Computational prediction and functional analysis of arsenic-binding proteins in human cells
Computational prediction and functional analysis of arsenic-binding proteins in human cells
复制标题
人类细胞中砷结合蛋白的计算预测和功能分析
DOI：
10.1007/s40484-019-0169-6
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发表时间：
2019-10
影响因子：
3.1
通讯作者：
Wang J
中科院分区：
文献类型：
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作者：
Pang S;Yang J;Zhao Y;Li Y;Wang J
研究背景砷对血液系统恶性肿瘤和实体瘤具有广泛的抗癌作用。为了系统地了解砷的生物学功能，我们需要鉴定人体细胞中的砷结合蛋白。然而，由于缺乏有效的理论工具和实验方法，只有少数砷结合蛋白已被确定。MethodsBased的ArsM的晶体结构，我们产生了一个单一的突变的自由能曲线砷结合自由能微扰方法。多重验证表明，我们的计算模型能够以理想的准确度预测砷结合蛋白。随后，我们应用这个计算模型扫描整个人类基因组，以确定所有潜在的砷结合蛋白。ResultsThe计算预测的砷结合蛋白显示了广泛的生物学功能，特别是在信号转导途径。在信号转导途径中，砷直接与关键因子（例如，Notch受体，Notch配体，Wnt家族蛋白，TGF-β，及其相互作用蛋白），并导致其酶活性的显着抑制，进一步对相关的信号通路产生至关重要的影响。砷与蛋白质的结合可导致靶蛋白功能障碍，对信号通路和基因转录产生重要影响。我们希望计算预测的砷结合蛋白和功能分析可以为砷的生物学功能提供新的见解，揭示砷的广泛抗癌机制。
BackgroundArsenic 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.MethodsBased 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.ResultsThe 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.ConclusionsArsenic 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.
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影响因子：
14.9
作者：
Smedley D;Haider S;Durinck S;Pandini L;Provero P;Allen J;Arnaiz O;Awedh MH;Baldock R;Barbiera G;Bardou P;Beck T;Blake A;Bonierbale M;Brookes AJ;Bucci G;Buetti I;Burge S;Cabau C;Carlson JW;Chelala C;Chrysostomou C;Cittaro D;Collin O;Cordova R;Cutts RJ;Dassi E;Di Genova A;Djari A;Esposito A;Estrella H;Eyras E;Fernandez-Banet J;Forbes S;Free RC;Fujisawa T;Gadaleta E;Garcia-Manteiga JM;Goodstein D;Gray K;Guerra-Assunção JA;Haggarty B;Han DJ;Han BW;Harris T;Harshbarger J;Hastings RK;Hayes RD;Hoede C;Hu S;Hu ZL;Hutchins L;Kan Z;Kawaji H;Keliet A;Kerhornou A;Kim S;Kinsella R;Klopp C;Kong L;Lawson D;Lazarevic D;Lee JH;Letellier T;Li CY;Lio P;Liu CJ;Luo J;Maass A;Mariette J;Maurel T;Merella S;Mohamed AM;Moreews F;Nabihoudine I;Ndegwa N;Noirot C;Perez-Llamas C;Primig M;Quattrone A;Quesneville H;Rambaldi D;Reecy J;Riba M;Rosanoff S;Saddiq AA;Salas E;Sallou O;Shepherd R;Simon R;Sperling L;Spooner W;Staines DM;Steinbach D;Stone K;Stupka E;Teague JW;Dayem Ullah AZ;Wang J;Ware D;Wong-Erasmus M;Youens-Clark K;Zadissa A;Zhang SJ;Kasprzyk A
通讯作者：
Kasprzyk A
影响因子：
2
作者：
R. Dmochowski
通讯作者：
R. Dmochowski
DOI：
10.1016/j.respe.2020.09.004
发表时间：
2020-10-10
期刊：
Revue D'Epidemiologie et De Sante Publique
影响因子：
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作者：
La rédaction
通讯作者：
La rédaction
影响因子：
9.3
作者：
通讯作者：
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