De Maeyer M, Desmet J, Lasters I
Center for Transgene Technology and Gene Therapy, Flanders Interuniversity Institute for Biotechnology, KU Leuven, Belgium.
Fold Des. 1997;2(1):53-66. doi: 10.1016/s1359-0278(97)00006-0.
About a decade ago, the concept of rotamer libraries was introduced to model sidechains given known mainchain coordinates. Since then, several groups have developed methods to handle the challenging combinatorial problem that is faced when searching rotamer libraries. To avoid a combinatorial explosion, the dead-end elimination method detects and eliminates rotamers that cannot be members of the global minimum energy conformation (GMEC). Several groups have applied and further developed this method in the fields of homology modelling and protein design.
This work addresses at the same time increased prediction accuracy and calculation speed improvements. The proposed enhancements allow the elimination of more than one-third of the possible rotameric states before applying the dead-end elimination method. This is achieved by using a highly detailed rotamer library allowing the safe application of an energy-based rejection criterion without risking the elimination of a GMEC rotamer. As a result, we gain both in modelling accuracy and in computational speed. Being completely automated, the current implementation of the dead-end elimination prediction of protein sidechains can be applied to the modelling of sidechains of proteins of any size on the high-end computer systems currently used in molecular modelling. The improved accuracy is highlighted in a comparative study on a collection of proteins of varying size for which score results have previously been published by multiple groups. Furthermore, we propose a new validation method for the scoring of the modelled structure versus the experimental data based upon the volume overlap of the predicted and observed sidechains. This overlap criterion is discussed in relation to the classic RMSD and the frequently used +/- 40 degrees window in comparing chi 1 and chi 2 angles.
We have shown that a very detailed library allows the introduction of a safe energy threshold rejection criterion, thereby increasing both the execution speed and the accuracy of the modelling program. We speculate that the current method will allow the sidechain prediction of medium-sized proteins and complex protein interfaces involving up to 150 residues on low-end desktop computers.
大约十年前，引入了旋转异构体库的概念，用于在已知主链坐标的情况下对侧链进行建模。从那时起，几个研究小组开发了一些方法来处理在搜索旋转异构体库时面临的具有挑战性的组合问题。为了避免组合爆炸，死端消除方法可检测并消除那些不可能成为全局最低能量构象（GMEC）成员的旋转异构体。几个研究小组已在同源建模和蛋白质设计领域应用并进一步发展了该方法。
这项工作同时致力于提高预测准确性和计算速度。所提出的改进措施使得在应用死端消除方法之前能够消除超过三分之一的可能旋转异构体状态。这是通过使用高度详细的旋转异构体库实现的，该库允许安全地应用基于能量的排除标准，而不会有消除GMEC旋转异构体的风险。结果，我们在建模准确性和计算速度方面都有所收获。蛋白质侧链的死端消除预测当前实现方式完全自动化，可应用于目前分子建模中使用的高端计算机系统上对任何大小蛋白质的侧链进行建模。在一项针对不同大小蛋白质集合的比较研究中突出显示了提高的准确性，此前多个研究小组已公布了这些蛋白质的评分结果。此外，我们基于预测侧链与观察侧链的体积重叠，提出了一种针对建模结构与实验数据评分的新验证方法。在比较χ1和χ2角度时，讨论了这种重叠标准与经典均方根偏差（RMSD）以及常用的±40度窗口的关系。
我们已经表明，一个非常详细的库允许引入一个安全的能量阈值排除标准，从而提高建模程序的执行速度和准确性。我们推测，当前方法将允许在低端台式计算机上对包含多达150个残基的中等大小蛋白质和复杂蛋白质界面进行侧链预测。
