Department of Medical Education, Geisinger Commonwealth School of Medicine, 525 Pine Street, Scranton, PA 18509, USA.
Biomolecules. 2022 Oct 26;12(11):1559. doi: 10.3390/biom12111559.
We present the Pharmacorank search tool as an objective means to obtain prioritized protein drug targets and their associated medications according to user-selected diseases. This tool could be used to obtain prioritized protein targets for the creation of novel medications or to predict novel indications for medications that already exist. To prioritize the proteins associated with each disease, a gene similarity profiling method based on protein functions is implemented. The priority scores of the proteins are found to correlate well with the likelihoods that the associated medications are clinically relevant in the disease's treatment. When the protein priority scores are plotted against the percentage of protein targets that are known to bind medications currently indicated to treat the disease, which we termed the pertinency score, a strong correlation was observed. The correlation coefficient was found to be 0.9978 when using a weighted second-order polynomial fit. As the highly predictive fit was made using a broad range of diseases, we were able to identify a general threshold for the pertinency score as a starting point for considering drug repositioning candidates. Several repositioning candidates are described for proteins that have high predicated pertinency scores, and these provide illustrative examples of the applications of the tool. We also describe focused reviews of repositioning candidates for Alzheimer's disease. Via the tool's URL, https://protein.som.geisinger.edu/Pharmacorank/, an open online interface is provided for interactive use; and there is a site for programmatic access.
我们提出了 Pharmacorank 搜索工具,作为一种根据用户选择的疾病获取优先蛋白质药物靶点及其相关药物的客观方法。该工具可用于获得优先蛋白质靶点以创建新药物,或预测已存在药物的新适应症。为了优先考虑与每种疾病相关的蛋白质,我们实施了一种基于蛋白质功能的基因相似性分析方法。发现蛋白质的优先级得分与相关药物在疾病治疗中具有临床相关性的可能性密切相关。当将蛋白质优先级得分与已知与目前用于治疗该疾病的药物结合的蛋白质靶标百分比(我们称之为相关性得分)进行比较时,观察到了很强的相关性。使用加权二阶多项式拟合,相关系数为 0.9978。由于使用了广泛的疾病进行了高度预测拟合,因此我们能够确定相关性得分的一般阈值作为考虑药物重新定位候选物的起点。对于具有高预测相关性得分的蛋白质,我们描述了几个重新定位候选物,并提供了工具应用的示例。我们还描述了针对阿尔茨海默病的重新定位候选物的重点审查。通过工具的 URL https://protein.som.geisinger.edu/Pharmacorank/,提供了一个开放的在线交互界面;并且还有一个用于编程访问的站点。