Experimentally Tuned Protein-RNA Rosetta Score Function using Bayesian Optimization

Abstract

Protein-RNA complexes drive fundamental cellular processes such as transcription and translation. Despite the prevalence and importance of protein-RNA interactions, the field lacks reliable and accessible methods to quantify the energetic favorability of these interactions. We propose an experimentally tuned protein-RNA score function that can be directly implemented into ROSETTA. Fine-tuning these score functions for predictive tasks requires repeated evaluations on a set of protein-RNA complexes, which can be computationally expensive given the number of parameters to tune. We used Bayesian Optimization to efficiently improve the energetic agreement between ROSETTA and experimentation. We observe significant interactions for specific RNA subclasses, serving as further confirmation of the physical validity of the score function. Beyond protein-RNA interaction prediction, we establish a framework to efficiently fine-tune ROSETTA score functions for any protein-class interaction using Bayesian Optimization. TOC FIGURE O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/739244v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@c7d3edorg.highwire.dtl.DTLVardef@12a8fe2org.highwire.dtl.DTLVardef@14c4c5dorg.highwire.dtl.DTLVardef@297169_HPS_FORMAT_FIGEXP M_FIG C_FIG