4 | CONCLUSIONS
A key objective of CASP is to monitor progress in predictive performance
on different kinds of target protein. Thus, a robust and objective
classification of targets is essential. Although previous classification
has benefitted from detailed consideration by experts in protein
evolution, the new, purely automatic method introduced here provides a
new labor-saving foundation for CASP-to-CASP comparisons. We show that
it largely recapitulates previous classifications and, furthermore, may
provide numerical estimates of difficulty beyond the current four
classes, potentially facilitating future study of features correlating
with target difficulty.
Much as a purely automatic division of targets into EUs would also be
desirable, the CASP15 set illustrate why that seems not yet to be
possible. For example, a satisfactory EU definition for the ABC
transporter T1158 was only achieved by manual reference to a set of
structures and an understating of the structure-function relationship of
the target: none of the automated domain partitioning algorithms
produced sensible results. Nevertheless, clear and objective guidelines
were followed as far as possible relating, for example, to the gradients
of the Grishin plots. Finally, it is worth noting that although
consistent policy is followed for EU definition, the resulting sets may
still differ from CASP to CASP as predictions improve. Thus, as more
groups accurately capture domain packing there will be fewer instances
of splitting and more where larger multi-domain units are retained as
the EUs: this tendency towards larger EUs could tend to depress global
quality metrics and should be borne in mind by future assessors.
Table 1. CASP15 tertiary structure prediction targets, their split into
evaluation units (EUs) and classification to homology-based prediction
classes. Canceled targets are highlighted in red; targets that were
released as auxiliary structures for other prediction categories
(ligand, oligo, protein-RNA complex) are in yellow.