2.4 | Ensembles of alternative conformations.
Following the success of deep-learning methods for single structures, it is increasingly important to assess methods for predicting ensembles of alternative conformations. While deep learning and other methods have the potential to generate ensembles in some circumstances, these abilities have never been rigorously tested. In CASP15, we made a first attempt to include this category. For CASP purposes, we categorize ensembles 8 as: (1) cases where a macromolecule populates multiple conformations under the same environmental and chemical conditions (including intrinsically disordered proteins or parts of proteins; vibrational motion; local alternative conformations; ‘ghost’ conformations which are present at low level but are dominant in other conditions; and folding intermediates.). (2) Cases where a macromolecule adopts different conformations in response to environment or chemical change (ligand binding; macromolecular complex formation; post-translational modification; mutations; and crystal, pH and other environmental changes). A third category of ensembles we consider is the set of conformations consistent with the experimental data. The latter is an increasingly important category both because of the now common high accuracy of the computed structures and the inclusion of lower resolution data in CASP.
Targets for alternative conformers do not require separate prediction formats as they are 3D structures routinely processed in CASP, but they do require a mechanism for submitting multiple models. In CASP15, this need was handled in two different ways. In some cases, different alternative conformations were treated as separate targets. In particular,
In other cases, participants were encouraged to submit multiple conformers using the standard CASP five models target format. This approach was used for