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Related Concept Videos

Regulated mRNA Transport02:22

Regulated mRNA Transport

In eukaryotes, transcription and translation are compartmentalized; an mRNA is first synthesized in the nucleus and then selectively transported to the cytoplasm for protein synthesis. Before transport, a pre-mRNA undergoes several steps of post-transcriptional modifications including splicing, 5' capping, and the addition of a poly-adenine tail. Various proteins bind to the pre-mRNA during these modifications. The mRNA transport takes place with the help of multiple proteins playing specific...
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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...
Improving Translational Accuracy02:07

Improving Translational Accuracy

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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Nuclear Localization Signals and Import

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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Cotranslational Protein Translocation

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Related Experiment Video

Updated: May 24, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Enhancing protein subcellular localization prediction through language model-based knowledge embeddings and machine

Karthik Avinash1, S Tejas1, Sriram Mamidala2

  • 1Department of Computer Science and Engineering, Indian Institute of Information Technology Dharwad, India.

Analytical Biochemistry
|May 22, 2026
PubMed
Summary
This summary is machine-generated.

This study evaluates protein language models for predicting protein subcellular localization. ESM-2 with a Support Vector Machine achieved the best performance, improving accuracy by 3% over current models.

Keywords:
Bidirectional encoder representations from transformersEvolutionary scale modelingMulti-label classificationProtein embeddingProtein language modelsProtein subcellular localizationStrict accuracy

Related Experiment Videos

Last Updated: May 24, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Protein Science

Background:

  • Protein subcellular localization is crucial for function.
  • Traditional methods for PSCL prediction are becoming impractical due to large datasets.
  • High-accuracy multi-label PSCL prediction remains a challenge.

Purpose of the Study:

  • To evaluate embeddings from five Protein Language Models (PLMs) as input features for machine learning classifiers.
  • To assess the performance of different PLM architectures and classifier complexities for PSCL prediction.
  • To identify optimal combinations for improved prediction accuracy.

Main Methods:

  • Utilized five PLMs: ProtBERT-BFD, ESM-2, ProtALBERT, ProLLaMA, and ProtGPT-2.
  • Employed machine learning classifiers, including Support Vector Machine (SVM) with a polynomial kernel.
  • Conducted five-fold cross-validation on the Swiss-Prot dataset.

Main Results:

  • Encoder-based PLMs, especially ESM-2, combined with SVM (polynomial kernel), yielded the best performance.
  • Achieved a strict accuracy of 0.58, a 3 percentage point improvement over state-of-the-art models.
  • Demonstrated modest but consistent improvements in strict-accuracy metrics.

Conclusions:

  • ESM-2 embeddings with SVM offer a robust approach for PSCL prediction.
  • Current PLM embeddings provide strong performance but require further advancements.
  • Future work should focus on enhanced feature extraction and model architectures to significantly boost strict accuracy.