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Antimicrobial peptides (AMPs) are important innate immune molecules, which have been found effective against several pathogenic micro-organisms like bacteria, virus, fungus, parasites etc. AMPs have been found almost in all forms of life viz., animal, plant, bacteria, fungi etc. constituting the first line of host defense against microbes. Due to the growing resistance of microbes against conventional antibiotics, AMPs are gaining attention as an alternative to chemical antibiotics worldwide. The antimicrobial specificity of AMPs towards the target cells depends upon the interaction of peptides with microbial cells, which enables them to kill the target cells without affecting the host cells. The AMPs cause cell death either by disrupting the cell membrane of microbes or by disrupting its intracellular functions. Due to broad spectrum of activity and low propensity for resistance development, AMPs are receiving attention in clinical application. Identification and designing of AMPs through wet lab experiments may be resource intensive. Thus, computational identification will supplement in identifying and designing new antimicrobial agents. By using the computational tool, the best candidate peptide can be identified prior to synthesis and testing against microbes.



Please Cite:


Meher, P. K., Sahu, T. K., Saini, V. and Rao, A. R. (2017). Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC. Scientific Reports, 7(1), 1-12.