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FIND: Identifying Functionally and Structurally Important Features in Protein Sequences with Deep Neural Networks

Murali, Ranjani and Hemp, James and Orphan, Victoria and Bisk, Yonatan (2019) FIND: Identifying Functionally and Structurally Important Features in Protein Sequences with Deep Neural Networks. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20190401-084306692

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Abstract

The ability to correctly predict the functional role of proteins from their amino acid sequences would significantly advance biological studies at the molecular level by improving our ability to understand the biochemical capability of biological organisms from their genomic sequence. Existing methods that are geared towards protein function prediction or annotation mostly use alignment-based approaches and probabilistic models such as Hidden-Markov Models. In this work we introduce a deep learning architecture (Function Identification with Neural Descriptions or FIND) which performs protein annotation from primary sequence. The accuracy of our methods matches state of the art techniques, such as protein classifiers based on Hidden Markov Models. Further, our approach allows for model introspection via a neural attention mechanism, which weights parts of the amino acid sequence proportionally to their relevance for functional assignment. In this way, the attention weights automatically uncover structurally and functionally relevant features of the classified protein and find novel functional motifs in previously uncharacterized proteins. While this model is applicable to any database of proteins, we chose to apply this model to superfamilies of homologous proteins, with the aim of extracting features inherent to divergent protein families within a larger superfamily. This provided insight into the functional diversification of an enzyme superfamily and its adaptation to different physiological contexts. We tested our approach on three families (nitrogenases, cytochrome bd-type oxygen reductases and heme-copper oxygen reductases) and present a detailed analysis of the sequence characteristics identified in previously characterized proteins in the heme-copper oxygen reductase (HCO) superfamily. These are correlated with their catalytic relevance and evolutionary history. FIND was then applied to discover features in previously uncharacterized members of the HCO superfamily, providing insight into their unique sequence features. This modeling approach demonstrates the power of neural networks to recognize patterns in large datasets and can be utilized to discover biochemically and structurally important features in proteins from their amino acid sequences.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://doi.org/10.1101/592808DOIDiscussion Paper
ORCID:
AuthorORCID
Orphan, Victoria0000-0002-5374-6178
Additional Information: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-ND 4.0 International license. This work was funding by DARPA’s CwC program through ARO (W911NF-15-1-0543), the Moore Foundation(Award to VJO: GBMF3780), the Department of Energy (Award to VJO: DE-SC0016469) and the Center for Dark Biosphere Investigations (NSF Award to VJO: OCE-0939564). We are grateful to Daan Speth for reading the manuscript at multiple stages of completion and for his useful comments throughout the process. We would also like to thank Dr. Robert Gennis for reading the manuscript and offering helpful comments, Grayson Chadwick for useful discussion on bioinformatics strategies and Haley Sapers for her instructive critique of the figures.
Funders:
Funding AgencyGrant Number
Department of Energy (DOE)UNSPECIFIED
Army Research Office (ARO)W911NF-15-1-0543
Gordon and Betty Moore FoundationGBMF3780
Department of Energy (DOE)DE-SC0016469
NSFOCE-0939564
Record Number:CaltechAUTHORS:20190401-084306692
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190401-084306692
Official Citation:FIND: Identifying Functionally and Structurally Important Features in Protein Sequences with Deep Neural Networks Ranjani Murali, James Hemp, Victoria Orphan, Yonatan Bisk bioRxiv 592808; doi: https://doi.org/10.1101/592808
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94306
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:01 Apr 2019 17:26
Last Modified:03 Oct 2019 21:02

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