Machine learning reveals sequence-function relationships in family 7 glycoside hydrolases.

Content Provider	Europe PMC
Author	Gado, Japheth E. Harrison, Brent E. Sandgren, Mats Ståhlberg, Jerry Beckham, Gregg T. Payne, Christina M.
Copyright Year	2021
Abstract	Family 7 glycoside hydrolases (GH7) are among the principal enzymes for cellulose degradation in nature and industrially. These enzymes are often bimodular, including a catalytic domain and carbohydrate-binding module (CBM) attached via a flexible linker, and exhibit an active site that binds cello-oligomers of up to ten glucosyl moieties. GH7 cellulases consist of two major subtypes: cellobiohydrolases (CBH) and endoglucanases (EG). Despite the critical importance of GH7 enzymes, there remain gaps in our understanding of how GH7 sequence and structure relate to function. Here, we employed machine learning to gain data-driven insights into relationships between sequence, structure, and function across the GH7 family. Machine-learning models, trained only on the number of residues in the active-site loops as features, were able to discriminate GH7 CBHs and EGs with up to 99% accuracy, demonstrating that the lengths of loops A4, B2, B3, and B4 strongly correlate with functional subtype across the GH7 family. Classification rules were derived such that specific residues at 42 different sequence positions each predicted the functional subtype with accuracies surpassing 87%. A random forest model trained on residues at 19 positions in the catalytic domain predicted the presence of a CBM with 89.5% accuracy. Our machine learning results recapitulate, as top-performing features, a substantial number of the sequence positions determined by previous experimental studies to play vital roles in GH7 activity. We surmise that the yet-to-be-explored sequence positions among the top-performing features also contribute to GH7 functional variation and may be exploited to understand and manipulate function.
ISSN	00219258
Volume Number	297
PubMed Central reference number	PMC8329511
Issue Number	2
PubMed reference number	34216620
Journal	The Journal of Biological Chemistry [J. Biol. Chem]
e-ISSN	1083351X
DOI	10.1016/j.jbc.2021.100931
Language	English
Publisher	American Society for Biochemistry and Molecular Biology
Publisher Date	2021-07-01
Access Restriction	Open
Rights License	This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). © 2021 The Authors
Subject Keyword	cellulase Trichoderma reesei bioinformatics tryptophan statistics glycoside hydrolase CBH, cellobiohydrolase CBM, carbohydrate-binding module CD, catalytic domain EG, endoglucanase GH7, family 7 glycoside hydrolase GH, glycoside hydrolase HMM, hidden Markov model KNN, k-nearest neighbor LPMO, lytic polysaccharide monooxygenase ML, machine learning MSA, multiple sequence alignment
Content Type	Text
Resource Type	Article
Subject	Cell Biology Molecular Biology Biochemistry