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Pai PP, Dattatreya RK, Mondal S. Ensemble Architecture for Prediction of Enzyme‐ligand Binding Residues Using Evolutionary Information. Mol Inform 2017. [DOI: 10.1002/minf.201700021] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Priyadarshini P. Pai
- Department of Biological SciencesBirla Institute of Technology and Science-Pilani, K.K. Birla Goa Campus. Near NH17 Bypass Road Zuarinagar, Goa India
| | - Rohit Kadam Dattatreya
- Department of EconomicsBirla Institute of Technology and Science-Pilani, K.K. Birla Goa Campus. Near NH17 Bypass Road Zuarinagar, Goa India, PIN: 403726
| | - Sukanta Mondal
- Department of Biological SciencesBirla Institute of Technology and Science-Pilani, K.K. Birla Goa Campus. Near NH17 Bypass Road Zuarinagar, Goa India
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Wang T, Mori H, Zhang C, Kurokawa K, Xing XH, Yamada T. DomSign: a top-down annotation pipeline to enlarge enzyme space in the protein universe. BMC Bioinformatics 2015; 16:96. [PMID: 25888481 PMCID: PMC4389672 DOI: 10.1186/s12859-015-0499-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2014] [Accepted: 02/18/2015] [Indexed: 12/27/2022] Open
Abstract
Background Computational predictions of catalytic function are vital for in-depth understanding of enzymes. Because several novel approaches performing better than the common BLAST tool are rarely applied in research, we hypothesized that there is a large gap between the number of known annotated enzymes and the actual number in the protein universe, which significantly limits our ability to extract additional biologically relevant functional information from the available sequencing data. To reliably expand the enzyme space, we developed DomSign, a highly accurate domain signature–based enzyme functional prediction tool to assign Enzyme Commission (EC) digits. Results DomSign is a top-down prediction engine that yields results comparable, or superior, to those from many benchmark EC number prediction tools, including BLASTP, when a homolog with an identity >30% is not available in the database. Performance tests showed that DomSign is a highly reliable enzyme EC number annotation tool. After multiple tests, the accuracy is thought to be greater than 90%. Thus, DomSign can be applied to large-scale datasets, with the goal of expanding the enzyme space with high fidelity. Using DomSign, we successfully increased the percentage of EC-tagged enzymes from 12% to 30% in UniProt-TrEMBL. In the Kyoto Encyclopedia of Genes and Genomes bacterial database, the percentage of EC-tagged enzymes for each bacterial genome could be increased from 26.0% to 33.2% on average. Metagenomic mining was also efficient, as exemplified by the application of DomSign to the Human Microbiome Project dataset, recovering nearly one million new EC-labeled enzymes. Conclusions Our results offer preliminarily confirmation of the existence of the hypothesized huge number of “hidden enzymes” in the protein universe, the identification of which could substantially further our understanding of the metabolisms of diverse organisms and also facilitate bioengineering by providing a richer enzyme resource. Furthermore, our results highlight the necessity of using more advanced computational tools than BLAST in protein database annotations to extract additional biologically relevant functional information from the available biological sequences. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0499-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Tianmin Wang
- Department of Biological Information, Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, 2-12-1 M6-3, Ookayama, Meguro-ku, Tokyo, 152-8550, Japan. .,Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China.
| | - Hiroshi Mori
- Department of Biological Information, Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, 2-12-1 M6-3, Ookayama, Meguro-ku, Tokyo, 152-8550, Japan. .,Earth-Life Science Institute, Tokyo Institute of Technology, 2-12-1-E3-10 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.
| | - Chong Zhang
- Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China.
| | - Ken Kurokawa
- Department of Biological Information, Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, 2-12-1 M6-3, Ookayama, Meguro-ku, Tokyo, 152-8550, Japan. .,Earth-Life Science Institute, Tokyo Institute of Technology, 2-12-1-E3-10 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.
| | - Xin-Hui Xing
- Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China.
| | - Takuji Yamada
- Department of Biological Information, Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, 2-12-1 M6-3, Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.
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Nath N, Mitchell JBO, Caetano-Anollés G. The natural history of biocatalytic mechanisms. PLoS Comput Biol 2014; 10:e1003642. [PMID: 24874434 PMCID: PMC4038463 DOI: 10.1371/journal.pcbi.1003642] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2013] [Accepted: 04/09/2014] [Indexed: 11/29/2022] Open
Abstract
Phylogenomic analysis of the occurrence and abundance of protein domains in proteomes has recently showed that the α/β architecture is probably the oldest fold design. This holds important implications for the origins of biochemistry. Here we explore structure-function relationships addressing the use of chemical mechanisms by ancestral enzymes. We test the hypothesis that the oldest folds used the most mechanisms. We start by tracing biocatalytic mechanisms operating in metabolic enzymes along a phylogenetic timeline of the first appearance of homologous superfamilies of protein domain structures from CATH. A total of 335 enzyme reactions were retrieved from MACiE and were mapped over fold age. We define a mechanistic step type as one of the 51 mechanistic annotations given in MACiE, and each step of each of the 335 mechanisms was described using one or more of these annotations. We find that the first two folds, the P-loop containing nucleotide triphosphate hydrolase and the NAD(P)-binding Rossmann-like homologous superfamilies, were α/β architectures responsible for introducing 35% (18/51) of the known mechanistic step types. We find that these two oldest structures in the phylogenomic analysis of protein domains introduced many mechanistic step types that were later combinatorially spread in catalytic history. The most common mechanistic step types included fundamental building blocks of enzyme chemistry: "Proton transfer," "Bimolecular nucleophilic addition," "Bimolecular nucleophilic substitution," and "Unimolecular elimination by the conjugate base." They were associated with the most ancestral fold structure typical of P-loop containing nucleotide triphosphate hydrolases. Over half of the mechanistic step types were introduced in the evolutionary timeline before the appearance of structures specific to diversified organisms, during a period of architectural diversification. The other half unfolded gradually after organismal diversification and during a period that spanned ∼2 billion years of evolutionary history.
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Affiliation(s)
- Neetika Nath
- Biomedical Sciences Research Complex and EaStCHEM School of Chemistry, University of St. Andrews, North Haugh, St. Andrews, Scotland, United Kingdom
| | - John B. O. Mitchell
- Biomedical Sciences Research Complex and EaStCHEM School of Chemistry, University of St. Andrews, North Haugh, St. Andrews, Scotland, United Kingdom
| | - Gustavo Caetano-Anollés
- Evolutionary Bioinformatics Laboratory, Department of Crop Sciences, University of Illinois, Urbana, Illinois, United States of America
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Nagao C, Nagano N, Mizuguchi K. Prediction of detailed enzyme functions and identification of specificity determining residues by random forests. PLoS One 2014; 9:e84623. [PMID: 24416252 PMCID: PMC3885575 DOI: 10.1371/journal.pone.0084623] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2013] [Accepted: 11/15/2013] [Indexed: 12/03/2022] Open
Abstract
Determining enzyme functions is essential for a thorough understanding of cellular processes. Although many prediction methods have been developed, it remains a significant challenge to predict enzyme functions at the fourth-digit level of the Enzyme Commission numbers. Functional specificity of enzymes often changes drastically by mutations of a small number of residues and therefore, information about these critical residues can potentially help discriminate detailed functions. However, because these residues must be identified by mutagenesis experiments, the available information is limited, and the lack of experimentally verified specificity determining residues (SDRs) has hindered the development of detailed function prediction methods and computational identification of SDRs. Here we present a novel method for predicting enzyme functions by random forests, EFPrf, along with a set of putative SDRs, the random forests derived SDRs (rf-SDRs). EFPrf consists of a set of binary predictors for enzymes in each CATH superfamily and the rf-SDRs are the residue positions corresponding to the most highly contributing attributes obtained from each predictor. EFPrf showed a precision of 0.98 and a recall of 0.89 in a cross-validated benchmark assessment. The rf-SDRs included many residues, whose importance for specificity had been validated experimentally. The analysis of the rf-SDRs revealed both a general tendency that functionally diverged superfamilies tend to include more active site residues in their rf-SDRs than in less diverged superfamilies, and superfamily-specific conservation patterns of each functional residue. EFPrf and the rf-SDRs will be an effective tool for annotating enzyme functions and for understanding how enzyme functions have diverged within each superfamily.
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Affiliation(s)
- Chioko Nagao
- National Institute of Biomedical Innovation, Ibaraki, Osaka, Japan
- * E-mail: (CN); (KM)
| | - Nozomi Nagano
- Computational Biology Research Center, AIST, Koto-ku, Tokyo, Japan
| | - Kenji Mizuguchi
- National Institute of Biomedical Innovation, Ibaraki, Osaka, Japan
- * E-mail: (CN); (KM)
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5
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Almonacid DE, Babbitt PC. Toward mechanistic classification of enzyme functions. Curr Opin Chem Biol 2011; 15:435-42. [PMID: 21489855 DOI: 10.1016/j.cbpa.2011.03.008] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2011] [Accepted: 03/17/2011] [Indexed: 11/15/2022]
Abstract
Classification of enzyme function should be quantitative, computationally accessible, and informed by sequences and structures to enable use of genomic information for functional inference and other applications. Large-scale studies have established that divergently evolved enzymes share conserved elements of structure and common mechanistic steps and that convergently evolved enzymes often converge to similar mechanisms too, suggesting that reaction mechanisms could be used to develop finer-grained functional descriptions than provided by the Enzyme Commission (EC) system currently in use. Here we describe how evolution informs these structure-function mappings and review the databases that store mechanisms of enzyme reactions along with recent developments to measure ligand and mechanistic similarities. Together, these provide a foundation for new classifications of enzyme function.
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Affiliation(s)
- Daniel E Almonacid
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, 1700 4th Street, MC 2550, San Francisco, CA 94158, USA
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de Melo-Minardi RC, Bastard K, Artiguenave F. Identification of subfamily-specific sites based on active sites modeling and clustering. ACTA ACUST UNITED AC 2010; 26:3075-82. [PMID: 20980272 DOI: 10.1093/bioinformatics/btq595] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
MOTIVATION Current computational approaches to function prediction are mostly based on protein sequence classification and transfer of annotation from known proteins to their closest homologous sequences relying on the orthology concept of function conservation. This approach suffers a major weakness: annotation reliability depends on global sequence similarity to known proteins and is poorly efficient for enzyme superfamilies that catalyze different reactions. Structural biology offers a different strategy to overcome the problem of annotation by adding information about protein 3D structures. This information can be used to identify amino acids located in active sites, focusing on detection of functional polymorphisms residues in an enzyme superfamily. Structural genomics programs are providing more and more novel protein structures at a high-throughput rate. However, there is still a huge gap between the number of sequences and available structures. Computational methods, such as homology modeling provides reliable approaches to bridge this gap and could be a new precise tool to annotate protein functions. RESULTS Here, we present Active Sites Modeling and Clustering (ASMC) method, a novel unsupervised method to classify sequences using structural information of protein pockets. ASMC combines homology modeling of family members, structural alignment of modeled active sites and a subsequent hierarchical conceptual classification. Comparison of profiles obtained from computed clusters allows the identification of residues correlated to subfamily function divergence, called specificity determining positions. ASMC method has been validated on a benchmark of 42 Pfam families for which previous resolved holo-structures were available. ASMC was also applied to several families containing known protein structures and comprehensive functional annotations. We will discuss how ASMC improves annotation and understanding of protein families functions by giving some specific illustrative examples on nucleotidyl cyclases, protein kinases and serine proteases. AVAILABILITY http://www.genoscope.fr/ASMC/.
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