Year: 2024
Type: article
Abstract: Understanding and predicting the diverse conformational states of membrane proteins is essential for elucidating their biological functions. Despite advancements in computational methods, accurately capturing these complex structural changes remains a significant challenge. Here, we introduce a computational approach to generate diverse and biologically relevant conformations of membrane proteins using a conditional diffusion model. Our approach integrates forward and backward diffusion processes, incorporating state classifiers and additional conditioners to control the generation gradient of conformational states. We specifically targeted the P-type ATPases, a critical family of membrane transporters, and constructed a comprehensive data set through a combination of experimental structures and molecular dynamics simulations. Our model, incorporating a graph neural network with specialized membrane constraints, demonstrates exceptional accuracy in generating a wide range of P-type ATPase conformations associated with different functional states. This approach represents a meaningful step forward in the computational generation of membrane protein conformations using AI and holds promise for studying the dynamics of other membrane proteins.
Authors: Jingtian Xu, Yong Wang
Institution: Zhejiang University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W4403941381