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Yamada KD, Nishi H, Nakata J, Kinoshita K. Structural characterization of single nucleotide variants at ligand binding sites and enzyme active sites of human proteins. Biophys Physicobiol 2016; 13:157-163. [PMID: 27924270 PMCID: PMC5042176 DOI: 10.2142/biophysico.13.0_157] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2016] [Accepted: 04/10/2016] [Indexed: 12/15/2022] Open
Abstract
Functional sites on proteins play an important role in various molecular interactions and reactions between proteins and other molecules. Thus, mutations in functional sites can severely affect the overall phenotype. Progress of genome sequencing projects has yielded a wealth of information on single nucleotide variants (SNVs), especially those with less than 1% minor allele frequency (rare variants). To understand the functional influence of genetic variants at a protein level, we investigated the relationship between SNVs and protein functional sites in terms of minor allele frequency and the structural position of variants. As a result, we observed that SNVs were less abundant at ligand binding sites, which is consistent with a previous study on SNVs and protein interaction sites. Additionally, we found that non-rare variants tended to be located slightly apart from enzyme active sites. Examination of non-rare variants revealed that most of the mutations resulted in moderate changes of the physico-chemical properties of amino acids, suggesting the existence of functional constraints. In conclusion, this study shows that the mapping of genetic variants on protein structures could be a powerful approach to evaluate the functional impact of rare genetic variations.
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Affiliation(s)
- Kazunori D Yamada
- Graduate School of Information Sciences, Tohoku University, Miyagi 980-8597, Japan
| | - Hafumi Nishi
- Graduate School of Information Sciences, Tohoku University, Miyagi 980-8597, Japan
| | - Junichi Nakata
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi 980-8573, Japan
| | - Kengo Kinoshita
- Graduate School of Information Sciences, Tohoku University, Miyagi 980-8597, Japan; Tohoku Medical Megabank Organization, Tohoku University, Miyagi 980-8573, Japan; Institute of Development, Aging, and Cancer, Tohoku University, Miyagi 980-8575, Japan
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2
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Singh H, Raghava GPS. BLAST-based structural annotation of protein residues using Protein Data Bank. Biol Direct 2016; 11:4. [PMID: 26810894 PMCID: PMC4727276 DOI: 10.1186/s13062-016-0106-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2015] [Accepted: 01/18/2016] [Indexed: 11/10/2022] Open
Abstract
Background In the era of next-generation sequencing where thousands of genomes have been already sequenced; size of protein databases is growing with exponential rate. Structural annotation of these proteins is one of the biggest challenges for the computational biologist. Although, it is easy to perform BLAST search against Protein Data Bank (PDB) but it is difficult for a biologist to annotate protein residues from BLAST search. Results A web-server StarPDB has been developed for structural annotation of a protein based on its similarity with known protein structures. It uses standard BLAST software for performing similarity search of a query protein against protein structures in PDB. This server integrates wide range modules for assigning different types of annotation that includes, Secondary-structure, Accessible surface area, Tight-turns, DNA-RNA and Ligand modules. Secondary structure module allows users to predict regular secondary structure states to each residue in a protein. Accessible surface area predict the exposed or buried residues in a protein. Tight-turns module is designed to predict tight turns like beta-turns in a protein. DNA-RNA module developed for predicting DNA and RNA interacting residues in a protein. Similarly, Ligand module of server allows one to predicted ligands, metal and nucleotides ligand interacting residues in a protein. Conclusions In summary, this manuscript presents a web server for comprehensive annotation of a protein based on similarity search. It integrates number of visualization tools that facilitate users to understand structure and function of protein residues. This web server is available freely for scientific community from URL http://crdd.osdd.net/raghava/starpdb. Reviewers This article was reviewed by Prof Michael Gromiha, Prof. Thomas Dandekar and Dr. I. King Jordan. Electronic supplementary material The online version of this article (doi:10.1186/s13062-016-0106-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Harinder Singh
- Bioinformatics Centre, Institute of Microbial Technology, Sector 39-A, Chandigarh, India.
| | - Gajendra P S Raghava
- Bioinformatics Centre, Institute of Microbial Technology, Sector 39-A, Chandigarh, India.
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McCarthy FM, Mahony TJ, Parcells MS, Burgess SC. Understanding animal viruses using the Gene Ontology. Trends Microbiol 2009; 17:328-35. [PMID: 19577474 DOI: 10.1016/j.tim.2009.04.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2008] [Revised: 04/27/2009] [Accepted: 04/29/2009] [Indexed: 11/18/2022]
Abstract
Understanding the effects of viral infection has typically focused on specific virus-host interactions such as tissue tropism, immune responses and histopathology. However, modeling viral pathogenesis requires information about the functions of gene products from both virus and host, and how these products interact. Recent developments in the functional annotation of genomes using Gene Ontology (GO) and in modeling functional interactions among gene products, together with an increased interest in systems biology, provide an excellent opportunity to generate global interaction models for viral infection. Here, we review how the GO is being used to model viral pathogenesis, with a focus on animal viruses.
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Affiliation(s)
- Fiona M McCarthy
- Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, MS 39762, USA.
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Loewenstein Y, Raimondo D, Redfern OC, Watson J, Frishman D, Linial M, Orengo C, Thornton J, Tramontano A. Protein function annotation by homology-based inference. Genome Biol 2009; 10:207. [PMID: 19226439 PMCID: PMC2688287 DOI: 10.1186/gb-2009-10-2-207] [Citation(s) in RCA: 148] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Where information on homologous proteins is available,
progress is being made in automated prediction of protein function
from sequence and structure. With many genomes now sequenced, computational annotation methods to characterize genes and proteins from their sequence are increasingly important. The BioSapiens Network has developed tools to address all stages of this process, and here we review progress in the automated prediction of protein function based on protein sequence and structure.
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Affiliation(s)
- Yaniv Loewenstein
- Department of Biological Chemistry, The Hebrew University of Jerusalem, Sudarsky Center, Jerusalem 91904, Israel
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Giuliani SE, Frank AM, Collart FR. Functional assignment of solute-binding proteins of ABC transporters using a fluorescence-based thermal shift assay. Biochemistry 2009; 47:13974-84. [PMID: 19063603 DOI: 10.1021/bi801648r] [Citation(s) in RCA: 42] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
We have used a fluorescence-based thermal shift (FTS) assay to identify amino acids that bind to solute-binding proteins in the bacterial ABC transporter family. The assay was validated with a set of six proteins with known binding specificity and was consistently able to map proteins with their known binding ligands. The assay also identified additional candidate binding ligands for several of the amino acid-binding proteins in the validation set. We extended this approach to additional targets and demonstrated the ability of the FTS assay to unambiguously identify preferential binding for several homologues of amino acid-binding proteins with known specificity and to functionally annotate proteins of unknown binding specificity. The assay is implemented in a microwell plate format and provides a rapid approach to validate an anticipated function or to screen proteins of unknown function. The ABC-type transporter family is ubiquitous and transports a variety of biological compounds, but the current annotation of the ligand-binding proteins is limited to mostly generic descriptions of function. The results illustrate the feasibility of the FTS assay to improve the functional annotation of binding proteins associated with ABC-type transporters and suggest this approach that can also be extended to other protein families.
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Affiliation(s)
- Sarah E Giuliani
- Biosciences Division, Argonne National Laboratory, Lemont, Illinois 60439, USA
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Reeves GA, Eilbeck K, Magrane M, O'Donovan C, Montecchi-Palazzi L, Harris MA, Orchard S, Jimenez RC, Prlic A, Hubbard TJP, Hermjakob H, Thornton JM. The Protein Feature Ontology: a tool for the unification of protein feature annotations. Bioinformatics 2008; 24:2767-72. [PMID: 18936051 PMCID: PMC2912506 DOI: 10.1093/bioinformatics/btn528] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION The advent of sequencing and structural genomics projects has provided a dramatic boost in the number of uncharacterized protein structures and sequences. Consequently, many computational tools have been developed to help elucidate protein function. However, such services are spread throughout the world, often with standalone web pages. Integration of these methods is needed and so far this has not been possible as there was no common vocabulary available that could be used as a standard language. RESULTS The Protein Feature Ontology has been developed to provide a structured controlled vocabulary for features on a protein sequence or structure and comprises approximately 100 positional terms, now integrated into the Sequence Ontology (SO) and 40 non-positional terms which describe features relating to the whole-protein sequence. In addition, post-translational modifications are described by using a pre-existing ontology, the Protein Modification Ontology (MOD). This ontology is being used to integrate over 150 distinct annotations provided by the BioSapiens Network of Excellence, a consortium comprising 19 partner sites in Europe. AVAILABILITY The Protein Feature Ontology can be browsed by accessing the ontology lookup service at the European Bioinformatics Institute (http://www.ebi.ac.uk/ontology-lookup/browse.do?ontName=BS).
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Affiliation(s)
- Gabrielle A Reeves
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
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Jenkinson AM, Albrecht M, Birney E, Blankenburg H, Down T, Finn RD, Hermjakob H, Hubbard TJP, Jimenez RC, Jones P, Kähäri A, Kulesha E, Macías JR, Reeves GA, Prlić A. Integrating biological data--the Distributed Annotation System. BMC Bioinformatics 2008; 9 Suppl 8:S3. [PMID: 18673527 PMCID: PMC2500094 DOI: 10.1186/1471-2105-9-s8-s3] [Citation(s) in RCA: 78] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The Distributed Annotation System (DAS) is a widely adopted protocol for dynamically integrating a wide range of biological data from geographically diverse sources. DAS continues to expand its applicability and evolve in response to new challenges facing integrative bioinformatics. RESULTS Here we describe the various infrastructure components of DAS and present a new extended version of the DAS specification. Version 1.53E incorporates several recent developments, including its extension to serve new data types and an ontology for protein features. CONCLUSION Our extensions to the DAS protocol have facilitated the integration of new data types, and our improvements to the existing DAS infrastructure have addressed recent challenges. The steadily increasing numbers of available data sources demonstrates further adoption of the DAS protocol.
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Statistical analysis of structural characteristics of protein Ca2+-binding sites. J Biol Inorg Chem 2008; 13:1169-81. [PMID: 18594878 DOI: 10.1007/s00775-008-0402-7] [Citation(s) in RCA: 44] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2008] [Accepted: 06/17/2008] [Indexed: 10/21/2022]
Abstract
To better understand the biological significance of Ca(2+), we report a comprehensive statistical analysis of calcium-binding proteins from the Protein Data Bank to identify structural parameters associated with EF-hand and non-EF-hand Ca(2+)-binding sites. Comparatively, non-EF-hand sites utilize lower coordination numbers (6 +/- 2 vs. 7 +/- 1), fewer protein ligands (4 +/- 2 vs. 6 +/- 1), and more water ligands (2 +/- 2 vs. 1 +/- 0) than EF-hand sites. The orders of ligand preference for non-EF-hand and EF-hand sites, respectively, were H(2)O (33.1%) > side-chain Asp (24.5%) > main-chain carbonyl (23.9%) > side-chain Glu (10.4%), and side-chain Asp (29.7%) > side-chain Glu (26.6%) > main-chain carbonyl (21.4%) > H(2)O (13.3%). Less formal negative charge was observed in the non-EF-hand than in the EF-hand binding sites (1 +/- 1 vs. 3 +/- 1). Additionally, over 20% of non-EF-hand sites had formal charge values of zero due to increased utilization of water and carbonyl oxygen ligands. Moreover, the EF-hand sites presented a narrower range of ligand distances and bond angles than non-EF-hand sites, possibly owing to the highly conserved helix-loop-helix motif. Significant differences between ligand types (carbonyl, side chain, bidentate) demonstrated that angles associated with each type must be classified separately, and the EF-hand side-chain Ca-O-C angles exhibited an unusual bimodal quality consistent with an Asp distribution that differed from the Gaussian model observed for non-EF-hand proteins. The results of this survey more accurately describe differences between EF-hand and non-EF-hand proteins and provide new parameters for the prediction and design of different classes of Ca(2+)-binding proteins.
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Abstract
The Gene Ontology (GO) project (http://www.geneontology.org/) provides a set of structured, controlled vocabularies for community use in annotating genes, gene products and sequences (also see http://www.sequenceontology.org/). The ontologies have been extended and refined for several biological areas, and improvements to the structure of the ontologies have been implemented. To improve the quantity and quality of gene product annotations available from its public repository, the GO Consortium has launched a focused effort to provide comprehensive and detailed annotation of orthologous genes across a number of ‘reference’ genomes, including human and several key model organisms. Software developments include two releases of the ontology-editing tool OBO-Edit, and improvements to the AmiGO browser interface.
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Affiliation(s)
- Dmitrij Frishman
- Department of Genome Oriented Bioinformatics, Technische Universität München, Wissenchaftszentrum Weihenstephan, 85350 Freising, Germany
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McCarthy FM, Bridges SM, Wang N, Magee GB, Williams WP, Luthe DS, Burgess SC. AgBase: a unified resource for functional analysis in agriculture. Nucleic Acids Res 2006; 35:D599-603. [PMID: 17135208 PMCID: PMC1751552 DOI: 10.1093/nar/gkl936] [Citation(s) in RCA: 84] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Analysis of functional genomics (transcriptomics and proteomics) datasets is hindered in agricultural species because agricultural genome sequences have relatively poor structural and functional annotation. To facilitate systems biology in these species we have established the curated, web-accessible, public resource 'AgBase' (www.agbase.msstate.edu). We have improved the structural annotation of agriculturally important genomes by experimentally confirming the in vivo expression of electronically predicted proteins and by proteogenomic mapping. Proteogenomic data are available from the AgBase proteogenomics link. We contribute Gene Ontology (GO) annotations and we provide a two tier system of GO annotations for users. The 'GO Consortium' gene association file contains the most rigorous GO annotations based solely on experimental data. The 'Community' gene association file contains GO annotations based on expert community knowledge (annotations based directly from author statements and submitted annotations from the community) and annotations for predicted proteins. We have developed two tools for proteomics analysis and these are freely available on request. A suite of tools for analyzing functional genomics datasets using the GO is available online at the AgBase site. We encourage and publicly acknowledge GO annotations from researchers and provide an online mechanism for agricultural researchers to submit requests for GO annotations.
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Affiliation(s)
- Fiona M. McCarthy
- Department of Basic Sciences, College of Veterinary Medicine, Mississippi State UniversityPO Box 6100, Mississippi, MS 39762, USA
- Institute for Digital Biology, Mississippi State UniversityMS 39762, USA
- To whom correspondence should be addressed. Tel: +1 662 325 5859; Fax: +1 662 325 1031;
| | - Susan M. Bridges
- Institute for Digital Biology, Mississippi State UniversityMS 39762, USA
- Department of Computer Science and Engineering, Bagley College of EngineeringPO Box 9637, Mississippi, MS 39762, USA
- To whom correspondence should be addressed. Tel: +1 662 325 5859; Fax: +1 662 325 1031;
| | - Nan Wang
- Institute for Digital Biology, Mississippi State UniversityMS 39762, USA
- Department of Computer Science and Engineering, Bagley College of EngineeringPO Box 9637, Mississippi, MS 39762, USA
| | - G. Bryce Magee
- Institute for Digital Biology, Mississippi State UniversityMS 39762, USA
- Department of Computer Science and Engineering, Bagley College of EngineeringPO Box 9637, Mississippi, MS 39762, USA
| | - W. Paul Williams
- USDA ARS Corn Host Plant Resistance Research UnitBox 5367, Mississippi, MS 39762, USA
| | - Dawn S. Luthe
- Department of Crop and Soil Sciences, The Pennsylvania State UniversityUniversity Park, PA 16802, USA
| | - Shane C. Burgess
- Department of Basic Sciences, College of Veterinary Medicine, Mississippi State UniversityPO Box 6100, Mississippi, MS 39762, USA
- Department of Computer Science and Engineering, Bagley College of EngineeringPO Box 9637, Mississippi, MS 39762, USA
- Mississippi Agricultural and Forestry Experiment Station, Mississippi State UniversityMS 39762, USA
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