A Multitask Deep-Learning Method for Predicting Membrane Associations and Secondary Structures of Proteins.

ABSTRACT: Prediction of residue-level structural attributes and protein-level structural classes helps model protein tertiary structures and understand protein functions. Existing methods are either specialized on only one class of proteins or developed to predict only a specific type of residue-level attribute. In this work, we develop a new deep-learning method, named Membrane Association and Secondary Structure Predictor (MASSP), for accurately predicting both residue-level structural attributes (secondary structure, location, orientation, and topology) and protein-level structural classes (bitopic, α-helical, β-barrel, and soluble). MASSP integrates a multilayer two-dimensional convolutional neural network (2D-CNN) with a long short-term memory (LSTM) neural network into a multitasking framework

SUBMITTER: Li B 

PROVIDER: S-EPMC8650144 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature