Protein loop modeling is a challenging yet highly nontrivial task in protein structure prediction.Despite recent progress,existing methods including knowledge-based,ab initio,hybrid,and deep learning(DL)methods fall s...Protein loop modeling is a challenging yet highly nontrivial task in protein structure prediction.Despite recent progress,existing methods including knowledge-based,ab initio,hybrid,and deep learning(DL)methods fall substantially short of either atomic accuracy or computational efficiency.To overcome these limitations,we present KarmaLoop,a novel paradigm that distinguishes itself as the first DL method centered on full-atom(encompassing both backbone and side-chain heavy atoms)protein loop modeling.Our results demonstrate that KarmaLoop considerably outperforms conventional and DL-based methods of loop modeling in terms of both accuracy and efficiency,with the average RMSDs of 1.77 and 1.95Åfor the CASP13+14 and CASP15 benchmark datasets,respectively,and manifests at least 2 orders of magnitude speedup in general compared with other methods.Consequently,our comprehensive evaluations indicate that KarmaLoop provides a state-of-the-art DL solution for protein loop modeling,with the potential to hasten the advancement of protein engineering,antibody-antigen recognition,and drug design.展开更多
基金supported by the National Key Research and Development Program of China(2022YFF1203000)the National Natural Science Foundation of China(22220102001,82204279,22007082,and 62006219)+2 种基金the Fundamental Research Funds for the Central Universities(226-2022-00220)the Natural Science Foundation of Zhejiang Province(LQ21B030013)Hong Kong Innovation and Technology Fund(Project No.ITS/241/21).
文摘Protein loop modeling is a challenging yet highly nontrivial task in protein structure prediction.Despite recent progress,existing methods including knowledge-based,ab initio,hybrid,and deep learning(DL)methods fall substantially short of either atomic accuracy or computational efficiency.To overcome these limitations,we present KarmaLoop,a novel paradigm that distinguishes itself as the first DL method centered on full-atom(encompassing both backbone and side-chain heavy atoms)protein loop modeling.Our results demonstrate that KarmaLoop considerably outperforms conventional and DL-based methods of loop modeling in terms of both accuracy and efficiency,with the average RMSDs of 1.77 and 1.95Åfor the CASP13+14 and CASP15 benchmark datasets,respectively,and manifests at least 2 orders of magnitude speedup in general compared with other methods.Consequently,our comprehensive evaluations indicate that KarmaLoop provides a state-of-the-art DL solution for protein loop modeling,with the potential to hasten the advancement of protein engineering,antibody-antigen recognition,and drug design.
