1
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Zhang C, Zhang X, Freddolino P, Zhang Y. BioLiP2: an updated structure database for biologically relevant ligand-protein interactions. Nucleic Acids Res 2024; 52:D404-D412. [PMID: 37522378 PMCID: PMC10767969 DOI: 10.1093/nar/gkad630] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/03/2023] [Accepted: 07/17/2023] [Indexed: 08/01/2023] Open
Abstract
With the progress of structural biology, the Protein Data Bank (PDB) has witnessed rapid accumulation of experimentally solved protein structures. Since many structures are determined with purification and crystallization additives that are unrelated to a protein's in vivo function, it is nontrivial to identify the subset of protein-ligand interactions that are biologically relevant. We developed the BioLiP2 database (https://zhanggroup.org/BioLiP) to extract biologically relevant protein-ligand interactions from the PDB database. BioLiP2 assesses the functional relevance of the ligands by geometric rules and experimental literature validations. The ligand binding information is further enriched with other function annotations, including Enzyme Commission numbers, Gene Ontology terms, catalytic sites, and binding affinities collected from other databases and a manual literature survey. Compared to its predecessor BioLiP, BioLiP2 offers significantly greater coverage of nucleic acid-protein interactions, and interactions involving large complexes that are unavailable in PDB format. BioLiP2 also integrates cutting-edge structural alignment algorithms with state-of-the-art structure prediction techniques, which for the first time enables composite protein structure and sequence-based searching and significantly enhances the usefulness of the database in structure-based function annotations. With these new developments, BioLiP2 will continue to be an important and comprehensive database for docking, virtual screening, and structure-based protein function analyses.
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Affiliation(s)
- Chengxin Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Xi Zhang
- Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Peter L Freddolino
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Computer Science, School of Computing, National University of Singapore, 117417, Singapore
- Cancer Science Institute of Singapore, National University of Singapore,117599, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 117596, Singapore
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2
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Bheemireddy S, Sandhya S, Srinivasan N, Sowdhamini R. Computational tools to study RNA-protein complexes. Front Mol Biosci 2022; 9:954926. [PMID: 36275618 PMCID: PMC9585174 DOI: 10.3389/fmolb.2022.954926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 09/20/2022] [Indexed: 11/19/2022] Open
Abstract
RNA is the key player in many cellular processes such as signal transduction, replication, transport, cell division, transcription, and translation. These diverse functions are accomplished through interactions of RNA with proteins. However, protein–RNA interactions are still poorly derstood in contrast to protein–protein and protein–DNA interactions. This knowledge gap can be attributed to the limited availability of protein-RNA structures along with the experimental difficulties in studying these complexes. Recent progress in computational resources has expanded the number of tools available for studying protein-RNA interactions at various molecular levels. These include tools for predicting interacting residues from primary sequences, modelling of protein-RNA complexes, predicting hotspots in these complexes and insights into derstanding in the dynamics of their interactions. Each of these tools has its strengths and limitations, which makes it significant to select an optimal approach for the question of interest. Here we present a mini review of computational tools to study different aspects of protein-RNA interactions, with focus on overall application, development of the field and the future perspectives.
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Affiliation(s)
- Sneha Bheemireddy
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Sankaran Sandhya
- Department of Biotechnology, Faculty of Life and Allied Health Sciences, M.S. Ramaiah University of Applied Sciences, Bengaluru, India
- *Correspondence: Sankaran Sandhya, ; Ramanathan Sowdhamini,
| | | | - Ramanathan Sowdhamini
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
- National Centre for Biological Sciences, TIFR, GKVK Campus, Bangalore, India
- Institute of Bioinformatics and Applied Biotechnology, Bangalore, India
- *Correspondence: Sankaran Sandhya, ; Ramanathan Sowdhamini,
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3
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Cheng J, Lin Y, Xu L, Chen K, Li Q, Xu K, Ning L, Kang J, Cui T, Huang Y, Zhao X, Wang D, Li Y, Su X, Yang B. ViRBase v3.0: a virus and host ncRNA-associated interaction repository with increased coverage and annotation. Nucleic Acids Res 2022; 50:D928-D933. [PMID: 34723320 PMCID: PMC8728225 DOI: 10.1093/nar/gkab1029] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 10/11/2021] [Accepted: 10/13/2021] [Indexed: 12/15/2022] Open
Abstract
As a means to aid in the investigation of viral infection mechanisms and identification of more effective antivirus targets, the availability of a source which continually collects and updates information on the virus and host ncRNA-associated interaction resources is essential. Here, we update the ViRBase database to version 3.0 (http://www.virbase.org/ or http://www.rna-society.org/virbase/). This update represents a major revision: (i) the total number of interaction entries is now greater than 820,000, an approximately 70-fold increment, involving 116 virus and 36 host organisms, (ii) it supplements and provides more details on RNA annotations (including RNA editing, RNA localization and RNA modification), ncRNA SNP and ncRNA-drug related information and (iii) it provides two additional tools for predicting binding sites (IntaRNA and PRIdictor), a visual plug-in to display interactions and a website which is optimized for more practical and user-friendly operation. Overall, ViRBase v3.0 provides a more comprehensive resource for virus and host ncRNA-associated interactions enabling researchers a more effective means for investigation of viral infections.
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Affiliation(s)
- Jun Cheng
- Affiliated Foshan Maternity and Child Healthcare Hospital, Southern Medical University (Foshan Maternity & Child Healthcare Hospital), Foshan 528000, China
| | - Yunqing Lin
- Center for Cell Lineage and Atlas (CCLA), Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou 510005, China
| | - Linfu Xu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Kechen Chen
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Qi Li
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Kaixin Xu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Lin Ning
- Dermatology Hospital, Southern Medical University, Guangzhou 510091, China
| | - Juanjuan Kang
- Affiliated Foshan Maternity and Child Healthcare Hospital, Southern Medical University (Foshan Maternity & Child Healthcare Hospital), Foshan 528000, China
| | - Tianyu Cui
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Yan Huang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Xiaoyang Zhao
- State Key Laboratory of Organ Failure Research, Department of Developmental Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Dong Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
- Dermatology Hospital, Southern Medical University, Guangzhou 510091, China
| | - Yanhui Li
- Institute of Cardiovascular Sciences and Key Laboratory of Molecular Cardiovascular Sciences, Ministry of Education, Peking University Health Science Center, Beijing, PR China
| | - Xi Su
- Affiliated Foshan Maternity and Child Healthcare Hospital, Southern Medical University (Foshan Maternity & Child Healthcare Hospital), Foshan 528000, China
| | - Bin Yang
- Dermatology Hospital, Southern Medical University, Guangzhou 510091, China
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4
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Zhao D, Wang C, Yan S, Chen R. Advances in the identification of long non-coding RNA binding proteins. Anal Biochem 2021; 639:114520. [PMID: 34896376 DOI: 10.1016/j.ab.2021.114520] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 12/04/2021] [Accepted: 12/04/2021] [Indexed: 02/06/2023]
Abstract
Long non-coding RNAs (lncRNAs) are transcripts longer than 200 nt without evident protein coding function. They play important regulatory roles in many biological processes, e.g., gene regulation, chromatin remodeling, and cell fate determination during development. Dysregulation of lncRNAs has been observed in various diseases including cancer. Interacting with proteins is a crucial way for lncRNAs to play their biological roles. Therefore, the characterization of lncRNA binding proteins is important to understand their functions and to delineate the underlying molecular mechanism. Large-scale studies based on mass spectrometry have characterized over a thousand new RNA binding proteins without known RNA-binding domains, thus revealing the complexity and diversity of RNA-protein interactions. In addition, several methods have been developed to identify the binding proteins for particular RNAs of interest. Here we review the progress of the RNA-centric methods for the identification of RNA-protein interactions, focusing on the studies involving lncRNAs, and discuss their strengths and limitations.
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Affiliation(s)
- Dongqing Zhao
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, 300072, China
| | - Chunqing Wang
- The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250014, China
| | - Shuai Yan
- Peking University First Hospital, Peking University Health Science Center, Beijing, 100191, China
| | - Ruibing Chen
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, 300072, China.
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5
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Baltoumas FA, Zafeiropoulou S, Karatzas E, Koutrouli M, Thanati F, Voutsadaki K, Gkonta M, Hotova J, Kasionis I, Hatzis P, Pavlopoulos GA. Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review. Biomolecules 2021; 11:1245. [PMID: 34439912 PMCID: PMC8391349 DOI: 10.3390/biom11081245] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 08/16/2021] [Accepted: 08/18/2021] [Indexed: 02/06/2023] Open
Abstract
Technological advances in high-throughput techniques have resulted in tremendous growth of complex biological datasets providing evidence regarding various biomolecular interactions. To cope with this data flood, computational approaches, web services, and databases have been implemented to deal with issues such as data integration, visualization, exploration, organization, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more and more difficult for an end user to know what the scope and focus of each repository is and how redundant the information between them is. Several repositories have a more general scope, while others focus on specialized aspects, such as specific organisms or biological systems. Unfortunately, many of these databases are self-contained or poorly documented and maintained. For a clearer view, in this article we provide a comprehensive categorization, comparison and evaluation of such repositories for different bioentity interaction types. We discuss most of the publicly available services based on their content, sources of information, data representation methods, user-friendliness, scope and interconnectivity, and we comment on their strengths and weaknesses. We aim for this review to reach a broad readership varying from biomedical beginners to experts and serve as a reference article in the field of Network Biology.
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Affiliation(s)
- Fotis A. Baltoumas
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Sofia Zafeiropoulou
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Evangelos Karatzas
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Mikaela Koutrouli
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Foteini Thanati
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Kleanthi Voutsadaki
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Maria Gkonta
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Joana Hotova
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Ioannis Kasionis
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Pantelis Hatzis
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
- Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Georgios A. Pavlopoulos
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
- Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece
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6
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Lin Y, Liu T, Cui T, Wang Z, Zhang Y, Tan P, Huang Y, Yu J, Wang D. RNAInter in 2020: RNA interactome repository with increased coverage and annotation. Nucleic Acids Res 2020; 48:D189-D197. [PMID: 31906603 PMCID: PMC6943043 DOI: 10.1093/nar/gkz804] [Citation(s) in RCA: 154] [Impact Index Per Article: 38.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Revised: 09/03/2019] [Accepted: 09/10/2019] [Indexed: 01/23/2023] Open
Abstract
Research on RNA-associated interactions has exploded in recent years, and increasing numbers of studies are not limited to RNA-RNA and RNA-protein interactions but also include RNA-DNA/compound interactions. To facilitate the development of the interactome and promote understanding of the biological functions and molecular mechanisms of RNA, we updated RAID v2.0 to RNAInter (RNA Interactome Database), a repository for RNA-associated interactions that is freely accessible at http://www.rna-society.org/rnainter/ or http://www.rna-society.org/raid/. Compared to RAID v2.0, new features in RNAInter include (i) 8-fold more interaction data and 94 additional species; (ii) more definite annotations organized, including RNA editing/localization/modification/structure and homology interaction; (iii) advanced functions including fuzzy/batch search, interaction network and RNA dynamic expression and (iv) four embedded RNA interactome tools: RIscoper, IntaRNA, PRIdictor and DeepBind. Consequently, RNAInter contains >41 million RNA-associated interaction entries, involving more than 450 thousand unique molecules, including RNA, protein, DNA and compound. Overall, RNAInter provides a comprehensive RNA interactome resource for researchers and paves the way to investigate the regulatory landscape of cellular RNAs.
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Affiliation(s)
- Yunqing Lin
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Tianyuan Liu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Tianyu Cui
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Zhao Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yuncong Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Puwen Tan
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Yan Huang
- Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan 528308, China
| | - Jia Yu
- State Key Laboratory of Medical Molecular Biology, Department of Biochemistry & Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences (CAMS) & Peking Union Medical College (PUMC), Beijing 100730, China
| | - Dong Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
- Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan 528308, China
- Dermatology Hospital, Southern Medical University, Guangzhou 510091, China
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 611731, China
- To whom correspondence should be addressed. Tel: +86 20 61648279; Fax: +86 20 61648279; or
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7
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Sullivan R, Adams MC, Naik RR, Milam VT. Analyzing Secondary Structure Patterns in DNA Aptamers Identified via CompELS. Molecules 2019; 24:molecules24081572. [PMID: 31010064 PMCID: PMC6515186 DOI: 10.3390/molecules24081572] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 04/09/2019] [Accepted: 04/15/2019] [Indexed: 12/12/2022] Open
Abstract
In contrast to sophisticated high-throughput sequencing tools for genomic DNA, analytical tools for comparing secondary structure features between multiple single-stranded DNA sequences are less developed. For single-stranded nucleic acid ligands called aptamers, secondary structure is widely thought to play a pivotal role in driving recognition-based binding activity between an aptamer sequence and its specific target. Here, we employ a competition-based aptamer screening platform called CompELS to identify DNA aptamers for a colloidal target. We then analyze predicted secondary structures of the aptamers and a large population of random sequences to identify sequence features and patterns. Our secondary structure analysis identifies patterns ranging from position-dependent score matrixes of individual structural elements to position-independent consensus domains resulting from global alignment.
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Affiliation(s)
- Richard Sullivan
- School of Materials Science and Engineering, Georgia Institute of Technology, 771 Ferst Dr. NW, Atlanta, GA 30332-0245, USA.
| | - Mary Catherine Adams
- School of Materials Science and Engineering, Georgia Institute of Technology, 771 Ferst Dr. NW, Atlanta, GA 30332-0245, USA.
| | - Rajesh R Naik
- 711 Human Performance Wing, Air Force Research Laboratory, Wright Patterson AFB, OH 45433, USA.
| | - Valeria T Milam
- School of Materials Science and Engineering, Georgia Institute of Technology, 771 Ferst Dr. NW, Atlanta, GA 30332-0245, USA.
- Wallace H. Coulter, Department of Biomedical Engineering, Georgia Institute of Technology, 313 Ferst Dr., Atlanta, GA 30332, USA.
- Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, 315 Ferst Dr., Atlanta, GA 30332-0363, USA.
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8
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Bush SJ, Chen L, Tovar-Corona JM, Urrutia AO. Alternative splicing and the evolution of phenotypic novelty. Philos Trans R Soc Lond B Biol Sci 2017; 372:rstb.2015.0474. [PMID: 27994117 DOI: 10.1098/rstb.2015.0474] [Citation(s) in RCA: 111] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/01/2016] [Indexed: 12/21/2022] Open
Abstract
Alternative splicing, a mechanism of post-transcriptional RNA processing whereby a single gene can encode multiple distinct transcripts, has been proposed to underlie morphological innovations in multicellular organisms. Genes with developmental functions are enriched for alternative splicing events, suggestive of a contribution of alternative splicing to developmental programmes. The role of alternative splicing as a source of transcript diversification has previously been compared to that of gene duplication, with the relationship between the two extensively explored. Alternative splicing is reduced following gene duplication with the retention of duplicate copies higher for genes which were alternatively spliced prior to duplication. Furthermore, and unlike the case for overall gene number, the proportion of alternatively spliced genes has also increased in line with the evolutionary diversification of cell types, suggesting alternative splicing may contribute to the complexity of developmental programmes. Together these observations suggest a prominent role for alternative splicing as a source of functional innovation. However, it is unknown whether the proliferation of alternative splicing events indeed reflects a functional expansion of the transcriptome or instead results from weaker selection acting on larger species, which tend to have a higher number of cell types and lower population sizes.This article is part of the themed issue 'Evo-devo in the genomics era, and the origins of morphological diversity'.
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Affiliation(s)
- Stephen J Bush
- The Roslin Institute, University of Edinburgh, Edinburgh EH25 9RG, UK
| | - Lu Chen
- West China Second University Hospital, State Key Laboratory of Biotherapy, Sichuan University, Chengdu 610041, People's Republic of China
| | | | - Araxi O Urrutia
- Department of Biology and Biochemistry, University of Bath, Bath BA2 7AY, UK .,Milner Centre for Evolution, University of Bath, Bath BA2 7AY, UK
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9
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A Brief Review of RNA-Protein Interaction Database Resources. Noncoding RNA 2017; 3:ncrna3010006. [PMID: 29657278 PMCID: PMC5832006 DOI: 10.3390/ncrna3010006] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2016] [Accepted: 01/23/2017] [Indexed: 12/25/2022] Open
Abstract
RNA–Protein interactions play critical roles in various biological processes. By collecting and analyzing the RNA–Protein interactions and binding sites from experiments and predictions, RNA–Protein interaction databases have become an essential resource for the exploration of the transcriptional and post-transcriptional regulatory network. Here, we briefly review several widely used RNA–Protein interaction database resources developed in recent years to provide a guide of these databases. The content and major functions in databases are presented. The brief description of database helps users to quickly choose the database containing information they interested. In short, these RNA–Protein interaction database resources are continually updated, but the current state shows the efforts to identify and analyze the large amount of RNA–Protein interactions.
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10
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Yi Y, Zhao Y, Li C, Zhang L, Huang H, Li Y, Liu L, Hou P, Cui T, Tan P, Hu Y, Zhang T, Huang Y, Li X, Yu J, Wang D. RAID v2.0: an updated resource of RNA-associated interactions across organisms. Nucleic Acids Res 2017; 45:D115-D118. [PMID: 27899615 PMCID: PMC5210540 DOI: 10.1093/nar/gkw1052] [Citation(s) in RCA: 156] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2016] [Revised: 10/18/2016] [Accepted: 10/20/2016] [Indexed: 02/05/2023] Open
Abstract
With the development of biotechnologies and computational prediction algorithms, the number of experimental and computational prediction RNA-associated interactions has grown rapidly in recent years. However, diverse RNA-associated interactions are scattered over a wide variety of resources and organisms, whereas a fully comprehensive view of diverse RNA-associated interactions is still not available for any species. Hence, we have updated the RAID database to version 2.0 (RAID v2.0, www.rna-society.org/raid/) by integrating experimental and computational prediction interactions from manually reading literature and other database resources under one common framework. The new developments in RAID v2.0 include (i) over 850-fold RNA-associated interactions, an enhancement compared to the previous version; (ii) numerous resources integrated with experimental or computational prediction evidence for each RNA-associated interaction; (iii) a reliability assessment for each RNA-associated interaction based on an integrative confidence score; and (iv) an increase of species coverage to 60. Consequently, RAID v2.0 recruits more than 5.27 million RNA-associated interactions, including more than 4 million RNA-RNA interactions and more than 1.2 million RNA-protein interactions, referring to nearly 130 000 RNA/protein symbols across 60 species.
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Affiliation(s)
- Ying Yi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yue Zhao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
- Department of Pathology, Harbin Medical University, Harbin 150081, China
| | - Chunhua Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Lin Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Huiying Huang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yana Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Lanlan Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Ping Hou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Tianyu Cui
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou 515041, China
| | - Puwen Tan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yongfei Hu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Ting Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yan Huang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Xiaobo Li
- Department of Pathology, Harbin Medical University, Harbin 150081, China
| | - Jia Yu
- State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, School of Basic Sciences & Institute of Basic Medical Sciences, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Dong Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou 515041, China
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11
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Walia RR, El-Manzalawy Y, Honavar VG, Dobbs D. Sequence-Based Prediction of RNA-Binding Residues in Proteins. Methods Mol Biol 2017; 1484:205-235. [PMID: 27787829 PMCID: PMC5796408 DOI: 10.1007/978-1-4939-6406-2_15] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Identifying individual residues in the interfaces of protein-RNA complexes is important for understanding the molecular determinants of protein-RNA recognition and has many potential applications. Recent technical advances have led to several high-throughput experimental methods for identifying partners in protein-RNA complexes, but determining RNA-binding residues in proteins is still expensive and time-consuming. This chapter focuses on available computational methods for identifying which amino acids in an RNA-binding protein participate directly in contacting RNA. Step-by-step protocols for using three different web-based servers to predict RNA-binding residues are described. In addition, currently available web servers and software tools for predicting RNA-binding sites, as well as databases that contain valuable information about known protein-RNA complexes, RNA-binding motifs in proteins, and protein-binding recognition sites in RNA are provided. We emphasize sequence-based methods that can reliably identify interfacial residues without the requirement for structural information regarding either the RNA-binding protein or its RNA partner.
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Affiliation(s)
| | - Yasser El-Manzalawy
- College of Information Sciences and Technology, Pennsylvania State University, University Park, PA, 16802, USA
| | - Vasant G Honavar
- College of Information Sciences and Technology, Pennsylvania State University, University Park, PA, 16802, USA
| | - Drena Dobbs
- Genetics, Development and Cell Biology Department, Iowa State University, 3112 Molecular Biology Building, Ames, IA, 50011-3650, USA.
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12
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A fast topological analysis algorithm for large-scale similarity evaluations of ligands and binding pockets. J Cheminform 2015; 7:42. [PMID: 26561508 PMCID: PMC4631714 DOI: 10.1186/s13321-015-0091-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2015] [Accepted: 07/22/2015] [Indexed: 11/10/2022] Open
Abstract
Motivation With the rapid increase of the structural data of biomolecular complexes, novel structural analysis methods have to be devised with high-throughput capacity to handle immense data input and to construct massive networks at the minimal computational cost. Moreover, novel methods should be capable of handling a broad range of molecular structural sizes and chemical natures, cognisant of the conformational and electrostatic bases of molecular recognition, and sufficiently accurate to enable contextually relevant biological inferences. Results A novel molecular topology comparison method was developed and tested. The method was tested for both ligand and binding pocket similarity analyses and a PDB-wide ligand topological similarity map was computed. Conclusion The unprecedentedly wide scope of ligand definition and large-scale topological similarity mapping can provide very robust tools, of performance unmatched by the present alignment-based methods. The method remarkably shows potential for application for scaffold hopping purposes. It also opens new frontiers in the areas of ligand-mediated protein connectivity, ligand-based molecular phylogeny, target fishing, and off-target predictions. Electronic supplementary material The online version of this article (doi:10.1186/s13321-015-0091-5) contains supplementary material, which is available to authorized users.
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13
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Nagarajan R, Chothani SP, Ramakrishnan C, Sekijima M, Gromiha MM. Structure based approach for understanding organism specific recognition of protein-RNA complexes. Biol Direct 2015; 10:8. [PMID: 25886642 PMCID: PMC4352265 DOI: 10.1186/s13062-015-0039-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2014] [Accepted: 02/03/2015] [Indexed: 12/11/2022] Open
Abstract
Background Protein-RNA interactions perform diverse functions within the cell. Understanding the recognition mechanism of protein-RNA complexes has been a challenging task in molecular and computational biology. In earlier works, the recognition mechanisms have been studied for a specific complex or using a set of non–redundant complexes. In this work, we have constructed 18 sets of same protein-RNA complexes belonging to different organisms from Protein Data Bank (PDB). The similarities and differences in each set of complexes have been revealed in terms of various sequence and structure based features such as root mean square deviation, sequence homology, propensity of binding site residues, variance, conservation at binding sites, binding segments, binding motifs of amino acid residues and nucleotides, preferred amino acid-nucleotide pairs and influence of neighboring residues for binding. Results We found that the proteins of mesophilic organisms have more number of binding sites than thermophiles and the binding propensities of amino acid residues are distinct in E. coli, H. sapiens, S. cerevisiae, thermophiles and archaea. Proteins prefer to bind with RNA using a single residue segment in all the organisms while RNA prefers to use a stretch of up to six nucleotides for binding with proteins. We have developed amino acid residue-nucleotide pair potentials for different organisms, which could be used for predicting the binding specificity. Further, molecular dynamics simulation studies on aspartyl tRNA synthetase complexed with aspartyl tRNA showed specific modes of recognition in E. coli, T. thermophilus and S. cerevisiae. Conclusion Based on structural analysis and molecular dynamics simulations we suggest that the mode of recognition depends on the type of the organism in a protein-RNA complex. Reviewers This article was reviewed by Sandor Pongor, Gajendra Raghava and Narayanaswamy Srinivasan. Electronic supplementary material The online version of this article (doi:10.1186/s13062-015-0039-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Raju Nagarajan
- Department of Biotechnology, Bhupat Jyoti Metha School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036, Tamilnadu, India.
| | - Sonia Pankaj Chothani
- Department of Biotechnology, Bhupat Jyoti Metha School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036, Tamilnadu, India. .,Philips Research North America, 345 Scarborough Road, Briarcliff Manor, NY, 10510, USA.
| | - Chandrasekaran Ramakrishnan
- Department of Biotechnology, Bhupat Jyoti Metha School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036, Tamilnadu, India.
| | - Masakazu Sekijima
- Global Scientific Information and Computing Center (GSIC), Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat Jyoti Metha School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036, Tamilnadu, India.
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14
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Zhang X, Wu D, Chen L, Li X, Yang J, Fan D, Dong T, Liu M, Tan P, Xu J, Yi Y, Wang Y, Zou H, Hu Y, Fan K, Kang J, Huang Y, Miao Z, Bi M, Jin N, Li K, Li X, Xu J, Wang D. RAID: a comprehensive resource for human RNA-associated (RNA-RNA/RNA-protein) interaction. RNA (NEW YORK, N.Y.) 2014; 20:989-993. [PMID: 24803509 PMCID: PMC4114696 DOI: 10.1261/rna.044776.114] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2014] [Accepted: 03/04/2014] [Indexed: 05/30/2023]
Abstract
Transcriptomic analyses have revealed an unexpected complexity in the eukaryote transcriptome, which includes not only protein-coding transcripts but also an expanding catalog of noncoding RNAs (ncRNAs). Diverse coding and noncoding RNAs (ncRNAs) perform functions through interaction with each other in various cellular processes. In this project, we have developed RAID (http://www.rna-society.org/raid), an RNA-associated (RNA-RNA/RNA-protein) interaction database. RAID intends to provide the scientific community with all-in-one resources for efficient browsing and extraction of the RNA-associated interactions in human. This version of RAID contains more than 6100 RNA-associated interactions obtained by manually reviewing more than 2100 published papers, including 4493 RNA-RNA interactions and 1619 RNA-protein interactions. Each entry contains detailed information on an RNA-associated interaction, including RAID ID, RNA/protein symbol, RNA/protein categories, validated method, expressing tissue, literature references (Pubmed IDs), and detailed functional description. Users can query, browse, analyze, and manipulate RNA-associated (RNA-RNA/RNA-protein) interaction. RAID provides a comprehensive resource of human RNA-associated (RNA-RNA/RNA-protein) interaction network. Furthermore, this resource will help in uncovering the generic organizing principles of cellular function network.
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Affiliation(s)
- Xiaomeng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Deng Wu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Liqun Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Xiang Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Jinxurong Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Dandan Fan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Tingting Dong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Mingyue Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Puwen Tan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Jintian Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Ying Yi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yuting Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Hua Zou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yongfei Hu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Kaili Fan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Juanjuan Kang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yan Huang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Zhengqiang Miao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Miaoman Bi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Nana Jin
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Kongning Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Jianzhen Xu
- College of Bioengineering, Henan University of Technology, Zhengzhou 450000, China
| | - Dong Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
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15
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Zhao H, Yang Y, Zhou Y. Prediction of RNA binding proteins comes of age from low resolution to high resolution. MOLECULAR BIOSYSTEMS 2013; 9:2417-25. [PMID: 23872922 PMCID: PMC3870025 DOI: 10.1039/c3mb70167k] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Networks of protein-RNA interactions is likely to be larger than protein-protein and protein-DNA interaction networks because RNA transcripts are encoded tens of times more than proteins (e.g. only 3% of human genome coded for proteins), have diverse function and localization, and are controlled by proteins from birth (transcription) to death (degradation). This massive network is evidenced by several recent experimental discoveries of large numbers of previously unknown RNA-binding proteins (RBPs). Meanwhile, more than 400 non-redundant protein-RNA complex structures (at 25% sequence identity or less) have been deposited into the protein databank. These sequences and structural resources for RBPs provide ample data for the development of computational techniques dedicated to RBP prediction, as experimentally determining RNA-binding functions is time-consuming and expensive. This review compares traditional machine-learning based approaches with emerging template-based methods at several levels of prediction resolution ranging from two-state binding/non-binding prediction, to binding residue prediction and protein-RNA complex structure prediction. The analysis indicates that the two approaches are complementary and their combinations may lead to further improvements.
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Affiliation(s)
- Huiying Zhao
- School of Informatics, Indiana University Purdue University, Indianapolis, Indiana 46202, USA.
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16
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Yang J, Roy A, Zhang Y. BioLiP: a semi-manually curated database for biologically relevant ligand-protein interactions. Nucleic Acids Res 2012; 41:D1096-103. [PMID: 23087378 PMCID: PMC3531193 DOI: 10.1093/nar/gks966] [Citation(s) in RCA: 454] [Impact Index Per Article: 37.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
BioLiP (http://zhanglab.ccmb.med.umich.edu/BioLiP/) is a semi-manually curated database for biologically relevant ligand–protein interactions. Establishing interactions between protein and biologically relevant ligands is an important step toward understanding the protein functions. Most ligand-binding sites prediction methods use the protein structures from the Protein Data Bank (PDB) as templates. However, not all ligands present in the PDB are biologically relevant, as small molecules are often used as additives for solving the protein structures. To facilitate template-based ligand–protein docking, virtual ligand screening and protein function annotations, we develop a hierarchical procedure for assessing the biological relevance of ligands present in the PDB structures, which involves a four-step biological feature filtering followed by careful manual verifications. This procedure is used for BioLiP construction. Each entry in BioLiP contains annotations on: ligand-binding residues, ligand-binding affinity, catalytic sites, Enzyme Commission numbers, Gene Ontology terms and cross-links to the other databases. In addition, to facilitate the use of BioLiP for function annotation of uncharacterized proteins, a new consensus-based algorithm COACH is developed to predict ligand-binding sites from protein sequence or using 3D structure. The BioLiP database is updated weekly and the current release contains 204 223 entries.
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Affiliation(s)
- Jianyi Yang
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109-2218, USA
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17
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Kubrycht J, Sigler K, Souček P. Virtual interactomics of proteins from biochemical standpoint. Mol Biol Int 2012; 2012:976385. [PMID: 22928109 PMCID: PMC3423939 DOI: 10.1155/2012/976385] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2012] [Revised: 05/18/2012] [Accepted: 05/18/2012] [Indexed: 12/24/2022] Open
Abstract
Virtual interactomics represents a rapidly developing scientific area on the boundary line of bioinformatics and interactomics. Protein-related virtual interactomics then comprises instrumental tools for prediction, simulation, and networking of the majority of interactions important for structural and individual reproduction, differentiation, recognition, signaling, regulation, and metabolic pathways of cells and organisms. Here, we describe the main areas of virtual protein interactomics, that is, structurally based comparative analysis and prediction of functionally important interacting sites, mimotope-assisted and combined epitope prediction, molecular (protein) docking studies, and investigation of protein interaction networks. Detailed information about some interesting methodological approaches and online accessible programs or databases is displayed in our tables. Considerable part of the text deals with the searches for common conserved or functionally convergent protein regions and subgraphs of conserved interaction networks, new outstanding trends and clinically interesting results. In agreement with the presented data and relationships, virtual interactomic tools improve our scientific knowledge, help us to formulate working hypotheses, and they frequently also mediate variously important in silico simulations.
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Affiliation(s)
- Jaroslav Kubrycht
- Department of Physiology, Second Medical School, Charles University, 150 00 Prague, Czech Republic
| | - Karel Sigler
- Laboratory of Cell Biology, Institute of Microbiology, Academy of Sciences of the Czech Republic, 142 20 Prague, Czech Republic
| | - Pavel Souček
- Toxicogenomics Unit, National Institute of Public Health, 100 42 Prague, Czech Republic
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18
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Barik A, Mishra A, Bahadur RP. PRince: a web server for structural and physicochemical analysis of protein-RNA interface. Nucleic Acids Res 2012; 40:W440-4. [PMID: 22689640 PMCID: PMC3394290 DOI: 10.1093/nar/gks535] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
We have developed a web server, PRince, which analyzes the structural features and physicochemical properties of the protein–RNA interface. Users need to submit a PDB file containing the atomic coordinates of both the protein and the RNA molecules in complex form (in ‘.pdb’ format). They should also mention the chain identifiers of interacting protein and RNA molecules. The size of the protein–RNA interface is estimated by measuring the solvent accessible surface area buried in contact. For a given protein–RNA complex, PRince calculates structural, physicochemical and hydration properties of the interacting surfaces. All these parameters generated by the server are presented in a tabular format. The interacting surfaces can also be visualized with software plug-in like Jmol. In addition, the output files containing the list of the atomic coordinates of the interacting protein, RNA and interface water molecules can be downloaded. The parameters generated by PRince are novel, and users can correlate them with the experimentally determined biophysical and biochemical parameters for better understanding the specificity of the protein–RNA recognition process. This server will be continuously upgraded to include more parameters. PRince is publicly accessible and free for use. Available at http://www.facweb.iitkgp.ernet.in/~rbahadur/prince/home.html.
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Affiliation(s)
- Amita Barik
- Department of Biotechnology, Indian Institute of Technology, Kharagpur 721302, India
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19
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Lewis BA, Walia RR, Terribilini M, Ferguson J, Zheng C, Honavar V, Dobbs D. PRIDB: a Protein-RNA interface database. Nucleic Acids Res 2010; 39:D277-82. [PMID: 21071426 PMCID: PMC3013700 DOI: 10.1093/nar/gkq1108] [Citation(s) in RCA: 95] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
The Protein–RNA Interface Database (PRIDB) is a comprehensive database of protein–RNA interfaces extracted from complexes in the Protein Data Bank (PDB). It is designed to facilitate detailed analyses of individual protein–RNA complexes and their interfaces, in addition to automated generation of user-defined data sets of protein–RNA interfaces for statistical analyses and machine learning applications. For any chosen PDB complex or list of complexes, PRIDB rapidly displays interfacial amino acids and ribonucleotides within the primary sequences of the interacting protein and RNA chains. PRIDB also identifies ProSite motifs in protein chains and FR3D motifs in RNA chains and provides links to these external databases, as well as to structure files in the PDB. An integrated JMol applet is provided for visualization of interacting atoms and residues in the context of the 3D complex structures. The current version of PRIDB contains structural information regarding 926 protein–RNA complexes available in the PDB (as of 10 October 2010). Atomic- and residue-level contact information for the entire data set can be downloaded in a simple machine-readable format. Also, several non-redundant benchmark data sets of protein–RNA complexes are provided. The PRIDB database is freely available online at http://bindr.gdcb.iastate.edu/PRIDB.
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Affiliation(s)
- Benjamin A Lewis
- Bioinformatics and Computational Biology Program, Iowa State University, Iowa, USA.
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20
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Zhou P, Zou J, Tian F, Shang Z. Geometric similarity between protein-RNA interfaces. J Comput Chem 2010; 30:2738-51. [PMID: 19399760 DOI: 10.1002/jcc.21300] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
A new method is described to measure the geometric similarity between protein-RNA interfaces quantitatively. The method is based on a procedure that dissects the interface geometry in terms of the spatial relationships between individual amino acid nucleotide pairs. Using this technique, we performed an all-on-all comparison of 586 protein-RNA interfaces deposited in the current Protein Data Bank, as the result, an interface-interface similarity score matrix was obtained. Based upon this matrix, hierarchical clustering was carried out which yielded a complete clustering tree for the 586 protein-RNA interfaces. By investigating the organizing behavior of the clustering tree and the SCOP classification of protein partners in complexes, a geometrically nonredundant, diverse data set (representative data set) consisting of 45 distinct protein-RNA interfaces was extracted for the purpose of studying protein-RNA interactions, RNA regulations, and drug design. We classified protein-RNA interfaces into three types. In type I, the families and interface structural classes of the protein partners, as well as the interface geometries are all similar. In type II, the interface geometries and the interface structural classes are similar, whereas the protein families are different. In type III, only the interface geometries are similar but the protein families and the interface structural classes are distinct. Furthermore, we also show two new RNA recognition themes derived from the representative data set.
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Affiliation(s)
- Peng Zhou
- Institute of Molecular Design and Molecular Thermodynamics, Department of Chemistry, Zhejiang University, Hangzhou 310027, China
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21
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Sutch BT, Chambers EJ, Bayramyan MZ, Gallaher TK, Haworth IS. Similarity of Protein-RNA Interfaces Based on Motif Analysis. J Chem Inf Model 2009; 49:2139-46. [DOI: 10.1021/ci900154a] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Brian T. Sutch
- Department of Pharmacology & Pharmaceutical Sciences, University of Southern California, Los Angeles, California 90089-9121
| | - Eric J. Chambers
- Department of Pharmacology & Pharmaceutical Sciences, University of Southern California, Los Angeles, California 90089-9121
| | - Melina Z. Bayramyan
- Department of Pharmacology & Pharmaceutical Sciences, University of Southern California, Los Angeles, California 90089-9121
| | - Timothy K. Gallaher
- Department of Pharmacology & Pharmaceutical Sciences, University of Southern California, Los Angeles, California 90089-9121
| | - Ian S. Haworth
- Department of Pharmacology & Pharmaceutical Sciences, University of Southern California, Los Angeles, California 90089-9121
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