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Jane S. Richardson

Duke University · US
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Area of research
Molecular Biology · Materials Chemistry
Research interest
Research interests include Enzyme Structure and Function, Protein Structure and Dynamics, RNA and protein synthesis mechanisms, and Glycosylation and Glycoproteins Research.
h-index
63
citations
92,434
works
241
NIH funding
primary concept
email

Recent publications

New targets and procedures for validating the valence geometry of nucleic acid structures
Nucleic Acids Research 2026cited by 1position: middledoi
Categorizing prediction modes within low-pLDDT regions of <i>AlphaFold</i> 2 structures: near-predictive, pseudostructure and barbed wire
Acta Crystallographica Section D Structural Biology 2025cited by 4position: lastdoi
<i>Cis</i> ‐ <scp>nonProline</scp> peptides: Genuine occurrences and their functional roles
Protein Science 2025cited by 2position: firstdoi
Categorizing prediction modes within low-pLDDT regions of AlphaFold2 structures
2025cited by 2position: lastdoi
RNAprecis: Prediction of full-detail RNA conformation from the experimentally best-observed sparse parameters
2025cited by 1position: middledoi
AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination.
2024cited by 258position: middledoi
Community recommendations on cryoEM data archiving and validation
IUCrJ 2024cited by 33position: middledoi
Community recommendations on cryoEM data archiving and validation.
2024cited by 21position: middledoi
Outcomes of the EMDataResource cryo-EM Ligand Modeling Challenge
Nature Methods 2024cited by 17position: middledoi
Outcomes of the EMDataResource cryo-EM Ligand Modeling Challenge.
2024cited by 16position: middledoi
Outcomes of the EMDataResource Cryo-EM Ligand Modeling Challenge
2024cited by 2position: middledoi
Accelerating crystal structure determination with iterative AlphaFold prediction.
2023cited by 52position: middledoi
The bad and the good of trends in model building and refinement for sparse-data regions: pernicious forms of overfitting versus good new tools and predictions
Acta Crystallographica Section D Structural Biology 2023cited by 6position: firstdoi
Improved AlphaFold modeling with implicit experimental information.
2022cited by 117position: middledoi
Improved AlphaFold modeling with implicit experimental information
2022cited by 9position: middledoi
AlphaFold predictions are valuable hypotheses, and accelerate but do not replace experimental structure determination
2022cited by 7position: middledoi
Accelerating crystal structure determination with iterative AlphaFold prediction
2022cited by 2position: middledoi
Cryo-EM model validation recommendations based on outcomes of the 2019 EMDataResource challenge
Nature Methods 2021cited by 119position: middledoi
Cryo-EM model validation recommendations based on outcomes of the 2019 EMDataResource challenge.
2021cited by 87position: middledoi
Making the invisible enemy visible
Nature Structural &amp; Molecular Biology 2021cited by 28position: middledoi
Improving SARS-CoV-2 structures: Peer review by early coordinate release
Biophysical Journal 2021cited by 27position: lastdoi
Seeing the PDB
Journal of Biological Chemistry 2021cited by 19position: firstdoi
The importance of residue‐level filtering and the Top2018 best‐parts dataset of high‐quality protein residues
Protein Science 2021cited by 18position: lastdoi
The importance of residue-level filtering, and the Top2018 best-parts dataset of high-quality protein residues
2021cited by 1position: middledoi
Improved chemistry restraints for crystallographic refinement by integrating the Amber force field into Phenix.
2020cited by 40position: middledoi
Making the invisible enemy visible
2020cited by 3position: middledoi
Outcomes of the 2019 EMDataResource model challenge: validation of cryo-EM models at near-atomic resolution
2020cited by 0position: middledoi
Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in <i>Phenix</i>
Acta Crystallographica Section D Structural Biology 2019cited by 7,316position: middledoi
New tools in MolProbity validation: CaBLAM for CryoEM backbone, UnDowser to rethink “waters,” and NGL Viewer to recapture online 3D graphics
Protein Science 2019cited by 177position: middledoi
CaBLAM for chiropraxis in cryoEM, UnDowser to rethink “waters”, and NGL Viewer to recapture online 3D graphics in MolProbity validation
2019cited by 2position: middledoi

Grants

No grants ingested yet.

Frequent collaborators

Christopher J. Williams · Duke University16 papers (2017–2026)Tristan I. Croll · Codexis (United States)9 papers (2019–2024)Vincent B. Chen · Duke University8 papers (2017–2025)David C. Richardson · Duke University7 papers (2019–2025)Thomas C. Terwilliger · Los Alamos National Laboratory6 papers (2022–2024)Randy J. Read · University of California, Berkeley6 papers (2022–2024)Paul D. Adams · University of Arkansas at Fayetteville6 papers (2020–2024) · 6 papers (2020–2024)Billy K. Poon · Lawrence Berkeley National Laboratory5 papers (2019–2023)Pavel V. Afonine · Lawrence Berkeley National Laboratory4 papers (2022–2023)Michael G. Prisant · Columbia University4 papers (2019–2025)Paul D. Adams · Lawrence Berkeley National Laboratory4 papers (2012–2019)Randy J. Read · University of Cambridge3 papers (2012–2019)Nigel W. Moriarty · Lawrence Berkeley National Laboratory3 papers (2012–2019) · 3 papers (2012–2019)Robert D. Oeffner · University of Cambridge3 papers (2022–2023)David Richardson · Duke University School of Medicine3 papers (2013–2019)David Baker · Howard Hughes Medical Institute2 papers (2013–2013)Bradley J. Hintze · Duke University2 papers (2017–2019)Ian Davis · The University of Texas at San Antonio2 papers (2012–2013)
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