Random Forest Prediction of Protein-Ligand Binding Affinities

小分子配体与其相互作用的蛋白质之间的结合亲和力不容易计算。事实上，它的计算预测仍然是计算生物化学科学中最重要和最困难的未解决问题之一。大多数药物，以及从农用化学品到杀虫剂的许多其他分子，都是与蛋白质结合的配体。所述蛋白质可以来自人，或来自病原性或不期望的生物体，如细菌。能够使用计算机预测结合亲和力将是非常有益的，因为制备非常多的分子并针对相关蛋白质或蛋白质测定它们的替代实验方法是困难的、昂贵的和耗时的。计算机使用称为评分函数的数学公式计算估计的结合亲和力。开发合适的评分功能，用于排列可能的三维蛋白质-配体相互作用的几何形状，特别是用于准确预测蛋白质-配体结合亲和力，仍然是一个相当大的挑战。评分函数必须捕获相互作用的所有重要方面，以便对结合亲和力进行准确可靠的预测。为了开发更好的评分功能，我们正在寻找机器学习和信息学领域，并将需要许多良好表征的蛋白质-配体复合物的已知结合亲和力和结构。幸运的是，数百种蛋白质-配体复合物具有可用的结构和结合亲和力。我们将使用的方法称为随机森林。森林是一组几百棵“决策树”，每棵树基本上都是一个流程图。我们将训练他们学习现有蛋白质-配体复合物的已知性质的模式，它们的结合亲和力和原子-原子相互作用距离的模式。然而，我们生成树的方式涉及计算机模拟骰子滚动。这将确保它们都是不同的，尽管基于相同的基本信息。然后，每个决策树对未知的结合亲和力进行预测。将这些预测平均以给出最终计算值。这种对许多决策树的平均最大限度地利用了底层数据中包含的信息，并产生比任何一棵决策树更准确的结果。我们的模型将通过使用它们来预测该算法以前从未见过的蛋白质-配体复合物的结合亲和力来进行验证。这确保了计算机不是简单地学习它正在训练的数据的特性。

The binding affinity between a small molecule ligand and the protein with which it interacts is not easy to calculate. Indeed, its computational prediction remains one of the most important and difficult unsolved problems in computational biochemical science. Most medicines, and many other molecules in uses from agrochemicals to deodorants, are ligands that bind to proteins. The proteins may be from the human, or from a pathogenic or undesirable organism such as a bacterium. It would be very beneficial to be able to predict binding affinities using a computer, because the alternative experimental approach of making very many molecules and assaying them against the relevant protein or proteins is difficult, expensive and time-consuming. The computer calculates an estimated binding affinity using a mathematical formula known as a scoring function. The development of suitable scoring functions for ranking possible three dimensional protein-ligand interaction geometries, and especially for accurate prediction of protein-ligand binding affinities, remains a considerable challenge. The scoring function must capture all the important aspects of the interaction in order to give an accurate and reliable prediction of the binding affinity. In order to develop better scoring functions, we are looking to the fields of machine learning and informatics, and will require the known binding affinities and structures of numerous well-characterised protein-ligand complexes. Fortunately, many hundreds of protein-ligand complexes have both structures and binding affinities available. The method we will use is called Random Forest. The forest is a set of several hundred 'decision trees', each of which is basically a flow diagram. We will train them to learn patterns in the known properties of existing protein-ligand complexes, their binding affinities and their patterns of atom-atom interaction distances. However, the way in which we will generate the trees involves computer-simulated dice-rolling. This will ensure that they are all different, though based on the same underlying information. The decision trees then each made a prediction of the unknown binding affinity. These predictions are averaged to give the final computed value. This averaging over many decision trees maximises the use of the information contained in the underlying data and produces results which are much more accurate than those of any one decision tree. Our models will be validated by using them to predict binding affinities of protein-ligand complexes that the algorithm has not seen before. This ensures that the computer is not simply learning the idiosyncrasies of the data on which it is being trained.