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MobiDB-lite:蛋白质内在无序的快速且高度特异的一致性预测

MobiDB-lite: fast and highly specific consensus prediction of intrinsic disorder in proteins.

作者信息

Necci Marco, Piovesan Damiano, Dosztányi Zsuzsanna, Tosatto Silvio C E

机构信息

Department of Biomedical Sciences, University of Padua, 35121 Padova, Italy.

Fondazione Edmund Mach, 38010 San Michele all'Adige, Italy.

出版信息

Bioinformatics. 2017 May 1;33(9):1402-1404. doi: 10.1093/bioinformatics/btx015.

Abstract

MOTIVATION

Intrinsic disorder (ID) is established as an important feature of protein sequences. Its use in proteome annotation is however hampered by the availability of many methods with similar performance at the single residue level, which have mostly not been optimized to predict long ID regions of size comparable to domains.

RESULTS

Here, we have focused on providing a single consensus-based prediction, MobiDB-lite, optimized for highly specific (i.e. few false positive) predictions of long disorder. The method uses eight different predictors to derive a consensus which is then filtered for spurious short predictions. Consensus prediction is shown to outperform the single methods when annotating long ID regions. MobiDB-lite can be useful in large-scale annotation scenarios and has indeed already been integrated in the MobiDB, DisProt and InterPro databases.

AVAILABILITY AND IMPLEMENTATION

MobiDB-lite is available as part of the MobiDB database from URL: http://mobidb.bio.unipd.it/. An executable can be downloaded from URL: http://protein.bio.unipd.it/mobidblite/.

CONTACT

silvio.tosatto@unipd.it.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

内在无序(ID)已被确认为蛋白质序列的一个重要特征。然而,由于在单残基水平上有许多性能相似的方法,其在蛋白质组注释中的应用受到了阻碍,这些方法大多没有针对预测与结构域大小相当的长ID区域进行优化。

结果

在此,我们专注于提供一种基于单一共识的预测方法MobiDB-lite,该方法针对长无序区域的高特异性(即假阳性少)预测进行了优化。该方法使用八种不同的预测器得出共识,然后对虚假的短预测进行过滤。在注释长ID区域时,共识预测的表现优于单一方法。MobiDB-lite在大规模注释场景中可能会很有用,并且实际上已经被整合到MobiDB、DisProt和InterPro数据库中。

可用性和实现方式

MobiDB-lite可作为MobiDB数据库的一部分从以下网址获取:http://mobidb.bio.unipd.it/。可从以下网址下载可执行文件:http://protein.bio.unipd.it/mobidblite/。

联系方式

silvio.tosatto@unipd.it

补充信息

补充数据可在《生物信息学》在线获取。

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