Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts, USA.
Biophys J. 2011 Oct 5;101(7):1690-8. doi: 10.1016/j.bpj.2011.07.052.
Computational determination of optimal side-chain conformations in protein structures has been a long-standing and challenging problem. Solving this problem is important for many applications including homology modeling, protein docking, and for placing small molecule ligands on protein-binding sites. Programs available as of this writing are very fast and reasonably accurate, as measured by deviations of side-chain dihedral angles; however, often due to multiple atomic clashes, they produce structures with high positive energies. This is problematic in applications where the energy values are important, for example when placing small molecules in docking applications; the relatively small binding energy of the small molecule is drowned by the large energy due to atomic clashes that hampers finding the lowest energy state of the docked ligand. To address this we have developed an algorithm for generating a set of side-chain conformations that is dense enough that at least one of its members would have a root mean-square deviation of no more than R Å from any possible side-chain conformation of the amino acid. We call such a set a side-chain cover set of order R for the amino acid. The size of the set is constrained by the energy of the interaction of the side chain to the backbone atoms. Then, side-chain cover sets are used to optimize the conformation of the side chains given the coordinates of the backbone of a protein. The method we use is based on a variety of dead-end elimination methods and the recently discovered dynamic programming algorithm for this problem. This was implemented in a computer program called Octopus where we use side-chain cover sets with very small values for R, such as 0.1 Å, which ensures that for each amino-acid side chain the set contains a conformation with a root mean-square deviation of, at most, R from the optimal conformation. The side-chain dihedral-angle accuracy of the program is comparable to other implementations; however, it has the important advantage that the structures produced by the program have negative energies that are very close to the energies of the crystal structure for all tested proteins.
计算蛋白质结构中最优侧链构象一直是一个长期存在且具有挑战性的问题。解决这个问题对于许多应用程序都很重要，包括同源建模、蛋白质对接以及将小分子配体放置在蛋白质结合位点上。截至本文撰写之时，可用的程序速度非常快，侧链二面角的偏差也相当准确；然而，由于原子之间的多次冲突，它们生成的结构具有很高的正能。在能量值很重要的应用中，这是一个问题，例如在对接应用程序中放置小分子时；小分子的相对较小的结合能被原子冲突产生的较大能量所掩盖，从而阻碍了找到对接配体的最低能量状态。为了解决这个问题，我们开发了一种生成一组侧链构象的算法，该构象足够密集，以至于其成员中的至少一个与氨基酸的任何可能的侧链构象的均方根偏差不超过 R Å。我们将这样的集合称为氨基酸的侧链覆盖集合，其阶数为 R。集合的大小受到侧链与骨架原子相互作用能量的限制。然后，根据蛋白质骨架的坐标，使用侧链覆盖集来优化侧链的构象。我们使用的方法基于各种死端消除方法和最近发现的该问题的动态规划算法。这在一个名为 Octopus 的计算机程序中实现，我们使用 R 值非常小的侧链覆盖集，例如 0.1 Å，这确保了对于每个氨基酸侧链，该集合包含一个构象，其均方根偏差最多为 R，从最佳构象。程序的侧链二面角精度与其他实现相当；然而，它具有一个重要的优势，即程序生成的结构的能量非常接近所有测试蛋白质的晶体结构的能量，而且是负值。
