Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC
By Prabina Kumar Meher, Tanmaya Kumar Sahu, Varsha Saini, Atmakuri Ramakrishna Rao. Scientific Reports, 2017
465 citations recorded by the source corpus.
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.
Subfields: Microbiology, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/5a7ed8945f1ebb003851091a0130e852ae9ea141
How researchers see this paper
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In Microbiology
In Molecular Biology
- Payel Das
- Amarda Shehu
- Vijil Chenthamarakshan
- C. D. Santos
- Uday Kamath
- Daniel Veltri
- Tom Sercu
- Matthew Riemer
Citations in the corpus
- PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences (2018, methodology): “A character-level LSTM language model (LM) Merity et al. [2017] trained on the labeled AMP/nonAMP sequences was used to estimate the perplexity (PPL) of the generated sequences.”
- Deep learning improves antimicrobial peptide recognition (2018, methodology): “…models (Fjell et al. , 2009), logistic regression (Veltri et al. , 2017; Randou et al. , 2013), random forests (RF) (Thomas et al. , 2009; Veltri, 2015) and support vector machines (SVM) (Lata et al. , 2010; Lee et al. , 2016; Meher et al. , 2017; Thomas et al. , 2009; Torrent et al. , 2011).”