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Yang L, He W, Yun Y, Gao Y, Zhu Z, Teng M, Liang Z, Niu L. Defining A Global Map of Functional Group-based 3D Ligand-binding Motifs. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022; 20:765-779. [PMID: 35288344 PMCID: PMC9881048 DOI: 10.1016/j.gpb.2021.08.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 06/30/2021] [Accepted: 09/27/2021] [Indexed: 01/31/2023]
Abstract
Uncovering conserved 3D protein-ligand binding patterns on the basis of functional groups (FGs) shared by a variety of small molecules can greatly expand our knowledge of protein-ligand interactions. Despite that conserved binding patterns for a few commonly used FGs have been reported in the literature, large-scale identification and evaluation of FG-based 3D binding motifs are still lacking. Here, we propose a computational method, Automatic FG-based Three-dimensional Motif Extractor (AFTME), for automatic mapping of 3D motifs to different FGs of a specific ligand. Applying our method to 233 naturally-occurring ligands, we define 481 FG-binding motifs that are highly conserved across different ligand-binding pockets. Systematic analysis further reveals four main classes of binding motifs corresponding to distinct sets of FGs. Combinations of FG-binding motifs facilitate the binding of proteins to a wide spectrum of ligands with various binding affinities. Finally, we show that our FG-motif map can be used to nominate FGs that potentially bind to specific drug targets, thus providing useful insights and guidance for rational design of small-molecule drugs.
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Affiliation(s)
- Liu Yang
- School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; Division of Molecular and Cellular Biophysics, Hefei National Laboratory for Physical Sciences at the Microscale, Hefei 230026, China
| | - Wei He
- School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; Division of Molecular and Cellular Biophysics, Hefei National Laboratory for Physical Sciences at the Microscale, Hefei 230026, China.
| | - Yuehui Yun
- School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; Division of Molecular and Cellular Biophysics, Hefei National Laboratory for Physical Sciences at the Microscale, Hefei 230026, China
| | - Yongxiang Gao
- School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; Division of Molecular and Cellular Biophysics, Hefei National Laboratory for Physical Sciences at the Microscale, Hefei 230026, China
| | - Zhongliang Zhu
- School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; Division of Molecular and Cellular Biophysics, Hefei National Laboratory for Physical Sciences at the Microscale, Hefei 230026, China
| | - Maikun Teng
- School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; Division of Molecular and Cellular Biophysics, Hefei National Laboratory for Physical Sciences at the Microscale, Hefei 230026, China
| | - Zhi Liang
- School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; Division of Molecular and Cellular Biophysics, Hefei National Laboratory for Physical Sciences at the Microscale, Hefei 230026, China.
| | - Liwen Niu
- School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; Division of Molecular and Cellular Biophysics, Hefei National Laboratory for Physical Sciences at the Microscale, Hefei 230026, China.
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Mining subgraph coverage patterns from graph transactions. INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2021; 13:105-121. [PMID: 34873579 PMCID: PMC8636072 DOI: 10.1007/s41060-021-00292-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 10/24/2021] [Indexed: 11/25/2022]
Abstract
Pattern mining from graph transactional data (GTD) is an active area of research with applications in the domains of bioinformatics, chemical informatics and social networks. Existing works address the problem of mining frequent subgraphs from GTD. However, the knowledge concerning the coverage aspect of a set of subgraphs is also valuable for improving the performance of several applications. In this regard, we introduce the notion of subgraph coverage patterns (SCPs). Given a GTD, a subgraph coverage pattern is a set of subgraphs subject to relative frequency, coverage and overlap constraints provided by the user. We propose the Subgraph ID-based Flat Transactional (SIFT) framework for the efficient extraction of SCPs from a given GTD. Our performance evaluation using three real datasets demonstrates that our proposed SIFT framework is indeed capable of efficiently extracting SCPs from GTD. Furthermore, we demonstrate the effectiveness of SIFT through a case study in computer-aided drug design.
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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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Valenzuela O, Rojas F, Rojas I, Glosekotter P. Main findings and advances in bioinformatics and biomedical engineering- IWBBIO 2018. BMC Bioinformatics 2020; 21:153. [PMID: 32366219 PMCID: PMC7199304 DOI: 10.1186/s12859-020-3467-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
In the current supplement, we are proud to present seventeen relevant contributions from the 6th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2018), which was held during April 25-27, 2018 in Granada (Spain). These contributions have been chosen because of their quality and the importance of their findings.
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Affiliation(s)
- Olga Valenzuela
- Faculty of Sciences, Applied Mathematics, University of Granada, Avenida de Fuente Nueva, Granada, 18071 Spain
| | - Fernando Rojas
- Information and Communications Technology Centre (CITIC and ETSIIT-UGR) University of Granada, Periodista Daniel Saucedo Aranda, Granada, 18071 Spain
| | - Ignacio Rojas
- Information and Communications Technology Centre (CITIC and ETSIIT-UGR) University of Granada, Periodista Daniel Saucedo Aranda, Granada, 18071 Spain
| | - Peter Glosekotter
- Department of Electrical Engineering and Computer Science, University of Applied Sciences of Munster, Stegerweldstr 39, Steinfurt, 48565 Germany
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