HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation
HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation
复制标题
DOI：
10.1093/bioinformatics/btaa160
复制
发表时间：
2020-06-01
期刊：
影响因子：
5.8
通讯作者：
Manavalan, Balachandran
中科院分区：
文献类型：
--
作者：
Hasan, Md. Mehedi;Schaduangrat, Nalini;Manavalan, Balachandran
动机：治疗性多肽在临床试验中失败可能归因于其毒性特征，如溶血活性，这阻碍了多肽作为候选药物的进一步发展。从给定的多肽中准确预测溶血肽（hlp）及其活性是免疫信息学中具有挑战性的任务之一，这对药物开发和基础研究至关重要。虽然在这方面已经提出了一些计算方法，但没有一种方法能够同时识别hlp及其活性。结果：在这项研究中，我们提出了一个双层预测框架，称为HLPpred-Fuse，可以准确和自动地预测溶血肽（HLPs或非HLPs）以及HLPs活性（高和低）。更具体地说，通过集成六种不同的机器学习分类器和九种不同的基于序列的编码，利用特征表示学习方案生成54个概率特征。因此，54个概率特征被融合以提供充分收敛的序列信息，这些信息被用作极端随机树的输入，用于开发两个独立识别HLP及其活性的最终预测模型。与经验交叉验证分析、独立测试和针对最先进方法的案例研究的性能比较表明，HLPpred-Fuse在鉴定溶血活性方面始终优于这些方法。
Motivation: Therapeutic peptides failing at clinical trials could be attributed to their toxicity profiles like hemolytic activity, which hamper further progress of peptides as drug candidates. The accurate prediction of hemolytic peptides (HLPs) and its activity from the given peptides is one of the challenging tasks in immunoinformatics, which is essential for drug development and basic research. Although there are a few computational methods that have been proposed for this aspect, none of them are able to identify HLPs and their activities simultaneously.Results: In this study, we proposed a two-layer prediction framework, called HLPpred-Fuse, that can accurately and automatically predict both hemolytic peptides (HLPs or non-HLPs) as well as HLPs activity (high and low). More specifically, feature representation learning scheme was utilized to generate 54 probabilistic features by integrating six different machine learning classifiers and nine different sequence-based encodings. Consequently, the 54 probabilistic features were fused to provide sufficiently converged sequence information which was used as an input to extremely randomized tree for the development of two final prediction models which independently identify HLP and its activity. Performance comparisons over empirical cross-validation analysis, independent test and case study against state-of-the-art methods demonstrate that HLPpred-Fuse consistently outperformed these methods in the identification of hemolytic activity.