Chaitanya Rastogi

Accurate sequence-to-affinity models for SH2 domains from multi-round peptide binding assays coupled with free-energy regression

The paper develops a sequence-to-affinity model for SH2 domains using multi-round peptide binding assays and free-energy regression to predict binding specificity and variant effects.

Optimized reporters for multiplexed detection of transcription factor activity

The paper systematically optimizes transcriptional reporters for 86 transcription factors and identifies 60 highly sensitive and specific reporters.

Optimisation of TP53 reporters by systematic dissection of synthetic TP53 response elements

The paper systematically examines how TP53 binding site architecture affects transcriptional output and develops a model to predict reporter activity.

Transcription factor paralogs orchestrate alternative gene regulatory networks by context-dependent cooperation with multiple cofactors

The paper shows Hox paralogs use different cofactors in distinct domains to activate specific gene regulatory networks in Drosophila legs.

Probing molecular specificity with deep sequencing and biophysically interpretable machine learning

The paper introduces ProBound, a machine learning framework that quantifies biophysical parameters of protein-ligand interactions using NGS data, and couples it with Kd-seq to determine absolute affinities.

Context-dependent gene regulation by transcription factor complexes

The paper investigates how DNA sequence influences the formation of transcription factor complexes and shows that non-canonical binding sites arise from direct interactions with functionally bound sites.

SelexGLM differentiates androgen and glucocorticoid receptor DNA-binding preference over an extended binding site

This study identifies differences in DNA-binding preferences between androgen and glucocorticoid receptors using SELEX-seq and SelexGLM, revealing AR's greater sequence sensitivity and enthalpy-driven binding.