Department of Chemistry, Seoul National University, Seoul, Republic of Korea.
Proteins. 2020 Aug;88(8):1009-1017. doi: 10.1002/prot.25859. Epub 2019 Dec 10.
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.
我们作为服务器预测器和人类预测器参与了 CARPI 轮次 38-45。这些 CAPRI 轮次为测试三类蛋白质相互作用的预测方法提供了极好的机会,即蛋白质-蛋白质、蛋白质-肽和蛋白质-寡糖相互作用。基于模板的方法(用于单体蛋白质的 GalaxyTBM、用于同源寡聚体蛋白质的 GalaxyHomomer、用于蛋白质-肽复合物的 GalaxyPepDock)和从头开始对接方法(用于蛋白质寡聚体的 GalaxyTongDock 和 GalaxyPPDock、用于蛋白质-肽复合物的 GalaxyPepDock-ab-initio、用于蛋白质-寡糖复合物的 GalaxyDock2 和 Galaxy7TM)都已进行了测试。基于模板的方法严重依赖于适当模板的可用性和模板-靶标相似度,而模板-靶标差异是基于模板模型不准确的原因。对于几个 CAPRI 目标,我们的基于物理能量优化的结构精修和环建模方法(GalaxyRefineComplex 和 GalaxyLoop)可以改进不准确的基于模板的模型。当前的从头开始对接方法需要准确的蛋白质结构作为输入。我们的对接方法可以考虑输入结构的小构象变化,为几个 CAPRI 目标生成最佳模型之一。然而,预测涉及蛋白质骨架的大构象变化仍然具有挑战性,并且对基于物理的方法进行全面探索仍然有待进行。