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Prediction of protein–ligand binding poses is an essential component for understanding protein–ligand interactions and computer‐aided drug design. Various proteins involve prosthetic groups such as heme for their functions, and adequate consideration of the prosthetic groups is vital for protein–ligand docking. Here, we extend the GalaxyDock2 protein–ligand docking algorithm to handle ligand docking to heme proteins. Docking to heme proteins involves increased complexity because the interaction of heme iron and ligand has covalent nature. GalaxyDock2‐HEME, a new protein–ligand docking program for heme proteins, has been developed based on GalaxyDock2 by adding an orientation‐dependent scoring term to describe heme iron‐ligand coordination interaction. This new docking program performs better than other noncommercial docking programs such as EADock with MMBP, AutoDock Vina, PLANTS, LeDock, and GalaxyDock2 on a heme protein–ligand docking benchmark set in which ligands are known to bind iron. In addition, docking results on two other sets of heme protein–ligand complexes in which ligands do not bind iron show that GalaxyDock2‐HEME does not have a high bias toward iron binding compared to other docking programs. This implies that the new docking program can distinguish iron binders from noniron binders for heme proteins. [ABSTRACT FROM AUTHOR]

Proteins perform their functions by interacting with other biomolecules. For these interactions, proteins often form homo‐ or hetero‐oligomers as well. Thus, oligomer protein structures provide important clues regarding the biological roles of proteins. To this end, computational prediction of oligomer structures may be a useful tool in the absence of experimentally resolved structures. Here, we describe our server and human‐expert methods used to predict oligomer structures in the CASP14 experiment. Examples are provided for cases in which manual domain‐splitting led to improved oligomeric domain structures by ab initio docking, automated oligomer structure refinement led to improved subunit orientation and terminal structure, and manual oligomer modeling utilizing literature information generated a reasonable oligomer model. We also discussed the results of post‐prediction docking calculations with AlphaFold2 monomers as input in comparison to our blind prediction results. Overall, ab initio docking of AlphaFold2 models did not lead to better oligomer structure prediction, which may be attributed to the interfacial structural difference between the AlphaFold2 monomer structures and the crystal oligomer structures. This result poses a next‐stage challenge in oligomer structure prediction after the success of AlphaFold2. For successful protein assembly structure prediction, a different approach that exploits further evolutionary information on the interface and/or flexible docking taking the interfacial conformational flexibilities of subunit structures into account is needed. [ABSTRACT FROM AUTHOR]

We participated in CARPI rounds 38‐45 both as a server predictor and a human predictor. These CAPRI rounds provided excellent opportunities for testing prediction methods for three classes of protein interactions, that is, protein‐protein, protein‐peptide, and protein‐oligosaccharide interactions. Both template‐based methods (GalaxyTBM for monomer protein, GalaxyHomomer for homo‐oligomer protein, GalaxyPepDock for protein‐peptide complex) and ab initio docking methods (GalaxyTongDock and GalaxyPPDock for protein oligomer, GalaxyPepDock‐ab‐initio for protein‐peptide complex, GalaxyDock2 and Galaxy7TM for protein‐oligosaccharide complex) have been tested. Template‐based methods depend heavily on the availability of proper templates and template‐target similarity, and template‐target difference is responsible for inaccuracy of template‐based models. Inaccurate template‐based models could be improved by our structure refinement and loop modeling methods based on physics‐based energy optimization (GalaxyRefineComplex and GalaxyLoop) for several CAPRI targets. Current ab initio docking methods require accurate protein structures as input. Small conformational changes from input structure could be accounted for by our docking methods, producing one of the best models for several CAPRI targets. However, predicting large conformational changes involving protein backbone is still challenging, and full exploration of physics‐based methods for such problems is still to come. [ABSTRACT FROM AUTHOR]

Predicting conformational changes of both the protein and the ligand is a major challenge when a protein–ligand complex structure is predicted from the unbound protein and ligand structures. Herein, we introduce a new protein–ligand docking program called GalaxyDock3 that considers the full ligand conformational flexibility by explicitly sampling the ligand ring conformation and allowing the relaxation of the full ligand degrees of freedom, including bond angles and lengths. This method is based on the previous version (GalaxyDock2) which performs the global optimization of a designed score function. Ligand ring conformation is sampled from a ring conformation library constructed from structure databases. The GalaxyDock3 score function was trained with an additional bonded energy term for the ligand on a large set of complex structures. The performance of GalaxyDock3 was improved compared to GalaxyDock2 when predicted ligand conformation was used as the input for docking, especially when the input ligand conformation differs significantly from the crystal conformation. GalaxyDock3 also compared favorably with other available docking programs on two benchmark tests that contained diverse ligand rings. The program is freely available at http://galaxy.seoklab.org/softwares/galaxydock.html. © 2019 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]