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Related Concept Videos

Mutations in Microorganisms01:18

Mutations in Microorganisms

Mutations are heritable changes in an organism’s genome involving alterations in the base sequence of DNA or RNA. These changes can influence cellular processes and phenotypic traits, potentially transforming the unaltered wild type into a mutant form. Such changes, termed forward mutations, are pivotal in shaping the genetic diversity of organisms.RNA viruses exhibit the highest mutation rates due to the absence of robust proofreading mechanisms during genome replication. In contrast,...
Viral Mutations00:36

Viral Mutations

A mutation is a change in the sequence of bases of DNA or RNA in a genome. Some mutations occur during replication of the genome due to errors made by the polymerase enzymes that replicate DNA or RNA. Unlike DNA polymerase, RNA polymerase is prone to errors because it is not capable of “proofreading” its work. Viruses with RNA-based genomes, like HIV, therefore accrue mutations faster than viruses with DNA-based genomes. Because mutation and recombination provide the raw material for adaptive...
Point and Frameshift Mutations01:30

Point and Frameshift Mutations

Point mutations are genetic alterations involving the change of a single nucleotide base pair in DNA. Depending on how the alteration affects protein synthesis, they can lead to various consequences.Point mutations fall into the following types:Silent mutations occur when a nucleotide change does not alter the amino acid sequence due to the redundancy of the genetic code. For instance, changing ACC to ACA still encodes threonine, leaving the protein function unaffected. This occurs because...
Size and Structure of Viral Genomes01:26

Size and Structure of Viral Genomes

Viral genomes exhibit remarkable diversity in size, structure, and composition, influencing their replication strategies and interactions with host cells. These genomes consist of either DNA or RNA and may be linear or circular. Additionally, they can be single-stranded or double-stranded, with each configuration affecting how the virus propagates within a host. RNA viruses, for instance, generally have smaller genomes than DNA viruses, a factor that contributes to their high mutation rates and...
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
Mutations01:39

Mutations

Overview

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Related Experiment Video

Updated: May 31, 2026

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
08:46

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Published on: December 9, 2015

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

Kaetlyn Gibson1, Po-E Li1, Valerie Li1

  • 1Genomics and Bioanalytics Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.

BMC Genomics
|May 29, 2026
PubMed
Summary

We developed Protein Representation Inference for Mutation Evaluation (PRIME) to accurately predict viral pathogen evolution. PRIME uses domain-specific fine-tuning and stratified validation, improving protein language model utility for biosurveillance.

Keywords:
BiosurveillanceGomology leakageHost tropism predictionMachine learning efficiencyModel fine-tuningPhenotype predictionPosition-stratified validationProtein language models

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Area of Science:

  • Computational Biology
  • Virology
  • Machine Learning

Background:

  • Protein language models (PLMs) show promise for protein fitness prediction.
  • Extreme sequence homology in viral pathogens causes data leakage in standard validation, inflating performance metrics.
  • Current PLM applications in viral pathogen surveillance lack real-world utility due to unreliable predictions.

Purpose of the Study:

  • To develop a robust framework for evaluating viral threats using PLMs.
  • To address the challenge of data leakage and improve the generalizability of PLMs for rapidly evolving viruses.
  • To establish a new benchmark for applying PLMs in pathogen surveillance.

Main Methods:

  • Introduced Protein Representation Inference for Mutation Evaluation (PRIME) framework.
  • Integrated domain-specific fine-tuning with position-stratified validation.
  • Utilized a dataset of 347,432 SARS-CoV-2 receptor binding domain (RBD) sequences for benchmarking.

Main Results:

  • Position-stratified validation revealed random splits yield deceptive R² values (>0.90) for SARS-CoV-2 RBD sequences.
  • Domain-specific fine-tuning of ESM-C 600M with stratified data achieved R² ~0.23 for predicting binding affinity and expression at unseen mutational sites.
  • PRIME identified 3.03% of bat coronavirus sequences as candidates for experimental prioritization based on functional similarity.

Conclusions:

  • PRIME sets a new benchmark for PLM application in pathogen surveillance.
  • State-of-the-art models and fine-tuning, combined with stratified validation, offer biologically meaningful insights.
  • This approach enhances understanding of pathogen evolution and zoonotic risk.