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  3. Discriminator-guided Inverse Folding For Multi-property Protein Design.
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  3. Discriminator-guided Inverse Folding For Multi-property Protein Design.

Related Experiment Video

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

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

Published on: July 25, 2013

Discriminator-Guided Inverse Folding for Multi-Property Protein Design.

Yuchuan Zheng1, Chuyi Liu2, Zhaoming Liu3,4

  • 1Institute for Advanced Study in Physics, Zhejiang University, Hangzhou, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|June 9, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Discriminator-Guided Inverse Folding (DGIF) enables simultaneous multi-property optimization for de novo protein design. This framework overcomes data limitations, improving protein thermostability and solubility by guiding inverse folding models.

Keywords:
discriminator‐guided optimizationinverse folding modelmulti‐property optimizationstructure‐based protein design

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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

Area of Science:

  • Protein engineering
  • Computational biology
  • Biochemistry

Background:

  • Designing proteins with multiple desired physicochemical properties is crucial for real-world applications.
  • Structure-based de novo protein design is a leading paradigm, but joint multi-property optimization remains a challenge.
  • Current methods struggle with multi-property optimization due to limited datasets with multiple property annotations.

Purpose of the Study:

  • To develop a novel framework for simultaneous multi-property optimization in de novo protein design.
  • To overcome the limitations of existing inverse folding methods that require multi-property annotated datasets.
  • To enable the design of proteins with improved and simultaneously optimized properties like thermostability and solubility.

Main Methods:

  • Introduced Discriminator-Guided Inverse Folding (DGIF), a framework guiding inverse folding models.
  • Utilized an auxiliary discriminator module integrating multiple single-property predictors.
  • Adjusted internal history states of the inverse folding model via the discriminator.

Main Results:

  • DGIF achieved substantial improvements in protein thermostability and solubility.
  • The framework successfully generated protein sequences optimized for multiple properties simultaneously.
  • Designed proteins demonstrated a significant shift towards the Pareto front, indicating optimal trade-offs.

Conclusions:

  • DGIF effectively enables multi-property optimization in structure-based protein design without multi-property datasets.
  • The developed framework significantly enhances key protein traits and facilitates joint optimization.
  • Experimental validation confirms DGIF's efficacy for designing proteins with tailored, multiple characteristics.