InstructPro2: Natural Language and Biological Grounding Guided De Novo Protein Design

Key points

• The ability to generate proteins with desired functions is a central goal of computational biology.1
• Although natural language can express diverse design objectives, existing protein generation methods often support only narrow tasks or make limited use of biological constraints.1
• On functional protein generation benchmarks, InstructPro2 outperforms Pinal, the strongest baseline, achieving a functional recovery F1 of 74.8 versus 62.2 and lowering global RMSD from 5.33A to 3.04A.1
• These findings highlight how integrating language-based instructions with biological conditioning can extend protein generation to distinct design settings.1

Sources (1)

• [1]InstructPro2: Natural Language and Biological Grounding Guided De Novo Protein Design
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 05:09 AM

  We develop InstructPro2 to translate natural-language design objectives into protein sequences while incorporating task-specific biological context.

  The ability to generate proteins with desired functions is a central goal of computational biology.

Extractive summary: sentences quoted from the sources.