Designing of Artificial Peptides for an Improved Antiviral Activity

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.