# Toward a Deeper Understanding of Allostery and Allotargeting by Computational Approaches

> **NIH GM R01** · STATE UNIVERSITY NEW YORK STONY BROOK · 2026 · $1,403,776

## Abstract

We propose to renew our R01 “Toward a deeper understanding of allostery and allotargeting by
computational approaches” (GM139297) building upon the significant progress we made during
the past funding term as well as breathtaking advances that took place in the applications of AI to
structural biology in recent years. A major breakthrough has been the introduction of AlphaFold
by Google DeepMind. The accessibility of > 200 million structures in the AlphaFold database,
together with our elastic network model (ENM)-based that can efficiently characterize structural
dynamics of proteins at the proteome scale, provided us with unique opportunities that we propose
to pursue in the renewal. An overarching theme of our R01 is the development of tools that
integrate physical-sciences-based models and methods with machine learning (ML) methods to
advance biological and pharmacological sciences. Our Aim 1 tackles a fundamental problem that
has been an obstacle to rational design of allosteric modulators, and which highlights the need
for physical-sciences-based models that can better inform ML methods. In Aim 1, we seek to
accurately model and distinguish the effects of positive and negative allosteric modulators (PAMs
and NAMs) on the structure and function of their target proteins. The challenge has been to
capture the subtle way local events at the small-molecule binding site elicit distinctive global
responses in the proteins. Data on these subtle events are sparse, highlighting the need for
physics-based models to tackle this challenge. We will develop a new methodology that combines
two powerful tools, our ProDy interface that provides a robust description of global/cooperative
mechanisms intrinsically accessible to allosteric proteins, and the weighted ensemble (WE)
enhanced sampling method that successfully samples at full atomic resolution events not
accessible by conventional MD, to generate pathways of subtle, local events that can be used to
train ML models or, on

## Key facts

- **NIH application ID:** 11227705
- **Project number:** 2R01GM139297-06
- **Recipient organization:** STATE UNIVERSITY NEW YORK STONY BROOK
- **Principal Investigator:** Ivet  Bahar
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** GM
- **Fiscal year:** 2026
- **Award amount:** $1,403,776
- **Award type:** 2
- **Project period:** 2021-08-05T00:00:00 → 2030-03-31T00:00:00

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11227705

## Citation

> US National Institutes of Health, RePORTER application 11227705, Toward a Deeper Understanding of Allostery and Allotargeting by Computational Approaches (2R01GM139297-06). Retrieved via AI Analytics 2026-05-20 from https://api.ai-analytics.org/grant/nih/11227705. Licensed CC0.

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