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化科学中最重要和最难解决的问题之一。大多数药物，以及从农用化学品到除臭剂的许多其他分子，都是与蛋白质结合的配体。所述蛋白质可以来自人体，也可以来自致病性或不受欢迎的生物体，如细菌。使用计算机预测结合亲和性将是非常有益的，因为另一种实验方法是制造非常多的分子并对相关蛋白质或蛋白质进行分析，这种方法既困难、昂贵又耗时。计算机使用称为评分函数的数学公式计算估计的绑定亲和度。开发合适的评分函数对可能的三维蛋白质-配体相互作用几何形状进行排序，特别是准确预测蛋白质-配体结合亲和力，仍然是一个相当大的挑战。评分函数必须捕捉到相互作用的所有重要方面，以便对绑定亲和性做出准确而可靠的预测。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，并且将需要已知的结合亲和力和许多特征良好的蛋白质配体复合物的结构。幸运的是，数以百计的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。森林是由几百棵“决策树”组成的集合，每棵树基本上都是一个流程图。我们将训练它们学习现有蛋白质-配体复合物的已知性质模式，它们的结合亲和力和原子-原子相互作用距离的模式。然而，我们生成树的方法涉及到计算机模拟的掷骰子。这将确保它们都是不同的，尽管它们基于相同的底层信息。然后每个决策树对未知的结合亲和度进行预测。对这些预测取平均值，得到最终的计算值。这种对许多决策树的平均可以最大限度地利用底层数据中包含的信息，并产生比任何一个决策树更准确的结果。我们的模型将通过使用它们来预测蛋白质-配体复合物的结合亲和力来验证，这是该算法以前从未见过的。这确保了计算机不是简单地学习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.