Protein–ligand interactions are increasingly profiled at high throughput using affinity selection and massively parallel sequencing. However, these assays do not provide the biophysical parameters that most rigorously quantify molecular interactions. Here we describe a flexible machine learning method, called ProBound, that accurately defines sequence recognition in terms of equilibrium binding constants or kinetic rates. This is achieved using a multi-layered maximum-likelihood framework that models both the molecular interactions and the data generation process. We show that ProBound quantifies transcription factor (TF) behavior with models that predict binding affinity over a range exceeding that of previous resources; captures the impact of DNA modifications and conformational flexibility of multi-TF complexes; and infers specificity directly from in vivo data such as ChIP-seq without peak calling. When coupled with an assay called K_D-seq, it determines the absolute affinity of protein–ligand interactions. We also apply ProBound to profile the kinetics of kinase–substrate interactions. ProBound opens new avenues for decoding biological networks and rationally engineering protein–ligand interactions.

使用亲和力选择和大规模并行测序在高通量下越来越多地分析蛋白质-配体相互作用。然而，这些测定并没有提供最严格量化分子相互作用的生物物理参数。在这里，我们描述了一种灵活的机器学习方法，称为 ProBound，它根据平衡结合常数或动力学速率准确定义序列识别。这是通过使用多层最大似然框架来实现的，该框架对分子相互作用和数据生成过程进行建模。我们表明，ProBound 通过模型来量化转录因子 (TF) 行为，该模型预测的结合亲和力范围超过了以前的资源；捕获 DNA 修饰和多 TF 复合物构象灵活性的影响；并直接从体内数据（例如 ChIP-seq）推断特异性，无需峰识别。当与称为K _D -seq的检测结合使用时，它可以确定蛋白质-配体相互作用的绝对亲和力。我们还应用 ProBound 来分析激酶-底物相互作用的动力学。ProBound 为解码生物网络和合理设计蛋白质-配体相互作用开辟了新途径。