Evidence map›Paper›PMID 41259415›Full record

ArticlePLoS computational biology2025

Molecular insights on the mechanism of α1-antitrypsin condensate formation and maturation.

Ignacio Sanchez-Burgos, Andres R Tejedor, Rosana Collepardo-Guevara, Jorge Bernardino de la Serna, Jorge R Espinosa

Abstract read
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Article in PLoS computational biology, 2025. 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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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

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3 · Its place in the literature

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Ignacio Sanchez-BurgosMaxwell Centre, Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge, United Kingdom.ORCID 0000-0002-1160-3945
Andres R TejedorYusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, United Kingdom.ORCID 0000-0002-9437-6169
Rosana Collepardo-GuevaraYusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, United Kingdom.
Jorge Bernardino de la SernaFaculty of Medicine, Imperial College London, South Kensington, London, United Kingdom.ORCID 0000-0002-1396-3338
Jorge R EspinosaMaxwell Centre, Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge, United Kingdom.ORCID 0000-0001-9530-2658

Funding

Emmanuel CollegeEuropean Research Council (ERC)European Union’s Horizon Europe research and innovation programmeRamon y Cajal fellowship
6 · The paper itself

Abstract

The deficiency of [Formula: see text]-antitrypsin protein is a genetic disorder characterized by the accumulation of misfolded protein aggregates within hepatocytes, leading to liver dysfunction. In the lung, it is found in macrophages, bronchial and epithelial alveolar cells type 2, leading to pulmonary emphysema. Despite extensive research, the precise mechanism underlying the formation of [Formula: see text]-antitrypsin inclusion bodies remain elusive. In this study, we combine equilibrium and non-equilibrium molecular dynamics simulations to elucidate the intricate process of [Formula: see text]-antitrypsin condensate formation and maturation. Our mechanistic model explains cluster accumulation-specifically the onset of this pathogenesis-through the emergence of phase-separated liquid-like protein droplets, which subsequently undergo inter-protein β-sheet transitions between misfolded variants, resulting in solid-like clusters. We find that this mechanism only applies to the misfolded variant, Z-[Formula: see text]-antitrypsin, which phase-separates driven by its disordered C-terminus. In contrast, the native protein, M-[Formula: see text]-antitrypsin, shows much lower propensity to phase-separate and later form kinetically trapped aggregates. Furthermore, we explore how Z-[Formula: see text]-antitrypsin exhibits an increased capacity to form condensates near external walls with different types of interactions. Such conditions can be similar to those found within the endoplasmic reticulum membrane, where phase separation and hardening take place. Overall, our results shed light on the molecular basis of [Formula: see text]-antitrypsin-related disorders and provide valuable microscopic insights for the development of therapeutic strategies targeting protein misfolding and aggregation-related disorders.

Indexed as

alpha 1-Antitrypsinalpha 1-Antitrypsin DeficiencyComputational BiologyHumansInclusion BodiesMolecular Dynamics SimulationProtein AggregatesProtein Foldingalpha 1-AntitrypsinProtein Aggregates

Identifiers

PMID41259415
PMCPMC12629480

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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.