Evidence map›Paper›PMID 40737735›Full record

ReviewCurrent opinion in structural biology2025

Approaches for regulating enzyme activities: Recent advances in experiment and computation.

Qiang Cui

Abstract readReview
In one paragraph

Review in Current opinion in structural biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

1 author.

Qiang CuiDepartments of Chemistry, Physics and Biomedical Engineering, Boston University, 590 Commonwealth Avenue, Boston, 02215, MA, USA. Electronic address: qiangcui@bu.edu.

Funding

Computational Analysis of Enzyme Catalysis and RegulationR35GM141930 · NIGMS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI Qiang Cui · 2021 to 2026
$2.7M
NIGMS NIH HHS R35 GM141930
6 · The paper itself

Abstract

Major progress has been made in recent years in terms of strategies for regulating enzyme activities. Novel high-throughput enzyme kinetic assays and efficient computational methodologies enabled a deeper understanding of molecular mechanisms that dictate the activity of enzymes, which provide guidance to rational modulation of enzyme catalysis. Continued development of efficient screening, directed evolution technologies, and machine learning-driven protein engineering tools make it possible to tune enzyme activities without having to understand the detailed mechanism of catalysis regulation. By combining these two limiting approaches, the efficiency of enzyme regulation can be substantially improved as a mechanistic understanding can help reduce the size of design space before the 'brute-force' engineering approach takes over. We briefly discuss relevant advances in both experiment and computation and comment on future developments that can further enhance mechanistic understanding and engineering capability for broad applications.

Indexed as

Computational BiologyEnzymesHumansMachine LearningProtein EngineeringEnzymes

Identifiers

PMID40737735
PMCPMC12790755

What OpenQuestion holds

Textmetadata
LicenceTDM
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.