Artificial Intelligence and Bioinformatics

Nicolas, Jacques

doi:10.1007/978-3-030-06170-8_7

Cited by 8 publications

(1 citation statement)

References 299 publications

(277 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Directly predicting the T opt of enzymes from sequences would undoubtedly accelerate enzyme screening. With the development of bioinformatics and artificial intelligence [ 2 ], researchers have designed various machine-learning models to predict the optimal catalytic temperature of proteins [ 3 ]. Among them, the TOME machine learning model developed by Li et al [ 4 ] combines the optimal growth temperature (OGT) of organisms with the sequence information of individual enzymes to predict the enzyme T opt .…”

Section: Introductionmentioning

confidence: 99%

Enhancing Machine-Learning Prediction of Enzyme Catalytic Temperature Optima through Amino Acid Conservation Analysis

Cao,

Qiu,

Ning

et al. 2024

IJMS

View full text Add to dashboard Cite

Enzymes play a crucial role in various industrial production and pharmaceutical developments, serving as catalysts for numerous biochemical reactions. Determining the optimal catalytic temperature (Topt) of enzymes is crucial for optimizing reaction conditions, enhancing catalytic efficiency, and accelerating the industrial processes. However, due to the limited availability of experimentally determined Topt data and the insufficient accuracy of existing computational methods in predicting Topt, there is an urgent need for a computational approach to predict the Topt values of enzymes accurately. In this study, using phosphatase (EC 3.1.3.X) as an example, we constructed a machine learning model utilizing amino acid frequency and protein molecular weight information as features and employing the K-nearest neighbors regression algorithm to predict the Topt of enzymes. Usually, when conducting engineering for enzyme thermostability, researchers tend not to modify conserved amino acids. Therefore, we utilized this machine learning model to predict the Topt of phosphatase sequences after removing conserved amino acids. We found that the predictive model’s mean coefficient of determination (R2) value increased from 0.599 to 0.755 compared to the model based on the complete sequences. Subsequently, experimental validation on 10 phosphatase enzymes with undetermined optimal catalytic temperatures shows that the predicted values of most phosphatase enzymes based on the sequence without conservative amino acids are closer to the experimental optimal catalytic temperature values. This study lays the foundation for the rapid selection of enzymes suitable for industrial conditions.

show abstract

Section: Introductionmentioning

confidence: 99%