Fusion process is known to be the initial step of viral infection and hence targeting the entry process is a promising strategy to design antiviral therapy. The self-inhibitory peptides derived from the envelope (E) proteins function to inhibit the protein-protein interactions in the membrane fusion step mediated by the viral E proteins. Thus, they have the potential to be developed into effective antiviral therapy. We developed a Monte Carlo-based computational method with the aim to identify and optimize potential peptide hits from the E proteins. Some novel peptides may inhibit the protein-protein interaction in the Dengue Virus (DENV) or Herpes Simplex Virus-! (HSV -1) fusion process and serve as starting points for the development of antiviral therapy to treat viral infection. Residue-specific all-atom conditional probability discriminatory function (RAPDF) has been justified effective for designing novel peptide inhibitors targeting E proteins in our computational method, The statistical potential has its universality and specificity which is decided by the reference state and the decoy sets. We built new distance-dependant statistical potentials using E, tetratricopeptide and ankyrin repeat proteins respectively. We demonstrated that the specific statistical potentials outperformed the general statistical potentials. Our computational method for identifying self-inhibitory peptides from three types of E proteins has also illustrated E proteins may have some unique features in common. In order to identify the active peptides from non-active peptides, we applied a support vector machine (SVM) approach for envelope peptide inhibitors activity prediction (EAP) based on the physicochemical properties, amino acid composition and statistical scoring function of peptide inhibitors which target E proteins. The results suggest that the rational connecting between properties of peptide inhibitors derived from E proteins and antivirus activity.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:603298 |
| Date | January 2013 |
| Creators | Xu, Yongtao |
| Publisher | Queen's University Belfast |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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