Evidence map›Paper›PMID 39416199›Full record

ArticlebioRxiv : the preprint server for biology2024

Regulating IL-2 immune signaling function via a core allosteric structural network.

Claire H Woodward, Shahlo O Solieva, Daniel Hwang, Viviane S De Paula, Charina S Fabilane, Michael C Young, Tony Trent, Ella C Teeley, Ananya Majumdar, Jamie B Spangler and 2 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Claire H WoodwardORCID 0000-0002-0373-6318
Viviane S De PaulaORCID 0000-0003-3171-6477
Charina S FabilaneORCID 0000-0003-3801-7927
Tony Trent
Ella C Teeley
Nikolaos G SgourakisORCID 0000-0003-3655-3902

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human interleukin-2 (IL-2) is a crucial cytokine for T cell regulation, with therapeutic potential in cancer and autoimmune diseases. However, IL-2's pleiotropic effects across different immune cell types often lead to toxicity and limited efficacy. Previous efforts to enhance IL-2's therapeutic profile have focused on modifying its receptor binding sites. Yet, the underlying dynamics and intramolecular networks contributing to IL-2 receptor recognition remain unexplored. This study presents a detailed characterization of IL-2 dynamics compared to two engineered IL-2 mutants, "superkines" S15 and S1, which exhibit biased signaling towards effector T cells. Using NMR spectroscopy and molecular dynamics simulations, we demonstrate significant variations in core dynamic pathways and conformational exchange rates across these three IL-2 variants. We identify distinct allosteric networks and excited state conformations in the superkines, despite their structural similarity to wild-type IL-2. Furthermore, we rationally design a mutation (L56A) in the S1 superkine's core network, which partially reverts its dynamics, receptor binding affinity, and T cell signaling behavior towards that of wild-type IL-2. Our results reveal that IL-2 superkine core dynamics play a critical role in their enhanced receptor binding and function, suggesting that modulating IL-2 dynamics and core allostery represents an untapped approach for designing immunotherapies with improved immune cell selectivity profiles. Highlights: NMR and molecular dynamics simulations revealed distinct conformational dynamics and allosteric networks in computationally re-designed IL-2 superkines compared to wild-type IL-2, despite their similar crystal structures.The superkines S1 and S15 exhibit altered sampling of excited state conformations at an intermediate timescale, with slower conformational exchange rates compared to wild-type IL-2.A rationally designed mutation (L56A) in the S1 superkine's core allosteric network partially reverted its dynamics, receptor binding affinity, and T cell signaling behavior towards that of wild-type IL-2.Our study demonstrates that IL-2 core dynamics play a critical role in receptor binding and signaling function, providing a foundation for engineering more selective IL-2-based immunotherapies.

Identifiers

PMID39416199
PMCPMC11482754

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.