Why Can't We Predict RNA Structure At Atomic Resolution?

TL;DR

Current RNA structure prediction algorithms, including FARNA-FARF, face significant challenges in achieving atomic resolution due to sampling, reliance on experimental data, and coarse-grained models, necessitating community-wide evaluation efforts.

Contribution

The paper reviews existing RNA structure prediction methods, highlights their limitations, and proposes a community-wide CASP-style experiment to evaluate and improve future algorithms.

Findings

Current algorithms cannot reliably predict atomic-resolution RNA structures.

Sampling and experimental data reliance are major limitations.

A community-wide evaluation framework is proposed for progress assessment.

Abstract

No existing algorithm can start with arbitrary RNA sequences and return the precise, three-dimensional structures that ensures their biological function. This chapter outlines current algorithms for automated RNA structure prediction (including our own FARNA-FARFAR), highlights their successes, and dissects their limitations, using a tetraloop and the sarcin/ricin motif as examples. The barriers to future advances are considered in light of three particular challenges: improving computational sampling, reducing reliance on experimentally solved structures, and avoiding coarse-grained representations of atomic-level interactions. To help meet these challenges and better understand the current state of the field, we propose an ongoing community-wide CASP-style experiment for evaluating the performance of current structure prediction algorithms.