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Multi-enzyme Screening Using a High-throughput Genetic Enzyme Screening System

Multi-enzyme Screening Using a High-throughput Genetic Enzyme Screening System

Published on: August 8, 2016

Semisupervised Gaussian Process for Automated Enzyme Search.

Joseph Mellor^1,2, Ioana Grigoras³, Pablo Carbonell⁴

¹School of Chemistry, University of Manchester , Manchester M13 9PL, U.K.

ACS Synthetic Biology

|March 24, 2016

Summary

This summary is machine-generated.

This study introduces a novel computational tool using Gaussian processes to predict enzyme function for synthetic biology. It aids in designing new biosynthesis pathways and estimating enzyme-substrate affinity (KM) for bioengineering applications.

Keywords:

Gaussian process regression enzyme kinetics enzyme screening metabolic engineering reaction fingerprint semisupervised Gaussian process

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Area of Science:

Synthetic biology
Bioinformatics
Computational chemistry

Background:

Designing novel biosynthesis pathways requires accurate enzyme selection.
Automating enzyme-reaction prediction is crucial for efficient bioengineering.

Purpose of the Study:

To develop an automated tool for predicting enzyme-catalyzed reactions.
To estimate enzyme-substrate affinity (KM) for bioengineering applications.

Main Methods:

Utilized reaction similarity signatures based on extended connectivity fingerprints.
Employed a semisupervised Gaussian process model for reaction probability estimation.
Applied Gaussian process regression to predict Michaelis constant (KM).

Main Results:

The tool accurately estimates the probability of an enzyme catalyzing a specific reaction.
Experimental validation confirmed the model's efficacy in identifying enzymes for novel metabolites.
Successfully predicted KM values for substrate-enzyme pairs.

Conclusions:

The developed tool assists in designing synthetic biology pathways by predicting enzyme function.
This approach offers a novel application of Gaussian processes in bioinformatics and enzyme engineering.
The method provides a robust framework for enzyme discovery and pathway optimization.