Application of PolyPRep tools on HIV protease polyproteins using molecular docking

In recent years, the development of high-throughput technologies for obtaining sequence data leveraged the possibility of analysis of protein data in silico. However, when it comes to viral polyprotein interaction studies, there is a gap in the representation of those proteins, given their size and length. The prepare for studies using state-of-the-art techniques such as Machine Learning, a good representation of such proteins is a must. We present an alternative to this problem, implementing a fragmentation and modeling protocol to prepare those polyproteins in the form of peptide fragments. Such procedure is made by several scripts, implemented together on the workflow we call PolyPRep, a tool written in Python script and available in GitHub. This software is freely available only for noncommercial users.

Accurate Prediction of Inhibitor Binding to HIV-1 Protease Using CANDOCK

The human immunodeficiency virus 1 (HIV-1) protease is an important target for treating HIV infection. Our goal was to benchmark a novel molecular docking protocol and determine its effectiveness as a therapeutic repurposing tool by predicting inhibitor potency to this target. To accomplish this, we predicted the relative binding scores of various inhibitors of the protease using CANDOCK, a hierarchical fragment-based docking protocol with a knowledge-based scoring function. We first used a set of 30 HIV-1 protease complexes as an initial benchmark to optimize the parameters for CANDOCK. We then compared the results from CANDOCK to two other popular molecular docking protocols Autodock Vina and Smina. Our results showed that CANDOCK is superior to both of these protocols in terms of correlating predicted binding scores to experimental binding affinities with a Pearson coefficient of 0.62 compared to 0.48 and 0.49 for Vina and Smina, respectively. We further leveraged the Database of Useful Decoys: Enhanced (DUD-E) HIV protease set to ascertain the effectiveness of each protocol in discriminating active versus decoy ligands for proteases. CANDOCK again displayed better efficacy over the other commonly used molecular docking protocols with area under the receiver operating characteristic curve (AUROC) of 0.94 compared to 0.71 and 0.74 for Vina and Smina. These findings support the utility of CANDOCK to help discover novel therapeutics that effectively inhibit HIV-1 and possibly other retroviral proteases.

Ritonavir and xk263 Binding-Unbinding with HIV-1 Protease: Pathways, Energy and Comparison

Understanding non-covalent biomolecular recognition, which includes drug–protein bound states and their binding/unbinding processes, is of fundamental importance in chemistry, biology, and medicine. Fully revealing the factors that govern the binding/unbinding processes can further assist in designing drugs with desired binding kinetics. HIV protease (HIVp) plays an integral role in the HIV life cycle, so it is a prime target for drug therapy. HIVp has flexible flaps, and the binding pocket can be accessible by a ligand via various pathways. Comparing ligand association and dissociation pathways can help elucidate the ligand–protein interactions such as key residues directly involved in the interaction or specific protein conformations that determine the binding of a ligand under certain pathway(s). Here, we investigated the ligand unbinding process for a slow binder, ritonavir, and a fast binder, xk263, by using unbiased all-atom accelerated molecular dynamics (aMD) simulation with a re-seeding approach and an explicit solvent model. Using ritonavir-HIVp and xk263-HIVp ligand–protein systems as cases, we sampled multiple unbinding pathways for each ligand and observed that the two ligands preferred the same unbinding route. However, ritonavir required a greater HIVp motion to dissociate as compared with xk263, which can leave the binding pocket with little conformational change of HIVp. We also observed that ritonavir unbinding pathways involved residues which are associated with drug resistance and are distal from catalytic site. Analyzing HIVp conformations sampled during both ligand–protein binding and unbinding processes revealed significantly more overlapping HIVp conformations for ritonavir-HIVp rather than xk263-HIVp. However, many HIVp conformations are unique in xk263-HIVp unbinding processes. The findings are consistent with previous findings that xk263 prefers an induced-fit model for binding and unbinding, whereas ritonavir favors a conformation selection model. This study deepens our understanding of the dynamic process of ligand unbinding and provides insights into ligand–protein recognition mechanisms and drug discovery.

Potentiation of drug toxicity through virus latency reversal promotes preferential elimination of HIV infected cells

Eliminating latently infected cells is a highly challenging, indispensable step towards the overall cure for HIV/AIDS. We recognized that the unique HIV protease cut site (Phe-Pro) can be reconstructed using a potent toxin, monomethyl auristatin F (MMAF), which features Phe at its C-terminus. We hypothesized that this presents opportunities to design prodrugs that are specifically activated by the HIV protease. To investigate this, a series of MMAF derivatives was synthesized and evaluated in cell culture using latently HIV-infected cells. Cytotoxicity of compounds was enhanced upon latency reversal by up to 11-fold. In a mixed cell population, nanomolar concentrations of the lead compound depleted predominantly the HIV-infected cells and in doing so markedly enriched the pool with the uninfected cells. Despite expectation, mechanism of action of the synthesized toxins was not as HIV protease-specific prodrugs, but likely through the synergy of toxicities between the toxin and the reactivated virus.

Improved Production of HIV-1 Subtype C Protease from Transgenic E. Coli

Background: Human Immunodeficiency Virus 1 (HIV-1) subtype C is responsible for the majority of infections of patients in Southern Africa. The HIV protease is a primary target for the development of highly efficient anti-retroviral pharmaceuticals because of its pivotal role in the maturation of the virus in the host cell. For target validation of novel HIV protease inhibitors, there is a need for the availability of an abundance of this protease. Objective: This study reports an optimized method to produce HIV-1 protease derived from HIV-1 subtype C. Methods: It involves the use of a transgenic E. coli strain that overexpresses the native form of the enzyme via inclusion bodies. A stringent method for the isolation, purification, and renaturation resulted in the production of highly pure active HIV-1 protease. In order to facilitate an increase in protease yields, an optimized growth strategy was developed. In this regard, a chemically defined medium with lower glucose content and devoid of essential amino acids of the TCA cycle was used as an alternative to the widely used nutrient-rich Luria Bertani (LB) medium. Results: Results indicated an increase in protease yield up to twice the amount, thereby making this medium an attractive alternative for increasing biomass and HIV protease production for future research. Conclusion: An optimized method for HIV-1 protease derived from HIV-1 subtype C production using chemically defined media was established. This was achieved using a known method to isolate and purify the enzyme with the use of a specialized feeding strategy.

HIV Protease and Integrase Empirical Substitution Models of Evolution: Protein-Specific Models Outperform Generalist Models

Diverse phylogenetic methods require a substitution model of evolution that should mimic, as accurately as possible, the real substitution process. At the protein level, empirical substitution models have traditionally been based on a large number of different proteins from particular taxonomic levels. However, these models assume that all of the proteins of a taxonomic level evolve under the same substitution patterns. We believe that this assumption is highly unrealistic and should be relaxed by considering protein-specific substitution models that account for protein-specific selection processes. In order to test this hypothesis, we inferred and evaluated four new empirical substitution models for the protease and integrase of HIV and other viruses. We found that these models more accurately fit, compared with any of the currently available empirical substitution models, the evolutionary process of these proteins. We conclude that evolutionary inferences from protein sequences are more accurate if they are based on protein-specific substitution models rather than taxonomic-specific (generalist) substitution models. We also present four new empirical substitution models of protein evolution that could be useful for phylogenetic inferences of viral protease and integrase.

Computational Simulation of HIV Protease Inhibitors to the Main Protease (Mpro) of SARS-CoV-2: Implications for COVID-19 Drugs Design

SARS-CoV-2 is highly homologous to SARS-CoV. To date, the main protease (Mpro) of SARS-CoV-2 is regarded as an important drug target for the treatment of Coronavirus Disease 2019 (COVID-19). Some experiments confirmed that several HIV protease inhibitors present the inhibitory effects on the replication of SARS-CoV-2 by inhibiting Mpro. However, the mechanism of action has still not been studied very clearly. In this work, the interaction mechanism of four HIV protease inhibitors Darunavir (DRV), Lopinavir (LPV), Nelfinavir (NFV), and Ritonavire (RTV) targeting SARS-CoV-2 Mpro was explored by applying docking, molecular dynamics (MD) simulations, and MM–GBSA methods using the broad-spectrum antiviral drug Ribavirin (RBV) as the negative and nonspecific control. Our results revealed that LPV, RTV, and NFV have higher binding affinities with Mpro, and they all interact with catalytic residues His41 and the other two key amino acids Met49 and Met165. Pharmacophore model analysis further revealed that the aromatic ring, hydrogen bond donor, and hydrophobic group are the essential infrastructure of Mpro inhibitors. Overall, this study applied computational simulation methods to study the interaction mechanism of HIV-1 protease inhibitors with SARS-CoV-2 Mpro, and the findings provide useful insights for the development of novel anti-SARS-CoV-2 agents for the treatment of COVID-19.

Structure-Function Characteristics of SARS-CoV-2 Proteases and Their Potential Inhibitors from Microbial Sources

The COVID-19 pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is considered the greatest challenge to the global health community of the century as it continues to expand. This has prompted immediate urgency to discover promising drug targets for the treatment of COVID-19. The SARS-CoV-2 viral proteases, 3-chymotrypsin-like protease (3CLpro) and papain-like cysteine protease (PLpro), have become the promising target to study due to their essential functions in spreading the virus by RNA transcription, translation, protein synthesis, processing and modification, virus replication, and infection of the host. As such, understanding of the structure and function of these two proteases is unavoidable as platforms for the development of inhibitors targeting this protein which further arrest the infection and spread of the virus. While the abundance of reports on the screening of natural compounds such as SARS-CoV-2 proteases inhibitors are available, the microorganisms-based compounds (peptides and non-peptides) remain less studied. Indeed, microorganisms-based compounds are also one of the potent antiviral candidates against COVID-19. Microbes, especially bacteria and fungi, are other resources to produce new drugs as well as nucleosides, nucleotides, and nucleic acids. Thus, we have compiled various reported literature in detail on the structures, functions of the SARS-CoV-2 proteases, and potential inhibitors from microbial sources as assistance to other researchers working with COVID-19. The compounds are also compared to HIV protease inhibitors which suggested the microorganisms-based compounds are advantageous as SARS-CoV2 proteases inhibitors. The information should serve as a platform for further development of COVID-19 drug design strategies.

Current Potential Therapeutic Approaches against SARS-CoV-2: A Review

The ongoing SARS-CoV-2 pandemic is a serious threat to public health worldwide and, to date, no effective treatment is available. Thus, we herein review the pharmaceutical approaches to SARS-CoV-2 infection treatment. Numerous candidate medicines that can prevent SARS-CoV-2 infection and replication have been proposed. These medicines include inhibitors of serine protease TMPRSS2 and angiotensin converting enzyme 2 (ACE2). The S protein of SARS-CoV-2 binds to the receptor in host cells. ACE2 inhibitors block TMPRSS2 and S protein priming, thus preventing SARS-CoV-2 entry to host cells. Moreover, antiviral medicines (including the nucleotide analogue remdesivir, the HIV protease inhibitors lopinavir and ritonavir, and wide-spectrum antiviral antibiotics arbidol and favipiravir) have been shown to reduce the dissemination of SARS-CoV-2 as well as morbidity and mortality associated with COVID-19.