Computational prediction and functional analysis of arsenic-binding proteins in human cells

Abstract

Background: Arsenic has a broad anti-cancer ability against hematologic malignancies and solid tumors. To systematically understand the biological functions of arsenic, we need to identify arsenic-binding proteins in human cells. However, due to lack of effective theoretical tools and experimental methods, only a few arsenic-binding proteins have been identified.

Methods: Based on the crystal structure of ArsM, we generated a single mutation free energy profile for arsenic binding using free energy perturbation methods. Multiple validations provide an indication that our computational model has the ability to predict arsenic-binding proteins with desirable accuracy. We subsequently apply this computational model to scan the entire human genome to identify all the potential arsenic-binding proteins.

Results: The computationally predicted arsenic-binding proteins show a wide range of biological functions, especially in the signaling transduction pathways. In the signaling transduction pathways, arsenic directly binds to the key factors (e.g., Notch receptors, Notch ligands, Wnt family proteins, TGF-beta, and their interacting proteins) and results in significant inhibitions on their enzymatic activities, further having a crucial impact on the related signaling pathways.

Conclusions: Arsenic has a significant impact on signaling transduction in cells. Arsenic binding to proteins can lead to dysfunctions of the target proteins, having crucial impacts on both signaling pathway and gene transcription. We hope that the computationally predicted arsenic-binding proteins and the functional analysis can provide a novel insight into the biological functions of arsenic, revealing a mechanism for the broad anti-cancer of arsenic.

Graphical abstract

Keywords

arsenic-binding proteins / free energy profile / position-specific score matrix / signaling transduction

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SUPPLEMENTARY MATERIALS

The supplementary materials can be found online with this article at https://doi.org/10.1007/s40484-019-0169-6.

ACKNOWLEDGEMENTS

This work was supported by the National Key R&D Program of China (Nos. 2016YFC0901704 and 2017YFA0505500), National High-Tech R&D Program (863 Program, No. 2015AA020105), the National Natural Science Foundation of China (Nos. 21377085 and 31770070), MOE New Century Excellent Talents in University (No. NCET-12-0354), and SJTU Med-Eng Joint Program (No. YG2016MS33) for financial supports.

COMPLIANCE WITH ETHICS GUIDELINES

The authors Shichao Pang, Junchen Yang, Yilei Zhao, Yixue Li and Jingfang Wang declare that they have no conflict of interests.

This article does not contain any studies with human or animal subjects performed by any of the authors.

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