A further leap of improvement in tertiary structure prediction in CASP13 prompts new routes for future assessments.

We present our assessment of tertiary structure predictions for hard targets in Critical Assessment of Structure Prediction round 13 (CASP13). The analysis includes (a) assignment and discussion of best models through scores-aided visual inspection of models for each evaluation unit (EU); (b) ranking of predictors resulting from this evaluation and from global scores; and (c) evaluation of progress, state of the art, and current limitations of protein structure prediction. We witness a sizable improvement in tertiary structure prediction building on the progress observed from CASP11 to CASP12, with (a) top models reaching backbone RMSD <3 å for several EUs of size <150 residues, contributed by many groups; (b) at least one model that roughly captures global topology for all EUs, probably unprecedented in this track of CASP; and (c) even quite good models for full, unsplit targets. Better structure predictions are brought about mainly by improved residue-residue contact predictions, and since this CASP also by distance predictions, achieved through state-of-the-art machine learning methods which also progressed to work with slightly shallower alignments compared to CASP12. As we reach a new realm of tertiary structure prediction quality, new directions are proposed and explored for future CASPs: (a) dropping splitting into EUs, (b) rethinking difficulty metrics probably in terms of contact and distance predictions, (c) assessing also side chains for models of high backbone accuracy, and (d) assessing residue-wise and possibly residue-residue quality estimates.

Assessment of data-assisted prediction by inclusion of crosslinking/mass-spectrometry and small angle X-ray scattering data in the 12th Critical Assessment of protein Structure Prediction experiment.

Integrative modeling approaches attempt to combine experiments and computation to derive structure-function relationships in complex molecular assemblies. Despite their importance for the advancement of life sciences, benchmarking of existing methodologies is rather poor. The 12th round of the Critical Assessment of protein Structure Prediction (CASP) offered a unique niche to benchmark data and methods from two kinds of experiments often used in integrative modeling, namely residue-residue contacts obtained through crosslinking/mass-spectrometry (CLMS), and small-angle X-ray scattering (SAXS) experiments. Upon assessment of the models submitted by predictors for 3 targets assisted by CLMS data and 11 targets by SAXS data, we observed no significant improvement when compared to the best data-blind models, although most predictors did improve relative to their own data-blind predictions. Only for target Tx892 of the CLMS-assisted category and for target Ts947 of the SAXS-assisted category, there was a net, albeit mild, improvement relative to the best data-blind predictions. We discuss here possible reasons for the relatively poor success, which point rather to inconsistencies in the data sources rather than in the methods, to which a few groups were less sensitive. We conclude with suggestions that could improve the potential of data integration in future CASP rounds in terms of experimental data production, methods development, data management and prediction assessment.

Assessment of hard target modeling in CASP12 reveals an emerging role of alignment-based contact prediction methods.

We present our assessment of CASP12 modeling efforts for targets with no obvious templates of high sequence/structure similarity in the PDB, that is for evaluation units of the free modeling (FM) and free modeling/template-based modeling (FM/TBM) categories. Models were clustered and ranked using the Global Distance Test-Total Score and 5 additional metrics developed in previous CASP rounds, producing short lists of models that were subject to visual inspection in comparison to the target structures. The whole procedure was implemented as a web app that facilitates model selection and visual inspection, and could become useful to facilitate and standardize future assessments. We describe cases of (1) targets with remarkably good predictions, (2) targets whose models captured some global shape and topology features, and (3) targets for which models fail to capture even coarse features. We note that despite this CASP being among the most challenging ones, a measurable improvement of the top predictions is apparent, that we attribute to the emergence of accurate contact prediction methods and the increased number of available sequences. We also briefly discuss current limitations in tertiary structure prediction exemplified by CASP12 targets. Overall, the Baker, Zhang, and Lee manual groups and servers were identified as the top global performing groups.

Definition and classification of evaluation units for tertiary structure prediction in CASP12 facilitated through semi-automated metrics.

For assessment purposes, CASP targets are split into evaluation units. We herein present the official definition of CASP12 evaluation units (EUs) and their classification into difficulty categories. Each target can be evaluated as one EU (the whole target) or/and several EUs (separate structural domains or groups of structural domains). The specific scenario for a target split is determined by the domain organization of available templates, the difference in server performance on separate domains versus combination of the domains, and visual inspection. In the end, 71 targets were split into 96 EUs. Classification of the EUs into difficulty categories was done semi-automatically with the assistance of metrics provided by the Prediction Center. These metrics account for sequence and structural similarities of the EUs to potential structural templates from the Protein Data Bank, and for the baseline performance of automated server predictions. The metrics readily separate the 96 EUs into 38 EUs that should be straightforward for template-based modeling (TBM) and 39 that are expected to be hard for homology modeling and are thus left for free modeling (FM). The remaining 19 borderline evaluation units were dubbed FM/TBM, and were inspected case by case. The article also overviews structural and evolutionary features of selected targets relevant to our accompanying article presenting the assessment of FM and FM/TBM predictions, and overviews structural features of the hardest evaluation units from the FM category. We finally suggest improvements for the EU definition and classification procedures.

The importance of dynamics in integrative modeling of supramolecular assemblies.

Revealing the atomistic architecture of supramolecular complexes is a fundamental step toward a deeper understanding of cellular functioning. To date, this formidable task is facilitated by an emerging array of integrative modeling approaches that combine experimental data from different sources. One major challenge these methods have to face is the treatment of the dynamic rearrangements of the individual subunits upon assembly. While this flexibility can be sampled at different levels, integrating native dynamic determinants with available experimental inputs can provide an effective way to reveal the molecular recognition mechanisms at the basis of supramolecular assembly.