The whole is greater than its parts: ensembling improves protein contact prediction.

ABSTRACT: The prediction of amino acid contacts from protein sequence is an important problem, as protein contacts are a vital step towards the prediction of folded protein structures. We propose that a powerful concept from deep learning, called ensembling, can increase the accuracy of protein contact predictions by combining the outputs of different neural network models. We show that ensembling the predictions made by different groups at the recent Critical Assessment of Protein Structure Prediction (CASP13) outperforms all individual groups. Further, we show that contacts derived from the distance predictions of three additional deep neural networks-AlphaFold, trRosetta, and ProSPr-can be substantially improved by ensembling all three networks. We also show that ensembling these recent deep neur

SUBMITTER: Billings WM 

PROVIDER: S-EPMC8044223 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature