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Understanding the relationship between amino acid sequence and protein function is a long-standing problem in molecular biology with far-reaching scientific implications.

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Using Deep Learning to Annotate the Protein Universe

Understanding the relationship between amino acid sequence and protein function is a long-standing problem in molecular biology with far-reaching scientific implications. Despite six decades of progress, state-of-the-art techniques cannot annotate 1/3 of microbial protein sequences, hampering our ability to exploit sequences collected from diverse organisms. In this code, i explore an alternative methodology based on deep learning that learns the relationship between unaligned amino acid sequences and their functional annotations across all 17929 families of the Pfam database. amino-acids-cover

My study focused on only 600 families out of all the families included in the dataset.

Model Architecture

#Architecture
Model download (83)

Result:

(Training) Accuracy vs Validation Accuracy (Training) Loss vs Validation Loss
result download (85) download (84)

Model Evaluation

image

Notice:

pre-trainde model: https://drive.google.com/file/d/12ZsTkRlEPG8DL50Wb_tdDmHINv9pKTbj/view?usp=share_link

pre-trainde model weights: https://drive.google.com/file/d/1bj4uJBu7rbO6OaIZg--IkOC5yke_WiLn/view?usp=share_link

Tokenizer: https://drive.google.com/file/d/1-01g2VBsa6hMSCRB-DGylfffJDrCRXu4/view?usp=share_link

References:

https://www.biorxiv.org/content/10.1101/626507v4.full.pdf

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Understanding the relationship between amino acid sequence and protein function is a long-standing problem in molecular biology with far-reaching scientific implications.

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