De novo protein structure prediction using ultra-fast molecular dynamics simulation.

ABSTRACT: Modern genomics sequencing techniques have provided a massive amount of protein sequences, but experimental endeavor in determining protein structures is largely lagging far behind the vast and unexplored sequences. Apparently, computational biology is playing a more important role in protein structure prediction than ever. Here, we present a system of de novo predictor, termed NiDelta, building on a deep convolutional neural network and statistical potential enabling molecular dynamics simulation for modeling protein tertiary structure. Combining with evolutionary-based residue-contacts, the presented predictor can predict the tertiary structures of a number of target proteins with remarkable accuracy. The proposed approach is demonstrated by calculations on a set of eighteen large protei

SUBMITTER: Cheung NJ 

PROVIDER: S-EPMC6245515 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature