Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13.

ABSTRACT: Predicting residue-residue distance relationships (eg, contacts) has become the key direction to advance protein structure prediction since 2014 CASP11 experiment, while deep learning has revolutionized the technology for contact and distance distribution prediction since its debut in 2012 CASP10 experiment. During 2018 CASP13 experiment, we enhanced our MULTICOM protein structure prediction system with three major components: contact distance prediction based on deep convolutional neural networks, distance-driven template-free (ab initio) modeling, and protein model ranking empowered by deep learning and contact prediction. Our experiment demonstrates that contact distance prediction and deep learning methods are the key reasons that MULTICOM was ranked 3rd out of all 98 predictors in bot

SUBMITTER: Hou J 

PROVIDER: S-EPMC6800999 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature