New methods for quantitative modeling of protein-DNA interactions

摘要 准确预测人类基因组中转录因子(TF)与DNA的相互作用对于 破译健康和疾病细胞中的转录调控网络，以及理解 非编码区基因多态的表型效应。然而，使用最广泛的模型 已知的Tf-DNA结合亲和力，即位置权重矩阵(PWM)，仅提供 TF的真正序列特异性，因为它假定TF结合位点中的碱基对之间是独立的。 已经提出了更复杂的结合模型，但它们相对于PWMS的改进也微乎其微 由于训练数据的限制(即，由于强烈的偏差、噪声、伪影或混杂因素)或 因为这些模型不够灵活，无法捕获TF结合位点中的复杂依赖关系。作为一名 结果：目前的dna结合模型预测非编码遗传变异的影响的能力有限。 在TF结合上，它们不能用来解决与 相似的DNA结合域，但在细胞中具有不同的调节作用。此应用程序的目标是 通过生成高质量的数据来克服这些限制，这些数据将用于训练灵活的统计模型 生成与体外实验分析类似的准确的TF-DNA结合亲和力预测。这个 根据初步结果和以前的工作，中心假设是，更好的亲和力数据和更好的 需要统计模型来预测具有显著更高的Tf-DNA相互作用的人类细胞 比目前的模型更准确。将在目标1中生成40个人TF的高质量结合亲和力数据 使用精心设计的体外测试的独特组合，将偏差和噪音降至最低，从而使 数据是训练复杂模型的理想选择。在AIM 2中将开发新的TF-DNA结合模型，使用状态- 最先进的统计方法：支持向量回归、非参数贝叶斯建模和条件张量 因式分解。这些模型将在体外进行实验测试，并利用来自 编码项目。在目标3中，新的绑定模型将用于两个应用：1)预测 非编码单核苷酸多态对转铁蛋白结合亲和力和转铁蛋白结合的定量影响 水平，以及2)预测具有相似DNA结合的密切相关的TF在体内的DNA结合差异 结构域，但在细胞中具有不同的调节功能。使用目前的模型，这样的应用是不可能的。 总体而言，我们预计在该项目中开发的绑定亲和力模型将允许更多 对调控因子-DNA相互作用的准确预测比使用当前模型可能的预测更准确，这一点意义重大 因为这将使我们更好地理解基因调控项目及其在 疾病，包括了解将基因变异与人类疾病联系起来的一连串事件。

ABSTRACT Accurate predictions of transcription factor (TF)-DNA interactions across the human genome are critical for deciphering transcriptional regulatory networks in healthy and diseased cells, as well as for understanding the phenotypic effects of polymorphisms in non-coding genomic regions. However, the most widely used model of TF-DNA binding affinity, the position weight matrix (PWM), is known to provide only an approximation of the true sequence specificity of TFs, because it assumes independence among the base pairs in TF binding sites. More complex binding models have been proposed, but their improvement over PWMs was marginal, either because of limitations of the training data (i.e. due to strong biases, noise, artifacts, or confounding factors) or because the models were not flexible enough to capture complex dependencies in TF binding sites. As a result, current DNA binding models have a limited ability to predict the effects of non-coding genetic variation on TF binding, and they cannot be used to resolve functional differences between closely related TFs with similar DNA binding domains but distinct regulatory roles in the cell. The objective of this application is to overcome these limitations by generating high quality data that will be used to train flexible statistical models to generate TF-DNA binding affinity predictions with accuracies similar to experimental in vitro assays. The central hypothesis, based on preliminary results and previous work, is that both better affinity data and better statistical models are needed in order to predict TF-DNA interactions in human cells with significantly higher accuracy than current models. High quality binding affinity data for 40 human TFs will be generated in Aim 1 using a unique combination of in vitro assays carefully designed to minimizes bias and noise, thus making the data ideal for training complex models. Novel TF-DNA binding models will be developed in Aim 2 using state- of-the-art statistical methods: support vector regression, nonparametric Bayes modeling, and conditional tensor factorization. The models will be tested experimentally in vitro, and by leveraging in vivo data from the ENCODE project. In Aim 3, the new binding models will be used in two applications: 1) to predict the quantitative effects of non-coding single nucleotide polymorphisms on TF binding affinities and TF binding levels, and 2) to predict differential in vivo DNA binding of closely related TFs with similar DNA binding domains but distinct regulatory functions in the cell. Such applications are not possible using current models. Overall, we anticipate that the binding affinity models developed in this project will allow for much more accurate predictions of regulatory TF-DNA interactions than possible using current models, which is significant because it will lead to a better understanding of gene regulatory programs and their misregulation during disease, including understanding the cascade of events that link genetic variation to human disease.