Abstract
Accurate prediction of atomic-level protein structure is important for annotating the biological functions of protein molecules and for designing new compounds to regulate the functions. Template-based modeling (TBM), which aims to construct structural models by copying and refining the structural frameworks of other known proteins, remains the most accurate method for protein structure prediction. Due to the difficulty in recognizing distant-homology templates, however, the accuracy of TBM decreases rapidly when the evolutionary relationship between the query and template vanishes. In this study, we propose a new method, CEthreader, which first predicts residue-residue contacts by coupling evolutionary precision matrices with deep residual convolutional neural-networks. The predicted contact maps are then integrated with sequence profile alignments to recognize structural templates from the PDB. The method was tested on two independent benchmark sets consisting collectively of 1,153 non-homologous protein targets, where CEthreader detected 176% or 36% more correct templates with a TM-score >0.5 than the best state-of-the-art profile- or contact-based threading methods, respectively, for the Hard targets that lacked homologous templates. Moreover, CEthreader was able to identify 114% or 20% more correct templates with the same Fold as the query, after excluding structures from the same SCOPe Superfamily, than the best profile- or contact-based threading methods. Detailed analyses show that the major advantage of CEthreader lies in the efficient coupling of contact maps with profile alignments, which helps recognize global fold of protein structures when the homologous relationship between the query and template is weak. These results demonstrate an efficient new strategy to combine ab initio contact map prediction with profile alignments to significantly improve the accuracy of template-based structure prediction, especially for distant-homology proteins.
Highlights
Given the rapidly increasing gap between the number of known protein sequences and the number of known structures, the demand for high-resolution, computer-based protein structure prediction has risen dramatically [1,2]
Previous studies have shown that the Protein Data Bank (PDB) library is complete for singledomain proteins and template-based modeling (TBM) is in principle sufficient to solve the structure prediction problem if the most similar structure in the PDB could be reliably identified and used as template for model reconstruction
The success of TBM depends on the availability of closely-homologous templates, where its accuracy and reliability decrease sharply when the evolutionary relationship between query and template becomes more distant
Summary
Given the rapidly increasing gap between the number of known protein sequences and the number of known structures, the demand for high-resolution, computer-based protein structure prediction has risen dramatically [1,2]. In an effort to recognize structure templates that have similar folds to the query, a variety of threading methods with different alignment scores and searching schemes have been developed in order to match the query sequence and template structures [8,11,12,13,14]. The success of these methods is, often limited to the targets that have closely-homologous templates in the PDB. There is evidence that the PDB library is complete and sufficient to solve the protein structure prediction problem [16], modeling of distantly-homologous proteins using TBM remains a major challenge in the field
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