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A novel method to improve recognition of antimicrobial peptides through distal sequence-based features

机译：通过基于远端序列的特征提高对抗菌肽的识别的新方法

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摘要

Growing bacterial resistance to antibiotics is urging the development of new lines of treatment. The discovery of naturally-occurring antimicrobial peptides (AMPs) is motivating many experimental and computational researchers to pursue AMPs as possible templates. In the experimental community, the focus is generally on systematic point mutation studies to measure the effect on antibacterial activity. In the computational community, the goal is to understand what determines such activity in a machine learning setting. In the latter, it is essential to identify biological signals or features in AMPs that are predictive of antibacterial activity. Construction of effective features has proven challenging. In this paper, we advance research in this direction. We propose a novel method to construct and select complex sequence-based features able to capture information about distal patterns within a peptide. Thorough comparative analysis in this paper indicates that such features compete with the state-of-the-art in AMP recognition while providing transparent summarizations of antibacterial activity at the sequence level. We demonstrate that these features can be combined with additional physicochemical features of interest to a biological researcher to facilitate specific AMP design or modification in the wet laboratory. Code, data, results, and analysis accompanying this paper are publicly available online at: http://cs.gmu.edu/~ashehu/?q=OurTools.
机译：细菌对抗生素的抵抗力不断增强，促使人们开发新的治疗方法。天然存在的抗菌肽（AMPs）的发现正促使许多实验和计算研究人员追求将AMPs作为可能的模板。在实验界，通常将重点放在系统的点突变研究上，以测量其对抗菌活性的影响。在计算社区中，目标是了解什么决定了机器学习环境中的此类活动。在后者中，必不可少的是在AMP中鉴定出可预测抗菌活性的生物学信号或特征。有效功能的构建已被证明具有挑战性。在本文中，我们朝着这个方向推进研究。我们提出了一种新颖的方法来构建和选择能够捕获有关肽段内远端模式信息的基于复杂序列的特征。本文全面的比较分析表明，这些特征与AMP识别领域的最新技术竞争，同时在序列水平上提供了抗菌活性的透明汇总。我们证明了这些功能可以与生物学研究人员感兴趣的其他物理化学功能相结合，以方便在潮湿实验室中进行特定的AMP设计或修改。本文随附的代码，数据，结果和分析可从以下网站在线在线获得：http://cs.gmu.edu/~ashehu/?q=OurTools。

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