Improving protein structure prediction with multicom
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[EMBARGOED UNTIL 12/01/2025] Proteins play essential roles in biological processes and understanding their three-dimensional (3D) structures is vital for revealing their complex functions. This study addresses the challenge of accurately predicting protein tertiary and quaternary structures from amino acid sequences, which offers an efficient and cost- effective alternative to experimental methods such as X-ray crystallography and cryo-electron microscopy. Despite advancements in deep learning, including methods like AlphaFold2, challenges remain in predicting protein tertiary and quaternary structures and assessing model quality due to limited evolutionary data and complex inter-chain interactions in multimers. This research aims to enhance protein structure prediction by integrating template-based and template-free methods, improving AlphaFold2 for tertiary structure predictions, and enhancing AlphaFold-Multimer for complex quaternary structures. Other key contributions include exploring the use of AlphaFold3 to predict protein complex stoichiometry and developing GATE, a novel quality assessment tool based on graph transformers and pairwise similarity graphs, for more precise model selection from large pools of predicted tertiary structures and quaternary structures. Results from the recent Critical Assessment of Structure Prediction (CASP) experiments (e.g., CASP14, CASP15 and CASP16) demonstrate the impact of these advancements, and the study concludes with potential future directions for protein structure prediction and quality assessment improvements.
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Ph. D.