AbstractMotivationMassive local minima on the protein energy surface often causes traditional conformation sampling algorithms to be easily trapped in local basin regions, because they are difficult to stride over high-energy barriers. Also, the lowest energy conformation may not correspond to the native structure due to the inaccuracy of energy models. This study investigates whether these two problems can be alleviated by a sequential niche technique without loss of accuracy.ResultsA sequential niche multimodal conformation sampling algorithm for protein structure prediction (SNfold) is proposed in this study. In SNfold, a derating function is designed based on the knowledge learned from the previous sampling and used to construct a series of sampling-guided energy functions. These functions then help the sampling algorithm stride over high-energy barriers and avoid the re-sampling of the explored regions. In inaccurate protein energy models, the high- energy conformation that may correspond to the native structure can be sampled with successively updated sampling-guided energy functions. The proposed SNfold is tested on 300 benchmark proteins and 24 CASP13 FM targets. Results show that SNfold is comparable with Rosetta restrained by distance (Rosetta-dist) and C-QUARK. SNfold correctly folds (TM-score ≥ 0.5) 231 out of 300 proteins. In particular, compared with Rosetta-dist protocol, SNfold achieves higher average TM- score and improves the sampling efficiency by more than 100 times. On the 24 CASP13 FM targets, SNfold is also comparable with four state-of-the-art methods in the CASP13 server group. As a plugin conformation sampling algorithm, SNfold can be extended to other protein structure prediction methods.AvailabilityThe source code and executable versions are freely available at https://github.com/iobio-zjut/[email protected]
Constructing effective energy functions for protein structure prediction through broadening attraction-basin and reverse Monte Carlo sampling