AI‑Designed Binders Target the “Undruggable” World of Disordered Proteins – The Babak Lab
The Babak Lab shared a post on LinkedIn about recent paper by Kejia Wu et al., published on Science.
“Scientific Wednesdays: AI‑Designed Binders Target the ‘Undruggable’ World of Disordered Proteins.
A groundbreaking report in Science (July 17, 2025) introduces a new approach where designed proteins can specifically bind intrinsically disordered protein regions (IDRs) — a previously untargetable part of the proteome.
Study Focus:
Develop a computational pipeline to design proteins (binders) that recognize diverse unstructured protein segments with high affinity and specificity.
Research Highlights:
- AI-guided pipeline combines Rosetta and RFdiffusion to generate template pockets that adapt to flexible IDR conformations
- Designed binders created for 39 diverse IDR targets—34 showed binding affinities between 100 pM and 100 nM
- Binders are specific: all‑by‑all binding tests confirmed target selectivity
- Functional in cells — as detection tools, inhibitors of GPCR signaling, or protein localization agents
- Broad applicability: from proteomics to drug targeting of cancer or neurodegeneration-associated IDRs
Key Findings:
- Disorder becomes an advantage — designed pockets accommodate unstructured targets
- Binders work across vastly different sequences and contexts
- New avenue to target previously ‘undruggable’ proteins, including those involved in cancer or Alzheimer’s
Conclusion:
This study marks a transformative leap in protein engineering: AI-designed binders unlock the ability to target intrinsically disordered regions — a once elusive but central element in many diseases.”
Maria Babak, Head of The Babak Lab and Assistant Professor at City University of Hong Kong, shared this post, adding:
“A new era for AI in science!
Context-aware models now reveal insights across genomics, imaging and beyond. Powerful step forward in biomedical discovery.”
Title: Design of intrinsically disordered region binding proteins
Authors: Kejia Wu, Hanlun Jiang, Derrick R. Hicks, Caixuan Liu, Edin Muratspahić, Theresa A. Ramelot, Yuexuan Liu, Kerrie McNally, Sebastian Kenny, Andrei Mihut, Amit Gaur, Brian Coventry, Wei Chen, Asim K. Bera, Alex Kang, Stacey Gerben, Mila Ya-Lan Lamb, Analisa Murray, Xinting Li, Madison A. Kennedy, Wei Yang, Zihao Song, Gudrun Schober, Stuart M. Brierley, John O’Neill, Michael H. Gelb, Gaetano T. Montelione, Emmanuel Derivery, and David Baker
Read The Full Article at Science.
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