PreDSLpmo: A neural network-based prediction tool for functional annotation of lytic polysaccharide monooxygenases

Journal Article · Mon Dec 09 00:00:00 EST 2019 · Journal of Biotechnology

DOI:https://doi.org/10.1016/j.jbiotec.2019.12.002· OSTI ID:1601231

Srivastava, Pulkit Anupam ^[1]; Hegg, Eric L. ^[2]; Fox, Brian G. ^[3]; Yennamalli, Ragothaman M. ^[4]

---

1. Jaypee Univ. of Information Technology, Waknaghat (India); Jaypee University of Information Technology Department of Biotechnology and Bioinformatics
2. Michigan State Univ., East Lansing, MI (United States)
3. Univ. of Wisconsin, Madison, WI (United States)
4. Jaypee Univ. of Information Technology, Waknaghat (India)

Lytic polysaccharide monooxygenases (LPMOs), a family of copper-dependent oxidative enzymes, boost the degradation of polysaccharides such as cellulose, chitin, and others. While experimental methods are used to validate LPMO function, a computational method that can aid experimental methods and provide fast and accurate classification of sequences into LPMOs and its families would be an important step towards understanding the breadth of contributions these enzymes make in deconstruction of recalcitrant polysaccharides. In this study, we developed a machine learning-based tool called PreDSLpmo that employs two different approaches to functionally classify protein sequences into the major LPMO families (AA9 and AA10). The first approach uses a traditional neural network or multilayer percerptron-based approach, while the second employs bi-directional long short-term memory for sequence classification. Finally, our method shows improvement in predictive power when compared with dbCAN2, an existing HMM-profile-based CAZyme predicting tool, on both validation and independent benchmark set.

• Research Organization: Univ. of Wisconsin, Madison, WI (United States). Great Lakes Bioenergy Research Center
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
• Grant/Contract Number: FC02-07ER64494; SC0018409
• OSTI ID: 1601231
• Journal Information: Journal of Biotechnology, Journal Name: Journal of Biotechnology Journal Issue: C Vol. 308; ISSN 0168-1656
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

59 BASIC BIOLOGICAL SCIENCES
Long short-term memory
Lytic polysaccharide monooxygenases
Machine learning technique
Neural network
Proteome