Computational identification of self‐inhibitory peptides from envelope proteins

Abstract

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 enveloped (E) proteins function to inhibit the protein-protein interactions in the membrane fusion step mediated by the viral E protein. Thus, they have the potential to be developed into effective antiviral therapy. Herein, we have developed a Monte Carlo-based computational method with the aim to identify and optimize potential peptide hits from the E proteins. The stability of the peptides, which indicates their potential to bind in situ to the E proteins, was evaluated by two different scoring functions, dipolar distance-scaled, finite, ideal-gas reference state and residue-specific all-atom probability discriminatory function. The method was applied to α-helical Class I HIV-1 gp41, β-sheet Class II Dengue virus (DENV) type 2 E proteins, as well as Class III Herpes Simplex virus-1 (HSV-1) glycoprotein, a E protein with a mixture of α-helix and β-sheet structural fold. The peptide hits identified are in line with the druggable regions where the self-inhibitory peptide inhibitors for the three classes of viral fusion proteins were derived. Several novel peptides were identified from either the hydrophobic regions or the functionally important regions on Class II DENV-2 E protein and Class III HSV-1 gB. They have potential to disrupt the protein-protein interaction in the fusion process and may serve as starting points for the development of novel inhibitors for viral E proteins.

Molecular Cloning and Characterization of Cystatin, a Cysteine Protease Inhibitor, fromBufo melanostictus

“…The structure of Cystatin-X was predicted with the Quark Server. 19) Three structural elements, G in the N-terminal region, QVVAG at the first binding loop connecting the second and third -strands, and LP at the second binding loop between the fourth and fifth strands of the -sheet, were similar to cystatin A (PDB 1G63) (Fig. 3).…”

Dengue Envelope Domain III-Peptide Binding Analysis <i>via</i> Tryptophan Fluorescence Quenching Assay

“…Screening of Ligand Binding by Fluorescence-Quenching Effect Peptides designed by Xu et al 12) were used to test if the intrinsic Trp321 of EIII can be used to monitor the peptides binding to the DENV2 EIII. To mimic the physiological environment, PBS at 1X concentration, pH 7.4 was used as the buffer in the binding assay.…”

“…10,11) In the current study, the Trp391 present in EIII was used as an intrinsic probe to monitor the change in fluorescence of the tryptophan residue upon binding to potential EIII peptide inhibitors. The peptides used in this study were designed based on a Monte Carlo method to identify the druggable regions in EIII.12) As described by Xu et al,12) the DS36wt and DN58wt peptides sequences were identified from the hydrophobic region in EIII, and were predicted to show promising self-inhibitory. The DS36opt and DN58opt peptides sequences were derived from DS36wt and DN58wt, respectively, and the stability of the substitutes amino acids which indicates its potential to bind in situ to E protein was evaluated with residue-specific all-atom probability discriminatory function (RAPDF) scoring function.…”

“…12) As described by Xu et al,12) the DS36wt and DN58wt peptides sequences were identified from the hydrophobic region in EIII, and were predicted to show promising self-inhibitory. The DS36opt and DN58opt peptides sequences were derived from DS36wt and DN58wt, respectively, and the stability of the substitutes amino acids which indicates its potential to bind in situ to E protein was evaluated with residue-specific all-atom probability discriminatory function (RAPDF) scoring function.…”

Assessment of semiempirical enthalpy of formation in solution as an effective energy function to discriminate native‐like structures in protein decoy sets

“…These decoy sets are created using a variety of sampling algorithms. [8,[10][11][12][13] Because protein structure prediction is usually based on the Anfinsen thermodynamic hypothesis, [14] a crucial property for an effective energy function is its ability to recognize when a conformation is at a free energy minimum. This means that a good test for an effective energy function is to score a large number of closely related structures to recognize native-like conformations, a purpose for which the decoy sets produced by the methods cited above are widely used.…”