A General Framework to Learn Tertiary Structure for Protein Sequence Characterization.

ABSTRACT: During the past five years, deep-learning algorithms have enabled ground-breaking progress towards the prediction of tertiary structure from a protein sequence. Very recently, we developed SAdLSA, a new computational algorithm for protein sequence comparison via deep-learning of protein structural alignments. SAdLSA shows significant improvement over established sequence alignment methods. In this contribution, we show that SAdLSA provides a general machine-learning framework for structurally characterizing protein sequences. By aligning a protein sequence against itself, SAdLSA generates a fold distogram for the input sequence, including challenging cases whose structural folds were not present in the training set. About 70% of the predicted distograms are statistically significant. Altho

SUBMITTER: Gao M 

PROVIDER: S-EPMC8301223 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature