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Related Experiment Video

Updated: Jul 15, 2025

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data

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Published on: February 18, 2022

cdsBERT - Extending Protein Language Models with Codon Awareness.

Logan Hallee¹, Nikolaos Rafailidis², Jason P Gleghorn³

¹Center for Bioinformatics and Computational Biology, University of Delaware.

Biorxiv : the Preprint Server for Biology

|September 25, 2023

Summary

This summary is machine-generated.

Protein language models (pLMs) now incorporate codon information, enhancing protein analysis. This codon-based approach, cdsBERT, improves predictions by capturing subtle sequence variations.

Keywords:

Artificial intelligence BERT Codon usage bias MELD Machine learning Protein language model

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Last Updated: Jul 15, 2025

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Residue-specific Incorporation of Noncanonical Amino Acids into Model Proteins Using an Escherichia coli Cell-free Transcription-translation System

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Identifying Amino Acid Overproducers Using Rare-Codon-Rich Markers

Identifying Amino Acid Overproducers Using Rare-Codon-Rich Markers

Published on: June 24, 2019

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Protein language models (pLMs) analyze protein sequences for high-throughput studies.
Codon usage bias is increasingly recognized for its predictive power in protein structure and function.

Approach:

Extended pLM vocabulary from amino acids to codons within CoDing Sequences (CDS).
Developed a novel pipeline (MELD) using token embedding seeding, masked language modeling, and knowledge distillation.
Transformed ProtBERT into cdsBERT, a codon-based pLM trained on a large CDS corpus.

Key Points:

cdsBERT variants generated a biochemically relevant latent space, outperforming amino acid-based models in enzyme commission number prediction.
Synonymous codon embeddings showed distinct movements, indicating information in "silent" mutations across phylogeny.
Embedding movement correlated with average usage bias, highlighting the significance of codon choice.

Conclusions:

Codon-based pLMs offer a novel way to improve protein structure and function prediction.
Future organism-specific codon pLMs could enhance codon usage fidelity.
Creating a codon pLM foundation model and enriching protein databases with CDS is crucial for advancing the field.