Finetuned DNA Language Model Based- Classifiers Captures Significant Enzymatic Activity from Metagenomic Datasets Öffentlichkeit

Abstract

The surge of metagenomic sequencing data demands functional annotation methods that move beyond traditional homology-based approaches. In this study, we utilize REBEAN (Read Embedding-Based Enzyme Annotator), a fine-tuned DNA language model designed to predict enzymatic activity directly from raw nucleotide sequences, and developed two classifiers, REBEAN-Halo and REBEAN-Nitro, targeting halogenase and nitrogenase functions, respectively. REBEAN-Halo identified functionally important regions within known halogenases and detected 92 candidates of novel halogenases from marine metagenomes. REBEAN-Nitro, though undertrained, successfully distinguished higher nitrogenase activity in unfertilized agricultural soils relative to fertilized ones, aligning with ecological expectations. Both models highlight REBEAN's potential to uncover functionally relevant but sequence-divergent enzymes in complex metagenomic datasets, offering a powerful tool for advancing enzyme discovery and microbiome functional profiling.

Table of Contents

Introduction .................................................................................................................................. 1

Results and Discussion .................................................................................................................. 4

Methods .......................................................................................................................................12

Bibliography .................................................................................................................................14

About this Honors Thesis

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School	Emory College
Department	Biology
Degree	B.S.
Submission	Honors Thesis
Language	English
Research Field	Biology, Molecular Biology, Bioinformatics
Stichwort	Artificial Intelligence Nitrogenases Metagenomics Function Prediction Halogenases
Committee Chair / Thesis Advisor	Yana Bromberg, Emory University
Committee Members	Carl J. Yang, Emory University Kyle F. Biegasiewicz, Emory University

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