Predicting allergenic proteins using wavelet transform
Predicting allergenic proteins using wavelet transform
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DOI：
10.1093/bioinformatics/bth286
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发表时间：
2004-11-01
期刊：
影响因子：
5.8
通讯作者：
Krishnan, A
中科院分区：
文献类型：
--
作者：
Li, KB;Issac, P;Krishnan, A
动机：随着今天许多转基因蛋白的引入，预测其潜在过敏原性的能力已成为一个重要的问题。以前的研究是基于序列相似性或从已知过敏原数据库中识别的蛋白质基序。基于相似性的方法，虽然能够产生高召回率，但通常具有较低的预测精度。先前基于模体的方法已被证明能够提高交叉验证实验的精度。在这项研究中，一个系统，它结合了基于相似性和基序的predictions.Results的优势：新的预测系统使用聚类算法，分组已知的过敏性蛋白成簇。每个簇内的蛋白质被认为携带一个或多个共同的基序。在多序列比对之后，每个簇中的蛋白质通过小波分析程序，从而鉴定保守的基序。然后将为每个识别的基序制备隐马尔可夫模型（HMM）图谱。不携带可检测的过敏原基序的过敏原将被保存在一个小数据库中。未知蛋白质的变应原性可以通过将其与HMM谱进行比较来预测，并且如果没有发现匹配的谱，则通过BLASTP与小变应原数据库进行比较。通过交叉验证实验，我们观察到超过70%的召回率和超过90%的精确率。使用整个Swiss-Prot作为查询，我们预测了大约2000种潜在的过敏原。
Motivation: With many transgenic proteins introduced today, the ability to predict their potential allergenicity has become an important issue. Previous studies were based on either sequence similarity or the protein motifs identified from known allergen databases. The similarity-based approaches, although being able to produce high recalls, usually have low prediction precisions. Previous motif-based approaches have been shown to be able to improve the precisions on cross-validation experiments. In this study, a system that combines the advantages of similarity-based and motif-based prediction is described.Results: The new prediction system uses a clustering algorithm that groups the known allergenic proteins into clusters. Proteins within each cluster are assumed to carry one or more common motifs. After a multiple sequence alignment, proteins in each cluster go through a wavelet analysis program whereby conserved motifs will be identified. A hidden Markov model (HMM) profile will then be prepared for each identified motif. The allergens that do not appear to carry detectable allergen motifs will be saved in a small database. The allergenicity of an unknown protein may be predicted by comparing it against the HMM profiles, and, if no matching profiles are found, against the small allergen database by BLASTP. Over 70% of recall and over 90% of precision were observed using cross-validation experiments. Using the entire Swiss-Prot as the query, we predicted about 2000 potential allergens.