Abstract
Protein natural diversity offers a vast sequence space for protein engineering, and deep learning enables its detection from metagenomes/proteomes without prior assumptions. DeepMetagenome, a Python-based method, explores protein diversity through modules for training and analyzing sequence datasets. The deep learning model includes Embedding, Conv1D, LSTM, and Dense layers, with sequence feature analysis for data cleaning. Applied to metallothioneins from a database of over 146 million coding features, DeepMetagenome identified over 500 high-confidence metallothionein sequences, outperforming DIAMOND and CNN-based models. It showed stable performance compared to a Transformer-based model over 25 epochs. Among 23 synthesized sequences, 20 exhibited metal resistance. The tool also successfully explored the diversity of three additional protein families and is freely available on GitHub with detailed instructions.