Remote Template Detection Algorithm and Its Application in Protein Structure Prediction
In the development process from traditional force field-driven protein structure prediction to current data-driven AI structure modeling,protein structure template detection is a key module in protein structure prediction,and how to detect high-precision protein structure remote templates is important to improve the prediction accuracy of structures.In this paper,a remote homology template detection algorithm ASEalign based on adaptive eigenvector extraction is proposed.Firstly,a deep learning technique of multi-feature information fusion is used to predict protein contact maps.Then,a multi-dimensional feature scoring function is designed to fuse contact maps,secondary structures,sequence profiles-profiles alignment and solvent accessibility,and the eigenvalue and eigenvector in the contact map matrix extracted by adaptive template alignment is performed.Finally,the de-tected high-quality templates are input to AlphaFold2 for structural modeling.Results on the test set of 135 proteins indicate that,compared to HHsearch,ASEalign improves the accuracy by 11.5％.Meanwhile,its accuracy of modeled structure is better than that of AlphaFold2.
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