Evidence map›Paper›PMID 39990461›Full record

ArticlebioRxiv : the preprint server for biology2025

Robust Prediction of Enzyme Variant Kinetics with RealKcat.

Karuna Anna Sajeevan, Abraham Osinuga, Arunraj B, Sakib Ferdous, Nabia Shahreen, Mohammed Sakib Noor, Shashank Koneru, Laura Mariana Santos-Correa, Rahil Salehi, Niaz Bahar Chowdhury and 4 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors.

Karuna Anna SajeevanDepartment of Chemical and Biological Engineering, Iowa State University, Ames, Iowa, USA.
Abraham OsinugaDepartment of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA.ORCID 0000-0002-2587-8704
Arunraj BDepartment of Chemical and Biological Engineering, Iowa State University, Ames, Iowa, USA.
Sakib FerdousDepartment of Chemical and Biological Engineering, Iowa State University, Ames, Iowa, USA.
Nabia ShahreenDepartment of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA.
Mohammed Sakib NoorDepartment of Chemical and Biological Engineering, Iowa State University, Ames, Iowa, USA.
Shashank KoneruDepartment of Chemical and Biological Engineering, Iowa State University, Ames, Iowa, USA.
Laura Mariana Santos-CorreaDepartment of Chemical and Biological Engineering, Iowa State University, Ames, Iowa, USA.
Rahil SalehiDepartment of Chemical and Biological Engineering, Iowa State University, Ames, Iowa, USA.
Niaz Bahar ChowdhuryDepartment of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA.
Brisa Calderon-LopezDepartment of Chemical and Biological Engineering, Iowa State University, Ames, Iowa, USA.
Ankur MaliDepartment of Computer Science and Engineering, University of South Florida, Tampa, Florida, USA.
Rajib SahaDepartment of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA.ORCID 0000-0002-2974-0243
Ratul ChowdhuryDepartment of Chemical and Biological Engineering, Iowa State University, Ames, Iowa, USA.ORCID 0000-0003-4522-6911

Funding

A Predictive Modeling Framework to Dissect the Dynamic Immunometabolic Responses to Pathogenic infection and the Kinetic Reprogramming of Metabolism in Cancer Cell SystemR35GM143009 · NIGMS · UNIVERSITY OF NEBRASKA LINCOLN · PI SAHA, RAJIB · 2021 to 2025
$1.8M
NIGMS NIH HHS R35 GM143009
6 · The paper itself

Abstract

Accurate prediction of kinetic parameters is crucial for understanding known and tailoring novel enzymes for biocatalysis. Current models fail to capture mutation effects on catalytically essential residues, limiting their utility in enzyme design. We grid-searched through ten model architectures (25,671 hyperparameter combinations) to identify a gradient-based additive framework called RealKcat, trained on 27,176 experimental entries curated manually (KinHub-27k) by screening 2,158 articles. Clustering catalytic turnover (

Indexed as

Bio-aware Machine learningBiocatalysisBiochemistryBiophysics and Computational BiologyDatabase curationEnzyme engineeringEnzyme kineticsSystems Biology

Identifiers

PMID39990461
PMCPMC11844551

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.