PTMcode: a database of known and predicted functional associations between post-translational modifications in proteins.
PTMcode: a database of known and predicted functional associations between post-translational modifications in proteins.
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DOI：
10.1093/nar/gks1230
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发表时间：
2013-01
影响因子：
14.9
通讯作者：
Bork P
中科院分区：
文献类型：
--
作者：
Minguez P;Letunic I;Parca L;Bork P
翻译后修饰（PTMs）参与真核生物蛋白的调控和结构稳定。单个PTM状态的组合是调节细胞功能的关键，这在一些经过充分研究的蛋白质中得到了证明。这种组合设置，被称为PTM代码，已被提议扩展到真核生物的整个蛋白质组。虽然我们还远远不能破译这样一种复杂的语言，但高通量技术正在绘制数千个蛋白质PTM位点，从而为比较分析提供了足够的数据。PTMcode （http://ptmcode.embl.de）旨在汇编已知和预测的PTM关联，以提供一个框架，使各种尺度的假设驱动的实验或计算分析成为可能。在其第一个版本中，PTMcode提供了8种真核生物蛋白中13种不同PTM类型的PTM功能关联。他们基于五个证据渠道：文献调查，残基共同进化，结构接近，相同残基的PTM和PTM高富集蛋白区域（热点）内的位置。PTMcode是一个基于蛋白质的可搜索数据库，具有交互式web界面，提供了bbb10 000个蛋白质中近75000个残基的共同调控背景。
Post-translational modifications (PTMs) are involved in the regulation and structural stabilization of eukaryotic proteins. The combination of individual PTM states is a key to modulate cellular functions as became evident in a few well-studied proteins. This combinatorial setting, dubbed the PTM code, has been proposed to be extended to whole proteomes in eukaryotes. Although we are still far from deciphering such a complex language, thousands of protein PTM sites are being mapped by high-throughput technologies, thus providing sufficient data for comparative analysis. PTMcode (http://ptmcode.embl.de) aims to compile known and predicted PTM associations to provide a framework that would enable hypothesis-driven experimental or computational analysis of various scales. In its first release, PTMcode provides PTM functional associations of 13 different PTM types within proteins in 8 eukaryotes. They are based on five evidence channels: a literature survey, residue co-evolution, structural proximity, PTMs at the same residue and location within PTM highly enriched protein regions (hotspots). PTMcode is presented as a protein-based searchable database with an interactive web interface providing the context of the co-regulation of nearly 75 000 residues in >10 000 proteins.
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影响因子：
56.9
作者：
Choudhary, Chunaram;Kumar, Chanchal;Mann, Matthias
通讯作者：
Mann, Matthias
影响因子：
14.9
作者：
Lee, Tzong-Yi;Huang, Hsien-Da;Hung, Jui-Hung;Huang, Hsi-Yuan;Yang, Yuh-Shyong;Wang, Tzu-Hao
通讯作者：
Wang, Tzu-Hao
影响因子：
14.9
作者：
Keshava Prasad TS;Goel R;Kandasamy K;Keerthikumar S;Kumar S;Mathivanan S;Telikicherla D;Raju R;Shafreen B;Venugopal A;Balakrishnan L;Marimuthu A;Banerjee S;Somanathan DS;Sebastian A;Rani S;Ray S;Harrys Kishore CJ;Kanth S;Ahmed M;Kashyap MK;Mohmood R;Ramachandra YL;Krishna V;Rahiman BA;Mohan S;Ranganathan P;Ramabadran S;Chaerkady R;Pandey A
通讯作者：
Pandey A
影响因子：
11.4
作者：
Landry, Christian R.;Levy, Emmanuel D.;Michnick, Stephen W.
通讯作者：
Michnick, Stephen W.
影响因子：
14.9
作者：
Hornbeck PV;Kornhauser JM;Tkachev S;Zhang B;Skrzypek E;Murray B;Latham V;Sullivan M
通讯作者：
Sullivan M