NanoAbLLaMA: construction of nanobody libraries with protein large language models.

ABSTRACT:

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 germl

SUBMITTER: Wang X 

PROVIDER: S-EPMC11893428 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature