Evidence map›Paper›PMID 41580068›Full record

ArticleJournal of molecular biology2026

ModelCIF Update: Supporting Emerging Classes of Computational Macromolecular Models.

Gerardo Tauriello, Yoriko Lill, Jacopo Sgrignani, Vincent Zoete, Benedikt Singer, Brinda Vallat, Benjamin M Webb, Thomas Garello, Stefan Bienert, Michael Feig and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of molecular biology, 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

17 authors.

Gerardo TaurielloBiozentrum, University of Basel, Basel, Switzerland; SIB Swiss Institute of Bioinformatics, Switzerland.
Yoriko LillSIB Swiss Institute of Bioinformatics, Switzerland; Department of Pharmaceutical Sciences, University of Basel, Basel, Switzerland.
Jacopo SgrignaniSIB Swiss Institute of Bioinformatics, Switzerland; Institute for Research in Biomedicine, Università della Svizzera italiana (USI), Faculty of Biomedical Sciences, Bellinzona, Switzerland.
Vincent ZoeteSIB Swiss Institute of Bioinformatics, Switzerland; Department of Oncology UNIL, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.
Benedikt SingerSIB Swiss Institute of Bioinformatics, Switzerland; Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Brinda VallatResearch Collaboratory for Structural Bioinformatics Protein Data Bank, and the Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Rutgers Cancer Institute, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA.
Benjamin M WebbDepartment of Bioengineering and Therapeutic Sciences, the Quantitative Biosciences Institute (QBI), and the Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94157, USA.
Thomas GarelloBiozentrum, University of Basel, Basel, Switzerland; SIB Swiss Institute of Bioinformatics, Switzerland.
Stefan BienertBiozentrum, University of Basel, Basel, Switzerland; SIB Swiss Institute of Bioinformatics, Switzerland.
Michael FeigDepartment of Biochemistry & Molecular Biology, Michigan State University, MI, USA.
Elena PapaleoCancer Structural Biology, Danish Cancer Institute, Copenhagen, Denmark; Cancer Systems Biology, Department of Health Technology Bioinformatics, Technical University of Denmark, Lyngby, Denmark.
Stephen K BurleyResearch Collaboratory for Structural Bioinformatics Protein Data Bank, and the Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Rutgers Cancer Institute, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA; Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA; Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Rutgers Artificial Intelligence and Data Science (RAD) Collaboratory, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
Andrej SaliDepartment of Bioengineering and Therapeutic Sciences, the Quantitative Biosciences Institute (QBI), and the Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94157, USA.
Markus A LillSIB Swiss Institute of Bioinformatics, Switzerland; Department of Pharmaceutical Sciences, University of Basel, Basel, Switzerland.
Andrea CavalliSIB Swiss Institute of Bioinformatics, Switzerland; Institute for Research in Biomedicine, Università della Svizzera italiana (USI), Faculty of Biomedical Sciences, Bellinzona, Switzerland.
Matteo Dal PeraroSIB Swiss Institute of Bioinformatics, Switzerland; Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Torsten SchwedeBiozentrum, University of Basel, Basel, Switzerland; SIB Swiss Institute of Bioinformatics, Switzerland. Electronic address: torsten.schwede@unibas.ch.

Funding

TR&D Project 4. The Imaging Stage: Multiscale Spatiotemporal Modeling of Macromolecular Systems in Cellular NeighborhoodsP41GM109824 · NIGMS · ROCKEFELLER UNIVERSITY · PI ROUT, MICHAEL P · 2014 to 2023
$18.8M
PDB Management by the Research Collaboratory for Structural BioinformaticsR01GM157729 · NIGMS · RUTGERS, THE STATE UNIV OF N.J. · PI STEPHEN K BURLEY · 2024 to 2026
$11.6M
IMP: Software for Hybrid Determination of Macromolecular Assembly StructuresR01GM083960 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI SALI, ANDREJ · 2008 to 2024
$5.2M
NIGMS NIH HHS P41 GM109824NIGMS NIH HHS R01 GM083960NIGMS NIH HHS R01 GM157729
6 · The paper itself

Abstract

The recent development of highly accurate protein structure prediction tools has led to a rapid expansion in the scope of computational structural biology, enabling a much wider range of modelling studies than ever before. These new in silico opportunities help life science researchers understand how proteins interact with their environment and support design of new molecules with desired properties. Ultimately, they have broad applications, e.g. in medicine, drug discovery or engineering. To ensure reproducibility and to facilitate data exchange and reuse, predicted structures or computed structure models can be stored using ModelCIF, a rich data representation designed to include the atomic coordinates/metadata. The previously published version of ModelCIF (1.4.4; 2022-12-21) mainly covered protein structure predictions generated by homology and ab initio modelling. In this work, we present an extension of the ModelCIF (https://github.com/ihmwg/ModelCIF) data standard and its associated tools. This extension supports important new use cases, including modelling protein-ligand and protein-protein interactions, sampling multiple conformational states and designing proteins de novo. We define guidelines for storage and validation of modelling results for those use cases by applying new and existing ModelCIF categories to capture protocols, inputs and outputs. Additionally, we outline updates to the software tools and resources that implement these new standards and provide functionality for model generation, validation, archiving, and visualisation. By enabling consistent metadata capture across different modelling workflows, this framework aims to support the FAIR dissemination of computational models, thereby promoting reproducibility and reusability in downstream applications.

Indexed as

Computational BiologyMacromolecular SubstancesModels, MolecularProteinsSoftwareComputer SimulationLigandsProtein ConformationLigandsMacromolecular SubstancesProteinsconformational statesdata standardmacromolecular structure predictionprotein complexesprotein design

Identifiers

PMID41580068
PMCPMC13495685

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.