Systematic benchmarking of deep-learning methods for tertiary RNA structure prediction.

ABSTRACT: The 3D structure of RNA critically influences its functionality, and understanding this structure is vital for deciphering RNA biology. Experimental methods for determining RNA structures are labour-intensive, expensive, and time-consuming. Computational approaches have emerged as valuable tools, leveraging physics-based-principles and machine learning to predict RNA structures rapidly. Despite advancements, the accuracy of computational methods remains modest, especially when compared to protein structure prediction. Deep learning methods, while successful in protein structure prediction, have shown some promise for RNA structure prediction as well, but face unique challenges. This study systematically benchmarks state-of-the-art deep learning methods for RNA structure prediction across d

SUBMITTER: Bahai A 

PROVIDER: S-EPMC11723642 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature