CASP15 cryo‐EM protein and RNA targets: Refinement and analysis using experimental maps

Abstract

CASP assessments primarily rely on comparing predicted coordinates with experimental reference structures. However, experimental structures by their nature are only models themselves—their construction involves a certain degree of subjectivity in interpreting density maps and translating them to atomic coordinates. Here, we directly utilized density maps to evaluate the predictions by employing a method for ranking the quality of protein chain predictions based on their fit into the experimental density. The fit‐based ranking was found to correlate well with the CASP assessment scores. Overall, the evaluation against the density map indicated that the models are of high accuracy, and occasionally even better than the reference structure in some regions of the model. Local assessment of predicted side chains in a 1.52 Å resolution map showed that side‐chains are sometimes poorly positioned. Additionally, the top 118 predictions associated with 9 protein target reference structures were selected for automated refinement, in addition to the top 40 predictions for 11 RNA targets. For both proteins and RNA, the refinement of CASP15 predictions resulted in structures that are close to the reference target structure. This refinement was successful despite large conformational changes often being required, showing that predictions from CASP‐assessed methods could serve as a good starting point for building atomic models in cryo‐EM maps for both proteins and RNA. Loop modeling continued to pose a challenge for predictors, and together with the lack of consensus amongst models in these regions suggests that modeling, in combination with model‐fit to the density, holds the potential for identifying more flexible regions within the structure.

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

“…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). Direct comparison of this model to the corresponding electron density map produces a very high SMOC score 22 of 0.87, just slightly below the 0.89 score of the experimental structure refined in the map. Consistent with this, the local fit of the model is also very good except for the termini and an 11-nucleotide region in one of the loops (residues 40-51).…”

“…For this target, it was possible to further refine some submitted models into specific conformation density maps so that they rival the reference structure. 22 Some types of ensemble were still beyond the state of the computational art this CASP. For the protein/RNA complex, new methods such as RosettaFold2…”

“…In another, it appears that to robustly achieve full sampling more extensive computation than standard would be required, and in the absence of the environmental fac- It is also possible to compare electron density implied by computed structures directly with maps derived from experiment, and this has already been explored in previous CASPs 29,30 as well as the current one. 22 In this CASP, the highly flexible RNA target R1156 was represented by 10 experimental atomic structures for each of four election density maps, 40 co-ordinate sets in all. A metric of electron density fit (SMOC 31 ) shows a similar ranking of overall model quality as the coordinate comparisons, but the map comparison provides useful insight into the local experiment/calculation mismatches.…”

Cryo-EM structure and B-factor refinement with ensemble representation

“…In a previous study, we developed a method that combined density-guided-refinement (which is similar to MDFF 40 ), with the hierarchical application of rigid-body restraints calculated using RIBFIND2 (version 2.0). This method was able to correct large structural changes in RNA complexes 41 – 43 . We applied this method to the refinements of Atm1 and were able to successfully refine the model from the closed conformation into the open cryo-EM map (Supplementary Fig.…”

A Reversible Spherical Geometric Conversion of Protein Backbone Structure Coordinate Matrix to Three Independent Vectors of ρ, θ and φ

“…In particular, the key for machine learning-based protein structure prediction is the extraction of essential structural features from protein data, which serve as the basis for predicting the threedimensional arrangement of atoms in a protein molecule [44][45][46][47][48][49][50][51][52][53]. Take AlphaFold for example, which is an artificial intelligence system developed by DeepMind, a subsidiary of Alphabet Inc. (Google's parent company).…”