Individually double minimum-distance definition of protein-RNA binding residues and application to structure-based prediction.

Identifying protein-RNA binding residues is essential for understanding the mechanism of protein-RNA interactions. So far, rigid distance thresholds are commonly used to define protein-RNA binding residues. However, after investigating 182 non-redundant protein-RNA complexes, we find that it would be unsuitable for a certain amount of complexes since the distances between proteins and RNAs vary widely. In this work, a novel definition method was proposed based on a flexible distance cutoff. This method can fully consider the individual differences among complexes by setting a variable tolerance limit of protein-RNA interactions, i.e. the double minimum-distance by which different distance thresholds are achieved for different complexes. In order to validate our method, a comprehensive comparison between our flexible method and traditional rigid methods was implemented in terms of interface structure, amino acid composition, interface area and interaction force, etc. The results indicate that this method is more reasonable because it incorporates the specificity of different complexes by extracting the important residues lost by rigid distance methods and discarding some redundant residues. Finally, to further test our double minimum-distance definition strategy, we developed a classifier to predict those binding sites derived from our new method by using structural features and a random forest machine learning algorithm. The model achieved a satisfactory prediction performance and the accuracy on independent data sets reaches to 85.0%. To the best of our knowledge, it is the first prediction model to define positive and negative samples using a flexible cutoff. So the comparison analysis and modeling results have demonstrated that our method would be a very promising strategy for more precisely defining protein-RNA binding sites.