Fallah Atanaki Fereshteh, Behrouzi Saman, Ariaeenejad Shohreh, Boroomand Amin, Kavousi Kaveh
Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 1417466191, Iran.
Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research, Education, and Extension Organization (AREEO), Karaj 31535-1897, Iran.
ACS Omega. 2020 Mar 26;5(13):7290-7297. doi: 10.1021/acsomega.9b04119. eCollection 2020 Apr 7.
Biofilms are biological systems that are formed by a community of microorganisms in which microbial cells are connected on a surface within a self-produced matrix of an extracellular polymeric substance. On some occasions, microorganisms use biofilms to protect themselves against the harmful effects of the host body immune system and the surrounding environment, hence increasing their chances of survival against the various anti-microbial agents. Biofilms play a crucial role in medicine and industry because of the problems they cause. Designing agents that inhibit bacterial biofilm formation is very costly and takes too much time in the laboratory to be discovered and validated. Therefore, developing computational tools for the prediction of biofilm inhibitor peptides is inevitable and important. Here, we present a computational prediction tool to screen the vast number of peptide sequences and select potential candidate peptides for further lab experiments and validation. In this learning model, different feature vectors, extracted from the peptide primary structure, are exploited to learn patterns from the sequence of biofilm inhibitory peptides. Various classification algorithms including SVM, random forest, and k-nearest neighbor have been examined to evaluate their performance. Overall, our approach showed better prediction in comparison with other prediction methods. In this study, for the first time, we applied features extracted from NMR spectra of amino acids along with physicochemical features. Although each group of features showed good discrimination potential alone, we used a combination of features to enhance the performance of our method. Our prediction tool is freely available.
生物膜是由微生物群落形成的生物系统，其中微生物细胞在细胞外聚合物的自身产生的基质内连接在表面上。在某些情况下，微生物利用生物膜保护自己免受宿主免疫系统和周围环境的有害影响，从而增加它们对抗各种抗菌剂的生存机会。由于生物膜引发的问题，它们在医学和工业中起着至关重要的作用。设计抑制细菌生物膜形成的药物成本非常高，并且在实验室中发现和验证需要花费太多时间。因此，开发用于预测生物膜抑制肽的计算工具是不可避免且重要的。在这里，我们提出了一种计算预测工具，用于筛选大量肽序列，并选择潜在的候选肽进行进一步的实验室实验和验证。在这个学习模型中，从肽一级结构中提取的不同特征向量被用来从生物膜抑制肽序列中学习模式。已经研究了包括支持向量机、随机森林和k近邻在内的各种分类算法，以评估它们的性能。总体而言，与其他预测方法相比，我们的方法显示出更好的预测效果。在本研究中，我们首次应用了从氨基酸核磁共振光谱中提取的特征以及物理化学特征。尽管每组特征单独显示出良好的区分潜力，但我们使用特征组合来提高我们方法的性能。我们的预测工具可免费获取。
