Seq-SetNet: directly exploiting multiple sequence alignment for protein secondary structure prediction

Abstract

Motivation Accurate prediction of protein structure relies heavily on exploiting multiple sequence alignment (MSA) for residue mutations and correlations as this information specifies protein tertiary structure. The widely used prediction approaches usually transform MSA into inter-mediate models, say position-specific scoring matrix or profile hidden Markov model. These inter-mediate models, however, cannot fully represent residue mutations and correlations carried by MSA; hence, an effective way to directly exploit MSAs is highly desirable. Results Here, we report a novel sequence set network (called Seq-SetNet) to directly and effectively exploit MSA for protein structure prediction. Seq-SetNet uses an ‘encoding and aggregation’ strategy that consists of two key elements: (i) an encoding module that takes a component homologue in MSA as input, and encodes residue mutations and correlations into context-specific features for each residue; and (ii) an aggregation module to aggregate the features extracted from all component homologues, which are further transformed into structural properties for residues of the query protein. As Seq-SetNet encodes each homologue protein individually, it could consider both insertions and deletions, as well as long-distance correlations among residues, thus representing more information than the inter-mediate models. Moreover, the encoding module automatically learns effective features and thus avoids manual feature engineering. Using symmetric aggregation functions, Seq-SetNet processes the homologue proteins as a sequence set, making its prediction results invariable to the order of these proteins. On popular benchmark sets, we demonstrated the successful application of Seq-SetNet to predict secondary structure and torsion angles of residues with improved accuracy and efficiency. Availability and implementation The code and datasets are available through https://github.com/fusong-ju/Seq-SetNet. Supplementary information Supplementary data are available at Bioinformatics online.

Deep learning for protein secondary structure prediction: Pre and post-AlphaFold

“…AlphaFold has successfully used MSA instead of sequence profiles to produce accurate structure prediction. Another study by Ju et al [163] suggested the use of MSA as a direct input for the PSSP model concerning that sequence profiles derived from MSA (PSSM and HMM profiles) may not be able to represent residue mutations and correlations. The model, called Seq-SetNet, performed two stages, encoding and aggregation, to deduce the structural properties of each amino acid in the input sequence.…”