RMalign: an RNA structural alignment tool based on a size independent scoring function

RNA-protein 3D complex structure prediction is still challenging. Recently, a template-based approach PRIME is proposed in our team to build RNA-protein complex 3D structure models with a higher success rate than computational docking software. However, scoring function of RNA alignment algorithm SARA in PRIME is size-dependent, which limits its ability to detect templates in some cases. Herein, we developed a novel RNA 3D structural alignment approach RMalign, which is based on a size-independent scoring function RMscore. The parameter in RMscore is then optimized in randomly selected RNA pairs and phase transition points (from dissimilar to similar) are determined in another randomly selected RNA pairs. In tRNA benchmarking, the precision of RMscore is higher than that of SARAscore (0.8771 and 0.7766, respectively) with phase transition points. In balance-FSCOR benchmarking, RMalign performed as good as ESA-RNA with a non-normalized score measuring RNA structure similarity. In balance-x-FSCOR benchmarking, RMalign achieves much better than a state-of-the-art RNA 3D structural alignment approach SARA due to a size-independent scoring function. Taking the advantage of RMalign, we update our RNA-protein modeling approach PRIME to version 2.0. The PRIME2.0 significantly improves about 10% success rate than PRIME.\n\nAuthor summaryRNA structures are important for RNA functions. With the increasing of RNA structures in PDB, RNA 3D structure alignment approaches have been developed. However, the scoring function which is used for measuring RNA structural similarity is still length dependent. This shortcoming limits its ability to detect RNA structure templates in modeling RNA structure or RNA-protein 3D complex structure. Thus, we developed a length independent scoring function RMscore to enhance the ability to detect RNA structure homologs. The benchmarking data shows that RMscore can distinct the similar and dissimilar RNA structure effectively. RMscore should be a useful scoring function in modeling RNA structures for the biological community. Based on RMscore, we develop an RNA 3D structure alignment RMalign. In both RNA structure and function classification benchmarking, RMalign obtains as good as or even better performance than the state-of-the-art approaches. With a length independent scoring function RMscore, RMalign should be useful for the modeling RNA structures. Based on above results, we update PRIME to PRIME2.0. We provide a more accurate RNA-protein 3D complex structure modeling tool PRIME2.0 which should be useful for the biological community.