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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Closing the loop: Experimentally validated methods in artificial intelligence-driven protein design.

Clayton W Kosonocky1, Sarah Alamdari2, Kevin K Yang2

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Artificial intelligence (AI) is revolutionizing protein design through advanced models. This review covers the AI pipeline from data to validation, highlighting successful methods for binders, antibodies, and enzymes.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) models trained on large datasets are transforming protein design.
  • AI-driven protein design involves a comprehensive pipeline: data curation, model development, candidate generation/filtering, and experimental validation.

Purpose of the Study:

  • To review AI-driven protein design methods across the entire pipeline.
  • To assess the performance of AI methods in key application areas: binders, antibodies, and enzymes.
  • To provide a practical reference for successful AI protein design strategies.

Main Methods:

  • Review of AI-driven protein design methodologies.
  • Analysis of an end-to-end pipeline encompassing data curation, model development, candidate generation, filtering, and experimental validation.
  • Consolidation of experimental outcomes from diverse AI approaches.

Main Results:

  • AI models can generate proteins with specified functions using sequence and structure data.
  • Performance assessment across binders, antibodies, and enzymes indicates successful AI applications.
  • Experimental feedback is crucial for advancing AI-driven protein design.

Conclusions:

  • AI has significantly reshaped the field of protein design.
  • The review consolidates current successful AI methods and their applications.
  • Continued integration of experimental validation is key for future progress in AI protein design.