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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]

Background and Aim: This study aimed to investigate the relationship between hepatic steatosis (HS) evaluated by biopsy and visceral adiposity assessed by computed tomography in lean living liver donor candidates and to determine the risk factors for lean non‐alcoholic fatty liver disease (NAFLD). Methods: This retrospective study included 250 lean (body mass index, < 23 kg/m2) potential living liver donors (mean age, 31.1 ± 8.6 years; 141 men) who had undergone liver biopsy and abdominal computed tomography between 2017 and 2018. Anthropometry, laboratory parameters, body composition, and the degree of HS were evaluated. Logistic regression was used to identify independent predictors of lean NAFLD. Results: The visceral fat area (VFA) was significantly correlated with the degree of HS in men (r = 0.408; P < 0.001) and women (r = 0.360; P < 0.001). The subcutaneous fat area was significantly correlated with the degree of HS in men (r = 0.398; P < 0.001), but not in women. The skeletal muscle area did not correlate with the degree of HS in either men or women. In the multivariable logistic regression analysis, the VFA (odds ratio [OR], 1.028; 95% confidence interval [CI], 1.013–1.044; P < 0.001) and subcutaneous fat area (OR, 1.016; 95% CI, 1.004–1.028; P = 0.009) were independent risk factors for lean NAFLD in men, and the VFA (OR, 1.036; 95% CI, 1.013–1.059; P = 0.002) was an independent risk factor for lean NAFLD in women. Conclusions: The severity of non‐alcoholic fatty liver was positively correlated with visceral fat accumulation in a lean Asian population. Visceral adiposity may be a risk factor for lean NAFLD in potential living liver donors. [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]

Many proteins need to form oligomers to be functional, so oligomer structures provide important clues to biological roles of proteins. Prediction of oligomer structures therefore can be a useful tool in the absence of experimentally resolved structures. In this article, we describe the server and human methods that we used to predict oligomer structures in the CASP13 experiment. Performances of the methods on the 42 CASP13 oligomer targets consisting of 30 homo‐oligomers and 12 hetero‐oligomers are discussed. Our server method, Seok‐assembly, generated models with interface contact similarity measure greater than 0.2 as model 1 for 11 homo‐oligomer targets when proper templates existed in the database. Model refinement methods such as loop modeling and molecular dynamics (MD)‐based overall refinement failed to improve model qualities when target proteins have domains not covered by templates or when chains have very small interfaces. In human predictions, additional experimental data such as low‐resolution electron microscopy (EM) map were utilized. EM data could assist oligomer structure prediction by providing a global shape of the complex structure. [ABSTRACT FROM AUTHOR]

Protein–protein docking methods are spotlighted for their roles in providing insights into protein–protein interactions in the absence of full structural information by experiment. GalaxyTongDock is an ab initio protein–protein docking web server that performs rigid‐body docking just like ZDOCK but with improved energy parameters. The energy parameters were trained by iterative docking and parameter search so that more native‐like structures are selected as top rankers. GalaxyTongDock performs asymmetric docking of two different proteins (GalaxyTongDock_A) and symmetric docking of homo‐oligomeric proteins with Cn and Dn symmetries (GalaxyTongDock_C and GalaxyTongDock_D). Performance tests on an unbound docking benchmark set for asymmetric docking and a model docking benchmark set for symmetric docking showed that GalaxyTongDock is better or comparable to other state‐of‐the‐art methods. Experimental and/or evolutionary information on binding interfaces can be easily incorporated by using block and interface options. GalaxyTongDock web server is freely available at http://galaxy.seoklab.org/tongdock. © 2019 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]