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Updated: Jun 4, 2025

Enhanced Genome Editing with Cas9 Ribonucleoprotein in Diverse Cells and Organisms
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Multi-Modal CLIP-Informed Protein Editing.

Mingze Yin1, Hanjing Zhou2, Yiheng Zhu2

  • 1School of Medicine, Zhejiang University, Hangzhou, China.

Health Data Science
|December 20, 2024
PubMed
Summary
This summary is machine-generated.

We developed ProtET, a novel machine learning method for protein editing using biotext instructions. This approach efficiently enhances protein functions like stability and binding, advancing artificial protein design.

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Genome Editing in Mammalian Cell Lines using CRISPR-Cas
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Area of Science:

  • Biotechnology
  • Computational Biology
  • Protein Engineering

Background:

  • Proteins are vital for biological functions, and controllable protein editing aids research and therapeutics.
  • Machine learning-assisted protein editing (MLPE) accelerates optimization but struggles with vast sequence spaces and direct human feedback integration.
  • Existing methods lack the ability to perform protein editing based on natural language instructions.

Purpose of the Study:

  • To introduce ProtET, an efficient method for CLIP-informed protein editing using multi-modality learning.
  • To enable protein editing guided by biotext instructions, enhancing human interactivity.
  • To overcome limitations in combinatorial space exploration for protein sequence generation.

Main Methods:

  • ProtET utilizes a two-stage approach: pretraining with contrastive learning to align protein and biotext representations via large language models (LLMs).
  • The protein editing stage fuses features from editing instructions and original protein sequences.
  • Generated target protein sequences are conditioned on these fused features for precise editing.

Main Results:

  • ProtET demonstrated superior performance in editing proteins for enhanced functionality across enzyme activity, stability, and antibody binding.
  • The method significantly improved protein stability, achieving gains of 16.67% and 16.90%.
  • ProtET outperformed existing state-of-the-art methods by a considerable margin.

Conclusions:

  • ProtET offers a powerful new capability for artificial protein editing guided by natural language.
  • This advancement has the potential to address unmet needs in academic, industrial, and clinical applications.
  • The method facilitates the creation of novel protein constructs with desired functionalities.