RyangGuk Kim, Rosario I Corona, Bo Hong, Jun-tao Guo
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引用次数: 12
Abstract
Structural insight from transcription factor-DNA (TF-DNA) complexes is of paramount importance to our understanding of the affinity and specificity of TF-DNA interaction, and to the development of structure-based prediction of TF binding sites. Yet the majority of the TF-DNA complexes remain unsolved despite the considerable experimental efforts being made. Computational docking represents a promising alternative to bridge the gap. To facilitate the study of TF-DNA docking, carefully designed benchmarks are needed for performance evaluation and identification of the strengths and weaknesses of docking algorithms.
We constructed two benchmarks for flexible and rigid TF-DNA docking respectively using a unified non-redundant set of 38 test cases. The test cases encompass diverse fold families and are classified into easy and hard groups with respect to the degrees of difficulty in TF-DNA docking. The major parameters used to classify expected docking difficulty in flexible docking are the conformational differences between bound and unbound TFs and the interaction strength between TFs and DNA. For rigid docking in which the starting structure is a bound TF conformation, only interaction strength is considered.
We believe these benchmarks are important for the development of better interaction potentials and TF-DNA docking algorithms, which bears important implications to structure-based prediction of transcription factor binding sites and drug design.
转录因子- dna (TF- dna)复合物的结构洞察对于我们理解TF- dna相互作用的亲和力和特异性以及基于结构的TF结合位点预测的发展至关重要。然而,尽管进行了大量的实验努力,大多数TF-DNA复合物仍未得到解决。计算对接代表了一个有希望的替代方案,以弥合差距。为了便于对TF-DNA对接的研究,需要精心设计基准来评估对接算法的性能并识别其优缺点。我们使用38个统一的非冗余测试用例分别构建了柔性和刚性TF-DNA对接的两个基准。测试用例包含不同的折叠家族,并根据TF-DNA对接的困难程度分为容易组和困难组。柔性对接中对预期对接难度进行分类的主要参数是结合与未结合的tf之间的构象差异以及tf与DNA之间的相互作用强度。对于初始结构为有界TF构象的刚性对接,只考虑相互作用强度。我们相信这些基准对于开发更好的相互作用潜力和TF-DNA对接算法非常重要,这对基于结构的转录因子结合位点预测和药物设计具有重要意义。
期刊介绍:
BMC Structural Biology is an open access, peer-reviewed journal that considers articles on investigations into the structure of biological macromolecules, including solving structures, structural and functional analyses, and computational modeling.