Using Bibliometric Analysis and Machine Learning to Identify Compounds Binding to Sialidase-1.
Using Bibliometric Analysis and Machine Learning to Identify Compounds Binding to Sialidase-1.
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使用文献计量分析和机器学习来识别与唾液酸酶-1结合的化合物。
DOI：
10.1021/acsomega.0c05591
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发表时间：
2021-02-02
期刊：
影响因子：
4.1
通讯作者：
Ekins S
中科院分区：
文献类型：
--
作者：
Klein JJ;Baker NC;Foil DH;Zorn KM;Urbina F;Puhl AC;Ekins S
罕见疾病影响着全世界数亿人。然而，由于财政资源有限，受影响的患者数量较少，生物活性数据往往不存在，支持临床前开发工作的动物模型也很少，因此治疗这种罕见疾病的方法很少。唾液酸病是一种溶酶体储存障碍，NEU1基因突变导致溶酶体酶唾液酸酶-1的缺失。这种酶催化从糖蛋白和糖脂中去除唾液酸部分。因此，蛋白质缺陷或缺陷会导致唾液酸糖蛋白的积聚，以及唾液酸病的几个特征症状，包括视力障碍、共济失调、肝肿大、多发性骨质疏松症和发育迟缓。在这项研究中，我们使用了一种文献计量学工具来生成溶酶体储存疾病(LSD)目标和现有生物活性数据之间的链接，这些数据可以被整理，以便建立机器学习模型和筛选电子计算机中的化合物。我们以唾液酸酶为例，使用文献中收集的数据建立贝叶斯模型，然后用该模型对化合物文库进行评分，并对这些分子进行体外测试。用微量热电泳法从体外实验中鉴定了两个化合物，即磺胺类化合物(Kd2.15±1.02μM)和美克森酮(Kd8.88±4.02μM)，这验证了我们鉴定与该蛋白结合的新分子的方法，这可能代表了可能的候选药物，可以进一步评估为这种目前尚无治疗方法的超广谱溶酶体疾病的潜在伴侣。将文献计量学和机器学习方法结合起来，能够分别协助整理小分子数据和建立模型，用于罕见疾病药物的发现。这种方法也有能力识别潜在的候选药物的新化合物。
Rare diseases impact hundreds of millions of individuals worldwide. However, few therapies exist to treat the rare disease population because financial resources are limited, the number of patients affected is low, bioactivity data is often nonexistent, and very few animal models exist to support preclinical development efforts. Sialidosis is an ultrarare lysosomal storage disorder in which mutations in the NEU1 gene result in the deficiency of the lysosomal enzyme sialidase-1. This enzyme catalyzes the removal of sialic acid moieties from glycoproteins and glycolipids. Therefore, the defective or deficient protein leads to the buildup of sialylated glycoproteins as well as several characteristic symptoms of sialidosis including visual impairment, ataxia, hepatomegaly, dysostosis multiplex, and developmental delay. In this study, we used a bibliometric tool to generate links between lysosomal storage disease (LSD) targets and existing bioactivity data that could be curated in order to build machine learning models and screen compounds in silico. We focused on sialidase as an example, and we used the data curated from the literature to build a Bayesian model which was then used to score compound libraries and rank these molecules for in vitro testing. Two compounds were identified from in vitro testing using microscale thermophoresis, namely sulfameter (Kd 2.15 ± 1.02 μM) and mexenone (Kd 8.88 ± 4.02 μM), which validated our approach to identifying new molecules binding to this protein, which could represent possible drug candidates that can be evaluated further as potential chaperones for this ultrarare lysosomal disease for which there is currently no treatment. Combining bibliometric and machine learning approaches has the ability to assist in curating small molecule data and model building, respectively, for rare disease drug discovery. This approach also has the capability to identify new compounds that are potential drug candidates.
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影响因子：
3.7
作者：
Perryman AL;Patel JS;Russo R;Singleton E;Connell N;Ekins S;Freundlich JS
通讯作者：
Freundlich JS
影响因子：
4.9
作者：
Minerali E;Foil DH;Zorn KM;Lane TR;Ekins S
通讯作者：
Ekins S
影响因子：
4.5
作者：
Aldenhoven, Mieke;Kurtzberg, Joanne
通讯作者：
Kurtzberg, Joanne
影响因子：
7.3
作者：
Guo, Tianlin;Heon-Roberts, Rachel;Cairo, Christopher W.
通讯作者：
Cairo, Christopher W.
DOI：
10.1016/0005-2795(75)90109-9
发表时间：
1975-01-01
期刊：
BIOCHIMICA ET BIOPHYSICA ACTA
影响因子：
--
作者：
MATTHEWS, BW
通讯作者：
MATTHEWS, BW