# Computational modulator design and machine learning to target protein-protein interactions

> **NIH NIH R35** · NEW YORK UNIVERSITY · 2024 · $590,705

## Abstract

Abstract
The overall goal of my research program is to develop and apply state-of-the-art machine learning and
molecular modeling tools to facilitate the rational design of modulators of important cellular pathways for
therapeutic use. Protein-protein interactions (PPIs) are central factors in cellular signaling and biological
networks, and their mis-regulations lead to diseases states. Thus PPIs are biologically compelling targets for
drug discovery. Despite a few notable successes, most PPIs have not been successfully targeted and remain
challenging for therapeutic intervention. The fundamental challenge derives from their intrinsic structural
features: the binding surfaces of many PPIs are generally large in area, flat, and dynamic. PPIs are often
transient and involve multivalent contacts. One of the most promising PPI inhibitor discovery strategies is to
use miniature protein domain mimetics (PDMs) to reproduce the key interface contacts utilized by nature.
PDMs are advantageous as medium-sized molecules with high surface complementarity and a broader set of
contact points than typical small molecules, but are still limited because—by definition—only a portion of the
total PPI binding energy is captured in the interaction. The binding affinity of the synthetic domains is often
lower than the cognate full-length proteins. In last five years, we have significantly advanced a pocket-guided
rational design approach based on AlphaSpace to tackle this challenge. We have successfully optimized a
PDM to target the KIX domain of the p300/CBP coactivator by introducing non-natural amino acids to improve
pocket-fragment binding; rationally designed a novel NEMO coiled coil mimic that disrupts virus-induced NF-κB
signaling and induces cell death; and successfully targeted a new binding pocket on MDM2 and MDMX with a
potent dual inhibitor by elaborating hydrogen-bond stabilized alpha-helix mimetics. Meanwhile, we have
developed state-of-the-art scoring functions for protein-ligand docking as well as virtual screening, advanced
deep learning models to predict molecular properties and chemical reactions, and established strong and
fruitful collaborations with several outstanding experimental labs in chemical biology and biophysics to discover
new modulators of biomolecular interactions. These significant advances set the stage for us to further push
the frontier of integrating machine learning and molecular modeling for rational drug design. Our focus in the
next few years will be to establish a robust pocket-guided design platform based on AlphaSpace and machine
learning for PPI orthosteric inhibitor optimization, provide physical/chemical insights and develop novel
computational strategies for allosteric modulator discovery, and explore chemical space with deep
sequence/graph/geometric representation learning for multi-objective molecular design. Our modulator design
efforts in close collaborations with our experimental colleagues will not only rigorousl...

## Key facts

- **NIH application ID:** 10849794
- **Project number:** 5R35GM127040-07
- **Recipient organization:** NEW YORK UNIVERSITY
- **Principal Investigator:** Yingkai Zhang
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $590,705
- **Award type:** 5
- **Project period:** 2018-05-01 → 2028-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10849794

## Citation

> US National Institutes of Health, RePORTER application 10849794, Computational modulator design and machine learning to target protein-protein interactions (5R35GM127040-07). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10849794. Licensed CC0.

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