Highly Accurate and Efficient Deep Learning Paradigm for Full-Atom Protein Loop Modeling with KarmaLoop
Tianyue Wang,
Xujun Zhang,
Odin Zhang,
Guangyong Chen,
Peichen Pan,
Ercheng Wang,
Jike Wang,
Jialu Wu,
Donghao Zhou,
Langcheng Wang,
Ruofan Jin,
Shicheng Chen,
Chao Shen,
Yu Kang,
Chang-Yu Hsieh,
Tingjun Hou
Affiliations
Tianyue Wang
Innovation Institute for Artificial Intelligence in Medicine of
Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Xujun Zhang
Innovation Institute for Artificial Intelligence in Medicine of
Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Odin Zhang
Innovation Institute for Artificial Intelligence in Medicine of
Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Innovation Institute for Artificial Intelligence in Medicine of
Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Ercheng Wang
Innovation Institute for Artificial Intelligence in Medicine of
Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Jike Wang
Innovation Institute for Artificial Intelligence in Medicine of
Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Jialu Wu
Innovation Institute for Artificial Intelligence in Medicine of
Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Donghao Zhou
Shenzhen Institute of Advanced Technology,
Chinese Academy of Sciences, Shenzhen 518055, Guangdong, China.
Langcheng Wang
Department of Pathology,
New York University Medical Center, New York, NY 10016, USA.
Ruofan Jin
Innovation Institute for Artificial Intelligence in Medicine of
Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Shicheng Chen
Innovation Institute for Artificial Intelligence in Medicine of
Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Chao Shen
Innovation Institute for Artificial Intelligence in Medicine of
Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Yu Kang
Innovation Institute for Artificial Intelligence in Medicine of
Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Chang-Yu Hsieh
Innovation Institute for Artificial Intelligence in Medicine of
Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Tingjun Hou
Innovation Institute for Artificial Intelligence in Medicine of
Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
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 1.95 Å for 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.
