Evidence map›Paper›PMID 42872055›Full record

ArticlePNAS nexus2026

CatRange enables robust prediction of enzyme variant kinetic regimes.

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 6 more

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Article in PNAS nexus, 2026. 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

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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

16 authors.

Karuna Anna SajeevanDepartment of Chemical and Biological Engineering, Iowa State University, Ames, IA 50011, USA.
Abraham OsinugaDepartment of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.ORCID https://orcid.org/0000-0002-2587-8704
Arunraj BDepartment of Chemical and Biological Engineering, Iowa State University, Ames, IA 50011, USA.
Sakib FerdousDepartment of Chemical and Biological Engineering, Iowa State University, Ames, IA 50011, USA.
Nabia ShahreenDepartment of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.
Mohammed Sakib NoorDepartment of Chemical and Biological Engineering, Iowa State University, Ames, IA 50011, USA.ORCID https://orcid.org/0000-0003-3120-0040
Shashank KoneruDepartment of Chemical and Biological Engineering, Iowa State University, Ames, IA 50011, USA.
Laura Mariana Santos-CorreaDepartment of Chemical and Biological Engineering, Iowa State University, Ames, IA 50011, USA.ORCID https://orcid.org/0009-0007-0353-4680
Rahil SalehiDepartment of Chemical and Biological Engineering, Iowa State University, Ames, IA 50011, USA.
Niaz Bahar ChowdhuryDepartment of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.ORCID https://orcid.org/0009-0005-3833-6380
Randy AryeeDepartment of Chemical and Biological Engineering, Iowa State University, Ames, IA 50011, USA.
Brisa Calderon-LopezDepartment of Chemical and Biological Engineering, Iowa State University, Ames, IA 50011, USA.
Supantha DeyDepartment of Chemical and Biological Engineering, Iowa State University, Ames, IA 50011, USA.ORCID https://orcid.org/0000-0003-1957-4613
Ankur MaliDepartment of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA.ORCID https://orcid.org/0000-0001-5813-3584
Rajib SahaDepartment of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.ORCID https://orcid.org/0000-0002-2974-0243
Ratul ChowdhuryDepartment of Chemical and Biological Engineering, Iowa State University, Ames, IA 50011, USA.ORCID https://orcid.org/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

Predicting enzyme kinetics directly from sequence remains a central challenge in computational biology, particularly for mutations at catalytically essential residues. Existing models often default to wild-type predictions even when mutations cause substantial activity loss. This limits their utility for enzyme design and mechanistic inference. Here, we introduce CatRange, a machine learning framework trained on CatLog-27k, a human-in-the-loop, AI-agent curated dataset of 27,176 in vitro enzyme-substrate kinetic records. CatLog-27k was created through systematic audit and correction of BRENDA and SABIO-RK, with mutant entries manually reconciled against 2,158 source articles. CatRange reframes kinetic prediction as classification into log

Indexed as

bio-aware machine learningbiocatalysisdatabase curationenzyme engineeringenzyme kinetics

Identifiers

PMID42872055
PMCPMC13659915

What OpenQuestion holds

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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.