Evidence map›Paper›PMID 42215857›Full record

ArticleBMC genomics2026

PRIME: An evaluation framework for protein representation inference and generalization in viral mutation space.

Kaetlyn Gibson, Po-E Li, Valerie Li, Martha Dix, Li-Wei Hung, George Widgery Stelle, Michal Babinski, Patrick Chain, Bin Hu

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Article in BMC genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Kaetlyn GibsonGenomics and Bioanalytics Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
Po-E LiGenomics and Bioanalytics Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
Valerie LiGenomics and Bioanalytics Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
Martha DixGenomics and Bioanalytics Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
Li-Wei HungBiochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
George Widgery StelleApplied Computer Science Group, Computer, Computational and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
Michal BabinskiGenomics and Bioanalytics Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
Patrick ChainGenomics and Bioanalytics Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
Bin HuGenomics and Bioanalytics Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. bhu@lanl.gov.

Funding

Los Alamos National Laboratory 20250637DI, 20250638DI, 20250639DI, 20240734DI, 20210767DI, 20200732ER
6 · The paper itself

Abstract

backgroundProtein language models (PLMs) have revolutionized protein fitness prediction, yet their application to rapidly evolving viral pathogens is often confounded by extreme sequence homology. This homology leads to "data leakage" in standard random validation splits, yielding inflated performance metrics that fail to translate into real-world biosurveillance utility.

resultsWe present Protein Representation Inference for Mutation Evaluation (PRIME), a framework that integrates domain-specific fine-tuning with a rigorous position-stratified validation protocol to evaluate viral threats. Using a dataset of 347,432 SARS-CoV-2 receptor binding domain (RBD) sequences, we demonstrate that while random training data split yields deceptive R

conclusionPRIME establishes a new benchmark for the application of PLMs in pathogen surveillance. Our findings demonstrate that state-of-the-art models and fine-tuning, when paired with stratified validation, provide biologically meaningful insights into pathogen evolution and zoonotic risk.

Indexed as

MutationSARS-CoV-2Spike Glycoprotein, CoronavirusBinding SitesHumansProtein DomainsSpike Glycoprotein, CoronavirusBiosurveillanceGomology leakageHost tropism predictionMachine learning efficiencyModel fine-tuningPhenotype predictionPosition-stratified validationProtein language models

Identifiers

PMID42215857
PMCPMC13425921

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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.