Thus, it is essential to comprehensively and quantitatively profile sequence recognition to decode these molecular networks. The strength and specificity of such ‘sequence recognition’ often spans orders of magnitude, and even weak ligands can be functional 1, 2, 3. ProBound opens new avenues for decoding biological networks and rationally engineering protein–ligand interactions.Ĭritical cellular processes, such as gene regulation and signal transduction, rely on sequence-specific molecular recognition to guide constituent proteins to preferentially interact with specific nucleic acid or polypeptide ligands. We also apply ProBound to profile the kinetics of kinase–substrate interactions. When coupled with an assay called K D-seq, it determines the absolute affinity of protein–ligand interactions.

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. This is achieved using a multi-layered maximum-likelihood framework that models both the molecular interactions and the data generation process. Here we describe a flexible machine learning method, called ProBound, that accurately defines sequence recognition in terms of equilibrium binding constants or kinetic rates. However, these assays do not provide the biophysical parameters that most rigorously quantify molecular interactions. Protein–ligand interactions are increasingly profiled at high throughput using affinity selection and massively parallel sequencing.

Machine transcriptions