ClassAMP: A Prediction Tool for Classification of Antimicrobial Peptides
01 Sep 2012-IEEE/ACM Transactions on Computational Biology and Bioinformatics (IEEE)-Vol. 9, Iss: 5, pp 1535-1538
TL;DR: An algorithm called ClassAMP has been developed to predict the propensity of a protein sequence to have antibacterial, antifungal, or antiviral activity.
Abstract: 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/.
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TL;DR: This study made an attempt to develop a support vector machine (SVM) based computational approach for prediction of AMPs with improved accuracy, and achieved higher accuracy than several existing approaches, while compared using benchmark dataset.
Abstract: Antimicrobial peptides (AMPs) are important components of the innate immune system that have been found to be effective against disease causing pathogens. Identification of AMPs through wet-lab experiment is expensive. Therefore, development of efficient computational tool is essential to identify the best candidate AMP prior to the in vitro experimentation. In this study, we made an attempt to develop a support vector machine (SVM) based computational approach for prediction of AMPs with improved accuracy. Initially, compositional, physico-chemical and structural features of the peptides were generated that were subsequently used as input in SVM for prediction of AMPs. The proposed approach achieved higher accuracy than several existing approaches, while compared using benchmark dataset. Based on the proposed approach, an online prediction server iAMPpred has also been developed to help the scientific community in predicting AMPs, which is freely accessible at http://cabgrid.res.in:8080/amppred/. The proposed approach is believed to supplement the tools and techniques that have been developed in the past for prediction of AMPs.
313 citations
TL;DR: This work reviews the available strategies for their synthesis, bioinformatics tools for the rational design of antimicrobial peptides with enhanced therapeutic indices, hurdles and shortcomings limiting the large-scale production of AMPs, as well as the challenges that the pharmaceutical industry faces on their use as therapeutic agents.
Abstract: Antimicrobial peptides are small molecules with activity against bacteria, yeasts, fungi, viruses, bacteria, and even tumor cells that make these molecules attractive as therapeutic agents. Due to the alarming increase of antimicrobial resistance, interest in alternative antimicrobial agents has led to the exploitation of antimicrobial peptides, both synthetic and from natural sources. Thus, many peptide-based drugs are currently commercially available for the treatment of numerous ailments, such as hepatitis C, myeloma, skin infections, and diabetes. Initial barriers are being increasingly overcome with the development of cost-effective, more stable peptides. Herein, we review the available strategies for their synthesis, bioinformatics tools for the rational design of antimicrobial peptides with enhanced therapeutic indices, hurdles and shortcomings limiting the large-scale production of AMPs, as well as the challenges that the pharmaceutical industry faces on their use as therapeutic agents.
159 citations
Cites background from "ClassAMP: A Prediction Tool for Cla..."
...2012)), and the Biomedical Informatics Center in Mumbai, respectively (ClassAMP (Joseph et al. 2012))....
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...…tools for the identification of antimicrobial regions and classification of antimicrobial peptide regions are also available at the Center for Genomic Regulation of Barcelona (AMPA (Torrent et al. 2012)), and the Biomedical Informatics Center in Mumbai, respectively (ClassAMP (Joseph et al. 2012))....
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TL;DR: It was observed that binary based model developed in this study preforms better than any model/method on a dataset containing compositionally similar antifungal and non-AFPs.
Abstract: This paper describes in silico models developed using a wide range of peptide features for predicting antifungal peptides. Our analyses indicate that certain types of residue (e.g., C, G, H, K, R, Y) are more abundant in antifungal peptides. The positional residue preference analysis reveals the prominence of the particular type of residues (e.g., R, V, K) at N-terminus and a certain type of residues (e.g., C, H) at C-terminus. In this study, models have been developed for predicting antifungal peptides using a wide range of peptide features (like residue composition, binary profile, terminal residues). The support vector machine based model developed using compositional features of peptides achieved maximum accuracy of 88.78% on the training dataset and 83.33% on independent or validation dataset. Our model developed using binary patterns of terminal residues of peptides achieved maximum accuracy 84.88% on training and 84.64% on validation dataset. We benchmark models developed in this study and existing methods on a dataset contain compositionally similar antifungal and non-antifungal peptides. It was observed that binary based model developed in this study preforms better than any model/method. In order to facilitate scientific community, we developed a mobile app, standalone and a user-friendly web server ‘Antifp' (http://webs.iiitd.edu.in/raghava/antifp).
92 citations
Cites methods from "ClassAMP: A Prediction Tool for Cla..."
...ClassAMP is one of the methods which predicts the given peptide as Antibacterial, Antiviral, Antifungal, etc. with a probability score (Joseph et al., 2012)....
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TL;DR: A systematic evaluation of ten publicly available AMP prediction methods finds that CAMPR3(RF) provides a statistically significant improvement in performance, as measured by the area under the receiver operating characteristic (ROC) curve, relative to the other five methods.
Abstract: Motivation Antimicrobial peptides (AMPs) are innate immune molecules that exhibit activities against a range of microbes, including bacteria, fungi, viruses and protozoa. Recent increases in microbial resistance against current drugs has led to a concomitant increase in the need for novel antimicrobial agents. Over the last decade, a number of AMP prediction tools have been designed and made freely available online. These AMP prediction tools show potential to discriminate AMPs from non-AMPs, but the relative quality of the predictions produced by the various tools is difficult to quantify. Results We compiled two sets of AMP and non-AMP peptides, separated into three categories-antimicrobial, antibacterial and bacteriocins. Using these benchmark data sets, we carried out a systematic evaluation of ten publicly available AMP prediction methods. Among the six general AMP prediction tools-ADAM, CAMPR3(RF), CAMPR3(SVM), MLAMP, DBAASP and MLAMP-we find that CAMPR3(RF) provides a statistically significant improvement in performance, as measured by the area under the receiver operating characteristic (ROC) curve, relative to the other five methods. Surprisingly, for antibacterial prediction, the original AntiBP method significantly outperforms its successor, AntiBP2 based on one benchmark dataset. The two bacteriocin prediction tools, BAGEL3 and BACTIBASE, both provide very good performance and BAGEL3 outperforms its predecessor, BACTIBASE, on the larger of the two benchmarks. Contact gaberemu@ngha.med.sa or william-noble@uw.edu. Supplementary information Supplementary data are available at Bioinformatics online.
84 citations
TL;DR: This review addresses the tools and techniques, and also their limitations, for mining AMPs from databases, which could be helpful for developing novel antimicrobial agents and combating resistant bacteria.
81 citations
Cites background or methods from "ClassAMP: A Prediction Tool for Cla..."
...…systems available as standalone programs or web servers, and these are AntiBP (Lata et al., 2007, 2010), CAMP (Thomas et al., 2010; Waghu et al., 2014), CS-AMPPred (Porto et al., 2010, 2012a), ClassAMP (Joseph et al., 2012), iAMP-2L (Xiao et al., 2013) and ADAM (Lee et al., 2015) (Table 4)....
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...ClassAMP: a prediction tool for classification of antimicrobial peptides....
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...Currently, there are three prediction systems available as standalone programs or web servers, and these are AntiBP (Lata et al., 2007, 2010), CAMP (Thomas et al., 2010; Waghu et al., 2014), CS-AMPPred (Porto et al., 2010, 2012a), ClassAMP (Joseph et al., 2012), iAMP-2L (Xiao et al., 2013) and ADAM (Lee et al., 2015) (Table 4)....
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...Porto et al. (2010, 2012a) ClassAMP This supports the algorithms SVM and RF and predicts the propensity for the peptide to be antibacterial, antifungal or antiviral....
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...Besides, it does not predict dual activity (e.g. antibacterial and antiviral) Joseph et al. (2012) iAMP-2L This supports the algorithm fuzzy K-nearest neighbour (FKNN) and it was trained using a pseudo amino acid composition strategy (Chou, 2001)....
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