Side‐chain modeling with an optimized scoring function

Abstract

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 1 , 78.3% for 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 1 , 73.2% for 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 1 , 4.7% for 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 1 + 2 prediction accuracy but may have little effect on 1 prediction accuracy.

Do rotamer libraries reproduce the side-chain conformations of peptidic ligands from the PDB?

“…Nevertheless, in spite of the large variability of such factors, side chains tend to exist in a limited number of low energy conformations, usually called ''rotamers'' [2]. Therefore, instead of considering the full geometrically possible conformational space, only a relatively small number of rotamers can be used to describe most naturally occurring conformers of aminoacidic residues [3].…”

An Experimental Analysis of the Performance of SideChain Packing Algorithms

“…• Dataset-65 [20]: 30 proteins were taken from [13]. For this subset, sequence identity cutoff was set to 50%, the resolution cutoff was set to 1.8Å, and the Rfactor cutoff was set to 20%.…”

Protein Structure Prediction: Assembly of Secondary Structure Elements by Basin‐Hopping

“…The efficiency of the trial moves can be increased by incorporating residue-specific structural preferences derived from experimental structures. 19,20 It is well known that the Φ and Ψ angles of the protein backbone are more densely centered around some regions with the distribution of the (Φ, Ψ) densities depending on the amino acid identity. 21 Likewise, protein side chains tend to exist in a limited number of low energy conformations called rotamers.…”