Leveraging conformational ensembles in allosteric drug discovery

Proteins involved in signaling networks, such as Ras, mammalian target of rapamycin (mTOR), and epidermal growth factor receptor (EGFR), exist as dynamic conformational ensembles in biomolecular condensates. These ensembles play a crucial role in allosteric drug discovery and action. Traditional approaches in drug discovery often trace back to the induced fit model, which viewed proteins as rigid entities with active and inactive states. However, this model's limitations hindered successful drug development. Advanced molecular dynamics simulations of oncogenic mutants and experiments reveal heterogeneous dynamic ensembles, which can uncover targetable spots like cryptic pockets and cooperative exosites that only exist transiently. In this review, we clarify traditional dogmas and show how recent knowledge improves allosteric drug design by leveraging conformational ensembles, with examples. We further discuss how ensemble-based approaches can advance promising therapeutics, unlocking their potential for more effective future strategies, including in biomolecular condensates.

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Publication Details

Journal
Trends in Pharmacological Sciences
Published
2026-02-27
DOI
https://doi.org/10.1016/j.tips.2026.01.006
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

Leveraging conformational ensembles in allosteric drug discovery

Hyunbum Jang, Ruth Nussinov, Clil Regev
Trends in Pharmacological Sciences
Computational Drug Discovery Methods
article

Leveraging conformational ensembles in allosteric drug discovery

Hyunbum Jang, Ruth Nussinov, Clil Regev
article en

Abstract

Proteins involved in signaling networks, such as Ras, mammalian target of rapamycin (mTOR), and epidermal growth factor receptor (EGFR), exist as dynamic conformational ensembles in biomolecular condensates. These ensembles play a crucial role in allosteric drug discovery and action. Traditional approaches in drug discovery often trace back to the induced fit model, which viewed proteins as rigid entities with active and inactive states. However, this model's limitations hindered successful drug development. Advanced molecular dynamics simulations of oncogenic mutants and experiments reveal heterogeneous dynamic ensembles, which can uncover targetable spots like cryptic pockets and cooperative exosites that only exist transiently. In this review, we clarify traditional dogmas and show how recent knowledge improves allosteric drug design by leveraging conformational ensembles, with examples. We further discuss how ensemble-based approaches can advance promising therapeutics, unlocking their potential for more effective future strategies, including in biomolecular condensates.

Trends in Pharmacological SciencesVol. 47(3)
Frederick National Laboratory for Cancer Research (US), National Cancer Institute (US)
Openalex Percentile: Top 39%
Computational Drug Discovery Methods
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