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Biochemistry, Biophysics and Molecular Biology, Roy J. Carver Department of, Baker Center for Bioinformatics and Biological Statistics

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Accepted Manuscript

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Proteins: Structure, Function, and Bioinformatics





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We have analyzed 29 different published matrices of protein pairwise contact potentials (CPs) between amino acids derived from different sets of proteins, either crystallographic structures taken from the Protein Data Bank (PDB) or computer-generated decoys. Each of the CPs is similar to 1 of the 2 matrices derived in the work of Miyazawa and Jernigan (Proteins 1999;34:49–68). The CP matrices of the first class can be approximated with a correlation of order 0.9 by the formula eij = hi + hj, 1 ≤ i, j ≤ 20, where the residue-type dependent factor h is highly correlated with the frequency of occurrence of a given amino acid type inside proteins. Electrostatic interactions for the potentials of this class are almost negligible. In the potentials belonging to this class, the major contribution to the potentials is the one-body transfer energy of the amino acid from water to the protein environment. Potentials belonging to the second class can be approximated with a correlation of 0.9 by the formula eij = c0 − hihj + qiqj, where c0 is a constant, h is highly correlated with the Kyte–Doolittle hydrophobicity scale, and a new, less dominant, residue-type dependent factor q is correlated (~0.9) with amino acid isoelectric points pI. Including electrostatic interactions significantly improves the approximation for this class of potentials. While, the high correlation between potentials of the first class and the hydrophobic transfer energies is well known, the fact that this approximation can work well also for the second class of potentials is a new finding. We interpret potentials of this class as representing energies of contact of amino acid pairs within an average protein environment.


This is the peer reviewed version of the following article: Pokarowski, Piotr, Andrzej Kloczkowski, Robert L. Jernigan, Neha S. Kothari, Maria Pokarowska, and Andrzej Kolinski. "Inferring ideal amino acid interaction forms from statistical protein contact potentials." PROTEINS: Structure, Function, and Bioinformatics 59, no. 1 (2005): 49-57, which has been published in final form at doi: 10.1002/prot.20380. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.

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Wiley-Liss, Inc.



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