The CASP16 experiment provided the first opportunity to benchmark AlphaFold3. In contrast to AlphaFold2, AlphaFold3 can predict the structure of non-protein molecules, and according to the benchmark presented by the developers, it should perform slightly better than AlphaFold2 for proteins. In this study, we assess the performance of AlphaFold3 using both automatic server submissions and manual predictions from the Elofsson group. All predictions were generated via the AlphaFold3 web server, with manual interventions applied to large targets and ligands. Compared to AlphaFold2-based methods, we found that AlphaFold3 performs slightly better for protein complexes. However, when massive sampling is applied to AlphaFold2, the difference disappears. It was also noted that in the official ranking from CASP, AlphaFold3 performs better than AlphaFold2 for easier targets, but not for harder targets. Further, the performance of the AlphaFold3 server is comparable to the best methods when taking the top-ranked predictions into account, but slightly behind when examining the best out of the five submitted models. Here, there exist targets where AlphaFold3 makes a good prediction and the top-ranked method failed, and vice-versa, indicating that a venue for progress could be to develop better strategies for identifying the best model. When using AlphaFold3 to predict the stoichiometry of larger protein complexes, the accuracy is limited, especially for heteromeric targets. When analyzing the predictions including nucleic acids, it was found that, in general, the accuracy is relatively low, but the AlphaFold3 performance was not far behind the top-ranked method. In summary, AlphaFold3 provides an easy-to-use method that offers close to state-of-the-art predictions in all categories of CASP.

The Critical Assessment of Structure Prediction (CASP) brings together a diverse group of scientists, from deep learning experts to NMR specialists, all aimed at developing accurate prediction algorithms that can effectively characterize the structural aspects of biomolecules relevant to their functions. Engagement within the CASP community has traditionally been limited to the prediction season and the conference, with limited discourse in the 1.5 years between CASP seasons. CASP special interest groups (SIGs) were established in 2023 to encourage continuous dialogue within the community. The online seminar series have drawn global participation from across disciplines and career stages. This has facilitated cross-disciplinary discussions fostering collaborations. The archives of these seminars have become a vital learning tool for newcomers to the field, lowering the barrier to entry.

Scoring docking solutions is a difficult task, and many methods have been developed for this purpose. In docking, only a handful of the hundreds of thousands of models generated by docking algorithms are acceptable, causing difficulties when developing scoring functions. Today’s best scoring functions can significantly increase the number of top-ranked models but still fails for most targets. Here, we examine the possibility of utilising predicted residues on a protein-protein interface to score docking models generated during the scan stage of a docking algorithm. Many methods have been developed to infer the portions of a protein surface that interact with another protein, but most have not been benchmarked using docking algorithms. Different interface prediction methods are systematically tested for scoring >300.000 low-resolution rigid-body template free docking decoys. Overall we find that BIPSPI is the best method to identify interface amino acids and score docking solutions. Further, using BIPSPI provides better docking results than state of the art scoring functions, with >12% of first ranked docking models being acceptable. Additional experiments indicated precision as a high-importance metric when estimating interface prediction quality, focusing on docking constraints production. We also discussed several limitations for the adoption of interface predictions as constraints in a docking protocol.