Title:Designing of Artificial Peptides for an Improved Antiviral Activity
VOLUME: 15 ISSUE: 4
Author(s):Speranta Avram, Adina-Luminita Milac, Livia-Cristina Borcan, Dan Mihailescu, Florin Borcan* and Miguel Castanho
Affiliation:Department of Anatomy, Animal Physiology and Biophysics, Faculty of Biology, University of Bucharest, Bucharest, Department of Anatomy, Animal Physiology and Biophysics, Faculty of Biology, University of Bucharest, Bucharest, Department of Occupational Health, Faculty of Medicine, "Victor Babes" University of Medicine and Pharmacy, Timisoara, Department of Anatomy, Animal Physiology and Biophysics, Faculty of Biology, University of Bucharest, Bucharest, Department of Analytical Chemistry, Faculty of Pharmacy, "Victor Babes" University of Medicine and Pharmacy, Timisoara, Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Lisbon
Keywords:Antiviral peptide, enfuvirtide, gp41 inhibitor, HIV-1 infection, QSAR, sifuvirtide.
Abstract:Background: Few HIV-1fusion and replication inhibitors were developed, with limited
clinical applications because of their short half-life, drug resistance and cross-reactivity with
preexisting antibodies in HIV-infected patients.
Objective: These limitations call for new strategies in the development of next anti-HIV-1 drugs.
Among anti-gp41HIV-1 inhibitors, short-peptides exhibit high antiviral activity but the mechanism of
action at molecular level has not been sufficiently addressed.
Method: We report potent QSAR (Quantitative Structure-Activity Relationship) models, used for
biological activity prediction of novel short HIV-1 gp41 inhibitor peptides in order to: (i) validate the
anti-HIV-1 activity of MT-sifuvirtide, MT-SC34EK, MT-C34 and HP23, expressed as IC50fusion and
IC50replication; (ii) predict inhibitory activity of SC24EK and its MT-derivative expressed as IC50resistant
HIV-1 NL4-3 variant; (iii) propose new derivatives DMT-SC22EK, DMT-SC29EK and DMT-sifuvirtide
through addition of aspartic acids by induced-mutagenesis; (iv) use molecular similarity established by
fingerprint models to correlate molecular spatial features with predicted biological activity of newly
generated inhibitors over parent compounds.
Results: We obtained good QSAR statistic parameters, demonstrating that our QSAR models are able
to predict biological activity of new HIV-1 inhibitors with suitable accuracy.
Conclusion: Despite acknowledged drawbacks of a reduced dataset, our results may enhance the
evaluation of biological activity of new and classical synthetic peptides as anti-HIV-1agents and
represent a good start for further studies in developing new antiviral drugs.
