Machine Learning Approaches to Predict Enzyme Function

预测酶功能的机器学习方法

• 批准号: BB/I00596X/1
• 负责人: John Mitchell
• 金额: $ 33.78万
• 依托单位: University of St Andrews
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2011
• 资助国家: 英国
• 起止时间: 2011 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FI00596X%2F1
• 关键词: Machine; Learning; Approaches; Predict; Enzyme

Proteins are amongst the most important of all molecules in biological systems. They are crucial to organisms which use them to carry out a huge variety of essential functions: catalysis, transport, storage, motor functions, signalling, chaperoning folding, regulation, molecular recognition, structural roles, and DNA Repair. As proteins are so ubiquitous in biology, understanding their properties is essential if we want to know about biological processes. This project is focused on one of the most significant of all protein functions: enzyme catalysis. Enzymes catalyse, or facilitate, the chemical reactions that occur in living organisms. Understanding how they work is both interesting in itself and useful in areas as diverse as drug design, diagnostics, biofuels, food science and laundry. This project is about the relationship between the structure of a protein and the enzyme function it carries out. We aim to predict the catalytic functionality from a knowledge of the protein structure. In order to achieve this, we will use machine learning methods, and in particular a technique called Random Forest. The forest consists 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 enzyme structures and the chemistry of the steps comprising the reactions they catalyse. 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 make a prediction of the unknown possible catalytic functions. These predictions are treated as votes as to the function of the protein. This voting process produces a consensus of many decision trees and maximises the use of the information contained in the underlying data, generating results which are much more accurate than those of any one decision tree. The prediction of enzyme function is immensely important for a number of reasons. Firstly, being able to predict enzyme function more accurately will improve the functional annotation of genomes and reduce the current risk of misannotations being propagated through bioinformatics databases. Rapid developments in structural genomics, high throughput structure determination of diverse proteins from a wide variety of organisms, mean that many structures are available for enzymes whose functions are not yet known. Secondly, this project will allow us to recognise chemical similarities between evolutionarily unrelated enzymes that catalyse similar steps, though not necessarily similar overall reactions. Thirdly, this work will help us to understand the key determinants of the complex relationship between protein structure, function and evolution, particularly in terms of catalysis of reaction steps. Fourthly, the project will facilitate the design of new enzymes with either novel functions or carefully modified versions of existing functions. This project sits at an interface between disciplines, combining chemistry, biology and computer science. A wide range of skills and expertise is necessary to increase our understanding of catalysis, which has long been an important academic goal. Commercially, this work lays a foundation which is directly useful to the pharmaceutical and biotechnology industries, where enzymes are used both as diagnostics and therapeutics; the agrochemical industry, whose products often target enzymes; in the development of biofuels, which need robust enzymes to improve productivity and reduce costs; in laundry, where enzymes are already used in everyday products; and in the nutrition and food industries. In particular this project will aid in the design of new and repurposed enzymes.

Proteins are amongst the most important of all molecules in biological systems. They are crucial to organisms which use them to carry out a huge variety of essential functions: catalysis, transport, storage, motor functions, signalling, chaperoning folding, regulation, molecular recognition, structural roles, and DNA Repair. As proteins are so ubiquitous in biology, understanding their properties is essential if we want to know about biological processes. This project is focused on one of the most significant of all protein functions: enzyme catalysis. Enzymes catalyse, or facilitate, the chemical reactions that occur in living organisms. Understanding how they work is both interesting in itself and useful in areas as diverse as drug design, diagnostics, biofuels, food science and laundry. This project is about the relationship between the structure of a protein and the enzyme function it carries out. We aim to predict the catalytic functionality from a knowledge of the protein structure. In order to achieve this, we will use machine learning methods, and in particular a technique called Random Forest. The forest consists 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 enzyme structures and the chemistry of the steps comprising the reactions they catalyse. 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 make a prediction of the unknown possible catalytic functions. These predictions are treated as votes as to the function of the protein. This voting process produces a consensus of many decision trees and maximises the use of the information contained in the underlying data, generating results which are much more accurate than those of any one decision tree. The prediction of enzyme function is immensely important for a number of reasons. Firstly, being able to predict enzyme function more accurately will improve the functional annotation of genomes and reduce the current risk of misannotations being propagated through bioinformatics databases. Rapid developments in structural genomics, high throughput structure determination of diverse proteins from a wide variety of organisms, mean that many structures are available for enzymes whose functions are not yet known. Secondly, this project will allow us to recognise chemical similarities between evolutionarily unrelated enzymes that catalyse similar steps, though not necessarily similar overall reactions. Thirdly, this work will help us to understand the key determinants of the complex relationship between protein structure, function and evolution, particularly in terms of catalysis of reaction steps. Fourthly, the project will facilitate the design of new enzymes with either novel functions or carefully modified versions of existing functions. This project sits at an interface between disciplines, combining chemistry, biology and computer science. A wide range of skills and expertise is necessary to increase our understanding of catalysis, which has long been an important academic goal. Commercially, this work lays a foundation which is directly useful to the pharmaceutical and biotechnology industries, where enzymes are used both as diagnostics and therapeutics; the agrochemical industry, whose products often target enzymes; in the development of biofuels, which need robust enzymes to improve productivity and reduce costs; in laundry, where enzymes are already used in everyday products; and in the nutrition and food industries. In particular this project will aid in the design of new and repurposed enzymes.

项目成果

The Parzen Window method: In terms of two vectors and one matrix.

• DOI: 10.1016/j.patrec.2015.06.002
• 发表时间: 2015-10-01
• 期刊: Pattern recognition letters
• 影响因子: 5.1
• 作者: Mussa HY;Mitchell JB;Afzal AM
• 通讯作者: Afzal AM

From sequence to enzyme mechanism using multi-label machine learning.

• DOI: 10.1186/1471-2105-15-150
• 发表时间: 2014-05-19
• 期刊: BMC bioinformatics
• 影响因子: 3
• 作者: De Ferrari L;Mitchell JB
• 通讯作者: Mitchell JB

A note on utilising binary features as ligand descriptors.

• DOI: 10.1186/s13321-015-0105-3
• 发表时间: 2015
• 期刊: Journal of cheminformatics
• 影响因子: 8.6
• 作者: Mussa HY;Mitchell JB;Glen RC
• 通讯作者: Glen RC

Enzyme mechanism prediction: a template matching problem on InterPro signature subspaces.

• DOI: 10.1186/s13104-015-1730-7
• 发表时间: 2015-12-03
• 期刊: BMC research notes
• 影响因子: 1.8
• 作者: Mussa HY;De Ferrari L;Mitchell JB
• 通讯作者: Mitchell JB

Machine learning methods in chemoinformatics.

• DOI: 10.1002/wcms.1183
• 发表时间: 2014-09-01
• 期刊: WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE
• 影响因子: 11.4
• 作者: Mitchell, John B. O.