Protein Solvent-Accessibility Prediction by a Stacked Deep Bidirectional Recurrent Neural Network.

ABSTRACT: Residue solvent accessibility is closely related to the spatial arrangement and packing of residues. Predicting the solvent accessibility of a protein is an important step to understand its structure and function. In this work, we present a deep learning method to predict residue solvent accessibility, which is based on a stacked deep bidirectional recurrent neural network applied to sequence profiles. To capture more long-range sequence information, a merging operator was proposed when bidirectional information from hidden nodes was merged for outputs. Three types of merging operators were used in our improved model, with a long short-term memory network performing as a hidden computing node. The trained database was constructed from 7361 proteins extracted from the PISCES server using a

SUBMITTER: Zhang B 

PROVIDER: S-EPMC6023031 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature