Wang Tianyue, Zhang Xujun, Zhang Odin, Chen Guangyong, Pan Peichen, Wang Ercheng, Wang Jike, Wu Jialu, Zhou Donghao, Wang Langcheng, Jin Ruofan, Chen Shicheng, Shen Chao, Kang Yu, Hsieh Chang-Yu, Hou Tingjun
Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Zhejiang Laboratory, Hangzhou 311100, Zhejiang, China.
Research (Wash D C). 2024 Jul 25;7:0408. doi: 10.34133/research.0408. eCollection 2024.
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.
在蛋白质结构预测中，蛋白质环建模是一项具有挑战性但又极为重要的任务。尽管最近取得了进展，但现有的方法，包括基于知识的方法、从头算方法、混合方法和深度学习（DL）方法，在原子精度或计算效率方面都存在很大不足。为了克服这些限制，我们提出了KarmaLoop，这是一种新颖的范式，它作为第一种以全原子（包括主链和侧链重原子）蛋白质环建模为中心的DL方法而脱颖而出。我们的结果表明，KarmaLoop在准确性和效率方面都大大优于传统的和基于DL的环建模方法，对于CASP13+14和CASP15基准数据集，平均均方根偏差（RMSD）分别为1.77 Å和1.95 Å，并且与其他方法相比，总体上至少有两个数量级的加速。因此，我们的综合评估表明，KarmaLoop为蛋白质环建模提供了一种最先进的DL解决方案，有可能加速蛋白质工程、抗体-抗原识别和药物设计的进展。
