NLPro: A Protein Foundation Model to Follow Human Instructions and Biological Context

Designing proteins with specified functions remains a fundamental challenge in computational biology. Motivated by the remarkable instruction-following capabilities of large language models (LLMs), natural language-guided protein generation offers a promising avenue for controllable, on-demand protein design. To this end, we present NLPro, a foundation model that integrates human instructions and biological context for flexible functional protein design. NLPro leverages a text encoder and specialized biological encoders to effectively capture functional descriptions alongside biological contextual constraints. To train a foundation model, we curate NLProBench from UniProtKB, containing 205 million <description, protein> pairs, and subsequently fine-tune the model on two important real-world tasks: ligand-binding protein design and antibody design. NLPro achieves state-of-the-art performance across all benchmarks, attaining a functional F1 score of 74.8%, a DockQ score of 0.39 in antibody design, and an ipTM score of 0.90 in ligand-binding protein design. These results demonstrate NLPro's capability to generate functional proteins for arbitrary goals, highlighting its strong potential for real-world biomedical applications.