Evidence map›Paper›PMID 42569124›Full record

ArticleACS omega2026

Deciphering the Structural Dynamics of Chicken Albumin through Molecular Dynamics Simulations and Deep Learning.

Darunee Traiphothon, Veerasit Kaewbundit, Jitti Niramitranon, Papis Wongchaisuwat, Prapasiri Pongprayoon

Abstract read
In one paragraph

Article in ACS omega, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

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.

Darunee TraiphothonDepartment of Chemistry, Faculty of Science, Kasetsart University, Chatuchak, Bangkok 10900, Thailand.
Veerasit KaewbunditDepartment of Industrial Engineering, Faculty of Engineering, Kasetsart University, Chatuchak, Bangkok 10900, Thailand.
Jitti NiramitranonDepartment of Computer Engineering, Faculty of Engineering, Kasetsart University, Chatuchak, Bangkok 10900, Thailand.
Papis WongchaisuwatDepartment of Industrial Engineering, Faculty of Engineering, Kasetsart University, Chatuchak, Bangkok 10900, Thailand.
Prapasiri PongprayoonDepartment of Chemistry, Faculty of Science, Kasetsart University, Chatuchak, Bangkok 10900, Thailand.ORCID https://orcid.org/0000-0002-1472-8241

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Serum albumin (SA), the most abundant protein carrier in blood plasma, is a major allergen source. Chicken serum albumin (CHSA) is associated with chicken meat allergy due to its cross-reactivity with human serum albumin (HSA). To elucidate the structural basis underlying this immunological cross-recognition, Molecular Dynamics (MD) simulations combined with data-driven artificial intelligence (AI) analyses were employed to compare the structural and dynamic characteristics of CHSA and HSA. Despite moderate sequence identity (∼46.6%), CHSA displays comparable protein compactness and backbone flexibility to HSA, with slightly greater compactness. Domains I and III of CHSA govern a conserved scissor-like motion like HSA. This dynamic similarity may facilitate structural mimicry, enabling antibody cross-recognition. Moreover, CHSA exhibits larger drug sites (Sudlow sites I and II) than HSA, affecting ligand-binding affinity and specificity. The reactive C34 in CHSA demonstrates a similar hydrogen bond network to that in HSA, suggesting comparable redox reactivity. C34 of CHSA can hydrogen bond with L31, Y84, and S35. Deep learning models distinguished HSA and CHSA based on their MD-derived features. To gain insight into the learned representations, an explainable AI approachTesting with Concept Activation Vectorswas employed. AI-based analysis identifies nuanced differences in spatial organization and dynamics patterns, particularly in flexibility, solvent exposure, and internal stabilization. These differences, not captured by averaged measures, may influence epitope presentation. This study provides molecular-level insights into CHSA-HSA similarities and differences, informing their potential biomedical applications and highlighting the need for further experimental validation.

Identifiers

PMID42569124
PMCPMC13448927

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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.