Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC

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.

DeepIon: Deep learning approach for classifying ion transporters and ion channels from membrane proteins

“…To avoid completely losing the sequence‐pattern information for proteins, the pseudo amino acid composition was proposed. Ever since then, it has been widely used in nearly all the areas of computational proteomics (see, e.g., as well as a long list of references cited in Ref. ).…”

Silver (Ag) doped magnesium phosphate microplatelets as next‐generation antibacterial orthopedic biomaterials

“…In contrast to conventional antibiotics, the biomaterials sector has witnessed the genesis of various kinds of innovative antimicrobial agents (Regiel‐Futyra et al, ). Materials like peptides (Marr, Gooderham, & Hancock, ; Meher, Sahu, Saini, & Rao, ), zinc/zinc oxide (ZnO) (Bhattacharjee et al, ; Cai et al, ; Raghupathi, Koodali, & Manna, ), N‐halamines (Dong et al, ; Kang et al, ), quaternary ammonium salt (Munoz‐Bonilla & Fernández‐García, ; Timofeeva & Kleshcheva, ) and povidone−iodine (PVP − I 2 ) (Gao, Wang, Liu, & Du, ) have been effectively used to treat surgical site infections (SSI) in orthopedics (Gao et al, ). However, compared with antibiotics and other antibacterial agents, silver (Ag) is recognized as a universal and traditional bactericidal agent that has shown a useful bactericidal effect on a broad spectrum of microbial species over decades (Chernousova & Epple, ).…”

pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins

“…Of these three, however, the jackknife test is deemed the least arbitrary that can always yield a unique outcome for a given benchmark dataset as elucidated in [32]. Accordingly, the jackknife test has been widely recognized and increasingly used by investigators to examine the quality of various predictors (see, e.g., [35,39,41,63,65,[102][103][104][105]). Accordingly, the jackknife test was also used in this study.…”

“…To deal with this problem, the PseAAC (Pseudo Amino Acid Composition) was introduced [53][54][55]. Ever since the concept of pseudo amino acid composition or Chou's PseAAC [55][56][57][58] was proposed, it has been widely used in many biomedicine and drug development areas [59,60] as well as nearly all the areas of computational proteomics(see, e.g., [39,43,45,[61][62][63][64][65][66][67][68][69][70][71][72][73] and a long list of references cited in two review papers [74,75]). Encouraged by the successes of using PseAAC to deal with protein/peptide sequences, its idea and approach have been extended to deal with DNA/RNA sequences [76][77][78][79][80][81][82] in computational genomics via PseKNC (Pseudo K-tuple Nucleotide Composition) [83,84].…”

“…According to the Chou's 5-step rule [32] that has been widely used by many recent investigators (see, e.g., [33][34][35][36][37][38][39][40][41][42][43][44][45][46][47]) for developing a statistical predictor, the first important and foremost thing is to construct or select a valid benchmark dataset to train and test the model [1,42,48]. In literature, the benchmark dataset usually consists of a training dataset and a testing dataset: the former is for the purpose of training a proposed model, while the latter for the purpose of testing it.…”