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SAPH-ire TFx – A Recommendation-based Machine Learning Model Captures a Broad Feature Landscape Underlying Functional Post-Translational Modifications

View ORCID ProfileNolan English, View ORCID ProfileMatthew Torres
doi: https://doi.org/10.1101/731026
Nolan English
1School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332
2Quantitative Biosciences Program, Georgia Institute of Technology, Atlanta, GA 30332
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  • ORCID record for Nolan English
Matthew Torres
1School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332
2Quantitative Biosciences Program, Georgia Institute of Technology, Atlanta, GA 30332
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  • For correspondence: mtorres35@gatech.edu
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ABSTRACT

Protein post-translational modifications (PTMs) are a rapidly expanding feature class of significant importance in cell biology. Due to a high burden of experimental proof, the number of functional PTMs in the eukaryotic proteome is currently underestimated. Furthermore, not all PTMs are functionally equivalent. Therefore, computational approaches that can confidently recommend the functional potential of experimental PTMs are essential. To address this challenge, we developed SAPH-ire TFx (https://saphire.biosci.gatech.edu/): a multi-feature neural network model and web resource optimized for recommending experimental PTMs with high potential for biological impact. The model is rigorously benchmarked against independent datasets and alternative models, exhibiting unmatched performance in the recall of known functional PTM sites and the recommendation of PTMs that were later confirmed experimentally. An analysis of feature contributions to model outcome provides further insight on the need for multiple rather than single features to capture the breadth of functional data in the public domain.

Contact mtorres35{at}gatech.edu

Supplementary Information See Tables S1-S6 & Figures S1-S4.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • New analysis of model feature contributions in SAPH-ire TFx conducted using Local Interpretable Model-Agnostic Explanations (LIME). Mock-experimental validation by re-analysis of newly discovered functional PTM data. New analysis of phosphosite type recall across multiple models. New SAPH-ire TFx recommendations for likely functional PTMs that intersect with functional Short Linear Motifs (SLiMs) and disease linked single nucleotide polymorphisms (SNPs).

  • https://saphire.biosci.gatech.edu

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted May 23, 2020.
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SAPH-ire TFx – A Recommendation-based Machine Learning Model Captures a Broad Feature Landscape Underlying Functional Post-Translational Modifications
Nolan English, Matthew Torres
bioRxiv 731026; doi: https://doi.org/10.1101/731026
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SAPH-ire TFx – A Recommendation-based Machine Learning Model Captures a Broad Feature Landscape Underlying Functional Post-Translational Modifications
Nolan English, Matthew Torres
bioRxiv 731026; doi: https://doi.org/10.1101/731026

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