Prediction of protein-ligand binding affinity from sequencing data with interpretable machine learning

Protein-ligand binding affinity is predicted quantitatively from sequencing data. 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.

Automated summary

This study presents a flexible machine learning method called ProBound, which accurately predicts the binding affinity or kinetic rates of protein-ligand interactions based on sequencing data. ProBound quantifies the behavior of transcription factors, captures the impact of DNA modifications and conformational flexibility, and infers specificity directly from in vivo data. It opens new avenues for decoding biological networks and rationally engineering protein-ligand interactions.