基于计算方法的抗菌肽预测

A Review on Prediction of Antimicrobial Peptides Based on Computational Methods

抗菌肽是由生物体免疫系统所产生的能抵抗微生物感染的一种小分子多肽,因其具有高效低毒的广谱抗菌活性且几乎无耐药性问题,被看做是抗生素的最佳替代品,对解决抗生素滥用问题具有重要的意义.抗菌肽预测是生物信息学的一个重要研究内容,对抗菌肽及其抗菌功能进行预测能有效帮助了解抗菌肽的作用机理,为抗菌肽药物的设计和改造提供理论依据.基于计算方法的抗菌肽预测是采用数学理论、计算机技术和生物信息学方法,通过对抗菌肽数据的分析来挖掘出抗菌肽的生物特征和抗菌活性之间的关联,从而自动地对抗菌肽的类别做出推断.由于不依赖于生物实验,而是依靠有效的算法和计算机的高速计算能力来完成预测工作,计算方法具有高效快捷、成本低廉等特点,且具有良好的可操作性和批量处理能力,非常适合大规模预测任务,因此已经引起了国内外学者越来越多的关注.文中对国内外的相关研究成果进行了阐述和总结,包括抗菌肽生物信息数据库、主流的预测方法和预测方法的性能检验等.抗菌肽数据库是专门针对抗菌肽建立的数据库,收录了大量的抗菌肽数据,使用者不仅可以从中提取所需要的信息,还可以使用数据库所提供的各类在线工具对数据进行处理.文中对常见的一些抗菌肽数据库进行了介绍,给出相关数据库的数据收录情况、功能特点和网址链接等,以方便读者查询使用.接着文中介绍了目前主要使用的抗菌肽预测方法,包括基于经验分析的预测方法和基于机器学习的预测方法,前者是根据已知的经验规则或者模式对某类抗菌肽的一些生化属性和抗菌活性之间的关联进行统计或建模来对该类抗菌肽进行识别,而后者则是利用机器学习技术,通过对抗菌肽的已知数据信息进行学习,建立合理的预测算法从中找出抗菌肽的特点和规律,并将其推广到未知多肽数据来进行预测.随后文中又给出了预测方法的评估方法和评价指标,这些性能检验结果既是评估一个方法预测性能好坏的标准,又是与其他方法进行比较的依据.最后,文中对抗菌肽预测的发展进行了思考和讨论,并展望了未来的研究方向.

Antimicrobial peptides represent a diverse class of natural small peptides derived from innate immune system of organisms to combat microorganism infection,and are considered as the best potential candidate substitution of antibiotics because antimicrobial peptides have properties of high efficiency,low toxicity,broad spectrum antimicrobial activity without drug resistance.Prediction of antimicrobial peptides is an important part of bioinformatics.Predicting antimicrobial peptides and their functional information can assist to comprehend their mechanism and provide theoretical supports for designing and improving antimicrobial peptide medicines.By using mathematical theory,computer technology and bioinformatics method,prediction of antimicrobial peptides based on computational methods analyzes antimicrobial peptide data to explore the connection between the biological feature and antibacterial function of antimicrobial peptides,to make decisions automatically for the samples’attribution.Being independent of biology experiments,the computational method relies on the effective algorithms as well as the computing power of computers to perform the prediction missions,therefore,this kind of approach is low cost,efficient,fast,and has excellent operability and processing batch ability to be quite proper for dealing with predicting tasks under large scale data,and then it has already attracted more and more attentions of both domestic and foreign scholars.This paper summarizes related researches at home and abroad,including antimicrobial peptide databases,current predicting methods for antimicrobial peptides,and the performance validation of prediction methods.The antimicrobial peptide databases are a class of databases specially created for researching antimicrobial peptides,which collect a mass of antimicrobial peptide data including information on antimicrobial peptides’amino acid residue sequences,sources of the recorded data,activities as well as functions and beyond.In addition,the users of these databases can not only download and extract the information they need but also process data by using the various online analysis tools provided by the databases.This article introduces some main open access antimicrobial peptide databases,presents their current inclusion of collected data,sample categories,functions,characters,Web sites with links,and so on,to provide convenience and guidance for readers when they try to use these databases.And then some mainstream methods for predicting antimicrobial peptides are proposed,including the approaches based on empirical analysis and the ones based on machine learning.The empirical analysis method gathers statistics of data and establishes a mathematical model for the connection between some antimicrobial peptide’s biochemistry properties and antimicrobial activities,according to known experiences,rules or pattern,to recognize this kind of antimicrobial peptides.And the machine learning method aims to mine and learn existing data information in the databases to design a proper algorithm,and finds out the antimicrobial peptide’s features and laws,and then extends the relevance to unseen peptide samples to deduce their functions for prediction.After that,this paper also introduces the model evaluation methods and validation criterions,which can both evaluate the performance of a prediction approach and provide a reference for comparing the effects of different algorithms.Finally,we discuss the development of antimicrobial peptides prediction,and propose some meaningful research directions in future.