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PEP-PRED:一个使用机器学习技术预测靶向电压门控 Na 通道的高特异性肽的网络服务器。

PEP-PRED: A web server for prediction of highly specific peptides targeting voltage-gated Na channels using machine learning techniques.

机构信息

Departamento de Ciencias Básicas, Facultad de Ciencias, Universidad Santo Tomas, Chile; Center of Molecular Biology and Pharmacogenetics, Scientific and Technological Bioresource Nucleus, Universidad de La Frontera, Chile.

Department of Chemical Engineering, Faculty of Engineering and Science, Universidad de La Frontera, Ave. Francisco Salazar, 01145, Temuco, Chile.

出版信息

Comput Biol Med. 2022 Jun;145:105414. doi: 10.1016/j.compbiomed.2022.105414. Epub 2022 Mar 26.

DOI:10.1016/j.compbiomed.2022.105414
PMID:35358751
Abstract

Voltage-gated sodium channel activity has long been associated with several diseases including epilepsy, chronic pain, cardiovascular diseases, cancers, immune system, neuromuscular and respiratory disorders. The strong participation of these channels in the development of diseases makes them excellent promising therapeutic targets. Voltage-gated Na channel blocking peptides come from a wide source of organisms such as venoms. However, the in vitro and in vivo identification and validation of these peptides are time-consuming and resource-intensive. In this work, we developed a bioinformatics tool called PEP-PRED for the highly specific prediction of voltage-gated Na channel blocking peptides. PEP-PRED is based on the random forest algorithm, which presented excellent performance measures during the cross-validation (sensitivity = 0.81, accuracy = 0.83, precision = 0.85, F-score = 0.83, specificity = 0.86, and Matthew's correlation coefficient = 0.67) and testing (sensitivity = 0.88, accuracy = 0.92, precision = 0.96, F-score = 0.91, specificity = 0.96, and Matthew's correlation coefficient = 0.84) phases. The PEP-PRED tool could be very useful in accelerating and reducing the costs of the discovery of new voltage-gated Na channel blocking peptides with therapeutic potential.

摘要

电压门控钠离子通道的活性长期以来与多种疾病相关,包括癫痫、慢性疼痛、心血管疾病、癌症、免疫系统、神经肌肉和呼吸系统疾病。这些通道在疾病发展中的强烈参与使它们成为极有前途的治疗靶点。电压门控 Na 通道阻断肽来自多种生物体来源,如毒液。然而,这些肽的体外和体内鉴定和验证既耗时又耗资源。在这项工作中,我们开发了一种名为 PEP-PRED 的生物信息学工具,用于高度特异性地预测电压门控 Na 通道阻断肽。PEP-PRED 基于随机森林算法,在交叉验证(敏感性 = 0.81、准确性 = 0.83、精确性 = 0.85、F 分数 = 0.83、特异性 = 0.86 和马修相关系数 = 0.67)和测试(敏感性 = 0.88、准确性 = 0.92、精确性 = 0.96、F 分数 = 0.91、特异性 = 0.96 和马修相关系数 = 0.84)阶段表现出优异的性能指标。PEP-PRED 工具对于加速和降低具有治疗潜力的新型电压门控 Na 通道阻断肽的发现成本可能非常有用。

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