Abstract
Protein-RNA interactions play important roles in many biological processes. Given the high cost and technique difficulties in experimental methods, computationally predicting the binding complexes from individual protein and RNA structures is pressingly needed, in which a reliable scoring function is one of the critical components. Here, we have developed a knowledge-based scoring function, referred to as ITScore-PR, for protein-RNA binding mode prediction by using a statistical mechanics-based iterative method. The pairwise distance-dependent atomic interaction potentials of ITScore-PR were derived from experimentally determined protein–RNA complex structures. For validation, we have compared ITScore-PR with 10 other scoring methods on four diverse test sets. For bound docking, ITScore-PR achieved a success rate of up to 86% if the top prediction was considered and up to 94% if the top 10 predictions were considered, respectively. For truly unbound docking, the respective success rates of ITScore-PR were up to 24 and 46%. ITScore-PR can be used stand-alone or easily implemented in other docking programs for protein–RNA recognition.
Highlights
Because of the importance of protein–RNA interactions on fundamental biological processes such as protein synthesis, DNA replication and repair, regulation of gene expression and defence against pathogens [1,2,3,4,5,6,7,8], determination of 3D protein–RNA complex structures would be valuable to understand the underlying recognition mechanisms at the atomic level [9,10,11,12,13,14]
We have developed a scoring function based on the atomic distance-dependent potentials derived from known protein–RNA structures, referred to as ITScorePR, for predicting protein–RNA complex structures from individual unbound protein/RNA structures
The crystal structure with the best resolution in each cluster was selected as a representative. These 175 protein–RNA complex structures were found to have some overlap with the protein–RNA docking benchmark 1.0 that we developed recently [49]
Summary
Because of the importance of protein–RNA interactions on fundamental biological processes such as protein synthesis, DNA replication and repair, regulation of gene expression and defence against pathogens [1,2,3,4,5,6,7,8], determination of 3D protein–RNA complex structures would be valuable to understand the underlying recognition mechanisms at the atomic level [9,10,11,12,13,14]. Molecular docking for protein–protein recognition has been developed for more than one decade [23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41], the protein–RNA docking field is still in infancy and has received attentions only recently, partially motivated by the protein–RNA example in the Critical Assessment of PRedicted Interactions (CAPRI) experiments [42]. It is more challenging to predict conformational changes in RNA molecules than in proteins on binding because of the aforementioned less correlation between RNA sequences and structures
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