Automated multiconformer model building for X-ray crystallography and cryo-EM.


Journal

eLife
ISSN: 2050-084X
Titre abrégé: Elife
Pays: England
ID NLM: 101579614

Informations de publication

Date de publication:
21 Jun 2024
Historique:
medline: 21 6 2024
pubmed: 21 6 2024
entrez: 21 6 2024
Statut: epublish

Résumé

In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift toward modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior R

Identifiants

pubmed: 38904665
doi: 10.7554/eLife.90606
pii: 90606
doi:
pii:

Substances chimiques

Proteins 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : NIH HHS
ID : GM145238
Pays : United States
Organisme : NIH HHS
ID : GM133769
Pays : United States
Organisme : Chan Zuckerberg Initiative
ID : EOSS5

Informations de copyright

© 2023, Wankowicz et al.

Déclaration de conflit d'intérêts

SW, AR, SS, BR, AR, DH, JF, DK, JF No competing interests declared, Hv The work in this publication does not overlap with Henry van den Bedem's role at Atomwise Inc, and there is no conflict of interest

Auteurs

Stephanie A Wankowicz (SA)

Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, United States.

Ashraya Ravikumar (A)

Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, United States.

Shivani Sharma (S)

Structural Biology Initiative, CUNY Advanced Science Research Center, New York, United States.
Ph.D. Program in Biology, The Graduate Center, City University of New York, New York, United States.

Blake Riley (B)

Structural Biology Initiative, CUNY Advanced Science Research Center, New York, United States.

Akshay Raju (A)

Structural Biology Initiative, CUNY Advanced Science Research Center, New York, United States.

Daniel W Hogan (DW)

Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, United States.

Jessica Flowers (J)

Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, United States.

Henry van den Bedem (H)

Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, United States.
Atomwise Inc, San Francisco, United States.

Daniel A Keedy (DA)

Structural Biology Initiative, CUNY Advanced Science Research Center, New York, United States.
Department of Chemistry and Biochemistry, City College of New York, New York, United States.
Ph.D. Programs in Biochemistry, Biology and Chemistry, The Graduate Center, City University of New York, New York, United States.

James S Fraser (JS)

Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, United States.

Articles similaires

Selecting optimal software code descriptors-The case of Java.

Yegor Bugayenko, Zamira Kholmatova, Artem Kruglov et al.
1.00
Software Algorithms Programming Languages
Photosynthesis Ribulose-Bisphosphate Carboxylase Carbon Dioxide Molecular Dynamics Simulation Cyanobacteria
Databases, Protein Protein Domains Protein Folding Proteins Deep Learning

Exploring blood-brain barrier passage using atomic weighted vector and machine learning.

Yoan Martínez-López, Paulina Phoobane, Yanaima Jauriga et al.
1.00
Blood-Brain Barrier Machine Learning Humans Support Vector Machine Software

Classifications MeSH