TY - JOUR T1 - Distance-guided protein folding based on generalized descent direction JF - bioRxiv DO - 10.1101/2021.05.16.444345 SP - 2021.05.16.444345 AU - Liujing Wang AU - Jun Liu AU - Yuhao Xia AU - Jiakang Xu AU - Xiaogen Zhou AU - Guijun Zhang Y1 - 2021/01/01 UR - http://biorxiv.org/content/early/2021/05/17/2021.05.16.444345.abstract N2 - Advances in the prediction of the inter-residue distance for a protein sequence have increased the accuracy to predict the correct folds of proteins with distance information. Here, we propose a distance-guided protein folding algorithm based on generalized descent direction, named GDDfold, which achieves effective structural perturbation and potential minimization in two stages. In the global stage, random-based direction is designed using evolutionary knowledge, which guides conformation population to cross potential barriers and explore conformational space rapidly in a large range. In the local stage, locally rugged potential landscape can be explored with the aid of conjugate-based direction integrated into a specific search strategy, which can improve exploitation ability. GDDfold is tested on 347 proteins of a benchmark set, 24 FM targets of CASP13 and 20 FM targets of CASP14. Results show that GDDfold correctly folds (TM-score ≥ 0.5) 316 out of 347 proteins, where 65 proteins have TM-scores that are greater than 0.8, and significantly outperforms Rosetta-dist (distance-assisted fragment assembly method) and L-BFGSfold (distance geometry optimization method). On CASP FM targets, GDDfold is comparable with five state-of-the-art methods, namely, Quark, RaptorX, Rosetta, MULTICOM and trRosetta in the CASP 13 and 14 server groups.Competing Interest StatementThe authors have declared no competing interest. ER -