Polar Profile of Antiviral Peptides from AVPpred Database

Abstract

Diseases of viral origin in humans are among the most serious threats to health and the global economy. As recent history has shown the virus has a high pandemic potential, among other reasons, due to its ability to spread by air, hence the identification, investigation, containment, and treatment of viral diseases should be considered of paramount importance. In this sense, the bioinformatics research has focused on finding fast and efficient algorithms that can identify highly toxic antiviral peptides and to serve as a first filter, so that trials in the laboratory are substantially reduced. The work presented here contributes to this effort through the use of an algorithm already published by this team, called polarity index method, which identifies with high efficiency antiviral peptides from the exhaustive analysis of the polar profile, using the linear sequence of the peptide. The test carried out included all peptides in APD2 Database and 60 antiviral peptides identified by Kumar and co-workers (Nucleic Acids Res 40:W199-204, 2012), to build its AVPpred algorithm. The validity of the method was focused on its discriminating capacity so we included the 15 sub-classifications of both Databases.

AVP‐IC50Pred: Multiple machine learning techniques‐based prediction of peptide antiviral activity in terms of half maximal inhibitory concentration (IC50)

“…It was observed that some properties like helix initiation parameter (c), frequency of C‐terminal non beta region (d), free energy in beta‐strand region (e, m) and frequency of helix (f, k) were more discriminative compared to others. Similar results were reported by Chang and Yang and Polanco et al while analyzing the physicochemical properties of AVPs.…”

Identification of proteins associated with Mycobacterium tuberculosis virulence pathway by their polar profile

“…The distribution of relative frequencies of the described groups indicates that the inflection points do not match in the groups. This has been already observed for other peptide and protein groups (Polanco et al, 2012;2013;2013a;2014a;2014b;2014c;2014d;2014e). We assume that their location is the identifier underlying the measurement of the electromagnetic balance of a protein.…”

“…In order to deepen the characterization of proteins associated with Mycobacterium tuberculosis strains on the basis of their physico-chemical properties, we calibrated the mathematical-computational Polarity Index Method (PIM) (Polanco et al, 2012), with all proteins associated with Mycobacterium tuberculosis virulence pathway (MTVP) group from the Tuberculist Database (Lew et al, 2011). The PIM is a supervised method that we used for the identification and characterization of various peptide and protein groups based on the linear representation of the protein (Polanco et al, 2012;2013;2013a;2014a;2014b;2014c;2014d;2014e). Its metric considers only polarity as a physico-chemical property, and it is based on a polar matrix.…”

Classifying lipoproteins based on their polar profiles.

“…In order to deepen the understanding of this important group of cargo-proteins specialized in the transport of lipids, we applied the supervised computational tool called Polarity index method (Polanco et al, 2012), which has already been used in the analysis of other groups of peptides and proteins (Polanco & Samaniego, 2009;Polanco et al, 2012;2013;2013a;2014a;2014b;2014c;2014d;2014e). Here, this technique was used to characterize Chylomicrons, VLDLs, IDLs, LDLs, HDLs, and Atherosclerosis, i.e.…”