Protein Function Prediction using Machine Learning by an Enhanced Novel Support Vector Logic-based Approach

蛋白质是一种生物分子，是生命的机械，参与许多生物过程，如分解食物以提供能量和防御细胞免受疾病侵袭。蛋白质采用复杂的三维(3D)结构，原子的位置可以通过实验来揭示。蛋白质的三维结构及其功能的知识往往为我们提供了对生物过程的重要洞察。此外，这些知识对新药的设计也大有裨益。由于生物学研究的进步，特别是人类、其他动物和许多细菌的基因组测序，科学界现在正在确定或预测许多蛋白质的三维结构，这些蛋白质的功能尚不清楚。此外，计算方法可以根据蛋白质的化学式(ITS序列)预测其可能的结构。这个项目是开发一种基于计算机的方法来提取实验确定的或预测的结构的蛋白质，并提出其功能。蛋白质的功能由关键残基的空间位置和这些残基所处的环境决定。我们将使用计算机算法从已知的蛋白质结构及其功能的例子中学习规则。特别是，机器学习方法将是来自支持向量机的逻辑推理和定量预测的组合，使用一种称为支持向量归纳逻辑编程(SVILP)的新方法。SVILP的优点是逻辑规则在描述空间关系方面很强大，而且很容易理解。然而，逻辑规则是或不是的，对于定量预测(例如，置信度或排名)，我们然后将逻辑规则馈入支持向量机。在这笔赠款中，我们将加强这一新颖的SVILP方法。这笔赠款将产生两个主要结果。首先，我们将开发一种增强的方法来为蛋白质结构分配功能，并开发一个供社区使用的网络服务器。其次，我们将开发一个增强的稳健版本的SVILP，其功能以具有挑战性的应用程序为基准，并以一种适合社区接受的形式将我们的方法应用于广泛的问题。

Proteins are biological molecules that are the machinery of life involved in numerous biological processes such as the breakdown of food to provide energy and the defence of a cell against disease. Proteins adopt complex three-dimensional (3D) structures and the location of the atoms can be revealed experimentally. Knowledge of the 3D structure of a protein and its function often provides major insight into biological processes. In addition, this knowledge is of substantial benefit to the design of novel drugs. As a result of advances in biological research, particularly the sequencing of the genomes of humans, other animals and many bacteria, the scientific community is now determining or predicting the 3D structures for many proteins whose functions are not yet known. In addition computational methods can predict the possible structure of a protein from its chemical formula (its sequence). This project is to develop a computer-based approach to take a protein of experimentally-determined or predicted structure and suggest its function. Protein function is determined by the spatial position of critical residues and the environment of these residues. We will use a computer algorithm to learn the rules from known examples of protein structures and their functions. In particular the machine learning approach will be a combination of logic reasoning and quantitative predictions from a support vector machine using a novel method known as Support Vector Inductive Logic Programming (SVILP). SVILP has the benefits that logic rules are powerful in describing spatial relationships and can be readily understood. However logic rules are yes or no and for quantitative prediction (e.g. confidence or rank) we then feed the logic rules into a support vector machine. In this grant we will enhance this novel SVILP methodology. There will be two major results from the grant. First we will have developed an enhanced method to assign function to protein structure and develop a web server for use by the community. Second we will have developed an enhanced robust version of SVILP with its power benchmarked on a challenging application and in a form suitable for uptake by the community to apply our method to a wide range of problems.