Creating PWMs of transcription factors using 3D structure-based computation of protein-DNA free binding energies
Creating PWMs of transcription factors using 3D structure-based computation of protein-DNA free binding energies
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DOI：
10.1186/1471-2105-11-225
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发表时间：
2010-05-03
影响因子：
3
通讯作者：
Kel, Alexander
中科院分区：
文献类型：
--
作者：
Alamanova, Denitsa;Stegmaier, Philip;Kel, Alexander
背景：转录因子-DNA结合模式的知识对于理解基因转录至关重要。许多DNA结合蛋白在文献中被注释为转录因子，然而，对于其中许多相应的DNA结合基序仍然uncharacterized. Results：位置权重矩阵（PWMs）的转录因子从不同的结构类已被确定使用基于知识的统计潜力。根据蛋白质-DNA接触的晶体学数据校准的评分函数恢复了广泛研究的转录因子家族（如p53和NF-κ B）的各种成员的PWM。在可能的情况下，与实验结合亲和力数据和其他物理模型进行了广泛的比较。虽然p50p50，p50RelB，和p50p65二聚体属于同一个家庭，特别是在他们的PWMs的差异进行检测，从而表明可能不同的体内结合模式。p63和p73的PWM的同源性建模的基础上计算，并使用85 p53/p73调节的人类基因的上游序列研究其性能。有趣的是，在总共126个启动子中，通过匹配算法报告的p63和p73命中中的大约一半位于相应转录起始位点上游2kb以上，这偏离了大多数调控位点位于更接近TSS的常见假设。在大多数情况下，p63和p73的结合位点与p53位点不重叠的事实表明，p63和p73可以协同影响p53的转录活性。新计算的p50p50 PWM恢复5个以上的实验结合位点比相应的TRANSFAC矩阵，而两个PWM表现出可比的receiver operator characteristics. Conclusions：一种新的算法被开发来计算位置权重矩阵从蛋白质-DNA复合物的结构。该算法对实验数据进行了广泛的验证。该方法进一步与同源建模相结合，以获得与DNA的晶体复合物尚不可用的因子的PWM。在这项工作中获得的PWMs相比，传统构建的矩阵的性能表明，基于结构的方法提出了一个很有前途的替代实验确定的转录因子结合特性。
Background: Knowledge of transcription factor-DNA binding patterns is crucial for understanding gene transcription. Numerous DNA-binding proteins are annotated as transcription factors in the literature, however, for many of them the corresponding DNA-binding motifs remain uncharacterized.Results: The position weight matrices (PWMs) of transcription factors from different structural classes have been determined using a knowledge-based statistical potential. The scoring function calibrated against crystallographic data on protein-DNA contacts recovered PWMs of various members of widely studied transcription factor families such as p53 and NF-kappa B. Where it was possible, extensive comparison to experimental binding affinity data and other physical models was made. Although the p50p50, p50RelB, and p50p65 dimers belong to the same family, particular differences in their PWMs were detected, thereby suggesting possibly different in vivo binding modes. The PWMs of p63 and p73 were computed on the basis of homology modeling and their performance was studied using upstream sequences of 85 p53/p73-regulated human genes. Interestingly, about half of the p63 and p73 hits reported by the Match algorithm in the altogether 126 promoters lay more than 2 kb upstream of the corresponding transcription start sites, which deviates from the common assumption that most regulatory sites are located more proximal to the TSS. The fact that in most of the cases the binding sites of p63 and p73 did not overlap with the p53 sites suggests that p63 and p73 could influence the p53 transcriptional activity cooperatively. The newly computed p50p50 PWM recovered 5 more experimental binding sites than the corresponding TRANSFAC matrix, while both PWMs showed comparable receiver operator characteristics.Conclusions: A novel algorithm was developed to calculate position weight matrices from protein-DNA complex structures. The proposed algorithm was extensively validated against experimental data. The method was further combined with Homology Modeling to obtain PWMs of factors for which crystallographic complexes with DNA are not yet available. The performance of PWMs obtained in this work in comparison to traditionally constructed matrices demonstrates that the structure-based approach presents a promising alternative to experimental determination of transcription factor binding properties.