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Protein and Protein Structure02:15

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Toward deep learning sequence-structure co-generation for protein design.

Chentong Wang1, Sarah Alamdari2, Carles Domingo-Enrich2

  • 1School of Life Sciences, Westlake University, Hangzhou, Zhejiang, 310024, China.

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|February 21, 2025
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Summary
This summary is machine-generated.

Deep generative models can design novel proteins by learning from natural sequences and structures. Emerging co-generation methods, modeling both protein sequence and structure simultaneously, offer more accurate and controllable protein design possibilities.

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Area of Science:

  • Computational biology
  • Protein engineering
  • Artificial intelligence in drug discovery

Background:

  • Deep generative models are advancing protein design by learning from natural protein data.
  • Current models often generate protein sequences or structures independently.
  • Simultaneous co-generation of sequence and structure offers enhanced design control and accuracy.

Purpose of the Study:

  • To review recent advancements in deep generative models for protein design.
  • To focus on sequence-structure co-generation methods.
  • To discuss future opportunities in this field.

Main Methods:

  • Review of literature on deep generative models for protein design.
  • Focus on methodologies for simultaneous sequence and structure co-generation.
  • Analysis of evaluation principles for co-generation models.

Main Results:

  • Highlighting key methodological and evaluation principles in sequence-structure co-generation.
  • Summarizing recent breakthroughs in the field.
  • Identifying current trends and challenges.

Conclusions:

  • Sequence-structure co-generation represents a promising frontier in protein design.
  • Further development is needed to fully realize the potential of these models.
  • Continued research can accelerate the creation of novel proteins with desired functions.