Gray Jeffrey J, Moughon Stewart, Wang Chu, Schueler-Furman Ora, Kuhlman Brian, Rohl Carol A, Baker David
Howard Hughes Medical Institute and Department of Biochemistry, University of Washington, J-567 Health Sciences, Box 357350, Seattle, WA 98195, USA.
J Mol Biol. 2003 Aug 1;331(1):281-99. doi: 10.1016/s0022-2836(03)00670-3.
Protein-protein docking algorithms provide a means to elucidate structural details for presently unknown complexes. Here, we present and evaluate a new method to predict protein-protein complexes from the coordinates of the unbound monomer components. The method employs a low-resolution, rigid-body, Monte Carlo search followed by simultaneous optimization of backbone displacement and side-chain conformations using Monte Carlo minimization. Up to 10(5) independent simulations are carried out, and the resulting "decoys" are ranked using an energy function dominated by van der Waals interactions, an implicit solvation model, and an orientation-dependent hydrogen bonding potential. Top-ranking decoys are clustered to select the final predictions. Small-perturbation studies reveal the formation of binding funnels in 42 of 54 cases using coordinates derived from the bound complexes and in 32 of 54 cases using independently determined coordinates of one or both monomers. Experimental binding affinities correlate with the calculated score function and explain the predictive success or failure of many targets. Global searches using one or both unbound components predict at least 25% of the native residue-residue contacts in 28 of the 32 cases where binding funnels exist. The results suggest that the method may soon be useful for generating models of biologically important complexes from the structures of the isolated components, but they also highlight the challenges that must be met to achieve consistent and accurate prediction of protein-protein interactions.
蛋白质-蛋白质对接算法提供了一种阐明目前未知复合物结构细节的方法。在此，我们提出并评估一种从未结合单体组分的坐标预测蛋白质-蛋白质复合物的新方法。该方法采用低分辨率、刚体蒙特卡罗搜索，随后使用蒙特卡罗最小化同时优化主链位移和侧链构象。进行多达10⁵次独立模拟，并使用由范德华相互作用、隐式溶剂化模型和取向依赖氢键势主导的能量函数对所得的“诱饵”进行排序。对排名靠前的诱饵进行聚类以选择最终预测结果。小扰动研究表明，在54个案例中的42个案例中，使用从结合复合物衍生的坐标时会形成结合漏斗，而在54个案例中的32个案例中，使用一个或两个单体的独立确定坐标时也会形成结合漏斗。实验结合亲和力与计算得分函数相关，并解释了许多靶点预测的成功或失败。在存在结合漏斗的32个案例中的28个案例中，使用一个或两个未结合组分进行全局搜索可预测至少25%的天然残基-残基接触。结果表明，该方法可能很快有助于从分离组分的结构生成生物学上重要复合物的模型，但它们也突出了在实现蛋白质-蛋白质相互作用的一致和准确预测方面必须克服的挑战。
