Department of Physics and Institute of Molecular Biophysics, Florida State University, Tallahassee, Florida, 32306.
Proteins. 2013 Dec;81(12):2229-36. doi: 10.1002/prot.24366. Epub 2013 Sep 14.
Predictions of protein-protein binders and binding affinities have traditionally focused on features pertaining to the native complexes. In developing a computational method for predicting protein-protein association rate constants, we introduced the concept of transient complex after mapping the interaction energy surface. The transient complex is located at the outer boundary of the bound-state energy well, having near-native separation and relative orientation between the subunits but not yet formed most of the short-range native interactions. We found that the width of the binding funnel and the electrostatic interaction energy of the transient complex are among the features predictive of binders and binding affinities. These ideas were very promising for the five affinity-related targets (T43-45, 55, and 56) of CAPRI rounds 20-27. For T43, we ranked the single crystallographic complex as number 1 and were one of only two groups that clearly identified that complex as a true binder; for T44, we ranked the only design with measurable binding affinity as number 4. For the nine docking targets, continuing on our success in previous CAPRI rounds, we produced 10 medium-quality models for T47 and acceptable models for T48 and T49. We conclude that the interaction energy landscape and the transient complex in particular will complement existing features in leading to better prediction of binding affinities.
传统的蛋白质-蛋白质结合物和结合亲和力的预测主要集中在与天然复合物相关的特征上。在开发一种用于预测蛋白质-蛋白质缔合速率常数的计算方法时,我们在映射相互作用能量表面后引入了瞬态复合物的概念。瞬态复合物位于结合态能量阱的外边界,亚基之间具有接近天然的分离和相对取向,但尚未形成大多数短程天然相互作用。我们发现,结合漏斗的宽度和瞬态复合物的静电相互能是预测结合物和结合亲和力的特征之一。这些想法对于 CAPRI 第 20-27 轮的五个亲和力相关靶标(T43-45、55 和 56)非常有前途。对于 T43,我们将单晶复合物排名第一,并且是仅有的两个明确将该复合物识别为真正结合物的小组之一;对于 T44,我们将唯一具有可测量结合亲和力的设计排名第四。对于九个对接靶标,我们在之前的 CAPRI 轮次中继续取得成功,为 T47 生成了 10 个中等质量的模型,为 T48 和 T49 生成了可接受的模型。我们得出的结论是,相互作用能量景观,特别是瞬态复合物,将补充现有特征,从而更好地预测结合亲和力。