Atomic protein structure refinement using all-atom graph representations and SE(3)-equivariant graph neural networks

Abstract

AbstractThree-dimensional (3D) protein structures reveal the fundamental information about protein function. The state-of-art protein structure prediction methods such as Alphafold are being widely used to predict structures of uncharacterized proteins in biomedical research. There is a significant need to further improve the quality and nativeness of the predicted structures to enhance their usability. Current machine learning methods of refining protein structures focus mostly on improving the backbone quality of predicted structures without effectively leveraging and enhancing the conformation of all atoms including side-chain, while molecular simulation methods are computationally intensive and time-consuming.In this work, we develop ATOMRefine, a deep learning-based, end-to-end, all-atom protein structural model refinement method. It uses a SE(3)-equivariant graph transformer network that is equivariant to the rotation and translation of 3D structures in conjunction with a novel graph representation of all atoms to directly refine protein atomic coordinates of all the atoms in a predicted tertiary structure represented as a molecular graph. The method is first trained and tested on the structural models in AlphafoldDB whose experimental structures are known, and then blindly tested on 69 CASP14 regular targets and 7 CASP14 refinement targets. ATOMRefine improves the quality of both backbone atoms and all-atom conformation of the initial structural models generated by AlphaFold. It also performs better than the state-of-the-art refinement methods in multiple evaluation metrics including an all-atom model quality score – the MolProbity score based on the analysis of all-atom contacts, bond length, atom clashes, torsion angles, and side-chain rotamers. As ATOMRefine can refine a protein structure quickly, it provides a viable, fast solution for improving protein geometry and fixing structural errors of predicted structures through direct coordinate refinement.