基于集成神经网络的蛋白质数据挖掘中的保守肽识别- LPMO案例研究

Q3 Mathematics
G. Dotsenko, A. Dotsenko
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引用次数: 0

摘要

蛋白质数据挖掘是现代生物信息学的一个新兴领域。在这项工作中,我们提出了一种新的挖掘蛋白质数据保守肽的方法-神经网络集成识别(CPRENN)。该方法应用于19种子囊菌、18种担子菌和18种细菌蛋白质组中水解多糖单加氧酶(LPMOs)的研究。LPMOs是近年来发现的一种酶,其开发与木质纤维素材料的生物技术具有重要意义。比较了两种传统的生物信息学方法——隐马尔可夫模型(hmm)搜索(hmm程序)和肽模式识别(结合Hotpep应用程序的PPR程序)。假设LPMO氨基酸序列数量最多的是由HMMER发现的。与保守肽识别相比,hmm搜索是一种更为敏感的LPMOs挖掘方法。总的来说,CPRENN分别发现了HMMER发现的假想子囊菌、担子菌和细菌LPMOs的76%、67%和65%。对于碳水化合物活性酶数据库(CAZy)中包含大部分LPMOs的AA9、AA10和AA11家族,CPRENN和PPR + Hotpep分别发现了HMMER发现的69 - 98%和62 - 95%的氨基酸序列。与PPR + Hotpep相比,CPRENN具有完美的精度,对担子菌和细菌LPMOs的挖掘更完整。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Conserved Peptides Recognition by Ensemble of Neural Networks for Mining Protein Data – LPMO Case Study
Mining protein data is a recent promising area of modern bioinformatics. In this work, we suggested a novel approach for mining protein data – conserved peptides recognition by ensemble of neural networks (CPRENN). This approach was applied for mining lytic polysaccharide monooxygenases (LPMOs) in 19 ascomycete, 18 basidiomycete, and 18 bacterial proteomes. LPMOs are recently discovered enzymes and their mining is of high relevance for biotechnology of lignocellulosic materials. CPRENN was compared with two conventional bioinformatic methods for mining protein data – profile hidden Markov models (HMMs) search (HMMER program) and peptide pattern recognition (PPR program combined with Hotpep application). The maximum number of hypothetical LPMO amino acid sequences was discovered by HMMER. Profile HMMs search proved to be more sensitive method for mining LPMOs than conserved peptides recognition. Totally, CPRENN found 76 %, 67 %, and 65 % of hypothetical ascomycete, basidiomycete, and bacterial LPMOs discovered by HMMER, respectively. For AA9, AA10, and AA11 families which contain the major part of all LPMOs in the carbohydrate-active enzymes database (CAZy), CPRENN and PPR + Hotpep found 69–98 % and 62–95 % of amino acid sequences discovered by HMMER, respectively. In contrast with PPR + Hotpep, CPRENN possessed perfect precision and provided more complete mining of basidiomycete and bacterial LPMOs.
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来源期刊
Mathematical Biology and Bioinformatics
Mathematical Biology and Bioinformatics Mathematics-Applied Mathematics
CiteScore
1.10
自引率
0.00%
发文量
13
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