哺乳动物转录因子DNA结合谱

基因差异表达是生物发育和对刺激作出应答的分子基础,转录因子在这种基因差异表达中发挥着重要的调控作用。因此,要弄清楚转录因子调控基因差异表达的机理,就必须鉴定出它们全部的靶基因并构建其操纵的转录调控网络。对基因组DNA的序列特异性结合是转录因子调控基因转录的关键环节,因此,要鉴定转录因子的靶基因,就必须从它们与DNA相互作用的分子水平,鉴定它们能够识别并结合的全部DNA序列,即转录因子DNA结合谱。近年来随着DNA微阵列芯片和高通量DNA测序技术的产生和快速发展,出现了建立转录因子体内及体外DNA结合谱的一系列革命性的新技术,对该领域的研究带来重大影响。这些新技术主要包括建立转录因子体内DNA结合谱的染色质免疫沉淀-芯片技术(ChIP-chip)和染色质免疫沉淀-测序技术(ChIP-Seq),以及建立转录因子体外DNA结合谱的双链DNA微阵列芯片技术(dsDNA microarray)、指数富集配体系统进化-系列分析基因表达技术(SELEX-SAGE)、结合-n-测序技术(Bind-n-Seq)、多重大规模并行SELEX技术(MMP-SELEX)、凝胶迁移实验-测序技术(EMSA-Seq)和高通量测序-荧光配体互作图谱分析技术(HiTS-FLIP)。文章将对这些新技术做一综述。     

A consensus DNA-binding site for the androgen receptor.

We have used a DNA-binding site selection assay to determine a consensus binding sequence for the androgen receptor (AR). A purified fusion protein containing the AR DNA-binding domain was incubated with a pool of random sequence oligonucleotides, and complexes were isolated by gel mobility shift assays. Individually selected sites were characterised by nucleotide sequencing and compiled to give a consensus AR-binding element. This sequence is comprised of two 6-basepair (bp) asymmetrical elements separated by a 3-bp spacer, 5'-GGA/TACANNNTGTTCT-3', similar to that described for the glucocorticoid response element. Inspection of the consensus revealed a slight preference for G or A nucleotides at the +1 position in the spacer and for A and T nucleotides in the 3'-flanking region. Therefore, a series of oligonucleotides was designed in which the spacer and flanking nucleotides were changed to the least preferred sequence. Competition experiments with these oligonucleotides and the AR fusion protein indicated that an oligonucleotide with both the spacer and flanking sequences changed had greater than 3-fold less affinity than the consensus sequence. The functional activity of these oligonucleotides was also assessed by placing them up-stream of a reporter gene in a transient transfection assay and correlated with the affinity with which the AR fusion protein bound to DNA. Therefore, sequences surrounding the two 6-bp half-sites influence both the binding affinity for the receptor and the functional activity of the response element.     

Residue-level prediction of DNA-binding sites and its application on DNA-binding protein predictions

Protein-DNA interactions are crucial to many cellular activities such as expression-control and DNA-repair. These interactions between amino acids and nucleotides are highly specific and any aberrance at the binding site can render the interaction completely incompetent. In this study, we have three aims focusing on DNA-binding residues on the protein surface: to develop an automated approach for fast and reliable recognition of DNA-binding sites; to improve the prediction by distance-dependent refinement; use these predictions to identify DNA-binding proteins. We use a support vector machines (SVM)-based approach to harness the features of the DNA-binding residues to distinguish them from non-binding residues. Features used for distinction include the residue's identity, charge, solvent accessibility, average potential, the secondary structure it is embedded in, neighboring residues, and location in a cationic patch. These features collected from 50 proteins are used to train SVM. Testing is then performed on another set of 37 proteins, much larger than any testing set used in previous studies. The testing set has no more than 20% sequence identity not only among its pairs, but also with the proteins in the training set, thus removing any undesired redundancy due to homology. This set also has proteins with an unseen DNA-binding structural class not present in the training set. With the above features, an accuracy of 66% with balanced sensitivity and specificity is achieved without relying on homology or evolutionary information. We then develop a post-processing scheme to improve the prediction using the relative location of the predicted residues. Balanced success is then achieved with average sensitivity, specificity and accuracy pegged at 71.3%, 69.3% and 70.5%, respectively. Average net prediction is also around 70%. Finally, we show that the number of predicted DNA-binding residues can be used to differentiate DNA-binding proteins from non-DNA-binding proteins with an accuracy of 78%. Results presented here demonstrate that machine-learning can be applied to automated identification of DNA-binding residues and that the success rate can be ameliorated as more features are added. Such functional site prediction protocols can be useful in guiding consequent works such as site-directed mutagenesis and macromolecular docking.