Predictive Model of Linear Antimicrobial Peptides Active against Gram-Negative Bacteria

Abstract

Antimicrobial peptides (AMPs) have been identified as a potential new class of anti-infectives for drug development. There are a lot of computational methods that try to predict AMPs. Most of them can only predict if a peptide will show any antimicrobial potency, but to the best of our knowledge, there are no tools which can predict antimicrobial potency against particular strains. Here we present a predictive model of linear AMPs being active against particular Gram-negative strains relying on a semi-supervised machine-learning approach with a density-based clustering algorithm. The algorithm can well distinguish peptides active against particular strains from others which may also be active but not against the considered strain. The available AMP prediction tools cannot carry out this task. The prediction tool based on the algorithm suggested herein is available on https://dbaasp.org.

Quantitative Structure-Activity Relationship Study of Antioxidant Tripeptides Based on Model Population Analysis

“…Quantitative structure-activity relationship (QSAR), which is a well-recognized tool for estimating chemical activities, has been widely applied for bioactive peptides prediction [5]. The QSAR models have been successfully built on ACE-inhibitory peptides [6], antimicrobial peptides [7], antioxidant peptides [8,9,10], antitumor peptides [11], bitter peptides [12], and etc. The QSAR study of antioxidant peptides mainly focused on di and tripeptides, because they can be absorbed intact from the intestinal lumen into the bloodstream and then produce biological effects at the tissue level [13].…”

A Pilot Study of Multi-Input Recurrent Neural Networks for Drug-Kinase Binding Prediction

“…For this reason, we represent each amino acid in a given protein kinase primary sequence by a vector of biochemical properties. Seventeen features ( Table 3 ) were chosen on the basis of general biological importance as well as having been found in previous studies [ 9 , 10 , 11 ] to be useful in characterizing protein activity. The values for each of these features, for each amino acid, were taken from the AAindex database (Kyoto University Bioinformatics Center, Kyoto, Japan) [ 28 ], then scaled to range between −1 and 1.…”

“…There exist numerous examples in the literature of the application of various types of machine learning classifiers for virtual screening [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. Commonly used classifiers include naïve Bayes, k-nearest neighbors, support vector machines, random forests, and artificial neural networks (ANNs).…”

Reprogramming biological peptides to combat infectious diseases

“…Another example of precise AMP prediction are initiatives to find or build molecules that are selectively active against microorganisms. Vishnepolsky et al 113 recently described a predictive model of small linear AMPs that present antimicrobial activity against particular Gram-negative strains. The authors accurately distinguished active peptides with specific activity against E. coli ATCC 25922 and P. aeruginosa ATCC 27853 using a semi-supervised machine-learning approach coupled to a density-based clustering algorithm.…”