A Multimodal Deep Learning Framework for Predicting PPI-Modulator Interactions

Sun, Heqi; Wang, Jianmin; Wu, Hongyan; Lin, Shenggeng; Chen, Junwei; Wei, Jinghua; Lv, Shuai; Xiong, Yi; Wei, Dong-Qing

doi:10.1021/acs.jcim.3c01527

Cited by 4 publications

(1 citation statement)

References 58 publications

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“…In the future, we can combine the rich conformational information on protein–protein complexes, the prediction of protein–protein interactions/binding affinity, PPI binding site prediction, the prediction of PPI–modulator interaction, and the generative design of PPI modulators for accelerating the study of PPI targets and the design/screening of modulators. , …”

Section: Discussionmentioning

confidence: 99%

Exploring the Conformational Ensembles of Protein–Protein Complex with Transformer-Based Generative Model

Wang,

Chu

et al. 2024

J. Chem. Theory Comput.

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Protein–protein interactions are the basis of many protein functions, and understanding the contact and conformational changes of protein–protein interactions is crucial for linking the protein structure to biological function. Although difficult to detect experimentally, molecular dynamics (MD) simulations are widely used to study the conformational ensembles and dynamics of protein–protein complexes, but there are significant limitations in sampling efficiency and computational costs. In this study, a generative neural network was trained on protein–protein complex conformations obtained from molecular simulations to directly generate novel conformations with physical realism. We demonstrated the use of a deep learning model based on the transformer architecture to explore the conformational ensembles of protein–protein complexes through MD simulations. The results showed that the learned latent space can be used to generate unsampled conformations of protein–protein complexes for obtaining new conformations complementing pre-existing ones, which can be used as an exploratory tool for the analysis and enhancement of molecular simulations of protein–protein complexes.

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