本文選題：抗菌肽預(yù)測 + 計算方法�。� 參考：《計算機(jī)學(xué)報》2017年12期

【摘要】：抗菌肽是由生物體免疫系統(tǒng)所產(chǎn)生的能抵抗微生物感染的一種小分子多肽,因其具有高效低毒的廣譜抗菌活性且?guī)缀鯚o耐藥性問題,被看做是抗生素的最佳替代品,對解決抗生素濫用問題具有重要的意義.抗菌肽預(yù)測是生物信息學(xué)的一個重要研究內(nèi)容,對抗菌肽及其抗菌功能進(jìn)行預(yù)測能有效幫助了解抗菌肽的作用機(jī)理,為抗菌肽藥物的設(shè)計和改造提供理論依據(jù).基于計算方法的抗菌肽預(yù)測是采用數(shù)學(xué)理論、計算機(jī)技術(shù)和生物信息學(xué)方法,通過對抗菌肽數(shù)據(jù)的分析來挖掘出抗菌肽的生物特征和抗菌活性之間的關(guān)聯(lián),從而自動地對抗菌肽的類別做出推斷.由于不依賴于生物實驗,而是依靠有效的算法和計算機(jī)的高速計算能力來完成預(yù)測工作,計算方法具有高效快捷、成本低廉等特點,且具有良好的可操作性和批量處理能力,非常適合大規(guī)模預(yù)測任務(wù),因此已經(jīng)引起了國內(nèi)外學(xué)者越來越多的關(guān)注.文中對國內(nèi)外的相關(guān)研究成果進(jìn)行了闡述和總結(jié),包括抗菌肽生物信息數(shù)據(jù)庫、主流的預(yù)測方法和預(yù)測方法的性能檢驗等.抗菌肽數(shù)據(jù)庫是專門針對抗菌肽建立的數(shù)據(jù)庫,收錄了大量的抗菌肽數(shù)據(jù),使用者不僅可以從中提取所需要的信息,還可以使用數(shù)據(jù)庫所提供的各類在線工具對數(shù)據(jù)進(jìn)行處理.文中對常見的一些抗菌肽數(shù)據(jù)庫進(jìn)行了介紹,給出相關(guān)數(shù)據(jù)庫的數(shù)據(jù)收錄情況、功能特點和網(wǎng)址鏈接等,以方便讀者查詢使用.接著文中介紹了目前主要使用的抗菌肽預(yù)測方法,包括基于經(jīng)驗分析的預(yù)測方法和基于機(jī)器學(xué)習(xí)的預(yù)測方法,前者是根據(jù)已知的經(jīng)驗規(guī)則或者模式對某類抗菌肽的一些生化屬性和抗菌活性之間的關(guān)聯(lián)進(jìn)行統(tǒng)計或建模來對該類抗菌肽進(jìn)行識別,而后者則是利用機(jī)器學(xué)習(xí)技術(shù),通過對抗菌肽的已知數(shù)據(jù)信息進(jìn)行學(xué)習(xí),建立合理的預(yù)測算法從中找出抗菌肽的特點和規(guī)律,并將其推廣到未知多肽數(shù)據(jù)來進(jìn)行預(yù)測.隨后文中又給出了預(yù)測方法的評估方法和評價指標(biāo),這些性能檢驗結(jié)果既是評估一個方法預(yù)測性能好壞的標(biāo)準(zhǔn),又是與其他方法進(jìn)行比較的依據(jù).最后,文中對抗菌肽預(yù)測的發(fā)展進(jìn)行了思考和討論,并展望了未來的研究方向.
[Abstract]:Antimicrobial peptides are small molecular peptides produced by the immune system of organisms that can resist microbial infection. Because of their high efficiency and low toxicity, antimicrobial peptides are considered as the best substitute for antibiotics because of their wide spectrum antibacterial activity and almost no drug resistance. It is of great significance to solve the problem of antibiotic abuse. The prediction of antimicrobial peptides is an important research content in bioinformatics. The prediction of antimicrobial peptides and its antibacterial function can effectively help to understand the mechanism of antimicrobial peptides and provide theoretical basis for the design and modification of antimicrobial peptides. The prediction of antimicrobial peptides based on computational methods is based on mathematical theory, computer technology and bioinformatics methods. By analyzing the data of antimicrobial peptides, we can find out the relationship between the biological characteristics and antibacterial activities of antimicrobial peptides. This automatically inferred the types of antimicrobial peptides. Because it does not depend on biological experiment, but relies on effective algorithm and high speed computing ability of computer to complete the prediction work, the calculation method has the characteristics of high efficiency, high speed, low cost, and has good maneuverability and batch processing ability. It is very suitable for large-scale prediction task, so it has attracted more and more attention from scholars at home and abroad. In this paper, the related research results at home and abroad are described and summarized, including the biological information database of antimicrobial peptides, the main prediction methods and the performance test of prediction methods. Antimicrobial peptide database is a database specially established for antimicrobial peptides, which contains a large number of antimicrobial peptide data. Users can not only extract the needed information from it, but also process the data by using all kinds of online tools provided by the database. In this paper, some common antimicrobial peptide databases are introduced, and the data collection, function features and URL links of the related databases are given, so as to facilitate the readers to inquire and use. Then, the paper introduces the main prediction methods of antimicrobial peptides, including empirical analysis based prediction and machine learning based prediction methods. The former uses known empirical rules or patterns to identify the relationship between some biochemical properties and antibacterial activities of a class of antimicrobial peptides, while the latter uses machine learning technology. By learning the known data information of antimicrobial peptides, a reasonable prediction algorithm is established to find out the characteristics and rules of antimicrobial peptides, and to extend them to unknown peptide data to predict. Then the evaluation methods and evaluation indexes of the prediction methods are given. These performance test results are not only the criteria for evaluating the performance of one method, but also the basis for comparison with other methods. Finally, the development of antimicrobial peptide prediction is discussed, and the future research direction is prospected.
【作者單位】： 大連理工大學(xué)控制科學(xué)與工程學(xué)院;
【基金】：國家自然科學(xué)基金(61502074) 中國博士后科學(xué)基金資助項目(2016M591430) 大連理工大學(xué)基本科研業(yè)務(wù)費科研項目(DUT17RC(4)09)資助~~
【分類號】：Q811.4;TP311.13

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本文編號：1947185