Biomedical Informatics Center of Indian Council of Medical Research, National Institute for Research in Reproductive Health, Parel, Mumbai, Maharashtra, India.
IEEE/ACM Trans Comput Biol Bioinform. 2012 Sep-Oct;9(5):1535-8. doi: 10.1109/TCBB.2012.89.
Antimicrobial peptides (AMPs) are gaining popularity as anti-infective agents. Information on sequence features that contribute to target specificity of AMPs will aid in accelerating drug discovery programs involving them. In this study, an algorithm called ClassAMP using Random Forests (RFs) and Support Vector Machines (SVMs) has been developed to predict the propensity of a protein sequence to have antibacterial, antifungal, or antiviral activity. ClassAMP is available at http://www.bicnirrh.res.in/classamp/.
抗菌肽(AMPs)作为抗感染药物越来越受到关注。有关有助于 AMP 靶向特异性的序列特征的信息将有助于加速涉及它们的药物发现计划。在这项研究中,开发了一种称为 ClassAMP 的算法,该算法使用随机森林(RFs)和支持向量机(SVMs)来预测蛋白质序列具有抗菌、抗真菌或抗病毒活性的倾向。ClassAMP 可在 http://www.bicnirrh.res.in/classamp/ 获得。