MCPath: Monte Carlo path generation approach to predict likely allosteric pathways and functional residues.
MCPath: Monte Carlo path generation approach to predict likely allosteric pathways and functional residues.
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DOI：
10.1093/nar/gkt284
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发表时间：
2013-07
影响因子：
14.9
通讯作者：
Haliloglu T
中科院分区：
文献类型：
--
作者：
Kaya C;Armutlulu A;Ekesan S;Haliloglu T
蛋白质的变构机制在生物分子信号传递中起着至关重要的作用。这一机制背后的一个重要方面是连接功能残基的通讯途径。本文提出并实现了一种蒙特卡罗(MC)路径生成方法，通过生成最大概率路径集合来定义可能的变构路径。蛋白质的结构被认为是一个氨基酸残基的网络，残基之间的相互作用用原子势函数来描述。以PDZ区域结构为例进行了研究。对牛视紫质和三种肌球蛋白结构的分析也作为补充案例研究提供。所建议的途径和构成途径的残基是最大可能的，并与以前的研究基本一致。综上所述，这些通讯途径可以是多条且内在地配置的，而MC路径生成方法为预测关键残基提供了一个有效的工具，这些残基在一系列途径和功能上可信的残基中介导变构通讯。Http://safir.prc.boun.edu.tr/clbet_server.上提供了MCPath服务器
Allosteric mechanism of proteins is essential in biomolecular signaling. An important aspect underlying this mechanism is the communication pathways connecting functional residues. Here, a Monte Carlo (MC) path generation approach is proposed and implemented to define likely allosteric pathways through generating an ensemble of maximum probability paths. The protein structure is considered as a network of amino acid residues, and inter-residue interactions are described by an atomistic potential function. PDZ domain structures are presented as case studies. The analysis for bovine rhodopsin and three myosin structures are also provided as supplementary case studies. The suggested pathways and the residues constituting the pathways are maximally probable and mostly agree with the previous studies. Overall, it is demonstrated that the communication pathways could be multiple and intrinsically disposed, and the MC path generation approach provides an effective tool for the prediction of key residues that mediate the allosteric communication in an ensemble of pathways and functionally plausible residues. The MCPath server is available at http://safir.prc.boun.edu.tr/clbet_server.
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DOI：
10.1016/j.str.2009.06.008
发表时间：
2009-08-12
期刊：
Structure (London, England : 1993)
影响因子：
--
作者：
del Sol A;Tsai CJ;Ma B;Nussinov R
通讯作者：
Nussinov R
影响因子：
9.9
作者：
通讯作者：
--
影响因子：
2.9
作者：
Harris, BZ;Lau, FW;Lim, WA
通讯作者：
Lim, WA
影响因子：
14.9
作者：
Huang Z;Zhu L;Cao Y;Wu G;Liu X;Chen Y;Wang Q;Shi T;Zhao Y;Wang Y;Li W;Li Y;Chen H;Chen G;Zhang J
通讯作者：
Zhang J
影响因子：
5.6
作者：
Kozlov, G;Banville, D;Ekiel, I
通讯作者：
Ekiel, I