Classifying Protein Families with Learned Compressed Representations

11 pages•Published: May 1, 2023

Ramin Dehghanpoor, Fatemeh Afrasiabi, Charles Fogel, Tung Dao, Suman Gautam, Aanab Nehela, Ahmad Nehela, Daniel Haehn and Nurit Haspel

Abstract

Classifying proteins into families is an important task when studying newly discovered proteins. If we can identify the family a protein belongs to, we can predict features without knowing the exact structure of such a protein.
However, this grouping process is challenging. We propose a two-stage algorithm that classifies proteins into families by combining a dimensionality reduction technique using a variational autoencoder with learned fingerprint representations using a Convolutional Neural Network (CNN). Our models use fewer parameters than existing methods but perform better, with our variational autoencoder achieving 94% accuracy in reconstructing the most common amino acid in a sequence alignment, and the neural network provides 98-100% accuracy in classifying protein families. We developed a software framework to access our algorithms. All code and data are publicly available at https://github.com/ramindehghanpoor/CLI.

Keyphrases: cnn, machine learning, neural network, protein family classification, vae

In: Hisham Al-Mubaid, Tamer Aldwairi and Oliver Eulenstein (editors). Proceedings of International Conference on Bioinformatics and Computational Biology (BICOB-2023), vol 92, pages 47-57.

Links:	https://easychair.org/publications/paper/VR1H
	https://doi.org/10.29007/qzzf

BibTeX entry

@inproceedings{BICOB-2023:Classifying_Protein_Families_with,
  author    = {Ramin Dehghanpoor and Fatemeh Afrasiabi and Charles Fogel and Tung Dao and Suman Gautam and Aanab Nehela and Ahmad Nehela and Daniel Haehn and Nurit Haspel},
  title     = {Classifying Protein Families with Learned Compressed Representations},
  booktitle = {Proceedings of International Conference on Bioinformatics and Computational Biology (BICOB-2023)},
  editor    = {Hisham Al-Mubaid and Tamer Aldwairi and Oliver Eulenstein},
  series    = {EPiC Series in Computing},
  volume    = {92},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/VR1H},
  doi       = {10.29007/qzzf},
  pages     = {47-57},
  year      = {2023}}

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