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A statistical approach to the prediction of p K a values in proteins
Author(s) -
He Yun,
Xu Jialin,
Pan XianMing
Publication year - 2007
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.21478
Subject(s) - chemistry , root mean square , absolute deviation , test set , statistical potential , data set , residue (chemistry) , set (abstract data type) , mathematics , training set , protein structure prediction , biological system , protein structure , statistics , physics , computer science , biochemistry , artificial intelligence , biology , quantum mechanics , programming language
Abstract We propose a simple model for the calculation of p K a values of ionizable residues in proteins. It is based on the premise that the p K a shift of ionizable residues is linearly correlated to the interaction between a particular residue and the local environment created by the surrounding residues. Despite its simplicity, the model displays good prediction performance. Under the sixfold cross test prediction over a data set of 405 experimental p K a values in 73 protein chains with known structures, the root‐mean‐square deviation (RMSD) between the experimental and calculated p K a was found to be 0.77. The accuracy of this model increases with increasing size of the data set: the RMSD is 0.609 for glutamate (the largest data set with 141 sites) and ∼1 pH unit for lysine, with a data set containing 45 sites. Proteins 2007. © 2007 Wiley‐Liss, Inc.