Normal Modes Expose Active Sites in Enzymes
Accurate prediction of active sites is an important tool in bioinformatics. Here we present an improved structure based technique to expose active sites that is based on large changes of solvent accessibility accompanying normal mode dynamics. The technique which detects EXPOsure of active SITes thr...
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description | Accurate prediction of active sites is an important tool in bioinformatics. Here we present an improved structure based technique to expose active sites that is based on large changes of solvent accessibility accompanying normal mode dynamics. The technique which detects EXPOsure of active SITes through normal modEs is named EXPOSITE. The technique is trained using a small 133 enzyme dataset and tested using a large 845 enzyme dataset, both with known active site residues. EXPOSITE is also tested in a benchmark protein ligand dataset (PLD) comprising 48 proteins with and without bound ligands. EXPOSITE is shown to successfully locate the active site in most instances, and is found to be more accurate than other structure-based techniques. Interestingly, in several instances, the active site does not correspond to the largest pocket. EXPOSITE is advantageous due to its high precision and paves the way for structure based prediction of active site in enzymes. |
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Here we present an improved structure based technique to expose active sites that is based on large changes of solvent accessibility accompanying normal mode dynamics. The technique which detects EXPOsure of active SITes through normal modEs is named EXPOSITE. The technique is trained using a small 133 enzyme dataset and tested using a large 845 enzyme dataset, both with known active site residues. EXPOSITE is also tested in a benchmark protein ligand dataset (PLD) comprising 48 proteins with and without bound ligands. EXPOSITE is shown to successfully locate the active site in most instances, and is found to be more accurate than other structure-based techniques. Interestingly, in several instances, the active site does not correspond to the largest pocket. EXPOSITE is advantageous due to its high precision and paves the way for structure based prediction of active site in enzymes.</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1005293</identifier><identifier>PMID: 28002427</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Binding sites ; Bioinformatics ; Biology and Life Sciences ; Catalytic Domain ; Computational Biology - methods ; Databases, Protein ; Datasets ; Enzyme kinetics ; Enzymes ; Enzymes - chemistry ; Enzymes - metabolism ; Enzymes - ultrastructure ; Funding ; Ligands ; Ligands (Biochemistry) ; Methods ; Models, Molecular ; Neural networks ; Observations ; Physical Sciences ; Physiological aspects ; Proteins ; Research and Analysis Methods ; Solvents ; Success</subject><ispartof>PLoS computational biology, 2016-12, Vol.12 (12), p.e1005293-e1005293</ispartof><rights>COPYRIGHT 2016 Public Library of Science</rights><rights>2016 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Glantz-Gashai Y, Meirson T, Samson AO (2016) Normal Modes Expose Active Sites in Enzymes. PLoS Comput Biol 12(12): e1005293. doi:10.1371/journal.pcbi.1005293</rights><rights>2016 Glantz-Gashai et al 2016 Glantz-Gashai et al</rights><rights>2016 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Glantz-Gashai Y, Meirson T, Samson AO (2016) Normal Modes Expose Active Sites in Enzymes. 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Here we present an improved structure based technique to expose active sites that is based on large changes of solvent accessibility accompanying normal mode dynamics. The technique which detects EXPOsure of active SITes through normal modEs is named EXPOSITE. The technique is trained using a small 133 enzyme dataset and tested using a large 845 enzyme dataset, both with known active site residues. EXPOSITE is also tested in a benchmark protein ligand dataset (PLD) comprising 48 proteins with and without bound ligands. EXPOSITE is shown to successfully locate the active site in most instances, and is found to be more accurate than other structure-based techniques. Interestingly, in several instances, the active site does not correspond to the largest pocket. 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Here we present an improved structure based technique to expose active sites that is based on large changes of solvent accessibility accompanying normal mode dynamics. The technique which detects EXPOsure of active SITes through normal modEs is named EXPOSITE. The technique is trained using a small 133 enzyme dataset and tested using a large 845 enzyme dataset, both with known active site residues. EXPOSITE is also tested in a benchmark protein ligand dataset (PLD) comprising 48 proteins with and without bound ligands. EXPOSITE is shown to successfully locate the active site in most instances, and is found to be more accurate than other structure-based techniques. Interestingly, in several instances, the active site does not correspond to the largest pocket. 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subjects | Binding sites Bioinformatics Biology and Life Sciences Catalytic Domain Computational Biology - methods Databases, Protein Datasets Enzyme kinetics Enzymes Enzymes - chemistry Enzymes - metabolism Enzymes - ultrastructure Funding Ligands Ligands (Biochemistry) Methods Models, Molecular Neural networks Observations Physical Sciences Physiological aspects Proteins Research and Analysis Methods Solvents Success |
title | Normal Modes Expose Active Sites in Enzymes |
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