibodies generated in prior infections or vaccinations. Prediction of antigenic relationships among influenza viruses is useful for designing (or updating the existing) influenza vaccines, provides important insights into the evolutionary mechanisms underpinning viral antigenic variations, and helps to understand viral epidemiology. In this study, we present a simple and physically interpretable model that can predict antigenic relationships among influenza A viruses, based on biophysical ideas, using both genomic amino acid sequences and experimental antigenic data. We demonstrate the applicability of the model using a benchmark dataset of four subtypes of influenza A (H1N1, H3N2, H5N1, and H9N2) viruses and report on its performance profiles. Additionally, analysis of the model’s parameters confirms several observations that are consistent with the findings of other previous studies, for which we provide plausible explanations.

The sea cucumber Apostichopus japonicus is a foodstuff with very high economic value in China, Japan and other countries in south-east Asia. It is at the heart of a multibillion-dollar industry and to meet demand for this product, aquaculture methods and facilities have been established. However, there are challenges associated with optimization of reproduction, feeding and growth in non-natural environments. Therefore, we need to learn more about the biology of A. japonicus, including processes such as aestivation, evisceration, regeneration and albinism. One of the major classes of molecules that regulate physiology and behaviour in animals are neuropeptides, and a few bioactive peptides have already been identified in A. japonicus. To facilitate more comprehensive investigations of neuropeptide function in A. japonicus, here we have analysed genomic and transcriptomic sequence data and proteomic data to identify neuropeptide precursors and neuropeptides in this species. We identified 44 transcripts encoding neuropeptide precursors or putative neuropeptide precursors, and in some instances neuropeptides derived from these precursors were confirmed by mass spectrometry. Furthermore, analysis of genomic sequence data enabled identification of the location of neuropeptide precursor genes on genomic scaffolds and linkage groups (chromosomes) and determination of gene structure. Many of the precursors identified contain homologs of neuropeptides that have been identified in other bilaterian animals. Precursors of neuropeptides that have thus far only been identified in echinoderms were identified, including L- and F-type SALMFamides, AN peptides and others. Precursors of several peptides that act as modulators of neuromuscular activity in A. japonicus were also identified. The discovery of a large repertoire of neuropeptide precursors and neuropeptides provides a basis for experimental studies that investigate the physiological roles of neuropeptide signaling systems in A. japonicus. Looking ahead, some of these neuropeptides may have effects that could be harnessed to enable improvements in the aquaculture of this economically important species.

SH3 domains are very abundant protein-protein interactions modules, involved in the regulation of several cellular processes. Whilst they have been associated to allosteric communication pathways between contiguous domains in multi-domain proteins, there is lack of information regarding the intra-domain allosteric cross-talk within the SH3 moiety. Here we scrutinize the presence of an allosteric network in the C-terminal SH3 domain of Grb2 protein, upon binding the Grb2-associated binding 2 protein. To explore allostery, we performed double mutant cycle analysis, a powerful quantitative approach based on mutagenesis in conjunction with kinetic experiments. Data reveal the presence of an unexpected allosteric sparse network that modulates the affinity between the SH3 domain and its physiological partner.

Protein-peptide interactions play an important role in major cellular processes, and are associated with several human diseases. To understand and potentially regulate these cellular function and diseases it is important to know the molecular details of the interactions. However, because of peptide flexibility and the transient nature of protein-peptide interactions, peptides are difficult to study experimentally. Thus, computational methods for predicting structural information about protein-peptide interactions are needed. Here we present InterPep, a pipeline for predicting protein-peptide interaction sites. It is a novel pipeline that, given a protein structure and a peptide sequence, utilizes structural template matches, sequence information, random forest machine learning, and hierarchical clustering to predict what region of the protein structure the peptide is most likely to bind. When tested on its ability to predict binding sites, InterPep successfully pinpointed 255 of 502 (50.7%) binding sites in experimentally determined structures at rank 1 and 348 of 502 (69.3%) among the top five predictions using only structures with no significant sequence similarity as templates. InterPep is a powerful tool for identifying peptide-binding sites; with a precision of 80% at a recall of 20% it should be an excellent starting point for docking protocols or experiments investigating peptide interactions. The source code for InterPred is available athttp://wallnerlab.org/InterPep/

Deguelin is a major active ingredient and principal component in several plants and it is a potential molecule to target proteins of cancer cell signaling pathway. As a complex natural extract, deguelin interacts with various molecular targets to exert its anti-tumor properties at nanomolar level. It induces cell apoptosis by blocking anti-apoptotic pathways, while inhibiting tumor cell multiplication and malignant transformation through p27-cyclin-E-pRb-E2F1- cell cycle control and HIF-1alphaVEGF antiangiogenic pathways. In silico studies of deguelin and its derivatives is performed to explore interactions with Cyclin D1 and Cyclin E to understand the molecular insights of derivatives with the receptors. Deguelin and its derivatives are minimized by Avogadro to achieve stable conformation. All docking simulation are performed with AutoDockVina and virtual screening of docked ligands are carried out based on binding energy and number of hydrogen bonds. Molecular dynamics (MD) and Simulation of Cyclin D1 and Cyclin E1 is performed for 100 ns and stable conformation is obtained at 78 ns and 19 ns respectively. Ligands thus obtained from docking studies may be probable target to inhibit cancer cell signaling pathways.

Protein-protein interactions (PPIs) underlie the molecular mechanisms of most biological processes. Mitogen-activated protein kinases (MAPKs) can be dephosphorylated by MAPK-specific phosphatases such as PP2C, which are critical to transduce extracellular signals into adaptive and programmed responses. However, the experimental approaches for identifying PPIs are expensive, time-consuming, laborious and challenging. In response, many computational methods have been developed to predict PPIs. Yet, these methods have inherent disadvantages such as high false positive and negative results. Thus, it is crucial to develop in silico approaches for predicting PPIs efficiently and accurately. In this study, we identified PPIs among 16 BdMAPKs and 86 BdPP2Cs in B. distachyon using a novel docking approach. Further, we systematically investigated the docking site (D-site) of BdPP2C which plays a vital role for recognition and docking of BdMAPKs. D-site analysis revealed that there were 96 pairs of PPIs including all BdMAPKs and most BdPP2Cs, which indicated that BdPP2C may play roles in other signaling networks. Moreover, most BdPP2Cs have a D-site for BdMAPKs in our prediction results, which suggested that our method can effectively predict PPIs, as confirmed by their 3D structure. In addition, we validated this methodology with known Arabidopsis and yeast phosphatase-MAPK interactions from the STRING database. The results obtained provide a vital research resource for exploring an accurate network of PPIs between BdMAPKs and BdPP2Cs.

RNA binding protein (RBP) plays an important role in cellular processes. Identifying RBPs by computation and experiment are both essential. Recently, an RBP predictor, RBPPred, is proposed in our group to predict RBPs. However, RBPPred is too slow for that it needs to generate PSSM matrix as its feature. Herein, based on the protein feature of RBPPred and Convolutional Neural Network (CNN), we develop a deep learning model called Deep-RBPPred. With the balance and imbalance training set, we obtain Deep-RBPPred-balance and Deep-RBPPred-imbalance models. Deep-RBPPred has three advantages comparing to previous methods. (1) Deep-RBPPred only needs few physicochemical properties based on protein sequences. (2) Deep-RBPPred runs much faster. (3) Deep-RBPPred has a good generalization ability. In the meantime, Deep-RBPPred is still as good as the state-of-the-art method. Testing in A. thaliana, S. cerevisiae and H. sapiens proteomes, MCC values are 0.82 (0.82), 0.65 (0.69) and 0.85 (0.80) for balance model (imbalance model) when the score cutoff is set to 0.5, respectively. In the same testing dataset, different machine learning algorithms (CNN and SVM) are also compared. The results show that CNN-based model can identify more RBPs than SVM-based. In comparing the balance and imbalance model, both CNN-base and SVM-based tend to favor the majority class in the imbalance set. Deep-RBPPred forecasts 280 (balance model) and 265 (imbalance model) of 299 new RBP. The sensitivity of balance model is about 7% higher than the state-of-the-art method. We also apply deep-RBPPred to 30 eukaryotes and 109 bacteria proteomes downloaded from Uniprot to estimate all possible RBPs. The estimating result shows that rates of RBPs in eukaryote proteomes are much higher than bacteria proteomes.

Mycoplasma pneumoniae is a significant cause of pneumonia and post infection sequelae affecting organ sites distant to the respiratory tract are common. It is also a model organism where extensive ‘omics’ studies have been conducted to gain insight into how minimal genome self-replicating organisms function. An N-terminome study undertaken here identified 4898 unique N-terminal peptides that mapped to 391 (56%) predicted M. pneumoniae proteins. True N-terminal sequences beginning with the initiating methionine (iMet) residue from the predicted Open Reading Frame (ORF) were identified for 163 proteins. Notably, almost half (317; 46%) of the ORFS derived from M. pneumoniae strain M129 are post-translationally modified, presumably by proteolytic processing, because dimethyl labelled neo-N-termini were characterised that mapped beyond the predicted N-terminus. An analysis of the N-terminome describes endoproteolytic processing events predominately targeting tryptic-like sites, though cleavages at negatively charged residues in P1′ (D and E) with lysine or serine/alanine in P2′ and P3′ positions also occurred frequently. Surfaceome studies identified 160 proteins (23% of the proteome) to be exposed on the extracellular surface of M. pneumoniae. The two orthogonal methodologies used to characterise the surfaceome each identified the same 116 proteins, a 72% (116/160) overlap. Apart from lipoproteins, transporters, and adhesins, 93/160 (58%) of the surface proteins lack signal peptides and have well characterised, canonical functions in the cell. Of the 160 surface proteins identified, 134 were also targets of endo-proteolytic processing. These processing events are likely to have profound implications for how the host immune system recognises and responds to M. pneumoniae.

Molecular evolution is an important step in the development of therapeutic antibodies. However, the current method of affinity maturation is overly costly and labor-intensive because of the repetitive mutation experiments needed to adequately explore sequence space. Here, we employed a long short term memory network (LSTM)—a widely used deep generative model—based sequence generation and prioritization procedure to efficiently discover antibody sequences with higher affinity. We applied our method to the affinity maturation of antibodies against kynurenine, which is a metabolite related to the niacin synthesis pathway. Kynurenine binding sequences were enriched through phage display panning using a kynurenine-binding oriented human synthetic Fab library. We defined binding antibodies using a sequence repertoire from the NGS data to train the LSTM model. We confirmed that likelihood of generated sequences from a trained LSTM correlated well with binding affinity. The affinity of generated sequences are over 1800-fold higher than that of the parental clone. Moreover, compared to frequency based screening using the same dataset, our machine learning approach generated sequences with greater affinity.

The interactions of the antibiotic proteins colicins/pyocins with immunity proteins is a seminal model system for studying protein–protein interactions and specificity. Yet, a precise and quantitative determination of which structural elements and residues determine their binding affinity and specificity is still lacking. Here, we used comparative structure-based energy calculations to map residues that substantially contribute to interactions across native and engineered complexes of colicins/pyocins and immunity proteins. We show that the immunity protein α1–α2 motif is a unique structurally-dissimilar element that restricts interaction specificity towards all colicins/pyocins, in both engineered and native complexes. This motif combines with a diverse and extensive array of electrostatic/polar interactions that enable the exquisite specificity that characterizes these interactions while achieving ultra-high affinity. Surprisingly, the divergence of these contributing colicin residues is reciprocal to residue conservation in immunity proteins. The structurally-dissimilar immunity protein α1–α2 motif is recognized by divergent colicins similarly, while the conserved immunity protein α3 helix interacts with diverse colicin residues. Electrostatics thus plays a key role in setting interaction specificity across all colicins and immunity proteins. Our analysis and resulting residue-level maps illuminate the molecular basis for these protein–protein interactions, with implications for drug development and rational engineering of these interfaces.

Treatment of antibiotic-resistant infections is dependent on the detection of specific bacterial genes or proteins in clinical assays. Identification of methicillin-resistant Staphylococcus aureus (MRSA) is often accomplished through the detection of penicillin-binding protein 2a (PBP2a). With greater dependence on mass spectrometry (MS)-based bacterial identification, complementary efforts to detect resistance have been hindered by the complexity of those proteins responsible. Initial characterization of PBP2a indicates the presence of glycan modifications. To simplify detection, we demonstrate a proof-of-concept tandem MS approach involving the generation of N-terminal PBP2a peptide-like fragments and detection of unique product ions during top-down proteomic sample analyses. This approach was implemented for two PBP2a variants, PBP2amecA and PBP2amecC, and was accurate across a representative panel of MRSA strains with different genetic backgrounds. Additionally, PBP2amecA was successfully detected from clinical isolates using a five-minute liquid chromatographic separation and implementation of this MS detection strategy. Our results highlight the capability of direct MS-based resistance marker detection and potential advantages for implementing these approaches in clinical diagnostics.

DNA synthesisin vitrohas enabled the rapid production of reference standards. These are used as controls, and allow measurement and improvement of the accuracy and quality of diagnostic tests. Current reference standards typically represent target genetic material, and act only as positive controls to assess test sensitivity. However, negative controls are also required to evaluate test specificity. Using a pair of chimeric A/B RNA standards, this allowed incorporation of positive and negative controls into diagnostic testing for the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). The chimeric standards constituted target regions for RT-PCR primer/probe sets that are joined in tandem across two separate synthetic molecules. Accordingly, a target region that is present in standard A provides a positive control, whilst being absent in standard B, thereby providing a negative control. This design enables cross-validation of positive and negative controls between the paired standards in the same reaction, with identical conditions. This enables control and test failures to be distinguished, increasing confidence in the accuracy of results. The chimeric A/B standards were assessed using the US Centers for Disease Control real-time RT-PCR protocol, and showed results congruent with other commercial controls in detecting SARS CoV-2 in patient samples. This chimeric reference standard design approach offers extensive flexibility, allowing representation of diverse genetic features and distantly related sequences, even from different organisms.

Many therapeutic drugs are compounds that can be represented by simple chemical structures, which contain important determinants of affinity at the site of action. Recently, graph convolutional neural network (GCN) models have exhibited excellent results in classifying the activity of such compounds. For models that make quantitative predictions of activity, more complex information has been utilized, such as the three-dimensional structures of compounds and the amino acid sequences of their respective target proteins. As another approach, we hypothesized that if sufficient experimental data were available and there were enough nodes in hidden layers, a simple compound representation would quantitatively predict activity with satisfactory accuracy. In this study, we report that GCN models constructed solely from the two-dimensional structural information of compounds demonstrated a high degree of activity predictability against 127 diverse targets from the ChEMBL database. Using the information entropy as a metric, we also show that the structural diversity had less effect on the prediction performance. Finally, we report that virtual screening using the constructed model identified a new serotonin transporter inhibitor with activity comparable to that of a marketed drug in vitro and exhibited antidepressant effects in behavioural studies., 化合物の薬理作用を予測する技術を開発 --薬理作用ビッグデータを用いて--. 京都大学プレスリリース. 2021-01-13.

Post translational modifications (PTMs) are covalent modifications of proteins that can range from small chemical modifications to addition of entire proteins. PTMs contribute to regulation of protein function and thereby greatly increase the functional diversity of the proteome. In the heart, a few well-studied PTMs, such as phosphorylation and glycosylation, are known to play essential roles for cardiac function. Yet, only a fraction of the ~ 300 known PTMs have been studied in a cardiac context. Here we investigated the proteome-wide map of PTMs present in human hearts by utilizing high-resolution mass spectrometry measurements and a suite of PTM identification algorithms. Our approach led to identification of more than 150 different PTMs across three of the chambers in human hearts. This finding underscores that decoration of cardiac proteins by PTMs is much more diverse than hitherto appreciated and provides insights in cardiac protein PTMs not yet studied. The results presented serve as a catalogue of which PTMs are present in human hearts and outlines the particular protein and the specific amino acid modified, and thereby provides a detail-rich resource for exploring protein modifications in human hearts beyond the most studied PTMs.

Sandalwood (Santalum album L.) is highly valued for its fragrant heartwood and extracted oil. Santalols, which are the main components of that oil, are terpenoids, and these are biosynthesized via the mevalonic acid (MVA) pathway. Mevalonate kinase (MK) and phosphomevalonate kinase (PMK) are key enzymes in the MVA pathway. Little is known about the genes that encode MK and PMK in S. album or the mechanism that regulates their expression. To isolate and identify the functional genes involved in santalol biosynthesis in S. album, an MK gene designated as SaMK, and a PMK gene designated as SaPMK, were cloned from S. album. The sequences of these genes were analyzed. A bioinformatics analysis was conducted to assess the homology of SaMK and SaPMK with MK and PMK genes from other plants. The subcellular localization of SaMK and SaPMK proteins was also investigated, as was the functional complementation of SaMK and SaPMK in yeast. Our results show that the full-length cDNA sequences of SaMK and SaPMK were 1409 bp and 1679 bp long, respectively. SaMK contained a 1381 bp open reading frame (ORF) encoding a polypeptide of 460 amino acids and SaPMK contained a 1527 bp ORF encoding a polypeptide of 508 amino acids. SaMK and SaPMK showed high homology with MK and PMK genes of other plant species. Functional complementation of SaMK in a MK-deficient mutant yeast strain YMR208W and SaPMK in a PMK-deficient mutant yeast strain YMR220W confirmed that cloned SaMK and SaPMK cDNA encode a functional MK and PMK, respectively, mediating MVA biosynthesis in yeast. An analysis of tissue expression patterns revealed that SaMK and SaPMK were constitutively expressed in all the tested tissues. SaMK was highly expressed in young leaves but weakly expressed in sapwood. SaPMK was highly expressed in roots and mature leaves, but weakly expressed in young leaves. Induction experiments with several elicitors showed that SaMK and SaPMK expression was upregulated by methyl jasmonate. These results will help to further study the role of MK and PMK genes during santalol biosynthesis in S. album.

Background Protein structure can provide insights that help biologists to predict and understand protein functions and interactions. However, the number of known protein structures has not kept pace with the number of protein sequences determined by high-throughput sequencing. Current techniques used to determine the structure of proteins, such as X-ray crystallography and Nuclear Magnetic Resonance (NMR) spectroscopy, are complex and may require a lot of time to analyze the experimental results, especially for large protein molecules. The limitations of these methods have motivated us to create a new approach for protein structure prediction.Results Here we describe a new approach that uses integration and analysis of torsion angle information from the Protein Data Bank to enable prediction of protein structures from amino acid sequences. Our prediction model performed well in comparison with previous methods when applied to the structural classification of two CATH datasets with more than 5000 protein domains. This new prediction model performs well with an average of 92.5% accuracy for structure classification, which is higher than the previous research. We also used our model to predict four known protein structures with a single amino acid sequence, while many other existing methods could only obtain one possible structure for a given sequence.Conclusions The results show that our method provides a new effective and reliable tool for protein structure prediction research.