Improving protein structure prediction using templates and sequence embedding.

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Abstract

Protein structure prediction has been greatly improved by deep learning, but the contribution of different information is yet to be fully understood. This paper studies the impacts of two kinds of information for structure prediction: template and MSA embedding. Templates have been used by some methods before, such as AlphaFold2, RoseTTAFold and RaptorX. AlphaFold2 and RosetTTAFold only used templates detected by HHsearch which may not perform very well on some targets. In addition, sequence embedding generated by pretrained protein language models has not been fully explored for structure prediction. In this paper, we study the impact of templates (including the number of templates, the template quality and how the templates are generated) on protein structure prediction accuracy, especially when the templates are detected by methods other than HHsearch. We also study the impact of sequence embedding (generated by MSATransformer and ESM-1b) on structure prediction.We have implemented a deep learning method for protein structure prediction that may take templates and MSA embedding as extra inputs. We study the contribution of templates and MSA embedding to structure prediction accuracy. Our experimental results show that templates can improve structure prediction on 71 of 110 CASP13 targets and 47 of 91 CASP14 targets, and templates are particularly useful for targets with similar templates. MSA embedding can improve structure prediction on 63 of 91 CASP14 targets and 87 of 183 CAMEO targets and is particularly useful for proteins with shallow MSAs. When both templates and MSA embedding are used, our method can predict correct folds (TMscore > 0.5) for 16 out of 23 CASP14 FM targets and 14 out of 18 CAMEO targets, outperforming RoseTTAFold by 5% and 7%, respectively.available at https://github.com/xluo233/RaptorXFold.Supplementary data are available at Bioinformatics online.© The Author(s) 2022. Published by Oxford University Press.