Caffeoyl malic acid is a potential dual inhibitor targeting TNFα/IL-4 evaluated by a combination strategy of network analysis-deep learning-molecular simulation.

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Abstract

Atopic dermatitis (AD) is a common inflammatory skin disease involving multiple signaling pathways. One of the effective treatment strategies of AD is to develop a new drug capable of regulating the key therapeutic targets. Here we report the combination use of network analysis, deep learning, and molecular simulation for the identification of key therapeutic targets for AD and screening of potential multi-target drugs. From the TCM@Taiwan database, we identify a small molecule, namely caffeoyl malic acid (CMA), to inhibit the key therapeutic targets (TNFα and IL-4) for AD. CMA is further identified as a TNFα inhibitor by a deep learning model based on convolutional neural network. Molecular simulations demonstrate that CMA can stably bind to TNFα and IL-4, thereby producing diverse effects on the structural fluctuation, structural flexibility, looseness, and motion strength of each protein. Furthermore, conformation alignments reveal that CMA makes the distance between chain A and C of TNFα become wider and the slit between the two α helices of IL-4 get narrow obviously. CMA leads to the change of protein conformation, which hinders the formation of the protein-receptor complex. Collectively, our findings suggest that CMA is a potential dual TNFα/IL-4 inhibitor for the treatment of AD.Copyright © 2022 Elsevier Ltd. All rights reserved.