Jane S. Richardson

Jane S. Richardson

Duke University

H-index: 66

North America-United States

About Jane S. Richardson

Jane S. Richardson, With an exceptional h-index of 66 and a recent h-index of 36 (since 2020), a distinguished researcher at Duke University, specializes in the field of structural biology, validation, informatics, molecular graphics of protein & RNA.

His recent articles reflect a diverse array of research interests and contributions to the field:

Community recommendations on cryoEM data archiving and validation

Outcomes of the EMDataResource Cryo-EM Ligand Modeling Challenge

AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination

Accelerating crystal structure determination with iterative AlphaFold prediction

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

A disulfide bridge survey and library

Improved AlphaFold modeling with implicit experimental information

The importance of residue‐level filtering and the Top2018 best‐parts dataset of high‐quality protein residues

Jane S. Richardson Information

University

Position

Professor of Biochemistry

Citations(all)

81689

Citations(since 2020)

30689

Cited By

62939

hIndex(all)

66

hIndex(since 2020)

36

i10Index(all)

125

i10Index(since 2020)

69

Email

University Profile Page

Duke University

Google Scholar

View Google Scholar Profile

Jane S. Richardson Skills & Research Interests

structural biology

validation

informatics

molecular graphics of protein & RNA

Top articles of Jane S. Richardson

Title

Journal

Author(s)

Publication Date

Community recommendations on cryoEM data archiving and validation

IUCrJ

Gerard J Kleywegt

Paul D Adams

Sarah J Butcher

Catherine L Lawson

Alexis Rohou

...

2024/3/1

Outcomes of the EMDataResource Cryo-EM Ligand Modeling Challenge

Research Square

Catherine Lawson

Andriy Kryshtafovych

Grigore Pintilie

Stephen Burley

Jiri Cerny

...

2024/1/25

AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination

Nature Methods

Thomas C Terwilliger

Dorothee Liebschner

Tristan I Croll

Christopher J Williams

Airlie J McCoy

...

2024/1

Accelerating crystal structure determination with iterative AlphaFold prediction

Acta Crystallographica Section D: Structural Biology

Thomas C Terwilliger

Pavel V Afonine

Dorothee Liebschner

Tristan I Croll

Airlie J McCoy

...

2023/3/1

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

Jane S Richardson

Christopher J Williams

Vincent B Chen

Michael G Prisant

David C Richardson

2023/12/1

A disulfide bridge survey and library

Acta Crystallographica Section A: Foundations and Advances

Christopher J Williams

Sushrit Pasumarthy

Jane S Richardson

2023/7/7

Improved AlphaFold modeling with implicit experimental information

Nature methods

Thomas C Terwilliger

Billy K Poon

Pavel V Afonine

Christopher J Schlicksup

Tristan I Croll

...

2022/11

The importance of residue‐level filtering and the Top2018 best‐parts dataset of high‐quality protein residues

Protein Science

Christopher J Williams

David C Richardson

Jane S Richardson

2022/1

Cryo-EM model validation recommendations based on outcomes of the 2019 EMDataResource challenge

Nature Methods

Catherine L Lawson

Andriy Kryshtafovych

Paul D Adams

Pavel V Afonine

Matthew L Baker

...

2021/2

Seeing the PDB

Jane S Richardson

David C Richardson

David S Goodsell

2021/1/1

Making the invisible enemy visible

Nature structural & molecular biology

Tristan I Croll

Kay Diederichs

Florens Fischer

Cameron D Fyfe

Yunyun Gao

...

2021/5

Improving SARS-CoV-2 structures: Peer review by early coordinate release

Biophysical journal

Tristan I Croll

Christopher J Williams

Vincent B Chen

David C Richardson

Jane S Richardson

2021/3/16

Making the invisible enemy visible (preprint)

Tristan Croll

Kay Diederichs

Florens Fischer

Cameron Fyfe

Yunyun Gao

...

2020

Mass spectrometric based detection of protein nucleotidylation in the RNA polymerase of SARS-CoV-2 (preprint)

Michael R Sussman

Brian J Conti

Robert N Kirchdoerfer

Cameron Fyfe

Yunyun Gao

...

2020

New tools in MolProbity validation: CaBLAM for CryoEM backbone, UnDowser to rethink “waters,” and NGL Viewer to recapture online 3D graphics

Protein Science

Michael G Prisant

Christopher J Williams

Vincent B Chen

Jane S Richardson

David C Richardson

2020/1

A new way to see RNAs

Nature Methods

Jane S Richardson

2020/7

Improved chemistry restraints for crystallographic refinement by integrating the Amber force field into Phenix

Acta Crystallographica Section D: Structural Biology

Nigel W Moriarty

Pawel A Janowski

Jason M Swails

Hai Nguyen

Jane S Richardson

...

2020/1/1

Outcomes of the 2019 EMDataResource model challenge: Validation of cryo-EM models at near-atomic resolution

BioRxiv

Catherine L Lawson

Andriy Kryshtafovych

Paul D Adams

Pavel V Afonine

Matthew L Baker

...

2020/6/15

Umbilical cord blood derived microglia-like cells to model COVID-19 exposure (preprint)

Roy H Perlis

Andrea G Edlow

Steven D Sheridan

Jessica M Thanos

Rose M De Guzman

...

2020

See List of Professors in Jane S. Richardson University(Duke University)

Co-Authors

H-index: 60
Jack Snoeyink

Jack Snoeyink

University of North Carolina at Chapel Hill

H-index: 55
David C. Richardson

David C. Richardson

Duke University

H-index: 45
Dr Airlie J. McCoy

Dr Airlie J. McCoy

University of Cambridge

H-index: 40
Simon Lovell

Simon Lovell

Manchester University

H-index: 33
Tristan Croll

Tristan Croll

University of Cambridge

H-index: 12
Gary Kapral

Gary Kapral

Duke University

academic-engine