Scoring model structure is an essential component of protein structure prediction that can affect the prediction accuracy tremendously. Users of protein structure prediction results also need to score models to select the best models for their application studies. In Critical Assessment of techniques for protein Structure Prediction (CASP), model accuracy estimation methods have been tested in a blind fashion by providing models submitted by the tertiary structure prediction servers for scoring. In CASP13, model accuracy estimation results were evaluated in terms of both global and local structure accuracy. Global structure accuracy estimation was evaluated by the quality of the models selected by the global structure scores and by the absolute estimates of the global scores. Residue‐wise, local structure accuracy estimations were evaluated by three different measures. A new measure introduced in CASP13 evaluates the ability to predict inaccurately modeled regions that may be improved by refinement. An intensive comparative analysis on CASP13 and the previous CASPs revealed that the tertiary structure models generated by the CASP13 servers show very distinct features. Higher consensus toward models of higher global accuracy appeared even for free modeling targets, and many models of high global accuracy were not well optimized at the atomic level. This is related to the new technology in CASP13, deep learning for tertiary contact prediction. The tertiary model structures generated by deep learning pose a new challenge for EMA (estimation of model accuracy) method developers. Model accuracy estimation itself is also an area where deep learning can potentially have an impact, although current EMA methods have not fully explored that direction.

In CASP, blind testing of model accuracy estimation methods has been conducted on models submitted by tertiary structure prediction servers. In CASP14, model accuracy estimation results were evaluated in terms of both global and local structure accuracy, as in the previous CASPs. Unlike the previous CASPs that did not show pronounced improvements in performance, the best single-model method (from the Baker group) showed an improved performance in CASP14, particularly in evaluating global structure accuracy when compared to both the best single-model methods in previous CASPs and the best multi-model methods in the current CASP. Although the CASP14 experiment on model accuracy estimation did not deal with the structures generated by AlphaFold2, new challenges that have arisen due to the success of AlphaFold2 are discussed. K E Y W O R D S CASP14 assessment, estimation of protein model accuracy, protein model quality assessment, protein structure prediction 1 | INTRODUCTION Estimating the accuracy of a protein model structure, or model quality assessment, is a crucial part of protein structure prediction and a gateway to proper usage of models in biomedical applications. Estimation of model accuracy (EMA, a.k.a. QA) has been a prediction category in CASP (Critical Assessment of techniques for protein Structure Prediction) since 2006. 1-7 The CASP Prediction Center has been providing a platform for evaluating EMA methods based on the protein model structures submitted by tertiary structure (TS) prediction servers.The success of AlphaFold2 (AF2) in predicting the threedimensional structures of single protein chains in CASP14 8-10 raises questions about the future role of EMA. Unfortunately, EMA methods were not tested on AF2 models in the regular season of CASP14 because AlphaFold2 was not registered as an automated server, but rather as a TS human group, and only TS server models were released for accuracy estimation. In CASP, human groups are given a longer deadline of 3 weeks (rather than 3 days allotted for automatic servers) and allowed to incorporate human intuition in various modeling steps such as in initial domain splitting or final model selection. In the CASP-COVID session, models submitted by AlphaFold (not identical to AlphaFold2) were evaluated along with lower-quality models by EMA methods for one target in a blind fashion and for three targets in a post-experiment 19 .In CASP14, the EMA methods were assessed in a manner similar to that used in the previous CASPs, using the same metrics. It is notable that progress from CASP13 was observed for single-model EMA methods in terms of selecting the top models. By definition, singlemodel methods evaluate models without taking advantage of information on other server model structures, unlike multi-model methods, which use consensus. The best EMA method in CASP14 was from the Baker group, 11 and it also performed better than other methods in CASP-COVID 19 .When models are accurate, the choice of evaluation measure, such as GDT-TS 12 or LDDT, 13 is less importa...

We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were

The 3D structure of a protein can be predicted from its amino acid sequence with high accuracy for a large fraction of cases because of the availability of large quantities of experimental data and the advance of computational algorithms. Recently, deep learning methods exploiting the coevolution information obtained by comparing related protein sequences have been successfully used to generate highly accurate model structures even in the absence of template structure information. However, structures predicted based on either template structures or related sequences require further improvement in regions for which information is missing. Refining a predicted protein structure with insufficient information on certain regions is critical because these regions may be connected to functional specificity that is not conserved among related proteins. The GalaxyRefine2 web server, freely available via http://galaxy.seoklab.org/refine2, is an upgraded version of the GalaxyRefine protein structure refinement server and reflects recent developments successfully tested through CASP blind prediction experiments. This method adopts an iterative optimization approach involving various structure move sets to refine both local and global structures. The estimation of local error and hybridization of available homolog structures are also employed for effective conformation search.

Critical Assessment of Structure Prediction (CASP) is an organization aimed at advancing the state of the art in computing protein structure from sequence. In the spring of 2020, CASP launched a community project to compute the structures of the most structurally challenging proteins coded for in the SARS-CoV-2 genome.Forty-seven research groups submitted over 3000 three-dimensional models and 700 sets of accuracy estimates on 10 proteins. The resulting models were released to the public. CASP community members also worked together to provide estimates of local and global accuracy and identify structure-based domain boundaries for some proteins. Subsequently, two of these structures (ORF3a and ORF8) have been solved experimentally, allowing assessment of both model quality and the accuracy estimates. Models from the AlphaFold2 group were found to have good agreement with the experimental structures, with main chain GDT_TS accuracy scores ranging from 63 (a correct topology) to 87 (competitive with experiment).