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Karasev DA, Sobolev BN, Lagunin AA, Filimonov DA, Poroikov VV. The method predicting interaction between protein targets and small-molecular ligands with the wide applicability domain. Comput Biol Chem 2022; 98:107674. [DOI: 10.1016/j.compbiolchem.2022.107674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 03/24/2022] [Accepted: 03/28/2022] [Indexed: 11/03/2022]
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Anashkina AA, Leberfarb EY, Orlov YL. Recent Trends in Cancer Genomics and Bioinformatics Tools Development. Int J Mol Sci 2021; 22:ijms222212146. [PMID: 34830028 PMCID: PMC8618360 DOI: 10.3390/ijms222212146] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 11/08/2021] [Indexed: 02/07/2023] Open
Affiliation(s)
- Anastasia A. Anashkina
- The Digital Health Institute, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), 119991 Moscow, Russia;
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
| | - Elena Y. Leberfarb
- Department of Medicinal Chemistry, Novosibirsk State Medical University, 630091 Novosibirsk, Russia;
| | - Yuriy L. Orlov
- The Digital Health Institute, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), 119991 Moscow, Russia;
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 630090 Novosibirsk, Russia
- Life Sciences Department, Novosibirsk State University, 630090 Novosibirsk, Russia
- Agrarian and Technological Institute, Peoples’ Friendship University of Russia, 117198 Moscow, Russia
- Correspondence: or
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Medical Genetics, Genomics and Bioinformatics Aid in Understanding Molecular Mechanisms of Human Diseases. Int J Mol Sci 2021; 22:ijms22189962. [PMID: 34576125 PMCID: PMC8467458 DOI: 10.3390/ijms22189962] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 09/09/2021] [Accepted: 09/13/2021] [Indexed: 12/14/2022] Open
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Pinzi L, Tinivella A, Gagliardelli L, Beneventano D, Rastelli G. LigAdvisor: a versatile and user-friendly web-platform for drug design. Nucleic Acids Res 2021; 49:W326-W335. [PMID: 34023895 PMCID: PMC8262749 DOI: 10.1093/nar/gkab385] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 04/19/2021] [Accepted: 04/27/2021] [Indexed: 12/17/2022] Open
Abstract
Although several tools facilitating in silico drug design are available, their results are usually difficult to integrate with publicly available information or require further processing to be fully exploited. The rational design of multi-target ligands (polypharmacology) and the repositioning of known drugs towards unmet therapeutic needs (drug repurposing) have raised increasing attention in drug discovery, although they usually require careful planning of tailored drug design strategies. Computational tools and data-driven approaches can help to reveal novel valuable opportunities in these contexts, as they enable to efficiently mine publicly available chemical, biological, clinical, and disease-related data. Based on these premises, we developed LigAdvisor, a data-driven webserver which integrates information reported in DrugBank, Protein Data Bank, UniProt, Clinical Trials and Therapeutic Target Database into an intuitive platform, to facilitate drug discovery tasks as drug repurposing, polypharmacology, target fishing and profiling. As designed, LigAdvisor enables easy integration of similarity estimation results with clinical data, thereby allowing a more efficient exploitation of information in different drug discovery contexts. Users can also develop customizable drug design tasks on their own molecules, by means of ligand- and target-based search modes, and download their results. LigAdvisor is publicly available at https://ligadvisor.unimore.it/.
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Affiliation(s)
- Luca Pinzi
- Department of Life Sciences, University of Modena and Reggio Emilia, Modena 41125, Italy
| | - Annachiara Tinivella
- Department of Life Sciences, University of Modena and Reggio Emilia, Modena 41125, Italy.,Clinical and Experimental Medicine, PhD Program, University of Modena and Reggio Emilia, Modena 41125, Italy
| | - Luca Gagliardelli
- Department of Engineering "Enzo Ferrari", University of Modena and Reggio Emilia, Modena 41125, Italy
| | - Domenico Beneventano
- Department of Engineering "Enzo Ferrari", University of Modena and Reggio Emilia, Modena 41125, Italy
| | - Giulio Rastelli
- Department of Life Sciences, University of Modena and Reggio Emilia, Modena 41125, Italy
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Timonina D, Sharapova Y, Švedas V, Suplatov D. Bioinformatic analysis of subfamily-specific regions in 3D-structures of homologs to study functional diversity and conformational plasticity in protein superfamilies. Comput Struct Biotechnol J 2021; 19:1302-1311. [PMID: 33738079 PMCID: PMC7933735 DOI: 10.1016/j.csbj.2021.02.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 02/08/2021] [Accepted: 02/09/2021] [Indexed: 02/07/2023] Open
Abstract
Local 3D-structural differences in homologous proteins contribute to functional diversity observed in a superfamily, but so far received little attention as bioinformatic analysis was usually carried out at the level of amino acid sequences. We have developed Zebra3D - the first-of-its-kind bioinformatic software for systematic analysis of 3D-alignments of protein families using machine learning. The new tool identifies subfamily-specific regions (SSRs) - patterns of local 3D-structure (i.e. single residues, loops, or secondary structure fragments) that are spatially equivalent within families/subfamilies, but are different among them, and thus can be associated with functional diversity and function-related conformational plasticity. Bioinformatic analysis of protein superfamilies by Zebra3D can be used to study 3D-determinants of catalytic activity and specific accommodation of ligands, help to prepare focused libraries for directed evolution or assist development of chimeric enzymes with novel properties by exchange of equivalent regions between homologs, and to characterize plasticity in binding sites. A companion Mustguseal web-server is available to automatically construct a 3D-alignment of functionally diverse proteins, thus reducing the minimal input required to operate Zebra3D to a single PDB code. The Zebra3D + Mustguseal combined approach provides the opportunity to systematically explore the value of SSRs in superfamilies and to use this information for protein design and drug discovery. The software is available open-access at https://biokinet.belozersky.msu.ru/Zebra3D.
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Affiliation(s)
- Daria Timonina
- Lomonosov Moscow State University, Faculty of Bioengineering and Bioinformatics, Lenin Hills 1-73, Moscow 119234, Russia
| | - Yana Sharapova
- Lomonosov Moscow State University, Faculty of Bioengineering and Bioinformatics, Lenin Hills 1-73, Moscow 119234, Russia
- Lomonosov Moscow State University, Belozersky Institute of Physicochemical Biology, Lenin Hills 1-73, Moscow 119234, Russia
| | - Vytas Švedas
- Lomonosov Moscow State University, Faculty of Bioengineering and Bioinformatics, Lenin Hills 1-73, Moscow 119234, Russia
- Lomonosov Moscow State University, Belozersky Institute of Physicochemical Biology, Lenin Hills 1-73, Moscow 119234, Russia
| | - Dmitry Suplatov
- Lomonosov Moscow State University, Belozersky Institute of Physicochemical Biology, Lenin Hills 1-73, Moscow 119234, Russia
- Corresponding author.
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Karasev D, Sobolev B, Lagunin A, Filimonov D, Poroikov V. Prediction of Protein-ligand Interaction Based on Sequence Similarity and Ligand Structural Features. Int J Mol Sci 2020; 21:ijms21218152. [PMID: 33142754 PMCID: PMC7663273 DOI: 10.3390/ijms21218152] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 10/28/2020] [Accepted: 10/29/2020] [Indexed: 01/09/2023] Open
Abstract
Computationally predicting the interaction of proteins and ligands presents three main directions: the search of new target proteins for ligands, the search of new ligands for targets, and predicting the interaction of new proteins and new ligands. We proposed an approach providing the fuzzy classification of protein sequences based on the ligand structural features to analyze the latter most complicated case. We tested our approach on five protein groups, which represented promised targets for drug-like ligands and differed in functional peculiarities. The training sets were built with the original procedure overcoming the data ambiguity. Our study showed the effective prediction of new targets for ligands with an average accuracy of 0.96. The prediction of new ligands for targets displayed the average accuracy 0.95; accuracy estimates were close to our previous results, comparable in accuracy to those of other methods or exceeded them. Using the fuzzy coefficients reflecting the target-to-ligand specificity, we provided predicting interactions for new proteins and new ligands; the obtained accuracy values from 0.89 to 0.99 were acceptable for such a sophisticated task. The protein kinase family case demonstrated the ability to account for subtle features of proteins and ligands required for the specificity of protein–ligand interaction.
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Affiliation(s)
- Dmitry Karasev
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia; (B.S.); (A.L.); (D.F.); (V.P.)
- Correspondence:
| | - Boris Sobolev
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia; (B.S.); (A.L.); (D.F.); (V.P.)
| | - Alexey Lagunin
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia; (B.S.); (A.L.); (D.F.); (V.P.)
- Department of Bioinformatics, Russian National Research Medical University, Moscow 117997, Russia
| | - Dmitry Filimonov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia; (B.S.); (A.L.); (D.F.); (V.P.)
| | - Vladimir Poroikov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia; (B.S.); (A.L.); (D.F.); (V.P.)
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Bioinformatics Methods in Medical Genetics and Genomics. Int J Mol Sci 2020; 21:ijms21176224. [PMID: 32872128 PMCID: PMC7504073 DOI: 10.3390/ijms21176224] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 08/25/2020] [Indexed: 02/06/2023] Open
Abstract
Medical genomics relies on next-gen sequencing methods to decipher underlying molecular mechanisms of gene expression. This special issue collects materials originally presented at the “Centenary of Human Population Genetics” Conference-2019, in Moscow. Here we present some recent developments in computational methods tested on actual medical genetics problems dissected through genomics, transcriptomics and proteomics data analysis, gene networks, protein–protein interactions and biomedical literature mining. We have selected materials based on systems biology approaches, database mining. These methods and algorithms were discussed at the Digital Medical Forum-2019, organized by I.M. Sechenov First Moscow State Medical University presenting bioinformatics approaches for the drug targets discovery in cancer, its computational support, and digitalization of medical research, as well as at “Systems Biology and Bioinformatics”-2019 (SBB-2019) Young Scientists School in Novosibirsk, Russia. Selected recent advancements discussed at these events in the medical genomics and genetics areas are based on novel bioinformatics tools.
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Poroikov VV. Computer-Aided Drug Design: from Discovery of Novel Pharmaceutical Agents to Systems Pharmacology. BIOCHEMISTRY (MOSCOW), SUPPLEMENT SERIES B: BIOMEDICAL CHEMISTRY 2020. [DOI: 10.1134/s1990750820030117] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
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Poroikov VV. [Computer-aided drug design: from discovery of novel pharmaceutical agents to systems pharmacology]. BIOMEDIT︠S︡INSKAI︠A︡ KHIMII︠A︡ 2020; 66:30-41. [PMID: 32116224 DOI: 10.18097/pbmc20206601030] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
New drug discovery is based on the analysis of public information about the mechanisms of the disease, molecular targets, and ligands, which interaction with the target could lead to the normalization of the pathological process. The available data on diseases, drugs, pharmacological effects, molecular targets, and drug-like substances, taking into account the combinatorics of the associative relations between them, correspond to the Big Data. To analyze such data, the application of computer-aided drug design methods is necessary. An overview of the studies in this area performed by the Laboratory for Structure-Function Based Drug Design of IBMC is presented. We have developed the approaches to identifying promising pharmacological targets, predicting several thousand types of biological activity based on the structural formula of the compound, analyzing protein-ligand interactions based on assessing local similarity of amino acid sequences, identifying likely molecular mechanisms of side effects of drugs, calculating the integral toxicity of drugs taking into account their metabolism, have been developed in the human body, predicting sustainable and sensitive options strains and evaluating the effectiveness of combinations of antiretroviral drugs in patients, taking into account the molecular genetic characteristics of the clinical isolates of HIV-1. Our computer programs are implemented as the web-services freely available on the Internet, which are used by thousands of researchers from many countries of the world to select the most promising substances for the synthesis and determine the priority areas for experimental testing of their biological activity.
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Affiliation(s)
- V V Poroikov
- Institute of Biomedical Chemistry, Moscow, Russia
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