Reduced HIV-1 integrase flexibility as a mechanism for raltegravir resistance

Analysis of highly frequent point mutations in glycoprotein C, glycoprotein N, and nucleoprotein of CCHFV

Crimean-Congo hemorrhagic fever virus (CCHFV) is classified among top 10 priority pathogens by World Health Organization. CCHFV belongs to Bunyaviridae family and negative sense ssRNA genome composed of three RNA segments: L, M, and S. RNA viruses show higher mutation rate as compared to DNA viruses. To gain deeper understanding of impact of point mutations in CCHFV M and S segment, mutation profiling, homology modeling, and molecular dynamic (MD) simulation were performed. Structural glycoproteins (glycoprotein C [Gc] and glycoprotein N [Gn]) of CCHFV are important for host-virus interaction and genome packaging, whereas CCHFV nucleoprotein (NP) is crucial for viral replication. Hence, current study is focused on evaluation of eight mutations in structural glycoproteins (Gc: 7 and Gn: 1) of M segment and seven mutations in NP of S segment. All these mutations were highly frequent, with mutation frequency between 0.81 and 1.0 and found to be persistent in the recent strains of CCHFV. Solubility analysis predicted that selected point mutations reduce solubility of Gc protein and increase solubility of Gn and NP proteins. MD simulation study deciphered that A1046V and G1158E in Gc protein, I778T in Gn protein, and H195R in NP protein displayed large deviation and fluctuation, and affected intramolecular interactions. In conclusion, we observed that point mutations could impact structure, stability, and host-virus interaction of protein, and might lead to evolution of new strains for better survival and drug resistance.

Protegrin-2, a potential inhibitor for targeting SARS-CoV-2 main protease Mpro

Background

SARS-CoV-2 variants continue to spread throughout the world and cause waves of COVID-19 infections. It is important to find effective antiviral drugs to combat SARS-CoV-2 and its variants. The main protease (Mpro) of SARS-CoV-2 is a promising therapeutic target due to its crucial role in viral replication and its conservation in all the variants. Therefore, the aim of this work was to identify an effective inhibitor of Mpro.

Methods

We studied around 200 antimicrobial peptides using in silico methods including molecular docking and allergenicity and toxicity prediction. One selected antiviral peptide was studied experimentally using a Bioluminescence Resonance Energy Transfer (BRET)-based Mpro biosensor, which reports Mpro activity through a decrease in energy transfer.

Results

Molecular docking identified one natural antimicrobial peptide, Protegrin-2, with high binding affinity and stable interactions with Mpro allosteric residues. Furthermore, free energy calculations and molecular dynamics simulation illustrated a high affinity interaction between the two. We also determined the impact of the binding of Protegrin-2 to Mpro using a BRET-based assay, showing that it inhibits the proteolytic cleavage activity of Mpro.

Conclusions

Our in silico and experimental studies identified Protegrin-2 as a potent inhibitor of Mpro that could be pursued further towards drug development against COVID-19 infection.

Exploring the Potential of Phytocompounds for Targeting Epigenetic Mechanisms in Rheumatoid Arthritis: An In Silico Study Using Similarity Indexing

Finding structurally similar compounds in compound databases is highly efficient and is widely used in present-day drug discovery methodology. The most-trusted and -followed similarity indexing method is Tanimoto similarity indexing. Epigenetic proteins like histone deacetylases (HDACs) inhibitors are traditionally used to target cancer, but have only been investigated very recently for their possible effectiveness against rheumatoid arthritis (RA). The synthetic drugs that have been identified and used for the inhibition of HDACs include SAHA, which is being used to inhibit the activity of HDACs of different classes. SAHA was chosen as a compound of high importance as it is reported to inhibit the activity of many HDAC types. Similarity searching using the UNPD database as a reference identified aglaithioduline from the Aglaia leptantha compound as having a ~70% similarity of molecular fingerprints with SAHA, based on the Tanimoto indexing method using ChemmineR. Aglaithioduline is abundantly present in the shell and fruits of A. leptantha. In silico studies with aglaithioduline were carried out against the HDAC8 protein target and showed a binding affinity of -8.5 kcal mol. The complex was further subjected to molecular dynamics simulation using Gromacs. The RMSD, RMSF, compactness and SASA plots of the target with aglaithioduline, in comparison with the co-crystallized ligand (SAHA) system, showed a very stable configuration. The results of the study are supportive of the usage of A. leptantha and A. edulis in Indian traditional medicine for the treatment of pain-related ailments similar to RA. Our study therefore calls for further investigation of A. leptantha and A. edulis for their potential use against RA by targeting epigenetic changes, using in vivo and in vitro studies.

Antiviral therapies: advances and perspectives

Viral infections cause high morbidity and mortality, threaten public health, and impose a socioeconomic burden. We have seen the recent emergence of SARS‐CoV‐2 (Severe Acute Respiratory Syndrome Coronavirus 2), the causative agent of COVID‐19 that has already infected more than 29 million people, with more than 900 000 deaths since its identification in December 2019. Considering the significant impact of viral infections, research and development of new antivirals and control strategies are essential. In this paper, we summarize 96 antivirals approved by the Food and Drug Administration between 1987 and 2019. Of these, 49 (51%) are used in treatments against human immunodeficiency virus (HIV), four against human papillomavirus, six against cytomegalovirus, eight against hepatitis B virus, five against influenza, six against herpes simplex virus, 17 against hepatitis C virus and one against respiratory syncytial virus. This review also describes future perspectives for new antiviral therapies such as nanotechnologies, monoclonal antibodies and the CRISPR‐Cas system. These strategies are suggested as inhibitors of viral replication by various means, such as direct binding to the viral particle, blocking the infection, changes in intracellular mechanisms or viral genes, preventing replication and virion formation. We also observed that a large number of viral agents have no therapy available and the majority of those approved in the last 32 years are restricted to some groups, especially anti‐HIV. Additionally, the emergence of new viruses and strains resistant to available antivirals has necessitated the formulation of new antivirals.

Molecular dynamic simulations to investigate the structural impact of known drug resistance mutations on HIV-1C Integrase-Dolutegravir binding

Resistance associated mutations (RAMs) threaten the long-term success of combination antiretroviral therapy (cART) outcomes for HIV-1 treatment. HIV-1 Integrase (IN) strand transfer inhibitors (INSTIs) have proven to be a viable option for highly specific HIV-1 therapy. The INSTI, Dolutegravir is recommended by the World Health Organization for use as first-line cART. This study aims to understand how RAMs affect the stability of IN, as well as the binding of the drug Dolutegravir to the catalytic pocket of the protein. A homology model of HIV-1 subtype C IN was successfully constructed and validated. The site directed mutator webserver was used to predict destabilizing and/or stabilizing effects of known RAMs while FoldX confirmed any changes in protein energy upon introduction of mutation. Also, interaction analysis was performed between neighbouring residues. Three mutations known to be associated with Raltegravir, Elvitegravir and Dolutegravir resistance were selected; E92Q, G140S and Y143R, for molecular dynamics simulations. The structural quality assessment indicated high reliability of the HIV-1C IN tetrameric structure, with more than 90% confidence in modelled regions. Change in free energy for the three mutants indicated different effects, while simulation analysis showed G140S to have the largest affect on protein stability and flexibility. This was further supported by weaker non-bonded pairwise interaction energy and binding free energy values between the drug DTG and E92Q, Y143R and G140S mutants suggesting reduced binding affinity, as indicated by interaction analysis in comparison to the WT. Our findings suggest the G140S mutant has the strongest effect on the HIV-1C IN protein structure and Dolutegravir binding. To the best of our knowledge, this is the first study that uses the consensus wild type HIV-1C IN sequence to build an accurate 3D model to understand the effect of three known mutations on DTG drug binding in a South Africa context.

Structure–activity relationship and binding mode studies for a series of diketo-acids as HIV integrase inhibitors by 3D-QSAR, molecular docking and molecular dynamics simulations

We explored the main factors affecting the activity of compounds by different statistical and computational methods.

Blind prediction of HIV integrase binding from the SAMPL4 challenge

Here, we give an overview of the protein-ligand binding portion of the Statistical Assessment of Modeling of Proteins and Ligands 4 (SAMPL4) challenge, which focused on predicting binding of HIV integrase inhibitors in the catalytic core domain. The challenge encompassed three components--a small "virtual screening" challenge, a binding mode prediction component, and a small affinity prediction component. Here, we give summary results and statistics concerning the performance of all submissions at each of these challenges. Virtual screening was particularly challenging here in part because, in contrast to more typical virtual screening test sets, the inactive compounds were tested because they were thought to be likely binders, so only the very top predictions performed significantly better than random. Pose prediction was also quite challenging, in part because inhibitors in the set bind to three different sites, so even identifying the correct binding site was challenging. Still, the best methods managed low root mean squared deviation predictions in many cases. Here, we give an overview of results, highlight some features of methods which worked particularly well, and refer the interested reader to papers in this issue which describe specific submissions for additional details.

Virtual screening with AutoDock Vina and the common pharmacophore engine of a low diversity library of fragments and hits against the three allosteric sites of HIV integrase: participation in the SAMPL4 protein-ligand binding challenge

To rigorously assess the tools and protocols that can be used to understand and predict macromolecular recognition, and to gain more structural insight into three newly discovered allosteric binding sites on a critical drug target involved in the treatment of HIV infections, the Olson and Levy labs collaborated on the SAMPL4 challenge. This computational blind challenge involved predicting protein-ligand binding against the three allosteric sites of HIV integrase (IN), a viral enzyme for which two drugs (that target the active site) have been approved by the FDA. Positive control cross-docking experiments were utilized to select 13 receptor models out of an initial ensemble of 41 different crystal structures of HIV IN. These 13 models of the targets were selected using our new "Rank Difference Ratio" metric. The first stage of SAMPL4 involved using virtual screens to identify 62 active, allosteric IN inhibitors out of a set of 321 compounds. The second stage involved predicting the binding site(s) and crystallographic binding mode(s) for 57 of these inhibitors. Our team submitted four entries for the first stage that utilized: (1) AutoDock Vina (AD Vina) plus visual inspection; (2) a new common pharmacophore engine; (3) BEDAM replica exchange free energy simulations, and a Consensus approach that combined the predictions of all three strategies. Even with the SAMPL4's very challenging compound library that displayed a significantly lower amount of structural diversity than most libraries that are conventionally employed in prospective virtual screens, these approaches produced hit rates of 24, 25, 34, and 27 %, respectively, on a set with 19 % declared binders. Our only entry for the second stage challenge was based on the results of AD Vina plus visual inspection, and it ranked third place overall according to several different metrics provided by the SAMPL4 organizers. The successful results displayed by these approaches highlight the utility of the computational structure-based drug discovery tools and strategies that are being developed to advance the goals of the newly created, multi-institution, NIH-funded center called the "HIV Interaction and Viral Evolution Center".