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Related Experiment Video

Updated: Jul 10, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Democratizing Protein Language Models with Parameter-Efficient Fine-Tuning.

Samuel Sledzieski^1,2, Meghana Kshirsagar², Minkyung Baek³

¹Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge MA 02139, USA.

Biorxiv : the Preprint Server for Biology

|November 21, 2023

Summary

This summary is machine-generated.

Parameter-efficient fine-tuning (PEFT) methods, like LoRA, are introduced to proteomics for protein language models. These methods significantly reduce computational resources for tasks like protein-protein interaction prediction and homooligomer symmetry prediction.

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Area of Science:

Computational Biology
Bioinformatics
Machine Learning in Proteomics

Background:

Large pre-trained protein language models (PLMs) revolutionize proteomics by learning sequence representations.
Fine-tuning PLMs for specific tasks is computationally intensive, posing a barrier for many researchers.
Parameter-efficient fine-tuning (PEFT) methods address similar challenges in natural language processing.

Conclusions:

PEFT methods offer a computationally efficient and effective approach for adapting PLMs in proteomics.
These methods democratize access to powerful PLM tuning for researchers with limited computational resources.
PEFT presents a viable alternative to traditional fine-tuning, even outperforming it in certain proteomic tasks.