Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterized proteins.

ABSTRACT: Deep learning-based prediction of protein structure usually begins by constructing a multiple sequence alignment (MSA) containing homologs of the target protein. The most successful approaches combine large feature sets derived from MSAs, and considerable computational effort is spent deriving these input features. We present a method that greatly reduces the amount of preprocessing required for a target MSA, while producing main chain coordinates as a direct output of a deep neural network. The network makes use of just three recurrent networks and a stack of residual convolutional layers, making the predictor very fast to run, and easy to install and use. Our approach constructs a directly learned representation of the sequences in an MSA, starting from a one-hot encoding of the sequence

SUBMITTER: Kandathil SM 

PROVIDER: S-EPMC8795500 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature