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Serverless Prediction of Peptide Properties with Recurrent Neural Networks

View ORCID ProfileMehrad Ansari, View ORCID ProfileAndrew D. White
doi: https://doi.org/10.1101/2022.05.18.492545
Mehrad Ansari
1Department of Chemical Engineering, University of Rochester
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Andrew D. White
1Department of Chemical Engineering, University of Rochester
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  • For correspondence: andrew.white@rochester.edu
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Abstract

We present three deep learning sequence prediction models for hemolysis, solubility, and resistance to non-specific interactions of peptides that achieve comparable results to the state-of-the art models. These predictive models share a common architecture of bidirectional recurrent neural networks (LSTM). These models are implemented in JavaScript so that they can be run on a static website without use of a dedicated server. This removes the cost, and long-term management of a server, while still enabling open and free access to the models. This “serverless” prediction model is a demonstration of edge computing bioinformatics and removes the dependence on cloud providers or self-hosting of resource-rich academic institutions. This is feasible because of the continued track of Moore’s law and ubiquitous hardware acceleration of deep learning computations on new phones and desktops.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • mehrad.ansari{at}rochester.edu

  • https://peptide.bio

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted May 19, 2022.
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Serverless Prediction of Peptide Properties with Recurrent Neural Networks
Mehrad Ansari, Andrew D. White
bioRxiv 2022.05.18.492545; doi: https://doi.org/10.1101/2022.05.18.492545
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Serverless Prediction of Peptide Properties with Recurrent Neural Networks
Mehrad Ansari, Andrew D. White
bioRxiv 2022.05.18.492545; doi: https://doi.org/10.1101/2022.05.18.492545

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