Prediction of antimicrobial peptides based on the adaptive neuro-fuzzy inference system application
Prediction of antimicrobial peptides based on the adaptive neuro-fuzzy inference system application
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DOI：
10.1002/bip.22066
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发表时间：
2012-01-01
期刊：
影响因子：
2.9
通讯作者：
Franco, Octavio L.
中科院分区：
文献类型：
--
作者：
Fernandes, Fabiano C.;Rigden, Daniel J.;Franco, Octavio L.
抗菌肽（Antimicrobial Peptides，AMPs）是一类分布广泛的防御分子，是解决抗生素耐药性问题的有效途径。然而，筛选推定的AMP所需的实验时间使得基于肽序列分析和/或分子建模的计算模拟非常有吸引力。作为模拟和预测工具的人工智能方法在帮助有效地发现和设计新颖的AMP方面具有重要意义。在本研究中，将使用不同预测方法和数据库的最新发表结果与自适应神经模糊推理系统（ANFIS）模型进行比较。结果表明，ANFIS模型的预测准确率为96.7%，马太相关系数（MCC）为0.936，是一种有效的抗菌肽预测模式识别模型。此外，ANFIS模型所需的输入参数数量更少，提高了预测的速度和易用性。综上所述，由于AMP理化性质的模糊性，ANFIS方法可以提供一个有效的解决方案，用于筛选推定的AMP序列和探索AMP的特性特征。(C)2012 Wiley Periodicals，Inc. Biopolymers（Pept Sci）98：280287，2012.
Antimicrobial peptides (AMPs) are widely distributed defense molecules and represent a promising alternative for solving the problem of antibiotic resistance. Nevertheless, the experimental time required to screen putative AMPs makes computational simulations based on peptide sequence analysis and/or molecular modeling extremely attractive. Artificial intelligence methods acting as simulation and prediction tools are of great importance in helping to efficiently discover and design novel AMPs. In the present study, state-of-the-art published outcomes using different prediction methods and databases were compared to an adaptive neuro-fuzzy inference system (ANFIS) model. Data from our study showed that ANFIS obtained an accuracy of 96.7% and a Matthew's Correlation Coefficient (MCC) of 0.936, which proved it to be an efficient model for pattern recognition in antimicrobial peptide prediction. Furthermore, a lower number of input parameters were needed for the ANFIS model, improving the speed and ease of prediction. In summary, due to the fuzzy nature of AMP physicochemical properties, the ANFIS approach presented here can provide an efficient solution for screening putative AMP sequences and for exploration of properties characteristic of AMPs. (C) 2012 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 98: 280287, 2012.