Assessment of three‐dimensional RNA structure prediction in CASP15

DOI: 10.1002/prot.26602

Authors: Rhiju Das (Stanford University School of Medicine; Stanford University) , Rachael C. Kretsch (Stanford University School of Medicine) , Adam J. Simpkin (The University of Liverpool) , Thomas Mulvaney (Leibniz-Institut für Virologie (LIV); University Medical Center Hamburg-Eppendorf (UKE)) , Phillip Pham (Stanford University School of Medicine) , Ramya Rangan (Stanford University School of Medicine) , Fan Bu (Guangzhou Laboratory; University of Science and Technology of China) , Ronan M. Keegan (The University of Liverpool; Diamond Light Source) , Maya Topf (Leibniz-Institut für Virologie (LIV);) , Daniel J. Rigden (The University of Liverpool) , Zhichao Miao (Guangzhou National Laboratory; Tongji University) , Eric Westhof (Institut de Biologie Moléculaire et Cellulaire du CNRS, Université de Strasbourg)
Co-authored by industrial partner: No

Type: Journal Paper
Journal: Proteins: Structure, Function, And Bioinformatics , VOL 147

State: Published (Approved)
Published: October 2023

Abstract: The prediction of RNA three-dimensional structures remains an unsolved problem. Here, we report assessments of RNA structure predictions in CASP15, the first CASP exercise that involved RNA structure modeling. Forty-two predictor groups submitted models for at least one of twelve RNA-containing targets. These models were evaluated by the RNA-Puzzles organizers and, separately, by a CASP-recruited team using metrics (GDT, lDDT) and approaches (Z-score rankings) initially developed for assessment of proteins and generalized here for RNA assessment. The two assessments independently ranked the same predictor groups as first (AIchemy_RNA2), second (Chen), and third (RNAPolis and GeneSilico, tied); predictions from deep learning approaches were significantly worse than these top ranked groups, which did not use deep learning. Further analyses based on direct comparison of predicted models to cryogenic electron microscopy (cryo-EM) maps and x-ray diffraction data support these rankings. With the exception of two RNA-protein complexes, models submitted by CASP15 groups correctly predicted the global fold of the RNA targets. Comparisons of CASP15 submissions to designed RNA nanostructures as well as molecular replacement trials highlight the potential utility of current RNA modeling approaches for RNA nanotechnology and structural biology, respectively. Nevertheless, challenges remain in modeling fine details such as noncanonical pairs, in ranking among submitted models, and in prediction of multiple structures resolved by cryo-EM or crystallography.

Journal Keywords: CASP15; conformational ensembles; cryogenic electron microscopy; deep learning; molecular replacement; ribonucleic acid; structure prediction

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Added On: 25/10/2023 09:43

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Proteins - 2023 - Das - Assessment of three‐dimensional RNA structure prediction in CASP15.pdf

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