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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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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.
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Advancing virulence factor prediction using protein language models.

Yitong Liu1, Xin Cao1, Jiani Li1

  • 1School of Life and Health Sciences, Hainan Province Key Laboratory of One Health, Collaborative Innovation Center of Life and Health, Hainan University, Haikou, 570228, Hainan, China.

BMC Biology
|October 15, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces pLM4VF, a new framework for predicting bacterial virulence factors (VFs) in both gram-positive and gram-negative bacteria. pLM4VF significantly improves prediction accuracy, offering a valuable tool for understanding bacterial diseases and developing new treatments.

Keywords:
Bacterial infectionMachine learningProtein language modelStacking strategyVirulence factor

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Area of Science:

  • Bacterial Pathogenesis and Virulence Factor Prediction
  • Computational Biology and Bioinformatics
  • Machine Learning in Microbiology

Background:

  • Bacterial infections are a major global health threat, with virulence factors (VFs) driving pathogenicity.
  • Accurate VF prediction is essential for understanding disease mechanisms and developing novel therapeutics.
  • Existing machine learning (ML) methods struggle with outdated features and lack differentiation between bacterial types (Gram-positive/negative).

Purpose of the Study:

  • To develop an advanced predictive framework, pLM4VF, for bacterial virulence factors.
  • To improve the accuracy and reliability of VF prediction, particularly for Gram-positive and Gram-negative bacteria.
  • To provide an accessible tool for researchers to predict VFs at a whole-genome scale.

Main Methods:

  • Utilized ESM protein language models for feature extraction specific to Gram-positive and Gram-negative bacteria.
  • Employed a stacking strategy to integrate separate models for enhanced predictive performance.
  • Conducted extensive benchmarking against state-of-the-art methods on independent test datasets.

Main Results:

  • pLM4VF demonstrated superior performance compared to existing methods, with accuracy improvements of 0.088-0.320 for Gram-positive and 0.063-0.307 for Gram-negative bacteria.
  • Biological validation through cytotoxicity and acute toxicity assays confirmed the framework's reliability.
  • An online tool was developed, facilitating whole-genome VF prediction for researchers with limited ML expertise.

Conclusions:

  • pLM4VF offers significant support for elucidating pathogenic mechanisms.
  • The framework aids in the development of novel antibacterial treatments and vaccines.
  • pLM4VF contributes to the prevention and management of bacterial diseases.