Evidence map›Paper›PMID 41278762›Full record

ArticlebioRxiv : the preprint server for biology2025

Large Numbers of New Human Paralogs Discovered.

Pradeep Bk, Weixia Deng, Mahsa Askary Hemmat, Issak Daniels, Robert L Jernigan

Abstract readPreprint
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

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.

Pradeep BkRoy J. Carver Department of Biochemistry, Biophysics and Molecular Biology Bioinformatics and Computational Biology Program Iowa State University Ames, IA 50011 USA.ORCID 0009-0008-5246-1470
Weixia DengRoy J. Carver Department of Biochemistry, Biophysics and Molecular Biology Bioinformatics and Computational Biology Program Iowa State University Ames, IA 50011 USA.ORCID 0009-0008-5073-1967
Mahsa Askary HemmatRoy J. Carver Department of Biochemistry, Biophysics and Molecular Biology Bioinformatics and Computational Biology Program Iowa State University Ames, IA 50011 USA.ORCID 0000-0002-0577-2777
Issak DanielsRoy J. Carver Department of Biochemistry, Biophysics and Molecular Biology Bioinformatics and Computational Biology Program Iowa State University Ames, IA 50011 USA.ORCID 0009-0003-1320-0845
Robert L JerniganRoy J. Carver Department of Biochemistry, Biophysics and Molecular Biology Bioinformatics and Computational Biology Program Iowa State University Ames, IA 50011 USA.ORCID 0000-0003-0996-8360

Funding

Novel Use of Genome Information to Understand MutationsR01HG012117 · NHGRI · IOWA STATE UNIVERSITY · PI JERNIGAN, ROBERT L, KLOCZKOWSKI, ANDRZEJ · 2021 to 2025
$2.3M
An effective statistical inference framework to develop innovative compensations for protein mutationsR01GM157600 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Zhao Ren · 2024 to 2026
$611k
Statistical methods for higher order dependences to understand protein functionsR01GM144961 · NIGMS · COLORADO STATE UNIVERSITY · PI ZHOU, WEN · 2021 to 2023
$600k
NHGRI NIH HHS R01 HG012117NIGMS NIH HHS R01 GM144961NIGMS NIH HHS R01 GM157600
6 · The paper itself

Abstract

The identification of paralogs is critical for understanding protein evolution, function, and for drug design, yet many human proteins remain unannotated and poorly classified. Sequence-based homology detection alone often fails to detect distant paralogs, especially in the "twilight zone" or beyond, regarding sequence identity. Here we present an integrated homolog detection framework that combines results from BLASTp, MMseqs2, Foldseek, and the large protein language model-based tool PROST, followed by validation based on comparison of structures and for enzymes comparison of the specific structures of the catalytic residues. Using all-versus-all exhaustive comparisons across the 20,647 human proteins, we systematically identify novel paralogs and assess their catalytic residues for two serine protease clans. We discovered 14 previously uncharacterized human serine carboxypeptidases, validated against experimentally determined PDB structures, with 11 of these displaying conserved catalytic triads. We further identify 203 new paralogs for human kinases, with 163 of these in the major clusters that represent previously uncharacterized kinase subtypes and 30 putative novel human transcription factors. Across both serine protease subtypes, structural alignments enable the prediction of the previously unknown catalytic residues for those lacking UniProt annotations of active site residues. By integrating sequence, structure, and LPLM embedding-based approaches, the framework enables the discovery of surprisingly large numbers of unknown paralogs, permitting defining catalytic residues, and expands the understanding of protein functional landscapes. These findings provide the foundation for a large number of future functional, evolutionary, and therapeutic investigations.

Identifiers

PMID41278762
PMCPMC12632886

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Registered trials

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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.