Jun 09 2023 RNA-Protein Complex Refinement using AI Modeling and Docking

This study presents an efficient technique for refining protein-RNA complexes using AI-based modeling and flexible docking. The method, utilizing parallel cascade selection molecular dynamics (PaCS-MD), accelerates conformational sampling of flexible RNA regions and produces high-quality complex models. Experimental validation demonstrates its superiority over template-based modeling, suggesting its potential for constructing complexes with non-canonical RNA-protein interactions.

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Abstract

An efficient structural refinement technique for protein-RNA complexes is proposed based on a combination of AI-based modeling and flexible docking. Specifically, an enhanced sampling method called parallel cascade selection molecular dynamics (PaCS-MD) was extended to include flexible docking to construct protein-RNA complexes from those obtained by AI-based modeling (AlphaFold2). With the present technique, the conformational sampling of flexible RNA regions is accelerated by PaCS-MD, enabling one to construct plausible models for protein-RNA complexes. For demonstration, PaCS-MD constructed several protein-RNA complexes of the RNA-binding Musashi-1 (MSI1) family of proteins, which were validated by comparing a group of crucial residues for RNA-binding with experimental complexes. Our analyses suggest that PaCS-MD improves the quality of complex modeling compared to the standard protocol based on template-based modeling (Phyre2). Furthermore, PaCS-MD could also be a beneficial technique for constructing complexes of non-native RNA-binding to proteins.

Citation

A Structural Refinement Technique for Protein-RNA Complexes Using a Combination of AI-based Modeling and Flexible Docking: A Study of Musashi-1 Protein
Nitchakan Darai, Kowit Hengphasatporn, Peter Wolschann, Michael T. Wolfinger, Yasuteru Shigeta, Thanyada Rungrotmongkol, Ryuhei Harada
B. Chem. Soc. Jpn. (2023)