Determination of specificity influencing residues for key transcription factor families.
Determination of specificity influencing residues for key transcription factor families.
复制标题
确定影响关键转录因子家族残基的特异性。
DOI：
10.1007/s40484-015-0045-y
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发表时间：
2015-09-01
期刊：
影响因子：
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通讯作者：
D Stormo G
中科院分区：
文献类型：
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作者：
Patel RY;Garde C;D Stormo G
转录因子 (TF) 是转录和后续细胞过程的主要调节剂。转录因子与特定调控元件的结合由其特异性决定。考虑到已知 TF 序列和特异性之间的差距，非常需要特异性预测框架。此类框架的关键输入是调节所考虑的 TF 特异性的蛋白质残基。由于蛋白质三维结构所施加的结构限制，诸如互信息（MI）之类的简单测量方法无法描述比对中影响特异性的残基（SIR）。氨基酸序列进化的结构限制导致错误 SIR 的识别。在这篇手稿中，我们扩展了三种方法（直接信息、PSICOV 和调整的相互信息），这些方法已用于将虚假的间接蛋白质残基-残基接触与直接接触分开，以从氨基酸和特异性的联合比对中识别 SIR。我们使用这些方法预测了 TF 的同源结构域 (HD)、螺旋-环-螺旋、LacI 和 GntR 家族的 SIR，并与 MI 进行了比较。通过各种测量，我们表明这三种方法的性能相当，但优于 MI。讨论了这些方法在特异性预测框架中的含义。这些方法以 R 包的形式实现，并可与stormo.wustl.edu/SpecPred 上的比对一起使用。
Transcription factors (TFs) are major modulators of transcription and subsequent cellular processes. The binding of TFs to specific regulatory elements is governed by their specificity. Considering the gap between known TFs sequence and specificity, specificity prediction frameworks are highly desired. Key inputs to such frameworks are protein residues that modulate the specificity of TF under consideration. Simple measures like mutual information (MI) to delineate specificity influencing residues (SIRs) from alignment fail due to structural constraints imposed by the three-dimensional structure of protein. Structural restraints on the evolution of the amino-acid sequence lead to identification of false SIRs. In this manuscript we extended three methods (Direct Information, PSICOV and adjusted mutual information) that have been used to disentangle spurious indirect protein residue-residue contacts from direct contacts, to identify SIRs from joint alignments of amino-acids and specificity. We predicted SIRs forhomeodomain (HD), helix-loop-helix, LacI and GntR families of TFs using these methods and compared to MI. Using various measures, we show that the performance of these three methods is comparable but better than MI. Implication of these methods in specificity prediction framework is discussed. The methods are implemented as an R package and available along with the alignments at stormo.wustl.edu/SpecPred.