Characterization of conformational flexibility in protein structures by applying artificial intelligence to molecular modeling

Recent AI applications have revolutionized the modeling of structurally unresolved protein regions, thereby complementing traditional computational methods. These state-of-the-art techniques can generate numerous candidate structures, significantly expanding the scope of structural biology. However, to effectively prioritize these models, a physics-based approach is required to assess the energy landscape. Such integration can bridge the gap between rapid model generation and precise determination of functional conformations. To address this challenge, we propose an integrated approach that combines molecular modeling with AI and HPC. Metadynamics simulations in latent space are used to explore potential energy landscapes based on initial approximations of flexible region structures derived from modeling tools such as AlphaFold, RosettaFold, Modeller, SwissModel, etc. The approach was validated by modeling folding of Trp-cage protein and conformational plasticity of ubiquitin. The predominant conformations of previously unresolved mobile regions in the active center of flavin-dependent 2-hydroxybiphenyl-3-monooxygenase (EC 1.14.13.44) were identified, while estimating the energy associated with these conformational changes.