Biology subjects

Hewage, C. M.

Publications and source records attributed to Hewage, C. M..

3 recordsLinked to original sources

bioRxiv2021

Conformation and membrane interaction studies of the potent antimicrobial and anticancer peptide palustrin-Ca

Palustrin-Ca (GFLDIIKDTGKEFAVKILNNLKCKLAGGCPP) is a host defense peptide with potent antimicrobial and anticancer activities, first isolated from the skin of the American bullfrog Lithobates catesbeianus. The peptide is 31 amino acid residues long, cationic and amphipathic. Two-dimensional NMR spectroscopy was employed to characterise its three-dimensional structure in a 50/50% water/2,2,2-trifluoroethanol-d3 mixture. The structure is defined by an -helix that spans between Ile6-Ala26, and a cyclic disulphide bridged domain at the C-terminal end of the peptide sequence, between residues 23 and 29. A molecular dynamics simulation was employed to model the peptides interactions with sodium dodecyl sulphate micelles, a widely used bacterial membrane-mimicking environment. Throughout the simulation, the peptide was found to maintain its -helical conformation between residues Ile6-Ala26, while adopting a position parallel to the surface to micelle, which is energetically-favourable due to many hydrophobic and electrostatic contacts with the micelle.

biochemistry↗

bioRxiv2021

ENNAVIA is an innovative new method which employs neural networks for antiviral and anti-coronavirus activity prediction for therapeutic peptides

Viruses represent one of the greatest threats to human health, necessitating the development of new antiviral drug candidates. Antiviral peptides often possess excellent biological activity and a favourable toxicity profile, and therefore represent a promising field of novel antiviral drugs. As the quantity of sequencing data grows annually, the development of an accurate in silico method for the prediction of peptide antiviral activities is important. This study leverages advances in deep learning and cheminformatics to produce a novel sequence-based deep neural network classifier for the prediction of antiviral peptide activity. The method out-performs the existent best-in-class, with an external test accuracy of 93.9%, Matthews correlation coefficient of 0.87 and an Area Under the Curve of 0.93 on the dataset of experimentally validated peptide activities. This cutting-edge classifier is available as an online web server at https://research.timmons.eu/ennavia, facilitating in silico screening and design of peptide antiviral drugs by the wider research community.

bioinformatics↗

bioRxiv2021

APPTEST is an innovative new method for the automatic prediction of peptide tertiary structures

Good knowledge of a peptides tertiary structure is important for understanding its function and its interactions with its biological targets. APPTEST is a novel computational method that employs a neural network architecture and simulated annealing methods for the prediction of peptide tertiary structure from the primary sequence. APPTEST works for both linear and cyclic peptides of 5-40 natural amino acids. APPTEST is computationally efficient, returning predicted structures within a number of minutes. APPTEST performance was evaluated on a set of 356 test peptides; the best structure predicted for each peptide deviated by an average of 1.9[A] from its experimentally determined backbone conformation, and a native or near-native structure was predicted for 97% of the target sequences. A comparison of APPTEST performance with PEP-FOLD, PEPstrMOD and Peplook across benchmark datasets of short, long and cyclic peptides shows that on average APPTEST produces structures more-native than the existing methods in all three categories. This innovative, cutting-edge peptide structure prediction method is available as an online web server at https://research.timmons.eu/apptest, facilitating in silico study and design of peptides by the wider research community.

bioinformatics↗