ProALIGN: Directly learning alignments for protein structure prediction via exploiting context-specific alignment motifs

Abstract

Template-based modeling (TBM), including homology modeling and protein threading, is one of the most reliable techniques for protein structure prediction. It predicts protein structure by building an alignment between the query sequence under prediction and the templates with solved structures. However, it is still very challenging to build the optimal sequence-template alignment, especially when only distantly-related templates are available. Here we report a novel deep learning approach ProALIGN that can predict much more accurate sequence-template alignment. Like protein sequences consisting of sequence motifs, protein alignments are also composed of frequently-occurring alignment motifs with characteristic patterns. Alignment motifs are context-specific as their characteristic patterns are tightly related to sequence contexts of the aligned regions. Inspired by this observation, we represent a protein alignment as a binary matrix (in which 1 denotes an aligned residue pair) and then use a deep convolutional neural network to predict the optimal alignment from the query protein and its template. The trained neural network implicitly but effectively encodes an alignment scoring function, which reduces inaccuracies in the handcrafted scoring functions widely used by the current threading approaches. For a query protein and a template, we apply the neural network to directly infer likelihoods of all possible residue pairs in their entirety, which could effectively consider the correlations among multiple residues. We further construct the alignment with maximum likelihood, and finally build structure model according to the alignment.Tested on three independent datasets with in total 6,688 protein alignment targets and 80 CASP13 TBM targets, our method achieved much better alignments and 3D structure models than the existing methods including HHpred, CNFpred, CEthreader and DeepThreader. These results clearly demonstrate the effectiveness of exploiting the context-specific alignment motifs by deep learning for protein threading.

Protein sequence‐to‐structure learning: Is this the end(‐to‐end revolution)?

“…NDThreader [77] and ProALIGN [78] are specifically designed to optimally align the query with the template in template-based modeling. Both methods exploit predicted or observed inter-residue distances to improve the sequence alignments, a strategy that proved powerful already in CASP13 [72,79,80].…”

A tale of solving two computational challenges in protein science: neoantigen prediction and protein structure prediction

“…Lately, several deep learning models (e.g. ResNet) have been developed to substantially improve sequence-template alignment for remotely similar templates [ 74 , 103 , 117 ]. ResNet-predicted contact and distance have also been used to improve sequence-template alignment [ 74 , 103 , 117–119 ].…”

“…ResNet) have been developed to substantially improve sequence-template alignment for remotely similar templates [ 74 , 103 , 117 ]. ResNet-predicted contact and distance have also been used to improve sequence-template alignment [ 74 , 103 , 117–119 ]. With very similar templates, traditional methods such as HHblits [ 73 ] and CNFpred [ 120 ] may already perform well on sequence-template alignment and thus deep learning is not essential for this step.…”

Protein sequence-to-structure learning: Is this the end(-to-end revolution)?

“…NDThreader [61] and ProALIGN [62] are specifically designed to optimally align the query with the template in template-based modeling. Both methods exploit predicted or observed inter-residue distances to improve the sequence alignments, a strategy that proved powerful already in CASP13 [56,63,64].…”