Evidence map›Paper›PMID 41001574›Full record

ArticleArXiv2025

SHREC 2025: Protein Surface Shape Retrieval including Electrostatic potential.

Taher Yacoub, Camille Depenveiller, Atsushi Tatsuma, Tin Barisin, Eugen Rusakov, Udo Göbel, Yuxu Peng, Shiqiang Deng, Yuki Kagaya, Joon Hong Park and 18 more

Abstract readPreprint
PubMed Full text at PMC
In one paragraph

Article in ArXiv, 2025. 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

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5 · Who and what money

Authors and funding

28 authors.

Taher YacoubLaboratoire GBCM, EA7528, Conservatoire Nationale des Arts et Métiers, Paris, France.
Camille DepenveillerLaboratoire GBCM, EA7528, Conservatoire Nationale des Arts et Métiers, Paris, France.
Atsushi TatsumaNational Institute of Technology, Tsuyama College, Tsuyama, Japan.
Tin BarisinCONTACT AI Research, CONTACT Software GmbH, Bremen, Germany.
Eugen RusakovCONTACT AI Research, CONTACT Software GmbH, Bremen, Germany.
Udo GöbelCONTACT AI Research, CONTACT Software GmbH, Bremen, Germany.
Yuxu PengChangsha University of Science and Technology, Hunan, China.
Shiqiang DengChangsha University of Science and Technology, Hunan, China.
Yuki KagayaDepartment of Biological Sciences, Purdue University, West Lafayette, IN, USA.
Joon Hong ParkDepartment of Computer Science, Purdue University, West Lafayette, IN, USA.
Daisuke KiharaDepartment of Biological Sciences, Purdue University, West Lafayette, IN, USA.
Marco GuerraCNR-IMATI, Genova, Italy.
Giorgio PalmieriCNR-IMATI, Genova, Italy.
Andrea RanieriCNR-IMATI, Genova, Italy.
Ulderico FugacciCNR-IMATI, Genova, Italy.
Silvia BiasottiCNR-IMATI, Genova, Italy.
Ruiwen HeDe Vinci Higher Education, De Vinci Research Center, Paris, France.
Halim BenhabilesIMT Nord Europe, Institut Mines-Télécom, Université de Lille, Center for Digital Systems, Lille, France.
Adnane CabaniUniversité de Rouen Normandie, ESIGELEC, IRSEEM, Rouen, France.
Karim HammoudiIRIMAS, Université de Haute-Alsace, Mulhouse, France.
Haotian LiYunnan Normal University, Kunming, Yunnan, China.
Hao HuangNew York University Abu Dhabi, Abu Dhabi, UAE.
Chunyan LiYunnan Normal University, Kunming, Yunnan, China.
Alireza TehraniDepartment of Chemistry, Queen's University, Kingston, ON, Canada.
Fanwang MengDepartment of Chemistry, Queen's University, Kingston, ON, Canada.
Farnaz Heidar-ZadehDepartment of Chemistry, Queen's University, Kingston, ON, Canada.
Tuan-Anh YangUniversity of Science, VNU-HCM, Ho Chi Minh City, Vietnam.
Matthieu MontesLaboratoire GBCM, EA7528, Conservatoire Nationale des Arts et Métiers, Paris, France.

Funding

Building protein structure models for intermediate resolution cryo-electron microscopy mapsR01GM133840 · NIGMS · PURDUE UNIVERSITY · PI KIHARA, DAISUKE · 2020 to 2023
$1.6M
Structural Modeling of Multifarious Protein ComplexesR01GM123055 · NIGMS · PURDUE UNIVERSITY · PI KIHARA, DAISUKE · 2017 to 2020
$1.2M
NIGMS NIH HHS R01 GM123055NIGMS NIH HHS R01 GM133840
6 · The paper itself

Abstract

This SHREC 2025 track dedicated to protein surface shape retrieval involved 9 participating teams. We evaluated the performance in retrieval of 15 proposed methods on a large dataset of 11,565 protein surfaces with calculated electrostatic potential (a key molecular surface descriptor). The performance in retrieval of the proposed methods was evaluated through different metrics (Accuracy, Balanced accuracy, F1 score, Precision and Recall). The best retrieval performance was achieved by the proposed methods that used the electrostatic potential complementary to molecular surface shape. This observation was also valid for classes with limited data which highlights the importance of taking into account additional molecular surface descriptors.

Indexed as

BioinformaticsComputer visionElectrostatic potentialMachine learningProtein shape classificationProtein shape retrieval

Identifiers

PMID41001574
PMCPMC12458584

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.