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Side‐chain modeling with an optimized scoring function
Author(s) -
Liang Shide,
Grishin Nick V.
Publication year - 2002
Publication title -
protein science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.353
H-Index - 175
eISSN - 1469-896X
pISSN - 0961-8368
DOI - 10.1110/ps.24902
Subject(s) - conformational isomerism , side chain , root mean square , protein structure prediction , standard deviation , function (biology) , protein structure , mathematics , physics , statistics , nuclear magnetic resonance , molecule , quantum mechanics , evolutionary biology , biology , polymer
Modeling side‐chain conformations on a fixed protein backbone has a wide application in structure prediction and molecular design. Each effort in this field requires decisions about a rotamer set, scoring function, and search strategy. We have developed a new and simple scoring function, which operates on side‐chain rotamers and consists of the following energy terms: contact surface, volume overlap, backbone dependency, electrostatic interactions, and desolvation energy. The weights of these energy terms were optimized to achieve the minimal average root mean square (rms) deviation between the lowest energy rotamer and real side‐chain conformation on a training set of high‐resolution protein structures. In the course of optimization, for every residue, its side chain was replaced by varying rotamers, whereas conformations for all other residues were kept as they appeared in the crystal structure. We obtained prediction accuracy of 90.4% for χ 1 , 78.3% for χ 1 + 2 , and 1.18 Å overall rms deviation. Furthermore, the derived scoring function combined with a Monte Carlo search algorithm was used to place all side chains onto a protein backbone simultaneously. The average prediction accuracy was 87.9% for χ 1 , 73.2% for χ 1 + 2 , and 1.34 Å rms deviation for 30 protein structures. Our approach was compared with available side‐chain construction methods and showed improvement over the best among them: 4.4% for χ 1 , 4.7% for χ 1 + 2 , and 0.21 Å for rms deviation. We hypothesize that the scoring function instead of the search strategy is the main obstacle in side‐chain modeling. Additionally, we show that a more detailed rotamer library is expected to increase χ 1 + 2 prediction accuracy but may have little effect on χ 1 prediction accuracy.