Moal Iain H, Dapkūnas Justas, Fernández-Recio Juan
Joint BSC-IRB Research Program in Computational Biology, Life Science Department, Barcelona Supercomputing Center, Barcelona, 08034, Spain.
Proteins. 2015 Apr;83(4):640-50. doi: 10.1002/prot.24761. Epub 2015 Feb 5.
Mutations at protein-protein recognition sites alter binding strength by altering the chemical nature of the interacting surfaces. We present a simple surface energy model, parameterized with empirical ΔΔG values, yielding mean energies of -48 cal mol(-1) Å(-2) for interactions between hydrophobic surfaces, -51 to -80 cal mol(-1) Å(-2) for surfaces of complementary charge, and 66-83 cal mol(-1) Å(-2) for electrostatically repelling surfaces, relative to the aqueous phase. This places the mean energy of hydrophobic surface burial at -24 cal mol(-1) Å(-2) . Despite neglecting configurational entropy and intramolecular changes, the model correlates with empirical binding free energies of a functionally diverse set of rigid-body interactions (r = 0.66). When used to rerank docking poses, it can place near-native solutions in the top 10 for 37% of the complexes evaluated, and 82% in the top 100. The method shows that hydrophobic burial is the driving force for protein association, accounting for 50-95% of the cohesive energy. The model is available open-source from http://life.bsc.es/pid/web/surface_energy/ and via the CCharpPPI web server http://life.bsc.es/pid/ccharppi/.
蛋白质 - 蛋白质识别位点的突变通过改变相互作用表面的化学性质来改变结合强度。我们提出了一个简单的表面能模型,用经验性的ΔΔG值进行参数化,相对于水相,疏水表面之间相互作用的平均能量为 -48 cal mol⁻¹ Å⁻²,互补电荷表面之间的平均能量为 -51至 -80 cal mol⁻¹ Å⁻²,静电排斥表面之间的平均能量为66 - 83 cal mol⁻¹ Å⁻²。这使得疏水表面埋藏的平均能量为 -24 cal mol⁻¹ Å⁻²。尽管该模型忽略了构型熵和分子内变化,但它与一组功能多样的刚体相互作用的经验结合自由能相关(r = 0.66)。当用于重新排列对接姿势时,对于37%的评估复合物,它可以将接近天然的解决方案排在前10位,对于82%的复合物可以排在前100位。该方法表明,疏水埋藏是蛋白质缔合驱动力,占内聚能的50 - 95%。该模型可从http://life.bsc.es/pid/web/surface_energy/开源获取,并可通过CCharpPPI网络服务器http://life.bsc.es/pid/ccharppi/获取。