Workshop: Machine Learning in Structural Biology

AutoFoldFinder: An Automated Adaptive Optimization Toolkit for De Novo Protein Fold Design

Shuhao Zhang · Youjun Xu · Jianfeng Pei · Luhua Lai


Although an explosive number of protein structures are revealed each year, the number of basic protein architecture - protein folds - stays stable. Because of the determining relationship between function and structure, it remains highly interesting to scientists to explore protein structure space and subsequently enrich the diversity of protein function space. Current protein structure exploration approaches either rely on sampling of natural protein fragments or require human-crafted constraints. To facilitate more emancipated structure space probing, we present an automated adaptive optimization toolkit for de novo protein fold design - AutoFoldFinder. We also further introduce CM-align to better quantify structure map similarity in the optimization process. Our results indicate a higher efficiency to produce novel yet biologically and physically meaningful folds compared with state-of-the-art methods, increasing novel fold reconstruction rate by 27.3%.

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