Abstract：
:The pathways of protein post-translational modifications (PTMs) have been shown to play particularly important roles for almost any biological process. Identification of PTM substrates along with information on the exact sites is fundamental for fully understanding or controlling biological processes. Alternative computational strategies would help to annotate PTMs in a high-throughput manner. Traditional algorithms are suited for identifying the common organisms and tissues that have a complete PTM atlas or extensive experimental data. While annotation of rare PTMs in most organisms is a clear challenge. In this work, to this end we have developed a novel homology-based pipeline named PTMProber that allows identification of potential modification sites for most of the proteomes lacking PTMs data. Cross-promotion E-value (CPE) as stringent benchmark has been used in our pipeline to evaluate homology to known modification sites. Independent-validation tests show that PTMProber achieves over 58.8% recall with high precision by CPE benchmark. Comparisons with other machine-learning tools show that PTMProber pipeline performs better on general predictions. In addition, we developed a web-based tool to integrate this pipeline at http://bioinfo.ncu.edu.cn/PTMProber/index.aspx. In addition to pre-constructed prediction models of PTM, the website provides an extensional functionality to allow users to customize models.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Chen X,Shi SP,Xu HD,Suo SB,Qiu JDdoi
10.1038/srep25801subject
Has Abstractpub_date
2016-05-13 00:00:00pages
25801issn
2045-2322pii
srep25801journal_volume
6pub_type
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pub_type： 杂志文章
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更新日期：2017-02-01 00:00:00
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journal_title：Scientific reports
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更新日期：2016-11-07 00:00:00
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journal_title：Scientific reports
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journal_title：Scientific reports
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pub_type： 已发布勘误
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更新日期：2016-05-20 00:00:00
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更新日期：2020-12-11 00:00:00
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更新日期：2018-03-12 00:00:00
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journal_title：Scientific reports
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pub_type： 临床试验,杂志文章
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journal_title：Scientific reports
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