The HIV-1 protease is critical for the process of viral maturation and as such, it is one of the most well characterized proteins in the Protein Data Bank. There is some evidence to suggest that the HIV-1 protease is capable of accommodating small molecule fragments at several locations on its surface outside of the active site. However, some pockets on the surface of proteins remain unformed in the apo structure and are termed \"cryptic sites.\" To date, no cryptic sites have been identified in the structure of HIV-1 protease. Here, we characterize a novel cryptic cantilever pocket on the surface of the HIV-1 protease through mixed-solvent molecular dynamics simulations using several probes. Interestingly, we noted that several homologous retroviral proteases exhibit evolutionarily conserved dynamics in the cantilever region and possess a conserved pocket in the cantilever region. Immobilization of the cantilever region of the HIV-1 protease via disulfide cross-linking resulted in curling-in of the flap tips and the propensity for the protease to adopt a semi-open flap conformation. Structure-based analysis and fragment-based screening of the cryptic cantilever pocket suggested that the pocket may be capable of accommodating ligand structures. Furthermore, molecular dynamics simulations of a top scoring fragment bound to the cryptic pocket illustrated altered flap dynamics of the fragment-bound enzyme. Together, these results suggest that the mobility of the cantilever region plays a key role in the global dynamics of retroviral proteases. Therefore, the cryptic cantilever pocket of the HIV-1 protease may represent an interesting target for future in vitro studies.

This study introduces a novel method named multiple parameter replica exchange Gaussian accelerated molecular dynamics (MP-Rex-GaMD), building on the Gaussian accelerated molecular dynamics (GaMD) algorithm. GaMD enhances sampling and retrieves free energy information for biomolecular systems by adding a harmonic boost potential to smooth the potential energy surface without the need for predefined reaction coordinates. Our innovative approach advances the acceleration power and energetic reweighting accuracy of GaMD by incorporating a replica exchange algorithm that enables the exchange of multiple parameters, including the GaMD boost parameters of force constant and energy threshold, as well as temperature. Applying MP-Rex-GaMD to the three model systems of dialanine, chignolin, and HIV protease, we demonstrate its superior capability over conventional molecular dynamics and GaMD simulations in exploring protein conformations and effectively navigating various biomolecular states across energy barriers. MP-Rex-GaMD allows users to accurately map free energy landscapes through energetic reweighting, capturing the ensemble of biomolecular states from low-energy conformations to rare high-energy transitions within practical computational time scales.

Drug resistance of pathogens, including viruses, is one of the reasons for decreased efficacy of therapy. Considering the impact of HIV type 1 (HIV-1) on the development of progressive immune dysfunction and the rapid development of drug resistance, the analysis of HIV-1 resistance is of high significance. Currently, a substantial amount of data has been accumulated on HIV-1 drug resistance that can be used to build both qualitative and quantitative models of HIV-1 drug resistance. Quantitative models of drug resistance can enrich the information about the efficacy of a particular drug in the scheme of antiretroviral therapy. In our study, we investigated the possibility of developing models for quantitative prediction of HIV-1 resistance to eight protease inhibitors based on the analysis of amino acid sequences of HIV-1 protease for 900 virus variants. We developed random forest regression (RFR), support vector regression (SVR), and self-consistent regression (SCR) models using binary vectors containing values from 0 or 1, depending on the presence of a specific peptide fragment in each amino acid sequence as independent variables, while fold ratio, reflecting the level of resistance, was the predicted variable. The SVR and SCR models showed the highest predictive performances. The models built demonstrate reasonable performances for eight out of nine (R2 varied from 0.828 to 0.909) protease inhibitors, while R2 for predicting tipranavir fold ratio was lower (R2 was 0.642). We believe that the developed approach can be applied to evaluate drug resistance of molecular targets of other viruses where appropriate experimental data are available.

OBJECTIVE: Routine viral load and drug resistance testing are well supported in most resource-rich settings and provide valuable benefits in the clinical care of PLWH in these communities. Undoubtedly, there exist financial and political constraints for the scale-up of viral load and drug resistance testing in Sub-Saharan Africa. To achieve the global UNAIDS 95/95/95 targets, there is the need to bridge this inequity in patient care and allow for a universal approach that leaves no community behind.
METHODS: Venous blood from 96 PLWH on second-line ART from Korle-Bu Teaching Hospital were collected and processed into plasma for CD4+ T- cell and viral load assessments. Ribonucleic acid (RNA) was extracted from stored plasma and the protease gene amplified, sequenced and analyzed for subtype and drug resistance mutations using the Stanford HIV drug resistance database.
RESULTS: Out of the 96 PLWH, 37 experienced virological failure with 8 patients\' samples successfully sequenced. The predominant HIV-1 subtype identified was CRF02_AG (6/8, 75.0%) with 12.5% (1/8) each of CFR06_cpx infection and one case unable to subtype. The major PI resistance mutations identified were; M46I, I54V, V82A, I47V, I84V and L90M.
CONCLUSIONS: Persons living with HIV who had experienced virologic failure in this study harboured drug resistance mutations to PI, thus compromise the effectiveness of the drugs in the second line. Resistance testing is strongly recommended prior to switching to a new regimen. This will help to inform the choice of drug and to achieve optimum therapeutic outcome among PLWH in Ghana.

The lack of success in clinical trials for HIV vaccines highlights the need to explore novel strategies for vaccine development. Research on highly exposed seronegative (HESN) HIV-resistant Kenyan female sex workers revealed naturally protective immunity is correlated with a focused immune response mediated by virus-specific CD8 T cells. Further studies indicated that the immune response is unconventionally focused on highly conserved sequences around HIV viral protease cleavage sites (VPCS). Thus, taking an unconventional approach to HIV vaccine development, we designed lipid nanoparticles loaded with mRNA that encodes multi-epitopes of VPCS (MEVPCS-mRNA LNP), a strategic design to boost antigen presentation by dendritic cells, promoting effective cellular immunity. Furthermore, we developed a novel cold-chain compatible mRNA LNP formulation, ensuring long-term stability and compatibility with cold-chain storage/transport, widening accessibility of mRNA LNP vaccine in low-income countries. The in-vivo mouse study demonstrated that the vaccinated group generated VPCS-specific CD8 memory T cells, both systemically and at mucosal sites of viral entry. The MEVPCS-mRNA LNP vaccine-induced CD8 T cell immunity closely resembled that of the HESN group and displayed a polyfunctional profile. Notably, it induced minimal to no activation of CD4 T cells. This proof-of-concept study underscores the potential of the MEVPCS-mRNA LNP vaccine in eliciting CD8 T cell memory specific to the highly conserved multiple VPCS, consequently having a broad coverage in human populations and limiting viral escape mutation. The MEVPCS-mRNA LNP vaccine holds promise as a candidate for an effective prophylactic HIV vaccine.

HIV-1 protease inhibitors are an essential component of antiretroviral therapy. However, drug resistance is a pervasive issue motivating a persistent search for novel therapies. Recent reports found that when protease activates within the host cell\'s cytosol, it facilitates the pyroptotic killing of infected cells. This has led to speculation that promoting protease activation, rather than inhibiting it, could help to eradicate infected cells and potentially cure HIV-1 infection. Here, we used a nanoscale flow cytometry-based assay to characterize protease resistance mutations and polymorphisms. We quantified protease activity, viral concentration, and premature protease activation and confirmed previous findings that major resistance mutations generally destabilize the protease structure. Intriguingly, we found evidence that common polymorphisms in the hinge domain of protease can influence its susceptibility to premature activation. This suggests that viral heterogeneity could pose a considerable challenge for therapeutic strategies aimed at inducing premature protease activation in the future.

The battle against viral drug resistance highlights the need for innovative approaches to replace time-consuming and costly traditional methods. Deep generative models offer automation potential, especially in the fight against Human immunodeficiency virus (HIV), as they can synthesize diverse molecules effectively. In this paper, an application of an LSTM-based deep generative model named \"LSTM-ProGen\" is proposed to be tailored explicitly for the de novo design of drug candidate molecules that interact with a specific target protein (HIV-1 protease). LSTM-ProGen distinguishes itself by employing a long-short-term memory (LSTM) architecture, to generate novel molecules target specificity against the HIV-1 protease. Following a thorough training process involves fine-tuning LSTM-ProGen on a diverse range of compounds sourced from the ChEMBL database. The model was optimized to meet specific requirements, with multiple iterations to enhance its predictive capabilities and ensure it generates molecules that exhibit favorable target interactions. The training process encompasses an array of performance evaluation metrics, such as drug-likeness properties. Our evaluation includes extensive silico analysis using molecular docking and PCA-based visualization to explore the chemical space that the new molecules cover compared to those in the training set. These evaluations reveal that a subset of 12 de novo molecules generated by LSTM-ProGen exhibit a striking ability to interact with the target protein, rivaling or even surpassing the efficacy of native ligands. Extended versions with further refinement of LSTM-ProGen hold promise as versatile tools for designing efficacious and customized drug candidates tailored to specific targets, thus accelerating drug development and facilitating the discovery of new therapies for various diseases.

The Gag-Pol polyprotein in human immunodeficiency virus type I (HIV-1) encodes enzymes that are essential for virus replication: protease (PR), reverse transcriptase (RT), and integrase (IN). The mature forms of PR, RT and IN are homodimer, heterodimer and tetramer, respectively. The precise mechanism underlying the formation of dimer or tetramer is not yet understood. Here, to gain insight into the dimerization of PR and RT in the precursor, we prepared a model precursor, PR-RT, incorporating an inactivating mutation at the PR active site, D25A, and including two residues in the p6* region, fused to a SUMO-tag, at the N-terminus of the PR region. We also prepared two mutants of PR-RT containing a dimer dissociation mutation either in the PR region, PR(T26A)-RT, or in the RT region, PR-RT(W401A). Size exclusion chromatography showed both monomer and dimer fractions in PR-RT and PR(T26A)-RT, but only monomer in PR-RT(W401A). SEC experiments of PR-RT in the presence of protease inhibitor, darunavir, significantly enhanced the dimerization. Additionally, SEC results suggest an estimated PR-RT dimer dissociation constant that is higher than that of the mature RT heterodimer, p66/p51, but slightly lower than the premature RT homodimer, p66/p66. Reverse transcriptase assays and RT maturation assays were performed as tools to assess the effects of the PR dimer-interface on these functions. Our results consistently indicate that the RT dimer-interface plays a crucial role in the dimerization in PR-RT, whereas the PR dimer-interface has a lesser role.

Fullerenes, particularly C60, exhibit unique properties that make them promising candidates for various applications, including drug delivery and nanomedicine. However, their interactions with biomolecules, especially proteins, remain not fully understood. This study implements both explicit and implicit C60 models into the UNRES coarse-grained force field, enabling the investigation of fullerene-protein interactions without the need for restraints to stabilize protein structures. The UNRES force field offers computational efficiency, allowing for longer timescale simulations while maintaining accuracy. Five model proteins were studied: FK506 binding protein, HIV-1 protease, intestinal fatty acid binding protein, PCB-binding protein, and hen egg-white lysozyme. Molecular dynamics simulations were performed with and without C60 to assess protein stability and investigate the impact of fullerene interactions. Analysis of contact probabilities reveals distinct interaction patterns for each protein. FK506 binding protein (1FKF) shows specific binding sites, while intestinal fatty acid binding protein (1ICN) and uteroglobin (1UTR) exhibit more generalized interactions. The explicit C60 model shows good agreement with all-atom simulations in predicting protein flexibility, the position of C60 in the binding pocket, and the estimation of effective binding energies. The integration of explicit and implicit C60 models into the UNRES force field, coupled with recent advances in coarse-grained modeling and multiscale approaches, provides a powerful framework for investigating protein-nanoparticle interactions at biologically relevant scales without the need to use restraints stabilizing the protein, thus allowing for large conformational changes to occur. These computational tools, in synergy with experimental techniques, can aid in understanding the mechanisms and consequences of nanoparticle-biomolecule interactions, guiding the design of nanomaterials for biomedical applications.

HIV genotyping is used to assess HIV susceptibility to antiretroviral drugs. The Applied Biosystems HIV-1 Genotyping Kit with Integrase (AB kit, Thermo Fisher Scientific) detects resistance-associated mutations (RAMs) in HIV protease (PR), reverse transcriptase (RT), and integrase (IN). We compared results from the AB kit with results obtained previously with the ViroSeq HIV-1 Genotyping System. DNA amplicons from the AB kit were also analyzed using next-generation sequencing (NGS). HIV RNA was extracted using the MagNA Pure 24 instrument (Roche Diagnostics; 96 plasma samples, HIV subtype B, viral load range: 530-737,741 copies/mL). FASTA files were generated from AB kit data using Exatype (Hyrax Biosciences). DNA amplicons from the AB kit were also analyzed by NGS using the Nextera XT kit (Illumina). Drug resistance was predicted using the Stanford HIV Drug Resistance Database. The mean genetic distance for sequences from ViroSeq and the AB kit was 0.02% for PR/RT and 0.04% for IN; 103 major RAMs were detected by both methods. Four additional major RAMs were detected by the AB kit only. These four major RAMs were also detected by NGS (detected in 18.1%-38.2% of NGS reads). NGS detected 27 major RAMs that were not detected with either of the Sanger sequencing-based kits. All major RAMs detected with ViroSeq were detected with the AB kit; additional RAMs were detected with the AB kit only. DNA amplicons from the AB kit can be used for NGS for more sensitive detection of RAMs.