Evidence map›Paper›PMID 40275092›Full record

ReviewNature computational science2025

Physics-based modeling in the new era of enzyme engineering.

Christopher Jurich, Qianzhen Shao, Xinchun Ran, Zhongyue J Yang

Abstract readReview
In one paragraph

Review in Nature computational science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters.Journal of chemical information and modeling · 2026
    Review
  10. Article
  11. Review
  12. Article
  13. Linker-mediated domain separation enhances cold adaptation in cellulases.Protein science : a publication of the Protein Society · 2025
    Article
  14. Article
  15. Physics-Inspired Single-Particle Tracking Accelerated with Parallelism.bioRxiv : the preprint server for biology · 2025
    Article
  16. Article
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

4 authors.

Christopher Jurich *Department of Chemistry, Vanderbilt University, Nashville, TN, USA.
Qianzhen Shao *Department of Chemistry, Vanderbilt University, Nashville, TN, USA.
Xinchun RanDepartment of Chemistry, Vanderbilt University, Nashville, TN, USA.
Zhongyue J YangDepartment of Chemistry, Vanderbilt University, Nashville, TN, USA. zhongyue.yang@vanderbilt.edu.ORCID 0000-0003-0395-6617

Funding

Chemistry-Biology Interface Training GrantT32GM065086 · NIGMS · VANDERBILT UNIVERSITY · PI BACHMANN, BRIAN O, SULIKOWSKI, GARY ALLEN · 2002 to 2022
$7.0M
Developing Computational Tools for Predicting and Designing Function-Enhancing Enzyme VariantsR35GM146982 · NIGMS · VANDERBILT UNIVERSITY · PI Zhongyue Yang · 2022 to 2026
$1.8M
Vanderbilt Chemical Biology Interface Training ProgramT32GM149371 · NIGMS · VANDERBILT UNIVERSITY · PI Lars Plate · 2023 to 2026
$1.7M
NIGMS NIH HHS R35 GM146982NIGMS NIH HHS T32 GM065086NIGMS NIH HHS T32 GM149371U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) R35GM146982
6 · The paper itself

Abstract

Enzyme engineering is entering a new era characterized by the integration of computational strategies. While bioinformatics and artificial intelligence methods have been extensively applied to accelerate the screening of function-enhancing mutants, physics-based modeling methods, such as molecular mechanics and quantum mechanics, are essential complements in many objectives. In this Perspective, we highlight how physics-based modeling will help the field of computational enzyme engineering reach its full potential by exploring current developments, unmet challenges and emerging opportunities for tool development.

Indexed as

EnzymesProtein EngineeringComputational BiologyQuantum TheoryEnzymes

Identifiers

PMID40275092
PMCPMC12239909

What OpenQuestion holds

Textmetadata
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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.