Analysis and Prediction of the Critical Regions of Antimicrobial Peptides Based on Conditional Random Fields

Abstract

Antimicrobial peptides (AMPs) are potent drug candidates against microbes such as bacteria, fungi, parasites, and viruses. The size of AMPs ranges from less than ten to hundreds of amino acids. Often only a few amino acids or the critical regions of antimicrobial proteins matter the functionality. Accurately predicting the AMP critical regions could benefit the experimental designs. However, no extensive analyses have been done specifically on the AMP critical regions and computational modeling on them is either non-existent or settled to other problems. With a focus on the AMP critical regions, we thus develop a computational model AMPcore by introducing a state-of-the-art machine learning method, conditional random fields. We generate a comprehensive dataset of 798 AMPs cores and a low similarity dataset of 510 representative AMP cores. AMPcore could reach a maximal accuracy of 90% and 0.79 Matthew’s correlation coefficient on the comprehensive dataset and a maximal accuracy of 83% and 0.66 MCC on the low similarity dataset. Our analyses of AMP cores follow what we know about AMPs: High in glycine and lysine, but low in aspartic acid, glutamic acid, and methionine; the abundance of α-helical structures; the dominance of positive net charges; the peculiarity of amphipathicity. Two amphipathic sequence motifs within the AMP cores, an amphipathic α-helix and an amphipathic π-helix, are revealed. In addition, a short sequence motif at the N-terminal boundary of AMP cores is reported for the first time: arginine at the P(-1) coupling with glycine at the P1 of AMP cores occurs the most, which might link to microbial cell adhesion.

Antimicrobial Protein Candidates from the Thermophilic Geobacillus sp. Strain ZGt-1: Production, Proteomics, and Bioinformatics Analysis

“…The proteins are in the molecular weight range of approximately 14–19 kDa, and a size range of 129–173 amino acid residues, which is in the range (10 to 100 s) for AMPs [33]. …”

Large-Scale Analysis of Antimicrobial Activities in Relation to Amphipathicity and Charge Reveals Novel Characterization of Antimicrobial Peptides

“…Among these validated AMPs, 2113 AMPs without any ambiguous amino acids such as “B”, “U”, “X”, and “Z” whose lengths range from 7 to 40 amino acids long were investigated. In our previous study [ 15 ], we identified that some AMPs would nest on other longer sequences. Here 138 nested AMP families were directly extracted from this dataset, and each nested family left one core sequence behind.…”

Characterization and Identification of Natural Antimicrobial Peptides on Different Organisms

“…Previous research has indicated that a more hydrophobic and amphiphilic C-terminal obviously infiltrated into the hydrophobic part of the target cell membrane [27]. Moreover, many physicochemical properties vary among AMPs and different effects on AMP activities such as antibacterial, antifungal, and antiviral activities [22]. Differences can be found in the terminal residue profiles between AMP and non-AMPs.…”

“…Previous research has indicated that the reason should be due to the dominance from disulfide-bonded and defensing-like molecules [17]. Meanwhile, the composition of "K" (lysine) was different between AMPs and non-AMPs, since the AMP structural cores mainly had positive net charges [22]. The composition of "G" (glycine) of AMPs was higher than the one for non-AMPs.…”