Analyzing effect of quadruple multiple sequence alignments on deep learning based protein inter-residue distance prediction.

ABSTRACT: Protein 3D structure prediction has advanced significantly in recent years due to improving contact prediction accuracy. This improvement has been largely due to deep learning approaches that predict inter-residue contacts and, more recently, distances using multiple sequence alignments (MSAs). In this work we present AttentiveDist, a novel approach that uses different MSAs generated with different E-values in a single model to increase the co-evolutionary information provided to the model. To determine the importance of each MSA's feature at the inter-residue level, we added an attention layer to the deep neural network. We show that combining four MSAs of different E-value cutoffs improved the model prediction performance as compared to single E-value MSA features. A further improvement

SUBMITTER: Jain A 

PROVIDER: S-EPMC8027171 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature