Detection and characterization of 3D-signature phosphorylation site motifs and their contribution towards improved phosphorylation site prediction in proteins.
Detection and characterization of 3D-signature phosphorylation site motifs and their contribution towards improved phosphorylation site prediction in proteins.
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DOI：
10.1186/1471-2105-10-117
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发表时间：
2009-04-21
影响因子：
3
通讯作者：
Walther D
中科院分区：
文献类型：
--
作者：
Durek P;Schudoma C;Weckwerth W;Selbig J;Walther D
蛋白质磷酸化在代谢和信号通路的调节和激活中发挥着至关重要的作用，是药物干预的重要靶点。磷酸化过程的核心是蛋白激酶识别特定靶位点，然后将磷酸基团共价连接到氨基酸丝氨酸、苏氨酸或酪氨酸上。磷酸化位点（P-位点）的实验鉴定和计算预测已被证明是一个具有挑战性的问题。计算方法主要集中于从磷酸化位点周围的局部一维序列信息中提取预测特征。我们描述了磷酸化位点的空间背景，并评估了其对于改进磷酸化位点预测的可用性。我们通过蛋白质数据库 (PDB) 中提供的三维 (3D) 结构信息确定了 750 个非冗余、经过实验验证的位点，并根据各自的激酶家族对它们进行了分组。我们研究了磷酸丝氨酸、磷酸苏氨酸和磷酸酪氨酸周围氨基酸的空间分布，以提取特征 3D 轮廓。磷酸化位点周围氨基酸残基类型的特征空间分布确实是可辨别的，特别是在分析激酶家族特异性靶位点时。为了测试使用空间信息进行磷酸化位点计算预测的附加值，使用序列和结构信息应用支持向量机。与仅基于序列的预测方法相比，当通过 3D 上下文信息进行预测时，获得了小但一致的性能改进。虽然观察到局部一维氨基酸序列信息具有大部分区分能力，但空间上下文信息被认为与激酶及其同源靶位点的识别相关，并且可用于改进磷酸化位点的预测。实现已开发的基于结构的 P 位点预测方法的基于网络的服务 (Phos3D) 已在 上提供。
Phosphorylation of proteins plays a crucial role in the regulation and activation of metabolic and signaling pathways and constitutes an important target for pharmaceutical intervention. Central to the phosphorylation process is the recognition of specific target sites by protein kinases followed by the covalent attachment of phosphate groups to the amino acids serine, threonine, or tyrosine. The experimental identification as well as computational prediction of phosphorylation sites (P-sites) has proved to be a challenging problem. Computational methods have focused primarily on extracting predictive features from the local, one-dimensional sequence information surrounding phosphorylation sites. We characterized the spatial context of phosphorylation sites and assessed its usability for improved phosphorylation site predictions. We identified 750 non-redundant, experimentally verified sites with three-dimensional (3D) structural information available in the protein data bank (PDB) and grouped them according to their respective kinase family. We studied the spatial distribution of amino acids around phosphorserines, phosphothreonines, and phosphotyrosines to extract signature 3D-profiles. Characteristic spatial distributions of amino acid residue types around phosphorylation sites were indeed discernable, especially when kinase-family-specific target sites were analyzed. To test the added value of using spatial information for the computational prediction of phosphorylation sites, Support Vector Machines were applied using both sequence as well as structural information. When compared to sequence-only based prediction methods, a small but consistent performance improvement was obtained when the prediction was informed by 3D-context information. While local one-dimensional amino acid sequence information was observed to harbor most of the discriminatory power, spatial context information was identified as relevant for the recognition of kinases and their cognate target sites and can be used for an improved prediction of phosphorylation sites. A web-based service (Phos3D) implementing the developed structure-based P-site prediction method has been made available at .
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影响因子：
--
作者：
Mah, Angie S.;Elia, Andrew E. H.;Deshaies, Raymond J.
通讯作者：
Deshaies, Raymond J.
影响因子：
19.7
作者：
HANLEY, JA;MCNEIL, BJ
通讯作者：
MCNEIL, BJ
影响因子：
5.6
作者：
Blom, N;Gammeltoft, S;Brunak, S
通讯作者：
Brunak, S
影响因子：
3
作者：
Diella F;Cameron S;Gemünd C;Linding R;Via A;Kuster B;Sicheritz-Pontén T;Blom N;Gibson TJ
通讯作者：
Gibson TJ
影响因子：
12.3
作者：
Jiménez JL;Hegemann B;Hutchins JR;Peters JM;Durbin R
通讯作者：
Durbin R