Peter Suzuki

The paper develops HT-recruit to measure transcriptional effector function across contexts and identifies context-dependent effectors for CRISPR-based transcription control.

The paper predicts and designs transcriptional repressor domains using mutational scans and a deep learning model called TENet, identifying functional variants and enabling synthetic design.

This study quantifies direct interactions between 204 human activation domains and eight co-activators using a microfluidic platform, revealing 334 novel binding pairs and demonstrating the utility of analytical models for predicting activation.

The paper systematically maps and characterizes transcriptional effector domains in human chromatin regulators and transcription factors, identifying new activation and repression domains and their functional features.

The paper develops HT-recruit, a high-throughput assay to identify transcriptional effectors in human proteins, finding repressor function correlates with evolutionary age and discovering new activator domains.