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Prediction of protein B‐factor profiles
Author(s) -
Yuan Zheng,
Bailey Timothy L.,
Teasdale Rohan D.
Publication year - 2005
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.20375
Subject(s) - support vector machine , sequence (biology) , distribution (mathematics) , computer science , correlation coefficient , biological system , molecular dynamics , pattern recognition (psychology) , artificial intelligence , algorithm , mathematics , chemistry , machine learning , biology , computational chemistry , mathematical analysis , biochemistry
Abstract The polypeptide backbones and side chains of proteins are constantly moving due to thermal motion and the kinetic energy of the atoms. The B‐factors of protein crystal structures reflect the fluctuation of atoms about their average positions and provide important information about protein dynamics. Computational approaches to predict thermal motion are useful for analyzing the dynamic properties of proteins with unknown structures. In this article, we utilize a novel support vector regression (SVR) approach to predict the B‐factor distribution (B‐factor profile) of a protein from its sequence. We explore schemes for encoding sequences and various settings for the parameters used in SVR. Based on a large dataset of high‐resolution proteins, our method predicts the B‐factor distribution with a Pearson correlation coefficient (CC) of 0.53. In addition, our method predicts the B‐factor profile with a CC of at least 0.56 for more than half of the proteins. Our method also performs well for classifying residues (rigid vs. flexible). For almost all predicted B‐factor thresholds, prediction accuracies (percent of correctly predicted residues) are greater than 70%. These results exceed the best results of other sequence‐based prediction methods. Proteins 2005. © 2005 Wiley‐Liss, Inc.