.png){kind=icon}
.png){kind=icon}
Return
Exploring Protein Conformational Changes Using a Large-Scale Biophysical Sampling Augmented Deep Learning Strategy
.png){kind=icon}
OA
AI
DOI：10.1002/advs.202400884.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Inspired by the success of deep learning in predicting static protein structures, researchers are now actively exploring other deep learning algorithms aimed at predicting the conformational changes of proteins. Currently, a major challenge in the development of such models lies in the limited training data characterizing different conformational transitions. To address this issue, molecular dynamics simulations is combined with enhanced sampling methods to create a large-scale database. To this end, the study simulates the conformational changes of 2635 proteins featuring two known stable states, and collects the structural information along each transition pathway. Utilizing this database, a general deep learning model capable of predicting the transition pathway for a given protein is developed. The model exhibits general robustness across proteins with varying sequence lengths (ranging from 44 to 704 amino acids) and accommodates different types of conformational changes. Great agreement is shown between predictions and experimental data in several systems and successfully apply this model to identify a novel allosteric regulation in an important biological system, the human beta-cardiac myosin. These results demonstrate the effectiveness of the model in revealing the nature of protein conformational changes. A large-scale database of protein conformational changes is constructed using molecular dynamics simulations. Building on this dataset, the present study develops a deep learning model that can predict the transition pathways of various proteins, including those with simple opening/closing transitions as well as those exhibiting significant global changes in folding topology. image
Keywords:
conformational changes
deep learning
proteins
transition pathway
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
14.1
Papers:
1.7W
Citations:
11.5W