In one paragraphArticle 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
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 registryThe 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 literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
5 · Who and what moneyAuthors and funding
14 authors.
Chapin E CavenderSkaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.ORCID 0000-0002-5899-7953 Julian C-H ChenBioscience Division, Los Alamos National Laboratory, Los Alamos, NM, USA; Department of Chemistry and Biochemistry, The University of Toledo, Toledo, OH, USA.ORCID 0000-0003-0341-165X Daniel A KeedyStructural Biology Initiative, CUNY Advanced Science Research Center, New York, NY, USA; Department of Chemistry and Biochemistry, City College of New York, New York, NY, USA; PhD Programs in Biochemistry, Biology, and Chemistry, CUNY Graduate Center, New York, NY, USA.ORCID 0000-0002-9184-7586 Kresten Lindorff-LarsenLinderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen N, Denmark.ORCID 0000-0002-4750-6039 David L MobleyDepartment of Pharmaceutical Sciences, University of California Irvine, Irvine, CA, USA.ORCID 0000-0002-1083-5533 O H Samuli OllilaInstitute of Biotechnology, University of Helsinki, Helsinki, Finland; VTT Technical Research Centre of Finland, Espoo, Finland.ORCID 0000-0002-8728-1006 Chris OostenbrinkInstitute for Molecular Modeling and Simulation, University of Natural Resources and Life Sciences, Vienna, Austria.ORCID 0000-0002-4232-2556 Michael E WallComputer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA; The Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA.ORCID 0000-0003-1000-688X David C WychComputer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA; The Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA.ORCID 0000-0001-9209-4371 Michael K GilsonSkaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.ORCID 0000-0002-3375-1738 Funding
Open data-driven infrastructure for building biomolecular force fields for predictive biophysics and drug designR01GM132386 · NIGMS · UNIVERSITY OF COLORADO · PI SHIRTS, MICHAEL R · 2020 to 2023
$3.1MMarkov State Model approaches for folding, binding and designR01GM123296 · NIGMS · TEMPLE UNIV OF THE COMMONWEALTH · PI VOELZ, VINCENT · 2017 to 2025
$3.1MNanuq Cryocooling SystemR35GM133769 · NIGMS · ADVANCED SCIENCE RESEARCH CENTER · PI Daniel A Keedy · 2019 to 2026
$2.9MCharacterizing the binding mechanisms of castration-resistant prostate cancer therapeutics to the intrinsically disordered N-terminal domain of the androgen receptorR35GM142750 · NIGMS · DARTMOUTH COLLEGE · PI Paul Robustelli · 2021 to 2026
$2.4MDeveloping a nucleic acid force field with direct chemical perception for computational modeling of nucleic acid therapeuticsF32GM150240 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI CAVENDER, CHAPIN E · 2023 to 2024
$143kNIGMS NIH HHS F32 GM150240NIGMS NIH HHS R01 GM123296NIGMS NIH HHS R01 GM132386NIGMS NIH HHS R35 GM133769NIGMS NIH HHS R35 GM142750
6 · The paper itselfAbstract
This review article provides an overview of structurally oriented experimental datasets that can be used to benchmark protein force fields, focusing on data generated by nuclear magnetic resonance (NMR) spectroscopy and room temperature (RT) protein crystallography. We discuss what the observables are, what they tell us about structure and dynamics, what makes them useful for assessing force field accuracy, and how they can be connected to molecular dynamics simulations carried out using the force field one wishes to benchmark. We also touch on statistical issues that arise when comparing simulations with experiment. We hope this article will be particularly useful to computational researchers and trainees who develop, benchmark, or use protein force fields for molecular simulations.
Identifiers
PMID40196146
PMCPMC11975311
What OpenQuestion holds
Textmetadata
LicenceCC BY
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