Incorporating knowledge-based biases into an energy-based side-chain modeling method: Application to comparative modeling of protein structure

The performance of the self-consistent mean-field theory (SCMFT) method for side-chain modeling, employing rotamer energies calculated with the flexib le rotamer model (FRM), is evaluated in the context of comparative modeling of protein structure. Predictions were carried out on a test of 56 model b ackbones of varying accuracy, to allow side-chain prediction accuracy to be analyzed as a function of backbone accuracy. A progressive decrease in the accuracy of prediction was observed as backbone accuracy decreased. Howeve r, even for very low backbone accuracy, prediction was substantially higher than random, indicating that the FRM can, in part, compensate for the erro rs in the modeled tertiary environment. It was also investigated whether th e introduction in the FRM-SCMFT method of knowledge-based biases, derived f rom a backbone-dependent rotamer library; could enhance its performance. A bias derived from the backbone-dependent rotamer conformations alone did no t improve prediction accuracy. However, a bias derived from the backbone-de pendent rotamer probabilities improved prediction accuracy considerably. Th is bias incorporated through two different strategies. In one (the indirect strategy), rotamer probabilities were used to reject unlikely rotamers a p riori, thus restricting prediction by FRM-SCMFT to a subset containing only the most probable rotamers in the library. In the other (the direct strate gy), rotamer energies were transformed into pseudo-energies that were added to the average potential energies of the respective rotamers, thereby crea ting hybrid energy-based/knowledge-based average rotamer energies, which we re used by the FRM-SCMFT method for prediction. For all degrees of backbone accuracy, an optimal strength of the knowledge-based bias existed for both strategies for which predictions were more accurate than pure energy-based predictions, and also than pure knowledge-based predictions. Hybrid knowle dge-based/energy-based methods were obtained from both strategies and compa red with the SCWRL method, a hybrid method based on the same backbone-depen dent rotamer library. The accuracy of the indirect method was approximately the same as that of the SCWRL method, but that of the direct method was si gnificantly higher. (C) 2001 John Wiley & Sons, Inc.