Novel peptides based on HIV-1 gp120 sequence with homology to chemokines inhibit HIV infection in cell culture

Engineering T-Cell Resistance to HIV-1 Infection via Knock-In of Peptides from the Heptad Repeat 2 Domain of gp41

Previous studies suggest that short peptides from the heptad repeat 2 (HR2) domain of gp41 expressed on the cell surface are more potent inhibitors of HIV-1 entry than soluble analogs. However, their therapeutic potential has only been examined using lentiviral vectors. Here, we aimed to develop CRISPR/Cas9-based fusion inhibitory peptide knock-in (KI) technology for the generation and selection of HIV-1-resistant T cells. First, we embedded a series of HIV-1 fusion inhibitory peptides in CD52, the shortest glycosylphosphatidylinositol (GPI)-anchored protein, which efficiently delivers epitope tags to the cell surface and maintains a sufficient level of KI. Among the seven peptides tested, MT-C34, HP-23L, and 2P23 exhibited significant activity against both cell-free and cell-to-cell HIV-1 infection. The shed variant of MT-C34 provided insufficient protection against HIV-1 due to its low concentration in the culture medium. Using Cas9 plasmids or ribonucleoprotein electroporation and peptide-specific antibodies, we sorted CEM/R5 cells with biallelic KI of MT-C34 and 2P23 peptides at the CXCR4 locus. In combination, these peptides provided a higher level of protection than individual KI. By extending homology arms and cloning donor DNA into a plasmid containing signals for nuclear localization, we achieved KI of MT-C34 into the CXCR4 locus and HIV-1 proviral DNA at levels of up to 35% in the T-cell line and up to 4 to 5% in primary CD4 lymphocytes. Compared to lentiviral delivery, KI resulted in the higher MT-C34 surface expression and stronger protection of lymphocytes from HIV-1. Thus, we demonstrate that KI is a viable strategy for peptide-based therapy of HIV infection. IMPORTANCE HIV is a human lentivirus that infects CD4-positive immune cells and, when left untreated, manifests in the fatal disease known as AIDS. Antiretroviral therapy (ART) does not lead to viral clearance, and HIV persists in the organism as a latent provirus. One way to control infection is to increase the population of HIV-resistant CD4 lymphocytes via entry molecule knockout or expression of different antiviral genes. Peptides from the heptad repeat (HR) domain of gp41 are potent inhibitors of HIV-1 fusion, especially when designed to express on the cell surface. Individual gp41 peptides encoded by therapeutic lentiviral vectors have been evaluated and some have entered clinical trials. However, a CRISPR/Cas9-based gp41 peptide delivery platform that operates through concomitant target gene modification has not yet been developed due to low knock-in (KI) rates in primary cells. Here, we systematically evaluated the antiviral activity of different HR2 peptides cloned into the shortest carrier molecule, CD52. The resulting small-size transgene constructs encoding selected peptides, in combination with improvements to enhance donor vector nuclear import, helped to overcome precise editing restrictions in CD4 lymphocytes. Using KI into CXCR4, we demonstrated different options for target gene modification, effectively protecting edited cells against HIV-1.

Rough Set Based Classification and Feature Selection Using Improved Harmony Search for Peptide Analysis and Prediction of Anti-HIV-1 Activities

AIDS, which is caused by the most widespread HIV-1 virus, attacks the immune system of the human body, and despite the incredible endeavors for finding proficient medication strategies, the continuing spread of AIDS and claiming subsequent infections has not yet been decreased. Consequently, the discovery of innovative medicinal methodologies is highly in demand. Some available therapies, based on peptides, proclaim the treatment for several deadly diseases such as AIDS and cancer. Since many experimental types of research are restricted by the analysis period and expenses, computational methods overcome the issues effectually. In computational technique, the peptide residues with anti-HIV-1 activity are predicted by classification method, and the learning process of the classification is improved with significant features. Rough set-based algorithms are capable of dealing with the gaps and imperfections present in real-time data. In this work, feature selection using Rough Set Improved Harmony Search Quick Reduct and Rough Set Improved Harmony Search Relative Reduct with Rough Set Classification framework is implemented to classify Anti-HIV-1 peptides. The primary objective of the proposed methodology is to predict the peptides with an anti-HIV-1 activity using effective feature selection and classification algorithms incorporated in the proposed framework. The results of the proposed algorithms are comparatively studied with existing rough set feature selection algorithms and benchmark classifiers, and the reliability of the algorithms implemented in the proposed framework is measured by validity measures, such as Precision, Recall, F-measure, Kulczynski Index, and Fowlkes–Mallows Index. The final results show that the proposed framework analyzed and classified the peptides with a high predictive accuracy of 96%. In this study, we have investigated the ability of a rough set-based framework with sequence-based numeric features to classify anti-HIV-1 peptides, and the experimentation results show that the proposed framework discloses the most satisfactory solutions, where it rapidly congregates in the problem space and finds the best reduct, which improves the prediction accuracy of the given dataset.

Identification of Entry Factors Involved in Hepatitis C Virus Infection Based on Host-Mimicking Short Linear Motifs

Host factors that facilitate viral entry into cells can, in principle, be identified from a virus-host protein interaction network, but for most viruses information for such a network is limited. To help fill this void, we developed a bioinformatics approach and applied it to hepatitis C virus (HCV) infection, which is a current concern for global health. Using this approach, we identified short linear sequence motifs, conserved in the envelope proteins of HCV (E1/E2), that potentially can bind human proteins present on the surface of hepatocytes so as to construct an HCV (envelope)-host protein interaction network. Gene Ontology functional and KEGG pathway analyses showed that the identified host proteins are enriched in cell entry and carcinogenesis functionalities. The validity of our results is supported by much published experimental data. Our general approach should be useful when developing antiviral agents, particularly those that target virus-host interactions.

Viruses recruit host proteins, called entry factors, to help gain entry to host cells. Identification of entry factors can provide targets for developing antiviral drugs. By exploring the concept that short linear peptide motifs involved in human protein-protein interactions may be mimicked by viruses to hijack certain host cellular processes and thereby assist viral infection/survival, we developed a bioinformatics strategy to computationally identify entry factors of hepatitis C virus (HCV) infection, which is a worldwide health problem. Analysis of cellular functions and biochemical pathways indicated that the human proteins we identified usually play a role in cell entry and/or carcinogenesis, and results of the analysis are generally supported by experimental studies on HCV infection, including the ~80% (15 of 19) prediction rate of known HCV hepatocyte entry factors. Because molecular mimicry is a general concept, our bioinformatics strategy is a timely approach to identify new targets for antiviral research, not only for HCV but also for other viruses.

Targeting strategies for delivery of anti-HIV drugs

Human Immunodeficiency Virus (HIV) infection remains a significant cause of mortality globally. Though antiretroviral therapy has significantly reduced AIDS-related morbidity and mortality, there are several drawbacks in the current therapy, including toxicity, drug–drug interactions, development of drug resistance, necessity for long-term drug therapy, poor bio-availability and lack of access to tissues and reservoirs. To circumvent these problems, recent anti-HIV therapeutic research has focused on improving drug delivery systems through drug delivery targeted specifically to host cells infected with HIV or could potentially get infected with HIV. In this regard, several surface molecules of both viral and host cell origin have been described in recent years, that would enable targeted drug delivery in HIV infection. In the present review, we provide a comprehensive overview of the need for novel drug delivery systems, and the successes and challenges in the identification of novel viral and host-cell molecules for the targeted drug delivery of anti-HIV drugs. Such targeted anti-retroviral drug delivery approaches could pave the way for effective treatment and eradication of HIV from the body.