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Related Experiment Video

Updated: Apr 7, 2026

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

Controllable Protein Design by Prefix-Tuning Protein Language Models.

Jiawei Luo¹, Jiahao Li¹, Xianliang Liu¹

¹School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055 Guangdong, China.

Journal of Chemical Information and Modeling

|April 6, 2026

Summary

This summary is machine-generated.

PrefixProt enables controllable protein design using virtual tokens for tailored functions. This framework efficiently generates proteins with desired structures and activities, advancing drug discovery.

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A Protocol for Computer-Based Protein Structure and Function Prediction

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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

Computational biology
Protein engineering
Bioinformatics

Background:

Designing novel proteins with specific functions is crucial for tackling biomedical challenges.
Existing methods for protein design can be limited in scope and efficiency.

Purpose of the Study:

To introduce PrefixProt, a novel framework for controllable protein design.
To enable multiobjective control over protein generation using adaptive virtual tokens.

Main Methods:

PrefixProt utilizes prefix-tuning to learn virtual tokens as control tags.
These virtual tokens are data-driven and combinatorially integrated for multiobjective control.
The framework was validated through experiments on protein structure and function design.

Main Results:

PrefixProt efficiently guides protein language models (ProtLM) by optimizing fewer parameters.
It outperforms other parameter-efficient fine-tuning methods and text-guided ProtLMs, especially with limited data.
The framework successfully generated proteins with multiple target properties, expanding design possibilities.

Conclusions:

PrefixProt offers a robust and flexible framework for de novo protein design.
The approach shows significant promise for applications in drug discovery and biomedicine.