Assessment of data-assisted prediction by inclusion of crosslinking/mass-spectrometry and small angle X-ray scattering data in the 12 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 12 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.