Evidence map›Paper›PMID 35254891›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2022

Accurate positioning of functional residues with robotics-inspired computational protein design.

Cody Krivacic, Kale Kundert, Xingjie Pan, Roland A Pache, Lin Liu, Shane O Conchúir, Jeliazko R Jeliazkov, Jeffrey J Gray, Michael C Thompson, James S Fraser and 1 more

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
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  5. Review
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

11 authors.

Cody KrivacicUC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA 94158.
Kale KundertDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158.
Xingjie PanUC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA 94158.ORCID 0000-0002-0060-4352
Roland A PacheDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158.ORCID 0000-0002-4723-6729
Lin LiuDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158.ORCID 0000-0003-1283-1410
Shane O ConchúirDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158.
Jeliazko R JeliazkovProgram in Molecular Biophysics, Johns Hopkins University, Baltimore, MD 21218.
Jeffrey J GrayDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218.ORCID 0000-0001-6380-2324
Michael C ThompsonDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158.
James S FraserUC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA 94158.ORCID 0000-0002-5080-2859
Tanja KortemmeUC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA 94158.ORCID 0000-0002-8494-680X

Funding

STRUCTURAL BIOLOGY TRAINING PROGRAMT32GM008284 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KORTEMME, TANJA · 1988 to 2022
$10.1M
Prediction of the Structure of Therapeutic Antibodies with their AntigensR01GM078221 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI GRAY, JEFFREY J · 2006 to 2020
$4.0M
Supplement to Resolving ensemble averaged conformations by multi-temperature x-ray crystallography - Equipment 2020R01GM123159 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI FRASER, JAMES SOLOMON · 2018 to 2021
$2.3M
Computational design of protein-based small-molecule biosensorsR01GM110089 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KORTEMME, TANJA · 2015 to 2022
$2.3M
NIGMS NIH HHS R01 GM078221NIGMS NIH HHS R01 GM110089NIGMS NIH HHS R01 GM123159NIGMS NIH HHS T32 GM008284
6 · The paper itself

Abstract

SignificanceComputational protein design promises to advance applications in medicine and biotechnology by creating proteins with many new and useful functions. However, new functions require the design of specific and often irregular atom-level geometries, which remains a major challenge. Here, we develop computational methods that design and predict local protein geometries with greater accuracy than existing methods. Then, as a proof of concept, we leverage these methods to design new protein conformations in the enzyme ketosteroid isomerase that change the protein's preference for a key functional residue. Our computational methods are openly accessible and can be applied to the design of other intricate geometries customized for new user-defined protein functions.

Indexed as

Computer-Aided DesignRoboticsAlgorithmsAmino AcidsComputational BiologyIsomerasesModels, MolecularProtein ConformationProtein EngineeringProteinsReproducibility of ResultsStructure-Activity RelationshipAmino AcidsIsomerasesProteinscomputational protein designdesign of functionRosettastructure prediction

Identifiers

PMID35254891
PMCPMC8931229

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.