Improved side-chain prediction accuracy using an ab initio potential energy function and a very large rotamer library

Abstract

Accurate prediction of the placement and comformations of protein side chains given only the backbone trace has a wide range of uses in protein design, structure prediction, and functional analysis. Prediction has most often relied on discrete rotamer libraries so that rapid fitness of side-chain rotamers can be assessed against some scoring function. Scoring functions are generally based on experimental parameters from small-molecule studies or empirical parameters based on determined protein structures. Here, we describe the NCN algorithm for predicting the placement of side chains. A predominantly first-principles approach was taken to develop the potential energy function incorporating van der Waals and electrostatics based on the OPLS parameters, and a hydrogen bonding term. The only empirical knowledge used is the frequency of rotameric states from the PDB. The rotamer library includes nearly 50,000 rotamers, and is the most extensive discrete library used to date. Although the computational time tends to be longer than most other algorithms, the overall accuracy exceeds all algorithms in the literature when placing rotamers on an accurate backbone trace. Considering only the most buried residues, 80% of the total residues tested, the placement accuracy reaches 92% for chi(1), and 83% for chi(1 + 2), and an overall RMS deviation of 1 A. Additionally, we show that if information is available to restrict chi(1) to one rotamer well, then this algorithm can generate structures with an average RMS deviation of 1.0 A for all heavy side-chains atoms and a corresponding overall chi(1 + 2) accuracy of 85.0%.

Figure 1.

The dependency of side-chain placement accuracy as a function of residue burial. The fraction of tested positions was those residues with accessibilities lower than a set threshold. Only these residues were considered in the generation of the data points. (A) The χ₁ dihedral accuracy is represented by the closed symbols, and the χ_{1 + 2} dihedral accuracy is represented by the open symbols. The red traces are the results for the NCN algorithm, the green traces are for the LGA, and the blue traces are for the SCAP algorithm. (B) The overall RMS deviation as a function of the fraction of total residues tested.

Figure 2.

The placement accuracy for each residue type is noted for each algorithm by dihedral angle: (A) χ₁ only, (B) χ_{1 + 2} only, (C) χ_{1 + 2 + 3} and χ_{1 + 2 + 3 + 4} dihedral angles. The last plot (D) shows the RMS deviation for each residue type averaged over all residues tested. Red is used for the results from the NCN algorithm, blue is for the LGA, and green is for the SCAP algorithm.