Mapping the Chemical Space of Active-Site Targeted Covalent Ligands for Protein Tyrosine Phosphatases.

Protein tyrosine phosphatases (PTPs) are an important class of enzymes that modulate essential cellular processes through protein dephosphorylation and are dysregulated in various disease states. There is demand for new compounds that target the active sites of these enzymes, for use as chemical tools to dissect their biological roles or as leads for the development of new therapeutics. In this study, we explore an array of electrophiles and fragment scaffolds to investigate the required chemical parameters for covalent inhibition of tyrosine phosphatases. Our analysis juxtaposes the intrinsic electrophilicity of these compounds with their potency against several classical PTPs, revealing chemotypes that inhibit tyrosine phosphatases while minimizing excessive, potentially non-specific reactivity. We also assess sequence divergence at key residues in PTPs to explain their differential susceptibility to covalent inhibition. We anticipate that our study will inspire new strategies to develop covalent probes and inhibitors for tyrosine phosphatases.

High-throughput profiling of sequence recognition by tyrosine kinases and SH2 domains using bacterial peptide display.

Tyrosine kinases and SH2 (phosphotyrosine recognition) domains have binding specificities that depend on the amino acid sequence surrounding the target (phospho)tyrosine residue. Although the preferred recognition motifs of many kinases and SH2 domains are known, we lack a quantitative description of sequence specificity that could guide predictions about signaling pathways or be used to design sequences for biomedical applications. Here, we present a platform that combines genetically encoded peptide libraries and deep sequencing to profile sequence recognition by tyrosine kinases and SH2 domains. We screened several tyrosine kinases against a million-peptide random library and used the resulting profiles to design high-activity sequences. We also screened several kinases against a library containing thousands of human proteome-derived peptides and their naturally-occurring variants. These screens recapitulated independently measured phosphorylation rates and revealed hundreds of phosphosite-proximal mutations that impact phosphosite recognition by tyrosine kinases. We extended this platform to the analysis of SH2 domains and showed that screens could predict relative binding affinities. Finally, we expanded our method to assess the impact of non-canonical and post-translationally modified amino acids on sequence recognition. This specificity profiling platform will shed new light on phosphotyrosine signaling and could readily be adapted to other protein modification/recognition domains.

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

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 KD-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.