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CAMP:研究抗菌肽的有用资源。

CAMP: a useful resource for research on antimicrobial peptides.

机构信息

Biomedical Informatics Center of Indian Council of Medical Research, National Institute for Research in Reproductive Health, Mumbai, India.

出版信息

Nucleic Acids Res. 2010 Jan;38(Database issue):D774-80. doi: 10.1093/nar/gkp1021. Epub 2009 Nov 18.

Abstract

Antimicrobial peptides (AMPs) are gaining popularity as better substitute to antibiotics. These peptides are shown to be active against several bacteria, fungi, viruses, protozoa and cancerous cells. Understanding the role of primary structure of AMPs in their specificity and activity is essential for their rational design as drugs. Collection of Anti-Microbial Peptides (CAMP) is a free online database that has been developed for advancement of the present understanding on antimicrobial peptides. It is manually curated and currently holds 3782 antimicrobial sequences. These sequences are divided into experimentally validated (patents and non-patents: 2766) and predicted (1016) datasets based on their reference literature. Information like source organism, activity (MIC values), reference literature, target and non-target organisms of AMPs are captured in the database. The experimentally validated dataset has been further used to develop prediction tools for AMPs based on the machine learning algorithms like Random Forests (RF), Support Vector Machines (SVM) and Discriminant Analysis (DA). The prediction models gave accuracies of 93.2% (RF), 91.5% (SVM) and 87.5% (DA) on the test datasets. The prediction and sequence analysis tools, including BLAST, are integrated in the database. CAMP will be a useful database for study of sequence-activity and -specificity relationships in AMPs. CAMP is freely available at http://www.bicnirrh.res.in/antimicrobial.

摘要

抗菌肽(AMPs)作为抗生素的更好替代品越来越受到关注。这些肽被证明对几种细菌、真菌、病毒、原生动物和癌细胞具有活性。了解 AMP 的一级结构在其特异性和活性中的作用对于它们作为药物的合理设计至关重要。抗菌肽数据库(CAMP)是一个免费的在线数据库,旨在提高对抗菌肽的现有认识。它是人工整理的,目前包含 3782 个抗菌序列。这些序列根据其参考文献分为经实验验证的(专利和非专利:2766)和预测的(1016)数据集。该数据库中捕获了 AMP 的来源生物、活性(MIC 值)、参考文献、靶标和非靶标生物等信息。基于机器学习算法(如随机森林(RF)、支持向量机(SVM)和判别分析(DA)),对经实验验证的数据集进一步开发了 AMP 预测工具。预测模型在测试数据集上的准确率分别为 93.2%(RF)、91.5%(SVM)和 87.5%(DA)。预测和序列分析工具,包括 BLAST,都集成在数据库中。CAMP 将成为研究 AMP 中序列-活性和 -特异性关系的有用数据库。CAMP 可免费在 http://www.bicnirrh.res.in/antimicrobial 获得。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87ac/2808926/6b5cc3ef4a90/gkp1021f1.jpg

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