QLoRA Fine-Tuning of Ministral LLM for Sequence-to-Function Protein Annotation

arXiv:2609.24538 · cs.CL, cs.AI, cs.NE, q-bio.QM · Submitted 2026-09-21 · Read on arXiv

cs.CL, cs.AI, cs.NE, q-bio.QM

Submitted: 2026-09-21

Updated: 2026-09-21

Code: https://github.com/PKU-YuanGroup/ProLLaMA

License: http://creativecommons.org/licenses/by/4.0/

The gist: Functional annotation of newly sequenced proteins remains a bottleneck in molecular biology: the number of sequences in public repositories grows far faster than the capacity for manual curation.

Terminology

Abstract

Functional annotation of newly sequenced proteins remains a bottleneck in molecular biology: the number of sequences in public repositories grows far faster than the capacity for manual curation. Most computational approaches consider annotation as multi-label classification over a fixed ontology, which constrains predictions to a predefined label set. In this work we study the the protein annotation as a sequence-to-text generation problem. We fine-tune the 3B-parameter Ministral 3 base model with QLoRA (4-bit NF4 quantization with low-rank adapters) on sequence annotation pairs. We assess predictions with an LLM-as-expert protocol: a GPT model prompted as a senior molecular-biology curator scores organism identification as binary and function annotation quality. We conclude that QLoRA-fine-tuned compact LLMs can generate curator-style annotations with genuine biological value for a substantial subset of proteins. We also discuss future directions in data quality, model scaling, and evidence grounding that are needed to make the approach sufficiently reliable for practical use.

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