Premium
Computational chemistry comparison of stable/nonstable protein mutants classification models based on 3D and topological indices
Author(s) -
GonzálezDíaz Humberto,
Pérezcastillo Yunierkis,
Podda Gianni,
Uriarte Eugenio
Publication year - 2007
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.20700
Subject(s) - stability (learning theory) , simplicity , sequence (biology) , computer science , process (computing) , protein stability , computational biology , topology (electrical circuits) , algorithm , mathematics , chemistry , machine learning , biology , genetics , physics , combinatorics , biochemistry , quantum mechanics , operating system
In principle, there are different protein structural parameters that can be used in computational chemistry studies to classify protein mutants according to thermal stability including: sequence, connectivity, and 3D descriptors. Connectivity parameters (called topological indices, TIs) are simpler than 3D parameters being then less computationally expensive. However, TIs ignore important aspects of protein structure and hence are expected to be inaccurate. In any case, a comparison of 3D and TIs has not been reported with respect to the power of discrimination of proteins according to stability. In this study, we compare both classes of indices in this sense by the first time. The best model found, based on 3D spectral moments correctly classified 507 out of 525 (96.6%) proteins while TIs model correctly classified 404 out of 525 (77.0%) proteins. We have shown that, in fact, 3D descriptor models gave more accurate results than TIs but interestingly, TIs give acceptable results in a timely way in spite of their simplicity. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2007