Protein Structure Discovery: software package to perform computational proteomics tasks

Software-information system Protein Structure Discovery was developed. The system can be used for the wide range of tasks in the field of computer proteomics including prediction of function, structure and immunological properties of proteins. A specially created section of the system allows evaluating the quantitative and qualitative effects of mutations on the structural and functional properties of proteins. There are 19 of different programs integrated into the system, including the database of protein functional sites PDBSite, a PDBSiteScan program for the prediction of functional sites in three-dimensional structures of proteins, and WebProAnalyst program for the quantitative analysis of the structure-activity relationship of proteins. Protein Structure Discovery program has a Web interface and is available for users through the Internet (http://www-bionet.sscc.ru/psd/). For example, binding sites of zinc ion and ADP showed high stability of the method to errors PDBSiteScan reconstruction of spatial structures of proteins in the recognition of functional sites in model structures.     

Novel structural tree of (α + β)-proteins containing abCd units

A database of 926 (α + β)-proteins and (α + β)-domains containing abCd units, including 401 non-homologous, was compiled from the Protein Data Bank (total of 2636 PDB entries). A novel structural tree of this structural class of proteins was constructed to include 286 possible polypeptide chain folds. A structural classification of (α + β)-proteins containing abCd unit was developed on the basis of the structural tree. The database and the structural tree are available at http://strees.protres.ru/.     

Computational method for predicting protein functional sites with the use of specificity determinants

The currently available body of decoded amino acid sequences of various proteins exceeds manifold the experimental capabilities of their functional annotation. Therefore, in silico annotation using bioinformatics methods becomes increasingly important. Such annotation is actually a prediction; however, this can be an important starting point for further laboratory research. This work describes a new method for predicting functionally important protein sites, SDPsite, on the basis of identification of specificity determinants. The algorithm proposed utilizes a protein family aglinment and a phylogenetic tree to predict the conserved positions and specificity determinants, map them onto the protein structure, and search for clusters of the predicted positions. Comparison of the resulting predictions with experimental data and published predictions of functional sites by other methods demonstrates that the results of SDPsite agree well with experimental data and exceed the results obtained with the majority of previous methods. SDPsite is publicly available at http://bioinf.fbb.msu.ru/SDPsite.     

PDBsum extras: SARS‐CoV‐2 and AlphaFold models

The PDBsum web server provides structural analyses of the entries in the Protein Data Bank (PDB). Two recent additions are described here. The first is the detailed analysis of the SARS‐CoV‐2 virus protein structures in the PDB. These include the variants of concern, which are shown both on the sequences and 3D structures of the proteins. The second addition is the inclusion of the available AlphaFold models for human proteins. The pages allow a search of the protein against existing structures in the PDB via the Sequence Annotated by Structure (SAS) server, so one can easily compare the predicted model against experimentally determined structures. The server is freely accessible to all at http://www.ebi.ac.uk/pdbsum.     

Prediction of Bacterial and Archaeal Allergenicity with AllPred Program

Nowadays, allergic disorders have become one of the most important social problems in the world. This can be related to the advent of new allergenic agents in the environment, as well as an increasing density of human contact with known allergens, including various proteins. Thus, the development of computer programs designed for the prediction of allergenic properties of proteins becomes one of the urgent tasks of modern bioinformatics. Previously we developed a web accessible Allpred Program (http://www-bionet.sscc.ru/ psd/cgi-bin/programs/Allpred/allpred.cgi) that allows users to assess the allergenicity of proteins by taking into account the characteristics of their spatial structure. In this paper, using AllPred, we predicted the allergenicity of proteins from 462 archaea and bacteria species for which a complete genome was available. The segregation of considered proteins on archaea and bacteria has shown that allergens are predicted more often among archaea than among bacteria. The division of these proteins into groups according to their intracellular localization has revealed that the majority of allergenic proteins were among the secreted proteins. The application of methods for predicting the level of gene expression of microorganisms based on DNA sequence analysis showed a statistically significant relationship between the expression level of the proteins and their allergenicity. This analysis has revealed that potentially allergenic proteins were more common among highly expressed proteins. Sorting microorganisms into the pathogenic and nonpathogenic groups has shown that pathogens can potentially be more allergenic because of a statistically significant greater number of allergens predicted among their proteins.     

Membrane profile-based probabilistic method for predicting transmembrane segments via multiple protein sequence alignment

Prediction of transmembrane (TM) segments of amino acid sequences of membrane proteins is a well-known and very important problem. The accuracy of its solution can be improved for approaches that do not use a homology search in an additional data bank. There is a lack of tested data in this area of research, because information on the structure of membrane proteins is scarce. In this work we created a test sample of structural alignments for membrane proteins. The TM segments of these proteins were mapped according to aligned 3D structures resolved for these proteins. A method for predicting TM segments in an alignment was developed on the basis of the forward-backward algorithm from the HMM theory. This method allows a user not only to predict TM segments, but also to create a probabilistic membrane profile, which can be employed in multiple alignment procedures taking the secondary structure of proteins into account. The method was implemented in a computer program available at http://bioinf.fbb.msu.ru/fwdbck/. It provides better results than the MEMSAT method, which is nearly the only tool predicting TM segments in multiple alignments, without a homology search.     

Nucleosome formation potential of exons, introns, and Alu repeats.

A program for constructing nucleosome formation potential profile was applied for investigation of exons, introns, and repetitive sequences. The program is available at http://wwwmgs.bionet.nsc.ru/mgs/programs/recon/. We have demonstrated that introns and repetitive sequences exhibit higher nucleosome formation potentials than exons. This fact may be explained by functional saturation of exons with genetic code, hindering the localization of efficient nucleosome positioning sites.     

Investigation and identification of functional post-translational modification sites associated with drug binding and protein-protein interactions

Background

Protein post-translational modification (PTM) plays an essential role in various cellular processes that modulates the physical and chemical properties, folding, conformation, stability and activity of proteins, thereby modifying the functions of proteins. The improved throughput of mass spectrometry (MS) or MS/MS technology has not only brought about a surge in proteome-scale studies, but also contributed to a fruitful list of identified PTMs. However, with the increase in the number of identified PTMs, perhaps the more crucial question is what kind of biological mechanisms these PTMs are involved in. This is particularly important in light of the fact that most protein-based pharmaceuticals deliver their therapeutic effects through some form of PTM. Yet, our understanding is still limited with respect to the local effects and frequency of PTM sites near pharmaceutical binding sites and the interfaces of protein-protein interaction (PPI). Understanding PTM’s function is critical to our ability to manipulate the biological mechanisms of protein.

Results

In this study, to understand the regulation of protein functions by PTMs, we mapped 25,835 PTM sites to proteins with available three-dimensional (3D) structural information in the Protein Data Bank (PDB), including 1785 modified PTM sites on the 3D structure. Based on the acquired structural PTM sites, we proposed to use five properties for the structural characterization of PTM substrate sites: the spatial composition of amino acids, residues and side-chain orientations surrounding the PTM substrate sites, as well as the secondary structure, division of acidity and alkaline residues, and solvent-accessible surface area. We further mapped the structural PTM sites to the structures of drug binding and PPI sites, identifying a total of 1917 PTM sites that may affect PPI and 3951 PTM sites associated with drug-target binding. An integrated analytical platform (CruxPTM), with a variety of methods and online molecular docking tools for exploring the structural characteristics of PTMs, is presented. In addition, all tertiary structures of PTM sites on proteins can be visualized using the JSmol program.

Conclusion

Resolving the function of PTM sites is important for understanding the role that proteins play in biological mechanisms. Our work attempted to delineate the structural correlation between PTM sites and PPI or drug-target binding. CurxPTM could help scientists narrow the scope of their PTM research and enhance the efficiency of PTM identification in the face of big proteome data. CruxPTM is now available at http://csb.cse.yzu.edu.tw/CruxPTM/.

     

The Protein Structure Initiative Structural Biology Knowledgebase Technology Portal: a structural biology web resource

The Technology Portal of the Protein Structure Initiative Structural Biology Knowledgebase (PSI SBKB; http://technology.sbkb.org/portal/ ) is a web resource providing information about methods and tools that can be used to relieve bottlenecks in many areas of protein production and structural biology research. Several useful features are available on the web site, including multiple ways to search the database of over 250 technological advances, a link to videos of methods on YouTube, and access to a technology forum where scientists can connect, ask questions, get news, and develop collaborations. The Technology Portal is a component of the PSI SBKB ( http://sbkb.org ), which presents integrated genomic, structural, and functional information for all protein sequence targets selected by the Protein Structure Initiative. Created in collaboration with the Nature Publishing Group, the SBKB offers an array of resources for structural biologists, such as a research library, editorials about new research advances, a featured biological system each month, and a functional sleuth for searching protein structures of unknown function. An overview of the various features and examples of user searches highlight the information, tools, and avenues for scientific interaction available through the Technology Portal.     

Prediction of linear B-cell epitopes

The use of antigenicity scales based on physicochemical properties and the sliding window method in combination with an averaging algorithm and subsequent search for the maximum value is the classical method for B-cell epitope prediction. However, recent studies have demonstrated that the best classical methods provide a poor correlation with experimental data. We review both classical and novel algorithms and present our own implementation of the algorithms. The AAPPred software is available at http://www.bioinf.ru/aappred/.     

Zebra: a web server for bioinformatic analysis of diverse protein families

During evolution of proteins from a common ancestor, one functional property can be preserved while others can vary leading to functional diversity. A systematic study of the corresponding adaptive mutations provides a key to one of the most challenging problems of modern structural biology – understanding the impact of amino acid substitutions on protein function. The subfamily-specific positions (SSPs) are conserved within functional subfamilies but are different between them and, therefore, seem to be responsible for functional diversity in protein superfamilies. Consequently, a corresponding method to perform the bioinformatic analysis of sequence and structural data has to be implemented in the common laboratory practice to study the structure–function relationship in proteins and develop novel protein engineering strategies. This paper describes Zebra web server – a powerful remote platform that implements a novel bioinformatic analysis algorithm to study diverse protein families. It is the first application that provides specificity determinants at different levels of functional classification, therefore addressing complex functional diversity of large superfamilies. Statistical analysis is implemented to automatically select a set of highly significant SSPs to be used as hotspots for directed evolution or rational design experiments and analyzed studying the structure–function relationship. Zebra results are provided in two ways – (1) as a single all-in-one parsable text file and (2) as PyMol sessions with structural representation of SSPs. Zebra web server is available at http://biokinet.belozersky.msu.ru/zebra.