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Protein Diffusion in the Membrane

Protein Diffusion in the Membrane

Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...

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Updated: Sep 9, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Protein A-like peptide generation based on generalized diffusion model.

Tianqian Zhou¹, Shibo Zhang¹, Huijia Song¹

¹College of Information Engineering, Beijing Institute of Petrochemical Technology, No. 19 Qingyuan North Road, Daxing District, Beijing, 102617, China.

Journal of Computer-Aided Molecular Design

|September 4, 2025

Summary

This summary is machine-generated.

This study introduces a novel generalized diffusion model for advanced protein design, enabling flexible generation of functional protein sequences. The method successfully created a high-affinity derivative, A_005, demonstrating its potential for drug development.

Keywords:

Diffusion models Generative models Protein design Screening

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Area of Science:

Biotechnology and Bioinformatics
Computational Biology and Protein Design
Generative Models in Life Sciences

Background:

Generative models are crucial for protein design in drug development, vaccine research, and biocatalysis.
Traditional diffusion models are limited by their reliance on Gaussian noise for sequence generation.
Advancements in biotechnology necessitate more flexible and adaptable protein design tools.

Purpose of the Study:

To propose and validate a generalized diffusion model for enhanced protein sequence generation.
To overcome limitations of traditional diffusion models in protein design flexibility.
To demonstrate the model's capability in designing functional proteins with high affinity.

Main Methods:

Protein sequences were encoded using one-hot encoding and processed by a generalized diffusion model.
Tertiary structures of generated proteins were predicted using AlphaFold.
Sequence selection involved structural alignment and backbone distance calculation using PyMOL, followed by experimental affinity assays.

Main Results:

The generalized diffusion model generated novel protein sequences with high structural and functional similarity to targets.
A derivative sequence, A_005, exhibited remarkably high affinity to Protein A parental.
A_005 demonstrated satisfactory dissociation and association rates, validating the model's efficacy.

Conclusions:

The generalized diffusion model offers a flexible and adaptable approach to protein design.
This method effectively generates protein sequences with desired structural and functional properties.
The findings provide a strong foundation for future applications in biomedicine and drug development.