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Proteins targeted to the nucleus carry short stretches of amino acid sequences called the nuclear localization signal or NLS. Classical nuclear localization signals are of two types: monopartite and bipartite NLS. Monopartite classical NLS (cNLS) consists of a single cluster of 4-8 amino acids. Bipartite cNLS consists of two clusters of  2-3 amino acids and a 9-12 residue long proline-rich linker bridging the two clusters. Signal clusters are rich in positively charged amino acids such as...
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mtx-COBRA: Subcellular localization prediction for bacterial proteins.

Isha Arora1, Arkadij Kummer1, Hao Zhou1

  • 1Moderna, Inc., 200 Technology Square, Cambridge, MA 02139, USA.

Computers in Biology and Medicine
|February 24, 2024
PubMed
Summary

A new pipeline, mtx-COBRA, accurately predicts bacterial protein subcellular localization (SCL), improving vaccine antigen identification. This method enhances the classification of proteins with unknown SCL, crucial for developing new vaccines against bacterial pathogens.

Keywords:
Bacterial subcellular localizationMachine learningProtein language modelReverse vaccinologymtx-COBRA

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Immunology

Background:

  • Bacteria pose significant health risks, necessitating vaccine development against infectious diseases.
  • Identifying bacterial antigens for immune recognition is key to vaccine design.
  • Protein subcellular localization (SCL) influences immune response, but experimental determination is challenging.

Purpose of the Study:

  • To introduce mtx-COBRA, a novel computational pipeline for predicting bacterial protein SCL.
  • To improve the identification of potential vaccine targets by addressing limitations of existing methods.

Main Methods:

  • mtx-COBRA integrates Meta's Evolutionary Scale Modeling (ESM) protein language model with an Extreme Gradient Boosting machine learning model.
  • The pipeline analyzes bacterial protein amino acid sequences for SCL prediction.
  • Training data was curated from UniProt and the ePSORTdb dataset.

Main Results:

  • mtx-COBRA demonstrates higher accuracy in predicting bacterial SCL compared to PSORTb.
  • Rigorous benchmarking, cross-validation, and leave-one-pathogen-out analyses validated the pipeline's performance.
  • The tool effectively classifies bacterial proteins with previously unknown SCL.

Conclusions:

  • mtx-COBRA offers an accessible and efficient solution for bacterial protein SCL prediction.
  • This advancement aids in identifying more vaccine candidates by resolving "Unknown" SCL classifications.
  • The pipeline surpasses current bioinformatic and experimental methods in efficiency and accuracy.