Evidence map›Paper›PMID 41037332›Full record

ArticleACS synthetic biology2025

A Three-Module Machine Learning Framework for Protein Sequence- and Temperature-Dependent

Mehmet Emre Erkanli, Yunseok Jang, Ali Malli, Khalid El-Halabi, Chaehyun Ryu, Jin Ryoun Kim

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in ACS synthetic biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters.Journal of chemical information and modeling · 2026
    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

6 authors.

Mehmet Emre ErkanliDepartment of Chemical and Biomolecular Engineering, New York University, 6 MetroTech Center, Brooklyn, New York 11201, United States.ORCID 0000-0002-3046-2674
Yunseok JangCenter for Data Science, New York University, 60 5th Ave, New York, New York 10011, United States.
Ali MalliDepartment of Chemical and Biomolecular Engineering, New York University, 6 MetroTech Center, Brooklyn, New York 11201, United States.ORCID 0000-0002-7981-9064
Khalid El-HalabiDepartment of Chemical and Biomolecular Engineering, New York University, 6 MetroTech Center, Brooklyn, New York 11201, United States.
Chaehyun RyuDepartment of Chemical and Biomolecular Engineering, New York University, 6 MetroTech Center, Brooklyn, New York 11201, United States.
Jin Ryoun KimDepartment of Chemical and Biomolecular Engineering, New York University, 6 MetroTech Center, Brooklyn, New York 11201, United States.ORCID 0000-0001-6156-8730

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The catalytic activity of enzymes is intricately determined by their amino acid sequences and assay conditions, particularly temperature. Navigating the complex interplay among sequence, temperature, and catalytic function is crucial for unlocking a multitude of enzyme applications. Machine learning has recently emerged as a tool for quantitative prediction of enzyme activity from protein sequences. Unfortunately, ML models designed to predict the comprehensive enzyme activity parameter,

Indexed as

beta-GlucosidaseMachine LearningAmino Acid SequenceKineticsTemperaturebeta-Glucosidaseenzymekcat/Kmmachine learningβ-glucosidase

Identifiers

PMID41037332
PMCPMC12538591

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.