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231223s2009 xx |||||o 00| ||eng c |
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|a 10.1002/jcc.21170
|2 doi
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|a pubmed24n0617.xml
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|a (DE-627)NLM185208096
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|a (NLM)19086060
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|a DE-627
|b ger
|c DE-627
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|a eng
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|a Concu, Riccardo
|e verfasserin
|4 aut
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|a Computational chemistry study of 3D-structure-function relationships for enzymes based on Markov models for protein electrostatic, HINT, and van der Waals potentials
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|c 2009
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Completed 30.07.2009
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|a Date Revised 18.11.2010
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|a published: Print
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|a Citation Status MEDLINE
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|a (c) 2008 Wiley Periodicals, Inc.
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|a In a significant work, Dobson and Doig (J Mol Biol 2003, 330, 771) illustrated protein prediction as enzymatic or not from spatial structure without resorting to alignments. They used 52 protein features and a nonlinear support vector machine model to classify more than 1000 proteins collected from the PDB with a 77% overall accuracy. The most useful features were: the secondary-structure content, the amino acid frequencies, the number of disulphide bonds, and the largest cleft size. Working on the same dataset used by D&D, in this article we reported a good and simple model, based on the Markov chain models (MCM), to classify protein 3D structures as enzymatic or not, taking into consideration the spatial structure without resorting to alignments. Here we define, for the first time, a general MCM to calculate the electrostatic potential, molecular vibrations, van der Waals (vdw) interactions, and hydrophobic interactions (HINT) and use them in comparative studies of potential fields and/or protein function prediction. The dataset is composed of 1371 proteins divided into 689 enzymes and 682 nonenzymes, all proteins were collected from the PDB. The best model we found was a linear model carried out with the linear discriminant analysis; it was able to classify 74.18% of the proteins using only two electrostatic potentials. In the work described here, we define 3D-HINT potentials (mu(k)) and use them for the first time to derive a classifier for protein enzymes. We analyzed ROC curves, domain of applicability, parametric assumptions, desirability maps, and also tested other nonlinear artificial neural network models which did not improve the linear model. In closing, this MCM allows a fast calculation and comparison of different potentials deriving into accurate protein 3D structure-function relationships, notably simpler than the previous
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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| 650 |
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|a Enzymes
|2 NLM
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| 700 |
1 |
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|a Podda, Gianni
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Uriarte, Eugenio
|e verfasserin
|4 aut
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| 700 |
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|a González-Díaz, Humberto
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 30(2009), 9 vom: 15. Juli, Seite 1510-20
|w (DE-627)NLM098138448
|x 1096-987X
|7 nnns
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| 773 |
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|g volume:30
|g year:2009
|g number:9
|g day:15
|g month:07
|g pages:1510-20
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| 856 |
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|u http://dx.doi.org/10.1002/jcc.21170
|3 Volltext
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|d 30
|j 2009
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