A protocol for single-sequence protein-RNA complex structure prediction using ProRNA3D-single

Abstract

Characterizing the interaction between protein and RNA is critically important for biology and medicine. Here, we present a protocol to predict three-dimensional protein-RNA complex structural models from their sequences using a deep learning-based approach named ProRNA3D-single. First, we delineate detailed steps for predicting protein-RNA interaction maps by leveraging pretrained language models. We then describe the procedure for generating the protein-RNA complex structure from the predicted interactions using optimization techniques. This protocol has potential applications in host-virus interactions and drug design. For complete details on the use and execution of this protocol, please refer to Roche et al. 1 • A protocol for generating three-dimensional protein-RNA complex structural models • Steps for preprocessing and feature generation • Instructions for executing the inference script and optimization algorithms Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Characterizing the interaction between protein and RNA is critically important for biology and medicine. Here, we present a protocol to predict three-dimensional protein-RNA complex structural models from their sequences using a deep learning-based approach named ProRNA3D-single. First, we delineate detailed steps for predicting protein-RNA interaction maps by leveraging pretrained language models. We then describe the procedure for generating the protein-RNA complex structure from the predicted interactions using optimization techniques. This protocol has potential applications in host-virus interactions and drug design.