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Identifying Amino Acid Overproducers Using Rare-Codon-Rich Markers

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Pichia-CLM: A language model-based codon optimization pipeline for Komagataella phaffii.

Harini Narayanan¹, J Christopher Love^1,2

¹Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139.

Proceedings of the National Academy of Sciences of the United States of America

|February 17, 2026

Summary

Scientists used language models to improve protein production in the yeast Komagataella phaffii. This codon usage bias optimization enhanced protein yields up to threefold, outperforming existing tools.

Keywords:

biotechnology codon usage bias encoder–decoder networks genetic sequences recombinant protein production

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Last Updated: Feb 19, 2026

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Published on: February 18, 2022

Removal of an Internal Translational Start Site from mRNA While Retaining Expression of the Full-Length Protein

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Published on: March 16, 2022

Area of Science:

Biotechnology
Computational Biology
Genomics

Background:

Synonymous codon usage bias (CUB) influences gene expression and protein production.
Factors like host organism, gene function, and codon position affect CUB.
Optimizing codon usage is crucial for efficient heterologous protein expression.

Purpose of the Study:

To develop a language model-based pipeline for learning and applying codon usage bias.
To enhance the production of heterologous proteins in the industrial host Komagataella phaffii.
To compare the performance of the developed pipeline against existing codon optimization tools.

Main Methods:

Utilized language models to learn codon usage patterns from the host genome.
Developed the Pichia-Codon language model (Pichia-CLM) pipeline.
Applied Pichia-CLM to optimize sequences for heterologous protein expression in K. phaffii.
Evaluated protein production levels and compared results with commercial tools.

Main Results:

Achieved up to threefold enhancement in heterologous protein production compared to native sequences.
Pichia-CLM consistently improved productivity for proteins of varying complexity.
Generated sequences mimicked host cell protein codon usage properties.
Successfully learned and avoided negative cis-regulatory and repeat elements.

Conclusions:

Language models can effectively learn codon usage patterns from genomic data.
Pichia-CLM offers a robust and efficient method for codon optimization.
The approach holds significant potential for improving heterologous protein production in industrial biotechnology.