logo
ResearchBunny Logo
Machine learning-aided design and screening of an emergent protein function in synthetic cells

Biology

Machine learning-aided design and screening of an emergent protein function in synthetic cells

S. Kohyama, B. P. Frohn, et al.

This groundbreaking research by Shunshi Kohyama, Béla P. Frohn, Leon Babl, and Petra Schwille showcases a machine learning-aided pipeline that successfully designs and screens proteins with new functionalities using the MinDE system. It highlights a high-scoring variant that completely replaces the wild-type MinE gene in E. coli, revealing vast potential for engineering cellular functions.

00:00
00:00
Playback language: English
Abstract
This research demonstrates a machine learning (ML)-aided pipeline for designing and screening proteins with emergent functions, using the bacterial MinDE system as a model. The study uses an MSA-VAE to generate MinE variants, a divide-and-conquer computational screening approach based on predicted sub-functions, and in vitro screening using synthetic cell mimics. A high-scoring variant fully substituted the wild-type MinE gene in E. coli, highlighting the potential of this integrated approach for engineering cellular functions.
Publisher
Nature Communications
Published On
Mar 05, 2024
Authors
Shunshi Kohyama, Béla P. Frohn, Leon Babl, Petra Schwille
Tags
machine learning
protein engineering
MinDE system
synthetic biology
E. coli

Related Publications

Explore these studies to deepen your understanding of the subject.

Stylistic and linguistic variations in compliments: an empirical analysis of children's gender schema development with machine learning algorithms
Linguistics and Languages

Stylistic and linguistic variations in compliments: an empirical analysis of children's gender schema development with machine learning algorithms

X. Liao and Y. Zhang

Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review
Medicine and Health

Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review

J. Zhang, F. Zhong, et al.

Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms
Engineering and Technology

Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms

M. A. Khan, B. Asad, et al.

Compositional design and phase formation capability of high-entropy rare-earth disilicates from machine learning and decision fusion
Engineering and Technology

Compositional design and phase formation capability of high-entropy rare-earth disilicates from machine learning and decision fusion

Y. Fan, Y. Bai, et al.

Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny