Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), C/Melchor Fernandez Almagro, 3, E-28029 Madrid, Spain.
BMC Bioinformatics. 2013 Nov 29;14:345. doi: 10.1186/1471-2105-14-345.
Protein kinases are involved in relevant physiological functions and a broad number of mutations in this superfamily have been reported in the literature to affect protein function and stability. Unfortunately, the exploration of the consequences on the phenotypes of each individual mutation remains a considerable challenge.
The wKinMut web-server offers direct prediction of the potential pathogenicity of the mutations from a number of methods, including our recently developed prediction method based on the combination of information from a range of diverse sources, including physicochemical properties and functional annotations from FireDB and Swissprot and kinase-specific characteristics such as the membership to specific kinase groups, the annotation with disease-associated GO terms or the occurrence of the mutation in PFAM domains, and the relevance of the residues in determining kinase subfamily specificity from S3Det. This predictor yields interesting results that compare favourably with other methods in the field when applied to protein kinases.Together with the predictions, wKinMut offers a number of integrated services for the analysis of mutations. These include: the classification of the kinase, information about associations of the kinase with other proteins extracted from iHop, the mapping of the mutations onto PDB structures, pathogenicity records from a number of databases and the classification of mutations in large-scale cancer studies. Importantly, wKinMut is connected with the SNP2L system that extracts mentions of mutations directly from the literature, and therefore increases the possibilities of finding interesting functional information associated to the studied mutations.
wKinMut facilitates the exploration of the information available about individual mutations by integrating prediction approaches with the automatic extraction of information from the literature (text mining) and several state-of-the-art databases.wKinMut has been used during the last year for the analysis of the consequences of mutations in the context of a number of cancer genome projects, including the recent analysis of Chronic Lymphocytic Leukemia cases and is publicly available at http://wkinmut.bioinfo.cnio.es.
蛋白激酶参与相关的生理功能,文献中报道了该超家族中大量的突变,这些突变影响蛋白的功能和稳定性。不幸的是,探索每个突变对表型的影响仍然是一个相当大的挑战。
wKinMut 网络服务器提供了来自多种方法的突变潜在致病性的直接预测,包括我们最近开发的基于组合来自多个不同来源的信息的预测方法,包括 FireDB 和 Swissprot 的理化性质和功能注释以及激酶特异性特征,如特定激酶组的成员资格、与疾病相关的 GO 术语注释或 PFAM 结构域中突变的发生,以及 S3Det 中残基在确定激酶亚家族特异性方面的相关性。当应用于蛋白激酶时,该预测器产生的结果与该领域的其他方法相比具有有趣的结果。除了预测外,wKinMut 还为突变分析提供了许多集成服务。这些服务包括:激酶的分类、从 iHop 提取的与其他蛋白质关联的激酶信息、将突变映射到 PDB 结构、来自多个数据库的致病性记录以及大规模癌症研究中的突变分类。重要的是,wKinMut 与 SNP2L 系统连接,该系统直接从文献中提取突变的提及,从而增加了找到与研究的突变相关的有趣功能信息的可能性。
wKinMut 通过将预测方法与文献(文本挖掘)和几个最先进的数据库中自动提取的信息集成,促进了对个体突变相关信息的探索。在过去的一年中,wKinMut 已用于分析癌症基因组项目背景下突变的后果,包括最近对慢性淋巴细胞白血病病例的分析,并且可在 http://wkinmut.bioinfo.cnio.es 上公开获得。