GraphKM: machine and deep learning for KM prediction of wildtype and mutant enzymes

Michaelis constant (K M ) is one of essential parameters for enzymes kinetics in the fields of protein engineering, enzyme engineering, and synthetic biology. As overwhelming experimental measurements of K M are difficult and time-consuming, prediction of the K M values from machine and deep learnin...

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Veröffentlicht in:BMC bioinformatics Jg. 25; H. 1; S. 1 - 12
Hauptverfasser: He, Xiao, Yan, Ming
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 28.03.2024
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
Algorithms
Amino acids
Application programming interface
Artificial intelligence
Bioinformatics
Biology
Biomedical and Life Sciences
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Datasets
Deep learning
Enzyme kinetics
Enzymes
Genetic aspects
Graph neural network
Graph neural networks
Graphical representations
Identification and classification
Kinetics
Learning algorithms
Life Sciences
Machine learning
Michaelis constant
Microarrays
Mutants
Mutation (Biology)
Neural networks
Prediction models
Protein engineering
Protein research
Proteins
Substrates
Tree boosting
China
Deep learning
Neural networks
Michaelis constant
Tree boosting
Graph neural network
ISSN:1471-2105, 1471-2105
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Zusammenfassung:Michaelis constant (K M ) is one of essential parameters for enzymes kinetics in the fields of protein engineering, enzyme engineering, and synthetic biology. As overwhelming experimental measurements of K M are difficult and time-consuming, prediction of the K M values from machine and deep learning models would increase the pace of the enzymes kinetics studies. Existing machine and deep learning models are limited to the specific enzymes, i.e., a minority of enzymes or wildtype enzymes. Here, we used a deep learning framework PaddlePaddle to implement a machine and deep learning approach (GraphKM) for K M prediction of wildtype and mutant enzymes. GraphKM is composed by graph neural networks (GNN), fully connected layers and gradient boosting framework. We represented the substrates through molecular graph and the enzymes through a pretrained transformer-based language model to construct the model inputs. We compared the difference of the model results made by the different GNN (GIN, GAT, GCN, and GAT-GCN). The GAT-GCN-based model generally outperformed. To evaluate the prediction performance of the GraphKM and other reported K M prediction models, we collected an independent K M dataset (HXKm) from literatures.
Bibliographie:ObjectType-Article-1
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-024-05746-1