Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment.

We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2-Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.

A graph theoretical approach for analysis of protein flexibility change at protein complex formation.

Hitherto analyses of protein complexes are frequently confined to the changes in the interface of the protein subunits undergoing interaction, while the holistic picture of the protein monomers' structure transformation, or the pervasive rigidity adopted by the newly formed complex are most often than not improperly evaluated in spite of the multiple and deep insights that they can yield about the interaction process itself at the molecular level, or at the higher level of genomic functional analyses for which relevant systems biological information can be obtained. To address this aspect of protein-protein interaction we propose in this work a newly developed algorithm that is based on graph theoretical instances and makes possible the evaluation of the changes in the flexibility of the interacting molecules and the rigidity adopted at complex formation. Since one can also figure out the opposite process, i.e. that in which the complex decomposes into its constituent subunits, each of which may accomplish another vital role in the organism, the methodology proposed here is also able to address such problem. The algorithm we propose performs a rigidity and/or flexibility evaluation of every node (atom) on the network constituted by the entire set of intra and inter-molecular inter-atomic interactions. Comparison of flexible or rigid molecular regions or domains within the complex with those in the respective isolated monomers leads to quantification of the loss (or gain) in the number of degrees of freedom at complex formation and their effects on protein complex formation mechanisms. This index is also valuable in the identification of collective motions within the protein that may play a critical role in the process of complex formation, and the influences they may have in the behavior and function of the complex (as well as the subunits constituting it) within the organism. Furthermore, the methodology, embedded in protein docking algorithms allows the development of a framework for categorizing and ranking decoys output by broadly used grid scoring type algorithms, one of which is the system for protein-protein interaction system MIAX that has been under continuous development in recent years.