INTRODUCTION: Traditional methods for constructing synthetic nanobody libraries are labor-intensive and time-consuming. This study introduces a novel approach leveraging protein large language models (LLMs) to generate germline-specific nanobody sequences, enabling efficient library construction through statistical analysis. METHODS: We developed NanoAbLLaMA, a protein LLM based on LLaMA2, fine-tuned using low-rank adaptation (LoRA) on 120,000 curated nanobody sequences. The model generates sequences conditioned on germlines (IGHV3-301 and IGHV3S5301). Training involved dataset preparation from SAbDab and experimental data, alignment with IMGT germline references, and structural validation using ImmuneBuilder and Foldseek. RESULTS: NanoAbLLaMA achieved near-perfect germline generation accuracy (100% for IGHV3-301, 95.5% for IGHV3S5301). Structural evaluations demonstrated superior predicted Local Distance Difference Test (pLDDT) scores (90.32 ± 10.13) compared to IgLM (87.36 ± 11.17), with comparable TM-scores. Generated sequences exhibited fewer high-risk post-translational modification sites than IgLM. Statistical analysis of CDR regions confirmed diversity, particularly in CDR3, enabling the creation of synthetic libraries with high humanization (>
99.9%) and low risk. DISCUSSION: This work establishes a paradigm shift in nanobody library construction by integrating LLMs, significantly reducing time and resource demands. While NanoAbLLaMA excels in germline-specific generation, limitations include restricted germline coverage and framework flexibility. Future efforts should expand germline diversity and incorporate druggability metrics for clinical relevance. The model's code, data, and resources are publicly available to facilitate broader adoption.