RNA target highlights in CASP15 : Evaluation of predicted models by structure providers

Self Cite

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.

Section: Resultsmentioning

confidence: 53%

Assessment of three‐dimensional RNA structure prediction in CASP15

Das,

Kretsch,

Simpkin

et al. 2023

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“…RNA structures as CASP targets. 14 One of these, R1138, was found to have two conformations (now PDB 7PTK and 7PTL, Figure 4):…”

Section: Rna Folding Intermediate R1138mentioning

confidence: 99%

“…They also generated 10 models from each of these maps. 14 For assessment purposes, the fit of each submitted structure to each of these 40 models was evaluated. Although this was generally a difficult target, one group succeeded in producing a computed model with reasonable fit to an experimental structure built from one of the maps (GDT_TS 51; TM 0.66).…”

Section: A Multi-conformational Rna An Example Of Defining An Experim...mentioning

confidence: 99%

Breaking the conformational ensemble barrier: Ensemble structure modeling challenges in CASP15

Kryshtafovych,

Montelione,

Rigden

et al. 2023

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For the first time, the 2022 CASP (Critical Assessment of Structure Prediction) community experiment included a section on computing multiple conformations for protein and RNA structures. There was full or partial success in reproducing the ensembles for four of the nine targets, an encouraging result. For protein structures, enhanced sampling with variations of the AlphaFold2 deep learning method was by far the most effective approach. One substantial conformational change caused by a single mutation across a complex interface was accurately reproduced. In two other assembly modeling cases, methods succeeded in sampling conformations near to the experimental ones even though environmental factors were not included in the calculations. An experimentally derived flexibility ensemble allowed a single accurate RNA structure model to be identified. Difficulties included how to handle sparse or low‐resolution experimental data and the current lack of effective methods for modeling RNA/protein complexes. However, these and other obstacles appear addressable.

“…Results for all these categories are summarized in this paper; other papers in this proteins issue describe detailed assessment results [9][10][11][12][13][14] and comments on the outcome by some of those contributing targets. 15,16 The issue also contains papers by selected participating groups. Some categories no longer seen as relevant were dropped: contact prediction which is now an integral part of deep learning methods, and refinement of initial models.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, computational methods should be able to reproduce multiple observed structural states. Results for all these categories are summarized in this paper; other papers in this proteins issue describe detailed assessment results 9–14 and comments on the outcome by some of those contributing targets 15,16 . The issue also contains papers by selected participating groups.…”

Section: Introductionmentioning

confidence: 99%

Critical assessment of methods of protein structure prediction (CASP)—Round XV

Kryshtafovych,

Schwede,

Topf

et al. 2023

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Computing protein structure from amino acid sequence information has been a long‐standing grand challenge. Critical assessment of structure prediction (CASP) conducts community experiments aimed at advancing solutions to this and related problems. Experiments are conducted every 2 years. The 2020 experiment (CASP14) saw major progress, with the second generation of deep learning methods delivering accuracy comparable with experiment for many single proteins. There is an expectation that these methods will have much wider application in computational structural biology. Here we summarize results from the most recent experiment, CASP15, in 2022, with an emphasis on new deep learning‐driven progress. Other papers in this special issue of proteins provide more detailed analysis. For single protein structures, the AlphaFold2 deep learning method is still superior to other approaches, but there are two points of note. First, although AlphaFold2 was the core of all the most successful methods, there was a wide variety of implementation and combination with other methods. Second, using the standard AlphaFold2 protocol and default parameters only produces the highest quality result for about two thirds of the targets, and more extensive sampling is required for the others. The major advance in this CASP is the enormous increase in the accuracy of computed protein complexes, achieved by the use of deep learning methods, although overall these do not fully match the performance for single proteins. Here too, AlphaFold2 based method perform best, and again more extensive sampling than the defaults is often required. Also of note are the encouraging early results on the use of deep learning to compute ensembles of macromolecular structures. Critically for the usability of computed structures, for both single proteins and protein complexes, deep learning derived estimates of both local and global accuracy are of high quality, however the estimates in interface regions are slightly less reliable. CASP15 also included computation of RNA structures for the first time. Here, the classical approaches produced better agreement with experiment than the new deep learning ones, and accuracy is limited. Also, for the first time, CASP included the computation of protein–ligand complexes, an area of special interest for drug design. Here too, classical methods were still superior to deep learning ones. Many new approaches were discussed at the CASP conference, and it is clear methods will continue to advance.