PreDSLpmo: A neural network-based prediction tool for functional annotation of lytic polysaccharide monooxygenases.
J Biotechnol
; 308: 148-155, 2020 Jan 20.
Article
in En
| MEDLINE
| ID: mdl-31830497
ABSTRACT
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. 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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Molecular Sequence Annotation
/
Mixed Function Oxygenases
Type of study:
Prognostic_studies
/
Risk_factors_studies
Language:
En
Journal:
J Biotechnol
Journal subject:
BIOTECNOLOGIA
Year:
2020
Document type:
Article
Affiliation country:
India