Predicting protein conformational motions using energetic frustration analysis and AlphaFold2.

ABSTRACT: 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 (

SUBMITTER: Guan X 

PROVIDER: S-EPMC11363347 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature