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New methods for quantitative modeling of protein-DNA interactions

蛋白质-DNA 相互作用定量建模的新方法

基本信息

批准号：
9150688
负责人：
Raluca Gordan
金额：
$ 35.02万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-25 至 2020-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9150688
关键词：
Address Affect Affinity Base Pairing Binding Binding Proteins Binding Sites Biological Assay Cell physiology Cells ChIP-seq Chromatin Complex Computer Simulation DNA DNA Binding DNA Binding Domain DNA Structure DNA-Protein Interaction Data Data Quality Dependency Development Disease Enhancers Environment Event Experimental Designs Gene Expression Gene Expression Regulation Genetic Genetic Polymorphism Genetic Variation Genome Genomic Segment Genomics Goals Human Human Genetics Human Genome In Vitro Investigation Lead Learning Link Measurement Methods Modeling Morphologic artifacts Mutation Noise Nucleotides Pattern Positioning Attribute Proteins Regulator Genes Role Single Nucleotide Polymorphism Site Software Tools Specificity Statistical Methods Statistical Models Testing Time Training Untranslated RNA Variant Weight Work base cofactor cost effective data modeling design falls flexibility genetic variant human disease improved in vitro Assay in vitro Model in vivo novel prevent programs trait transcription factor user friendly software vector

项目摘要

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.

摘要