Evidence map›Paper›PMID 39951500›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2025

AI protocol for retrieving protein dynamic structures from two-dimensional infrared spectra.

Sheng Ye, Lvshuai Zhu, Zhicheng Zhao, Fan Wu, Zhipeng Li, BinBin Wang, Kai Zhong, Changyin Sun, Shaul Mukamel, Jun Jiang

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Generation of protein dynamics by machine learning.Current opinion in structural biology · 2025
    Review
  4. AI protocol for retrieving protein dynamic structures from two-dimensional infrared spectra.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
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

10 authors.

Sheng Ye *Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology, School of AI, Anhui University, Hefei 230601, China.
Lvshuai Zhu *Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology, School of AI, Anhui University, Hefei 230601, China.ORCID 0009-0006-7726-3556
Zhicheng Zhao *Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology, School of AI, Anhui University, Hefei 230601, China.ORCID 0000-0002-2761-7399
Fan WuState Key Laboratory of Precision and Intelligent Chemistry, Hefei National Research Center for Physical Sciences at the Microscale, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, Anhui, China.
Zhipeng LiEngineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology, School of AI, Anhui University, Hefei 230601, China.
BinBin WangEngineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology, School of AI, Anhui University, Hefei 230601, China.
Kai ZhongZernike Institute for Advanced Materials, Department of Nanoscience and Materials Science, University of Groningen, Groningen 9747AG, Netherlands.
Changyin SunEngineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology, School of AI, Anhui University, Hefei 230601, China.
Shaul MukamelDepartment of Chemistry and Department of Physics & Astronomy, University of California, Irvine, CA 92697.ORCID 0000-0002-6015-3135
Jun JiangState Key Laboratory of Precision and Intelligent Chemistry, Hefei National Research Center for Physical Sciences at the Microscale, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, Anhui, China.ORCID 0000-0002-6116-5605

Funding

CAS Project for Young Scientists in Basic Research YSBR-005K. C. Wong Education Foundation GJTD-2020-15MOST | National Natural Science Foundation of China (NSFC) 21633007MOST | National Natural Science Foundation of China (NSFC) 22025304MOST | National Natural Science Foundation of China (NSFC) 22033007MOST | National Natural Science Foundation of China (NSFC) 22203001MOST | National Natural Science Foundation of China (NSFC) 22273093MOST | National Natural Science Foundation of China (NSFC) 61921004MOST | National Natural Science Foundation of China (NSFC) 62236002National Science Foundation (NSF) CHE-2246379| Natural Science Foundation of Anhui Province () 2208085Y04University Synergy Innovation Program of Anhui Province GXXT-2022-062
6 · The paper itself

Abstract

Understanding the dynamic evolution of protein structures is crucial for uncovering their biological functions. Yet, real-time prediction of these dynamic structures remains a significant challenge. Two-dimensional infrared (2DIR) spectroscopy is a powerful tool for analyzing protein dynamics. However, translating its complex, low-dimensional signals into detailed three-dimensional structures is a daunting task. In this study, we introduce a machine learning-based approach that accurately predicts dynamic three-dimensional protein structures from 2DIR descriptors. Our method establishes a robust "spectrum-structure" relationship, enabling the recovery of three-dimensional structures across a wide variety of proteins. It demonstrates broad applicability in predicting dynamic structures along different protein folding trajectories, spanning timescales from microseconds to milliseconds. This approach also shows promise in identifying the structures of previously uncharacterized proteins based solely on their spectral descriptors. The integration of AI with 2DIR spectroscopy offers insights and represents a significant advancement in the real-time analysis of dynamic protein structures.

Indexed as

Machine LearningProteinsProtein ConformationProtein FoldingSpectrophotometry, InfraredProteinsmachine learningprotein dynamicsspectrum-structure relationship

Identifiers

PMID39951500
PMCPMC11848431

What OpenQuestion holds

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Registered trials

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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.