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在寻找蛋白质侧链集合的全局最小能量构象时增强的穷举消除法。

Enhanced dead-end elimination in the search for the global minimum energy conformation of a collection of protein side chains.

作者信息

Lasters I, De Maeyer M, Desmet J

机构信息

Center for Transgene Technology and Gene Therapy, Vlaams Interuniversitair Instituut voor Biotechnologie, K.U. Leuven, Belgium.

出版信息

Protein Eng. 1995 Aug;8(8):815-22. doi: 10.1093/protein/8.8.815.

DOI:10.1093/protein/8.8.815
PMID:8637851
Abstract

Although the conformational states of protein side chains can be described using a library of rotamers, the determination of the global minimum energy conformation (GMEC) of a large collection of side chains, given fixed backbone coordinates, represents a challenging combinatorial problem with important applications in the field of homology modelling. Recently, we have developed a theoretical framework, called the dead-end elimination method, which allows us to identify efficiently rotamers that cannot be members of the GMEC. Such dead-ending rotamers can be iteratively removed from the system under study thereby tracking down the size of the combinatorial problem. Here we present new developments to the dead-end elimination method that allow us to handle larger proteins and more extensive rotamer libraries. These developments encompass (i) a procedure to determine weight factors in the generalized dead-end elimination theorem thereby enhancing the elimination of dead-ending rotamers and (ii) a novel strategy, mainly based on logical arguments derived from the logic pairs theorem, to use dead-ending rotamer pairs in the efficient elimination of single rotamers. These developments are illustrated for proteins of various sizes and the flow of the current method is discussed in detail. The effectiveness of dead-end elimination is increased by two orders of magnitude as compared with previous work. In addition, it now becomes feasible to use extremely detailed libraries. We also provide an appendix in which the validity of the generalized dead-end criterion is shown. Finally, perspectives for further applications which may now become within reach are discussed.

摘要

尽管蛋白质侧链的构象状态可以用旋转异构体库来描述,但在给定固定主链坐标的情况下,确定大量侧链的全局最小能量构象(GMEC)是一个具有挑战性的组合问题,在同源建模领域有着重要应用。最近,我们开发了一个名为死端消除法的理论框架,它使我们能够有效地识别那些不可能是全局最小能量构象一部分的旋转异构体。这些死端旋转异构体可以从正在研究的系统中迭代去除,从而逐步缩小组合问题的规模。在此,我们展示了死端消除法的新进展,这些进展使我们能够处理更大的蛋白质和更广泛的旋转异构体库。这些进展包括:(i)一种在广义死端消除定理中确定权重因子的程序,从而增强对死端旋转异构体的消除;(ii)一种主要基于从逻辑对定理推导出来的逻辑论证的新策略,用于利用死端旋转异构体对来高效消除单个旋转异构体。针对各种大小的蛋白质展示了这些进展,并详细讨论了当前方法的流程。与之前的工作相比,死端消除的有效性提高了两个数量级。此外,现在使用极其详细的库也变得可行。我们还提供了一个附录,其中展示了广义死端标准的有效性。最后,讨论了现在可能实现的进一步应用前景。

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