ИСТИНА

Modulation of enzyme functional properties is a challenging problem for both fundamental enzymology and enzyme engineering. The task can be solved by changing protein structure at mutation or modification, due to interaction of an enzyme with modulating molecules or a carrier, etc. To settle these problems we propose to use interdisciplinary approach that combines the strength of bioinformatics, molecular modeling, high performance computing, theoretical chemistry, protein engineering and is currently implemented as a platform of 8 public web-servers [1-8]. The developed methodology was applied for the different purposes [9-11]: i) to study structure-function relationship in several enzyme families (Ntn-hydrolases, penicillin-binding proteins, sialidases, α/β-hydrolases), ii) to search for function-related variable positions in protein structure that can be used as hotspots for mutations to modulate catalytic activity, substrate profile, stereoselectivity and stability of enzymes, iii) to identify previously unknown binding sites in enzyme structure for binding of modulating ligands that can be used to design selective inhibitors. The report will summarize laboratory's experience in building up and application of such a platform to create biocatalysts with improved properties and to design new selective inhibitors of enzymes of pathogens. The developed methodology can be recommended as a systematic tool to study structure-function relationship, characterize and rank enzyme binding sites for binding of new selective modulating molecules, identify function-related positions and use them as hotspots for mutation to rationalize different protein engineering approaches thus designing enzymes with requested functional properties. This work was supported by the Russian Science Foundation (grant #15-14-00069-P). 1. Suplatov D, Kopylov K, Popova N, Voevodin V, Švedas V. Mustguseal: Server for Multiple Structure-Guided Sequence Alignments of Protein Families. Bioinformatics. 2018, 34(9), 1583-1585. 2. Shegay M, Suplatov D, Popova N, Švedas V, Voevodin V. parMATT: parallel multiple alignment of protein 3D-structures for distributed-memory systems. Bioinformatics. 2019. DOI:10.1093/bioinformatics/btz224 3. Suplatov D, Sharapova Y, Timonina D, Kopylov K, Švedas V. The visualCMAT: a web-server to select and interpret correlated mutations/co-evolving residues in protein families. J Bioinform Comput Biol. 2018, 16(2), 1840005. DOI:10.1142/S021972001840005X 4. Suplatov D, Kirilin E, Takhaveev V, Švedas V. Zebra: a web server for bioinformatic analysis of diverse protein families. J Biomol Struct Dyn. 2014, 32(11), 1752-1758. DOI:10.1080/07391102.2013.834514 5. Suplatov D, Kirilin E, Arbatsky M, Takhaveev V, Švedas V. pocketZebra: a web-server for automated selection and classification of subfamily-specific binding sites by bioinformatic analysis of diverse protein families. Nucleic Acids Research. 2014, 42(W1), W344-W349. DOI:dx.doi.org 6. Suplatov D, Popova N, Zhumatiy S, Voevodin V, Švedas V. Parallel workflow manager for non-parallel bioinformatic applications to solve large-scale biological problems on a supercomputer, J Bioinform Comput Biol, 2016, 14(2), 1641008, doi:10.1142/S0219720016410080. 7. Suplatov D, Timonina D, Sharapova Y, Švedas V. Yosshi: a web-server for disulfide engineering by bioinformatics analysis of diverse protein families. Nucleic Acids Research. 2019, DOI: 10.1093/nar/gkz385 8. Zlobin A, Suplatov D, Kopylov K, Švedas V. CASBench: A Benchmarking Set of Proteins with Annotated Catalytic and Allosteric Sites in Their Structures. Acta Naturae. 2019, 11(1), 74-80. 9. Fesko K, Suplatov D, Švedas V. Bioinformatic analysis of the fold type I PLP‐dependent enzymes reveals determinants of reaction specificity in L‐threonine aldolase from Aeromonas jandaei. FEBS Open Bio. 2018, 8(6), 1013-1028. 10. Sharapova Y, Suplatov D, Švedas V. Neuraminidase A from Streptococcus pneumoniae has a modular organization of Catalytic and Lectin Domains separated by a flexible linker. FEBS J 2018, 285, 2428-2445. 11. Suplatov D., Panin N., Kirilin E., Shcherbakova T., Kudryavtsev P., Švedas V. Computational design of a pH stable enzyme: understanding molecular mechanism of penicillin acylase’s adaptation to alkaline conditions. PLoS ONE.2014, 9(6), e100643.