Department of Physics, National Laboratory of Solid State Microstructure, Nanjing University, Nanjing 210093, China.
Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325000, China.
Proc Natl Acad Sci U S A. 2024 Aug 27;121(35):e2410662121. doi: 10.1073/pnas.2410662121. Epub 2024 Aug 20.
Proteins perform their biological functions through motion. Although high throughput prediction of the three-dimensional static structures of proteins has proved feasible using deep-learning-based methods, predicting the conformational motions remains a challenge. Purely data-driven machine learning methods encounter difficulty for addressing such motions because available laboratory data on conformational motions are still limited. In this work, we develop a method for generating protein allosteric motions by integrating physical energy landscape information into deep-learning-based methods. We show that local energetic frustration, which represents a quantification of the local features of the energy landscape governing protein allosteric dynamics, can be utilized to empower AlphaFold2 (AF2) to predict protein conformational motions. Starting from ground state static structures, this integrative method generates alternative structures as well as pathways of protein conformational motions, using a progressive enhancement of the energetic frustration features in the input multiple sequence alignment sequences. For a model protein adenylate kinase, we show that the generated conformational motions are consistent with available experimental and molecular dynamics simulation data. Applying the method to another two proteins KaiB and ribose-binding protein, which involve large-amplitude conformational changes, can also successfully generate the alternative conformations. We also show how to extract overall features of the AF2 energy landscape topography, which has been considered by many to be black box. Incorporating physical knowledge into deep-learning-based structure prediction algorithms provides a useful strategy to address the challenges of dynamic structure prediction of allosteric proteins.
蛋白质通过运动发挥其生物学功能。尽管使用基于深度学习的方法已经证明可以高效地预测蛋白质的三维静态结构，但预测构象运动仍然是一个挑战。由于可用的构象运动实验数据仍然有限，纯粹基于数据的机器学习方法在解决这些运动方面遇到了困难。在这项工作中，我们开发了一种通过将物理能量景观信息集成到基于深度学习的方法中来生成蛋白质变构运动的方法。我们表明，局部能量挫败感（local energetic frustration），它代表了控制蛋白质变构动力学的能量景观的局部特征的量化，可以被用来增强 AlphaFold2（AF2）来预测蛋白质构象运动。从基态静态结构开始，这种集成方法使用输入的多重序列比对序列中能量挫败感特征的逐步增强，生成蛋白质构象运动的替代结构和途径。对于模型蛋白腺苷酸激酶，我们表明生成的构象运动与可用的实验和分子动力学模拟数据一致。将该方法应用于另外两种涉及大振幅构象变化的蛋白质 KaiB 和核糖结合蛋白，也可以成功生成替代构象。我们还展示了如何提取 AF2 能量景观地形的整体特征，许多人认为这是一个黑盒。将物理知识纳入基于深度学习的结构预测算法为解决变构蛋白质的动态结构预测挑战提供了一种有用的策略。
