Issue 15, 2024

Biomimetic fusion: Platyper's dual vision for predicting protein–surface interactions

Abstract

Predicting protein binding with the material surface still remains a challenge. Here, a novel approach, platypus dual perception neural network (Platyper), was developed to describe the interactions in protein–surface systems involving bioceramics with BMPs. The resulting model integrates a graph convolutional neural network (GCN) based on interatomic potentials with a convolutional neural network (CNN) model based on images of molecular structures. This dual-vision approach, inspired by the platypus's adaptive sensory system, addresses the challenge of accurately predicting the complex binding and unbinding dynamics in steered molecular dynamics (SMD) simulations. The model's effectiveness is demonstrated through its application in predicting surface interactions in protein–ligand systems. Notably, Platyper improves computational efficiency compared to classical SMD-based methods and overcomes the limitations of GNN-based methods for large-scale atomic simulations. The incorporation of heat maps enhances model's interpretability, providing valuable insights into its predictive capabilities. Overall, Platyper represents a promising advancement in the accurate and efficient prediction of protein–surface interactions in the context of bioceramics and growth factors.

Graphical abstract: Biomimetic fusion: Platyper's dual vision for predicting protein–surface interactions

Supplementary files

Article information

Article type
Communication
Submitted
19 Jan 2024
Accepted
03 May 2024
First published
07 May 2024

Mater. Horiz., 2024,11, 3528-3538

Biomimetic fusion: Platyper's dual vision for predicting protein–surface interactions

C. Hong, X. Wu, J. Huang and H. Dai, Mater. Horiz., 2024, 11, 3528 DOI: 10.1039/D4MH00066H

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements