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

> **NIH NIH R35** · NEW YORK UNIVERSITY · 2021 · $556,806

## Abstract

Abstract
The overall goal of my research program is to develop and apply computational 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 gene regulation networks. Their misregulation is associated with a
variety of diseases, including cancer, neurodegenerative disease, autoimmune disease, and diabetes.
Inevitably, many PPIs are biologically compelling targets for drug discovery. But despite a few notable
successes, most PPIs have not been successfully targeted and remain undruggable. 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. Currently, one most
promising PPI inhibitor discovery strategy 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. On the other hand,
targeted covalent inhibition is an orthogonal therapeutic approach fit to overcome the fundamental binding
limitations at PPIs, but has a well-known drawback: the high reactivity of typical covalent warheads leads to
nonspecific inhibition, and toxicity. Here we aim to develop computational methods for a new design strategy
that will leverage the strengths of these two methods—PDMs and covalent inhibition—while simultaneously
mitigating their respective limitations. The focus of the effort is to rationally discover potent inhibitors that will
non-covalently recognize and then covalently target protein-protein binding interfaces with exquisite specificity.
Furthermore, our development of robust scoring functions by integrating multitask machine learning and
molecular modeling would significantly accelerate the rational drug discovery process. The planned work
builds on our recent advances in three state-of-the-art computational approaches: AlphaSpace for fragment-
centric topographical mapping of PPI interfaces; ab initio QM/MM molecular dynamics for modeling covalent
inhibition; and a novel delta-machine learning strategy to simultaneously improve scoring, docking and
screening performance of a protein-ligand scoring function. Our design efforts will result in highly specific and
potent modulators of a variety of therapeutically important but previously undruggable PPI interfaces, providing
new leads for drug development.

## Key facts

- **NIH application ID:** 10152659
- **Project number:** 5R35GM127040-04
- **Recipient organization:** NEW YORK UNIVERSITY
- **Principal Investigator:** Yingkai Zhang
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $556,806
- **Award type:** 5
- **Project period:** 2018-05-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10152659

## Citation

> US National Institutes of Health, RePORTER application 10152659, Computational modulator design and machine learning to target protein-protein interactions (5R35GM127040-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10152659. Licensed CC0.

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