Evidence map›Paper›PMID 39981029›Full record

ReviewRSC chemical biology2025

Decoding allosteric landscapes: computational methodologies for enzyme modulation and drug discovery.

Ruidi Zhu, Chengwei Wu, Jinyin Zha, Shaoyong Lu, Jian Zhang

Abstract readReview
In one paragraph

Review in RSC chemical biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Review
  7. Article
  8. Review
  9. Article
  10. Article
  11. 'Intelligent' proteins.Cellular and molecular life sciences : CMLS · 2025
    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

5 authors.

Ruidi ZhuMedicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine Shanghai 200025 China jian.zhang@sjtu.edu.cn.ORCID https://orcid.org/0000-0002-3975-0090
Chengwei WuMedicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine Shanghai 200025 China jian.zhang@sjtu.edu.cn.
Jinyin ZhaMedicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine Shanghai 200025 China jian.zhang@sjtu.edu.cn.
Shaoyong LuMedicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine Shanghai 200025 China jian.zhang@sjtu.edu.cn.
Jian ZhangMedicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine Shanghai 200025 China jian.zhang@sjtu.edu.cn.ORCID https://orcid.org/0000-0002-6558-791X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Allosteric regulation is a fundamental mechanism in enzyme function, enabling dynamic modulation of activity through ligand binding at sites distal to the active site. Allosteric modulators have gained significant attention due to their unique advantages, including enhanced specificity, reduced off-target effects, and the potential for synergistic interaction with orthosteric agents. However, the inherent complexity of allosteric mechanisms has posed challenges to the systematic discovery and design of allosteric modulators. This review discusses recent advancements in computational methodologies for identifying and characterizing allosteric sites in enzymes, emphasizing techniques such as molecular dynamics (MD) simulations, enhanced sampling methods, normal mode analysis (NMA), evolutionary conservation analysis, and machine learning (ML) approaches. Advanced tools like PASSer, AlloReverse, and AlphaFold have further enhanced the understanding of allosteric mechanisms and facilitated the design of selective allosteric modulators. Case studies on enzymes such as Sirtuin 6 (SIRT6) and MAPK/ERK kinase (MEK) demonstrate the practical applications of these approaches in drug discovery. By integrating computational predictions with experimental validation, this review highlights the transformative potential of computational strategies in advancing allosteric drug discovery, offering innovative opportunities to regulate enzyme activity for therapeutic benefits.

Identifiers

PMID39981029
PMCPMC11836628

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.