Exact reaction coordinates for flap opening in HIV-1 protease.

The primary goal of protein science is to understand how proteins function, which requires understanding the functional dynamics responsible for transitions between different functional structures of a protein. A central concept is the exact reaction coordinates that can determine the value of committor for any protein configuration, which provide the optimal description of functional dynamics. Despite intensive efforts, identifying the exact reaction coordinates (RCs) in complex molecules remains a formidable challenge. Using the recently developed generalized work functional, we report the discovery of the exact RCs for an important functional process-the flap opening of HIV-1 protease. Our results show that this process has six RCs, each one is a linear combination of ~240 backbone dihedrals, providing the precise definition of collectivity and cooperativity in the functional dynamics of a protein. Applying bias potentials along each RC can accelerate flap opening by [Formula: see text] to [Formula: see text] folds. The success in identifying the RCs of a protein with 198 residues represents a significant progress beyond that of the alanine dipeptide, currently the only other complex molecule for which the exact RCs for its conformational changes are known. Our results suggest that the generalized work functional (GWF) might be the fundamental operator of mechanics that controls protein dynamics.

Preclinical characterization of a non-peptidomimetic HIV protease inhibitor with improved metabolic stability.

Protease inhibitors (PIs) remain an important component of antiretroviral therapy for the treatment of HIV-1 infection due to their high genetic barrier to resistance development. Nevertheless, the two most commonly prescribed HIV PIs, atazanavir and darunavir, still require co-administration with a pharmacokinetic boosting agent to maintain sufficient drug plasma levels which can lead to undesirable drug-drug interactions. Herein, we describe GS-9770, a novel investigational non-peptidomimetic HIV PI with unboosted once-daily oral dosing potential due to improvements in its metabolic stability and its pharmacokinetic properties in preclinical animal species. This compound demonstrates potent inhibitory activity and high on-target selectivity for recombinant HIV-1 protease versus other aspartic proteases tested. In cell culture, GS-9770 inhibits Gag polyprotein cleavage and shows nanomolar anti-HIV-1 potency in primary human cells permissive to HIV-1 infection and against a broad range of HIV subtypes. GS-9770 demonstrates an improved resistance profile against a panel of patient-derived HIV-1 isolates with resistance to atazanavir and darunavir. In resistance selection experiments, GS-9770 prevented the emergence of breakthrough HIV-1 variants at all fixed drug concentrations tested and required multiple protease substitutions to enable outgrowth of virus exposed to escalating concentrations of GS-9770. This compound also remained fully active against viruses resistant to drugs from other antiviral classes and showed no in vitro antagonism when combined pairwise with drugs from other antiretroviral classes. Collectively, these preclinical data identify GS-9770 as a potent, non-peptidomimetic once-daily oral HIV PI with potential to overcome the persistent requirement for pharmacological boosting with this class of antiretroviral agents.

Machine learning-guided design of potent darunavir analogs targeting HIV-1 proteases: A computational approach for antiretroviral drug discovery.

In the pursuit of novel antiretroviral therapies for human immunodeficiency virus type-1 (HIV-1) proteases (PRs), recent improvements in drug discovery have embraced machine learning (ML) techniques to guide the design process. This study employs ensemble learning models to identify crucial substructures as significant features for drug development. Using molecular docking techniques, a collection of 160 darunavir (DRV) analogs was designed based on these key substructures and subsequently screened using molecular docking techniques. Chemical structures with high fitness scores were selected, combined, and one-dimensional (1D) screening based on beyond Lipinski's rule of five (bRo5) and ADME (absorption, distribution, metabolism, and excretion) prediction implemented in the Combined Analog generator Tool (CAT) program. A total of 473 screened analogs were subjected to docking analysis through convolutional neural networks scoring function against both the wild-type (WT) and 12 major mutated PRs. DRV analogs with negative changes in binding free energy ( ΔΔ G bind ) compared to DRV could be categorized into four attractive groups based on their interactions with the majority of vital PRs. The analysis of interaction profiles revealed that potent designed analogs, targeting both WT and mutant PRs, exhibited interactions with common key amino acid residues. This observation further confirms that the ML model-guided approach effectively identified the substructures that play a crucial role in potent analogs. It is expected to function as a powerful computational tool, offering valuable guidance in the identification of chemical substructures for synthesis and subsequent experimental testing.

Performance of the Applied Biosystems HIV-1 Genotyping Kit with Integrase.

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.

HIV-1 reverse transcriptase stability correlates with Gag cleavage efficiency: reverse transcriptase interaction implications for modulating protease activation.

Proteolytic processing of human immunodeficiency virus type 1 particles mediated by viral protease (PR) is essential for acquiring virus infectivity. Activation of PR embedded in Gag-Pol is triggered by Gag-Pol dimerization during virus assembly. We previously reported that amino acid substitutions at the RT tryptophan repeat motif destabilize virus-associated RT and attenuate the ability of efavirenz (EFV, an RT dimerization enhancer) to increase PR-mediated Gag cleavage efficiency. Furthermore, a single amino acid change at RT significantly reduces virus yields due to enhanced Gag cleavage. These data raise the possibility of the RT domain contributing to PR activation by promoting Gag-Pol dimerization. To test this hypothesis, we investigated the putative involvement of a hydrophobic leucine repeat motif (LRM) spanning RT L282 to L310 in RT/RT interactions. We found that LRM amino acid substitutions led to RT instability and that RT is consequently susceptible to degradation by PR. The LRM mutants exhibited reduced Gag cleavage efficiencies while attenuating the EFV enhancement of Gag cleavage. In addition, an RT dimerization-defective mutant, W401A, reduced enhanced Gag cleavage via a leucine zipper (LZ) motif inserted at the deleted Gag-Pol region. Importantly, the presence of RT and integrase domains failed to counteract the LZ enhancement of Gag cleavage. A combination of the Gag cleavage enhancement factors EFV and W402A markedly impaired Gag cleavage, indicating a disruption of W402A Gag-Pol dimerization following EFV binding to W402A Gag-Pol. Our results support the idea that RT modulates PR activation by affecting Gag-Pol/Gag-Pol interaction. IMPORTANCE A stable reverse transcriptase (RT) p66/51 heterodimer is required for HIV-1 genome replication in host cells following virus entry. The activation of viral protease (PR) to mediate virus particle processing helps viruses acquire infectivity following cell release. RT and PR both appear to be major targets for inhibiting HIV-1 replication. We found a strong correlation between impaired p66/51RT stability and deficient PR-mediated Gag cleavage, suggesting that RT/RT interaction is critical for triggering PR activation via the promotion of adequate Gag-Pol dimerization. Accordingly, RT/RT interaction is a potentially advantageous method for anti-HIV/AIDS therapy if it is found to simultaneously block PR and RT enzymatic activity.

Rational design, synthesis and biological evaluation of novel HIV-1 protease inhibitors containing 2-phenylacetamide derivatives as P2 ligands with potent activity against DRV-Resistant HIV-1 variants.

A novel kind of potent HIV-1 protease inhibitors, containing diverse hydroxyphenylacetic acids as the P2-ligands and 4-substituted phenyl sulfonamides as the P2' ligands, were designed, synthesized and evaluated in this work. Majority of the target compounds exhibited good to excellent activity against HIV-1 protease with IC50 values below 200 nM. In particular, compound 18d with a 2-(3,4-dihydroxyphenyl) acetamide as the P2 ligand and a 4- methoxybenzene sulfonamide P2' ligand exhibited inhibitory activity IC50 value of 0.54 nM, which was better than that of the positive control darunavir (DRV). More importantly, no significant decline of the potency against HIV-1DRVRS (DRV-resistant mutation) and HIV-1NL4_3 variant (wild type) for 18d was detected. The molecular docking study of 18d with HIV-1 protease (PDB-ID: 1T3R, www.rcsb.org) revealed possible binding mode with the HIV-1 protease. These results suggested the validity of introducing phenol-derived moieties into the P2 ligand and deserve further optimization which was of great value for future discovery of novel HIV-1 protease.

HIV protease resistance mutations in patients receiving second-line antiretroviral therapy in Libreville, Gabon.

INTRODUCTION: In 2022, the WHO reported that 29.8 million people around the world were living with HIV (PLHIV) and receiving antiretroviral treatment (ART), including 25| 375 people in Gabon (54% of all those living with HIV in the country). The literature reports a frequency of therapeutic failure with first-line antiretrovirals (ARVs) of between 20% and 82%. Unfortunately, data relating to the failure of second-line ARVs are scarce in Gabon. This study aims to determine the profiles of HIV drug resistance mutations related to protease inhibitors in Gabon. METHODOLOGY: Plasma from 84 PLHIV receiving ARVs was collected from 2019 to 2021, followed by RNA extraction, amplification, and sequencing of the protease gene. ARV resistance profiles were generated using the Stanford interpretation algorithm version 8.9-1 ( https://hivdb.stanford.edu ) and statistical analyses were performed using EpiInfo software version 7.2.1.0 (CDC, USA). RESULTS: Of 84 HIV plasma samples collected from 45 men and 39 women, 342 mutations were detected. Of these, 43.3% (148/342) were associated with nucleoside reverse transcriptase inhibitors (NRTIs), 30.4% (104/342) with non-nucleoside reverse transcriptase inhibitors (NNRTIs), and 26.3% (90/342) with protease inhibitors (PIs). Most NRTI mutations were associated with thymidine analogues (TAMs) (50.7%; 75/148), including T215F/V (14.9%; 22/148), D67DN/E/G/N/T (10.1%; 15/148), M41L (9.5%; 14/148), and K70E/KN/S/R (9.5%; 14/148). Resistance mutations related to non-TAM NRTIs (33.1%; 49/148) were M184V (29.1%; 43/148), and L74I/V (8.1%; 12/148). NNRTI mutations were predominantly K103N/S (32.7%; 34/104), V108I (10.6%; 11/104), A98G (10.6%; 11/104), and P225H (9.6%; 10/104). Minor mutations associated with PIs (60.0%; 54/90) were predominantly K20I (15.6%; 14/90) and L10F/I/V (14.5%; 13/90). The major mutations associated with PIs (40.0%; 36/90) were M41L (12.2%; 11/90), I84V (6.7%; 06/90), and V82A (6.7%; 06/90). The four most prescribed therapeutic regimens were TDF + 3TC + LPV/r (20.3%; 17/84), ABC + DDI + LPV/r (17.9%; 15/84), TDF + FTC + LPV/r (11.9%; 10/84), and ABC + 3TC + LPV/r (11.9%; 10/84). CONCLUSION: This study revealed that HIV drug resistance mutations are common in Gabon. The major mutations associated with PIs were M41L, I84V, and V82A. There is a need for access to new NRTIs, NNRTIs, and PIs for a better therapeutic management of PLHIV in Gabon.

Advanced molecular mechanisms of modified DRV compounds in targeting HIV-1 protease mutations and interrupting monomer dimerization.

HIV-1 protease (PR) plays a crucial role in the treatment of HIV as a key target. The global issue of emerging drug resistance is escalating, and PR mutations pose a substantial challenge to the effectiveness of inhibitors. HIV-1 PR is an ideal model for studying drug resistance to inhibitors. The inhibitor, darunavir (DRV), exhibits a high genetic barrier to viral resistance, but with mutations of residues in the PR, there is also some resistance to DRV. Inhibitors can impede PR in two ways: one involves binding to the active site of the dimerization protease, and the other involves binding to the PR monomer, thereby preventing dimerization. In this study, we aimed to investigate the inhibitory effect of DRV with a modified inhibitor on PR, comparing the differences between wild-type and mutated PR, using molecular dynamics simulations. The inhibitory effect of the inhibitors on PR monomers was subsequently investigated. And molecular mechanics Poisson-Boltzmann surface area evaluated the binding free energy. The energy contribution of individual residues in the complex was accurately calculated by the alanine scanning binding interaction entropy method. The results showed that these inhibitors had strong inhibitory effects against PR mutations, with GRL-142 exhibiting potent inhibition of both the PR monomer and dimer. Improved inhibitors could strengthen hydrogen bonds and interactions with PR, thereby boosting inhibition efficacy. The binding of the inhibitor and mutation of the PR affected the distance between D25 and I50, preventing their dimerization and the development of drug resistance. This study could accelerate research targeting HIV-1 PR inhibitors and help to further facilitate drug design targeting both mechanisms.

Modeling and Analysis of HIV-1 Pol Polyprotein as a Case Study for Predicting Large Polyprotein Structures.

Acquired immunodeficiency syndrome (AIDS) is caused by human immunodeficiency virus (HIV). HIV protease, reverse transcriptase, and integrase are targets of current drugs to treat the disease. However, anti-viral drug-resistant strains have emerged quickly due to the high mutation rate of the virus, leading to the demand for the development of new drugs. One attractive target is Gag-Pol polyprotein, which plays a key role in the life cycle of HIV. Recently, we found that a combination of M50I and V151I mutations in HIV-1 integrase can suppress virus release and inhibit the initiation of Gag-Pol autoprocessing and maturation without interfering with the dimerization of Gag-Pol. Additional mutations in integrase or RNase H domain in reverse transcriptase can compensate for the defect. However, the molecular mechanism is unknown. There is no tertiary structure of the full-length HIV-1 Pol protein available for further study. Therefore, we developed a workflow to predict the tertiary structure of HIV-1 NL4.3 Pol polyprotein. The modeled structure has comparable quality compared with the recently published partial HIV-1 Pol structure (PDB ID: 7SJX). Our HIV-1 NL4.3 Pol dimer model is the first full-length Pol tertiary structure. It can provide a structural platform for studying the autoprocessing mechanism of HIV-1 Pol and for developing new potent drugs. Moreover, the workflow can be used to predict other large protein structures that cannot be resolved via conventional experimental methods.

Integrating Explicit and Implicit Fullerene Models into UNRES Force Field for Protein Interaction Studies.

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.

The HIV-1 Viral Protease Is Activated during Assembly and Budding Prior to Particle Release.

HIV-1 encodes a viral protease that is essential for the maturation of infectious viral particles. While protease inhibitors are effective antiretroviral agents, recent studies have shown that prematurely activating, rather than inhibiting, protease function leads to the pyroptotic death of infected cells, with exciting implications for efforts to eradicate viral reservoirs. Despite 40 years of research into the kinetics of protease activation, it remains unclear exactly when protease becomes activated. Recent reports have estimated that protease activation occurs minutes to hours after viral release, suggesting that premature protease activation is challenging to induce efficiently. Here, monitoring viral protease activity with sensitive techniques, including nanoscale flow cytometry and instant structured illumination microscopy, we demonstrate that the viral protease is activated within cells prior to the release of free virions. Using genetic mutants that lock protease into a precursor conformation, we further show that both the precursor and mature protease have rapid activation kinetics and that the activity of the precursor protease is sufficient for viral fusion with target cells. Our finding that HIV-1 protease is activated within producer cells prior to release of free virions helps resolve a long-standing question of when protease is activated and suggests that only a modest acceleration of protease activation kinetics is required to induce potent and specific elimination of HIV-infected cells. IMPORTANCE HIV-1 protease inhibitors have been a mainstay of antiretroviral therapy for more than 2 decades. Although antiretroviral therapy is effective at controlling HIV-1 replication, persistent reservoirs of latently infected cells quickly reestablish replication if therapy is halted. A promising new strategy to eradicate the latent reservoir involves prematurely activating the viral protease, which leads to the pyroptotic killing of infected cells. Here, we use highly sensitive techniques to examine the kinetics of protease activation during and shortly after particle formation. We found that protease is fully activated before virus is released from the cell membrane, which is hours earlier than recent estimates. Our findings help resolve a long-standing debate as to when the viral protease is initially activated during viral assembly and confirm that prematurely activating HIV-1 protease is a viable strategy to eradicate infected cells following latency reversal.

HIV-1 drug resistance profiling using amino acid sequence space cartography.

MOTIVATION: Human immunodeficiency virus (HIV) drug resistance is a global healthcare issue. The emergence of drug resistance influenced the efficacy of treatment regimens, thus stressing the importance of treatment adaptation. Computational methods predicting the drug resistance profile from genomic data of HIV isolates are advantageous for monitoring drug resistance in patients. However, existing computational methods for drug resistance prediction are either not suitable for emerging HIV strains with complex mutational patterns or lack interpretability, which is of paramount importance in clinical practice. The approach reported here overcomes these limitations and combines high accuracy of predictions and interpretability of the models. RESULTS: In this work, a new methodology based on generative topographic mapping (GTM) for biological sequence space representation and quantitative genotype-phenotype relationships prediction purposes was introduced. The GTM-based resistance landscapes allowed us to predict the resistance of HIV strains based on sequencing and drug resistance data for three viral proteins [integrase (IN), protease (PR) and reverse transcriptase (RT)] from Stanford HIV drug resistance database. The average balanced accuracy for PR inhibitors was 0.89 ± 0.01, for IN inhibitors 0.85 ± 0.01, for non-nucleoside RT inhibitors 0.73 ± 0.01 and for nucleoside RT inhibitors 0.84 ± 0.01. We have demonstrated in several case studies that GTM-based resistance landscapes are useful for visualization and analysis of sequence space as well as for treatment optimization purposes. Here, GTMs were applied for the in-depth analysis of the relationships between mutation pattern and drug resistance using mutation landscapes. This allowed us to predict retrospectively the importance of the presence of particular mutations (e.g. V32I, L10F and L33F in HIV PR) for the resistance development. This study highlights some perspectives of GTM applications in clinical informatics and particularly in the field of sequence space exploration. AVAILABILITY AND IMPLEMENTATION: https://github.com/karinapikalyova/ISIDASeq. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Evaluation of darunavir-derived HIV-1 protease inhibitors incorporating P2' amide-derivatives: Synthesis, biological evaluation and structural studies.

We report here the synthesis and biological evaluation of darunavir derived HIV-1 protease inhibitors and their functional effect on enzyme inhibition and antiviral activity in MT-2 cell lines. The P2' 4-amino functionality was modified to make a number of amide derivatives to interact with residues in the S2' subsite of the HIV-1 protease active site. Several compounds exhibited picomolar enzyme inhibitory and low nanomolar antiviral activity. The X-ray crystal structure of the chloroacetate derivative bound to HIV-1 protease was determined. Interestingly, the active chloroacetate group converted to the acetate functionality during X-ray exposure. The structure revealed that the P2' carboxamide functionality makes enhanced hydrogen bonding interactions with the backbone atoms in the S2'-subsite.

How to Compute Atomistic Insight in DFT Clusters: The REG-IQA Approach.

The relative energy gradient (REG) method is paired with the topological energy partitioning method interacting quantum atoms (IQA), as REG-IQA, to provide detailed and unbiased knowledge on the intra- and interatomic interactions. REG operates on a sequence of geometries representing a dynamical change of a system. Its recent application to peptide hydrolysis of the human immunodeficiency virus-1 (HIV-1) protease (PDB code: 4HVP) has demonstrated its full potential in recovering reaction mechanisms and through-space electrostatic and exchange-correlation effects, making it a compelling tool for analyzing enzymatic reactions. In this study, the computational efficiency of the REG-IQA method for the 133-atom HIV-1 protease quantum mechanical system is analyzed in every detail and substantially improved by means of three different approaches. The first approach of smaller integration grids for IQA integrations reduces the computational overhead by about a factor of 3. The second approach uses the line-simplification Ramer-Douglas-Peucker (RDP) algorithm, which outputs the minimal number of geometries necessary for the REG-IQA analysis for a predetermined root mean squared error (RMSE) tolerance. This cuts the computational time of the whole REG analysis by a factor of 2 if an RMSE of 0.5 kJ/mol is considered. The third approach consists of a "biased" or "unbiased" selection of a specific subset of atoms of the whole initial quantum mechanical model wave-function, which results in more than a 10-fold speed-up per geometry for the IQA calculation, without deterioration of the outcome of the REG-IQA analysis. Finally, to show the capability of these approaches, the findings gathered from the HIV-1 protease system are also applied to a different system named haloalcohol dehalogenase (HheC). In summary, this study takes the REG-IQA method to a computationally feasible and highly accurate level, making it viable for the analysis of a multitude of enzymatic systems.

Evolving Mutational Buildup in HIV-1 Protease Shifts Conformational Dynamics to Gain Drug Resistance.

Drug resistance in antiviral treatments is a serious public health problem. Viral proteins mutate very fast, giving them a way to escape drugs by lowering drug binding affinity but with compromised function. Human immunodeficiency virus type I (HIV-1) protease, a critical antiretroviral therapeutic target, represents a model for such viral regulation under inhibition. Drug inhibitors of HIV-1 protease lose effectiveness as the protein evolves through several variants to become more resistant. However, the detailed mechanism of drug resistance in HIV-1 protease is still unclear. Here, we test the hypothesis that mutations throughout the protease alter the protein conformational ensemble to weaken protein-inhibitor binding, resulting in an inefficient protease but still viable virus. Comparing conformational ensembles between variants and the wild type helps detect these function-related dynamical changes. All analyses of over 30 µs simulations converge to the conclusion that conformational dynamics of more drug-resistant variants are more different from that of the wild type. Distinct roles of mutations during viral evolution are discussed, including a mutation predominantly contributing to the increase of drug resistance and a mutation that is responsible (synergistically) for restoring catalytic efficiency. Drug resistance is mainly due to altered flap dynamics that hinder the access to the active site. The mutant variant showing the highest drug resistance has the most ″collapsed″ active-site pocket and hence the largest magnitude of hindrance of drug binding. An enhanced difference contact network community analysis is applied to understand allosteric communications. The method summarizes multiple conformational ensembles in one community network and can be used in future studies to detect function-related dynamics in proteins.

Co-evolution of drug resistance and broadened substrate recognition in HIV protease variants isolated from an Escherichia coli genetic selection system.

A genetic selection system for activity of HIV protease is described that is based on a synthetic substrate constructed as a modified AraC regulatory protein that when cleaved stimulate l-arabinose metabolism in an Escherichia coli araC strain. Growth stimulation on selective plates was shown to depend on active HIV protease and the scissile bond in the substrate. In addition, the growth of cells correlated well with the established cleavage efficiency of the sites in the viral polyprotein, Gag, when these sites were individually introduced into the synthetic substrate of the selection system. Plasmids encoding protease variants selected based on stimulation of cell growth in the presence of saquinavir or cleavage of a site not cleaved by wild-type protease, were indistinguishable with respect to both phenotypes. Also, both groups of selected plasmids encoded side chain substitutions known from clinical isolates or displayed different side chain substitutions but at identical positions. One highly frequent side chain substitution, E34V, not regarded as a major drug resistance substitution was found in variants obtained under both selective conditions and is suggested to improve protease processing of the synthetic substrate. This substitution is away from the substrate-binding cavity and together with other substitutions in the selected reading frames supports the previous suggestion of a substrate-binding site extended from the active site binding pocket itself.

Predominance of positive epistasis among drug resistance-associated mutations in HIV-1 protease.

Drug-resistant mutations often have deleterious impacts on replication fitness, posing a fitness cost that can only be overcome by compensatory mutations. However, the role of fitness cost in the evolution of drug resistance has often been overlooked in clinical studies or in vitro selection experiments, as these observations only capture the outcome of drug selection. In this study, we systematically profile the fitness landscape of resistance-associated sites in HIV-1 protease using deep mutational scanning. We construct a mutant library covering combinations of mutations at 11 sites in HIV-1 protease, all of which are associated with resistance to protease inhibitors in clinic. Using deep sequencing, we quantify the fitness of thousands of HIV-1 protease mutants after multiple cycles of replication in human T cells. Although the majority of resistance-associated mutations have deleterious effects on viral replication, we find that epistasis among resistance-associated mutations is predominantly positive. Furthermore, our fitness data are consistent with genetic interactions inferred directly from HIV sequence data of patients. Fitness valleys formed by strong positive epistasis reduce the likelihood of reversal of drug resistance mutations. Overall, our results support the view that strong compensatory effects are involved in the emergence of clinically observed resistance mutations and provide insights to understanding fitness barriers in the evolution and reversion of drug resistance.

Effectively predicting HIV-1 protease cleavage sites by using an ensemble learning approach.

BACKGROUND: The site information of substrates that can be cleaved by human immunodeficiency virus 1 proteases (HIV-1 PRs) is of great significance for designing effective inhibitors against HIV-1 viruses. A variety of machine learning-based algorithms have been developed to predict HIV-1 PR cleavage sites by extracting relevant features from substrate sequences. However, only relying on the sequence information is not sufficient to ensure a promising performance due to the uncertainty in the way of separating the datasets used for training and testing. Moreover, the existence of noisy data, i.e., false positive and false negative cleavage sites, could negatively influence the accuracy performance. RESULTS: In this work, an ensemble learning algorithm for predicting HIV-1 PR cleavage sites, namely EM-HIV, is proposed by training a set of weak learners, i.e., biased support vector machine classifiers, with the asymmetric bagging strategy. By doing so, the impact of data imbalance and noisy data can thus be alleviated. Besides, in order to make full use of substrate sequences, the features used by EM-HIV are collected from three different coding schemes, including amino acid identities, chemical properties and variable-length coevolutionary patterns, for the purpose of constructing more relevant feature vectors of octamers. Experiment results on three independent benchmark datasets demonstrate that EM-HIV outperforms state-of-the-art prediction algorithm in terms of several evaluation metrics. Hence, EM-HIV can be regarded as a useful tool to accurately predict HIV-1 PR cleavage sites.

Prediction of HIV-1 protease cleavage site from octapeptide sequence information using selected classifiers and hybrid descriptors.

BACKGROUND: In most parts of the world, especially in underdeveloped countries, acquired immunodeficiency syndrome (AIDS) still remains a major cause of death, disability, and unfavorable economic outcomes. This has necessitated intensive research to develop effective therapeutic agents for the treatment of human immunodeficiency virus (HIV) infection, which is responsible for AIDS. Peptide cleavage by HIV-1 protease is an essential step in the replication of HIV-1. Thus, correct and timely prediction of the cleavage site of HIV-1 protease can significantly speed up and optimize the drug discovery process of novel HIV-1 protease inhibitors. In this work, we built and compared the performance of selected machine learning models for the prediction of HIV-1 protease cleavage site utilizing a hybrid of octapeptide sequence information comprising bond composition, amino acid binary profile (AABP), and physicochemical properties as numerical descriptors serving as input variables for some selected machine learning algorithms. Our work differs from antecedent studies exploring the same subject in the combination of octapeptide descriptors and method used. Instead of using various subsets of the dataset for training and testing the models, we combined the dataset, applied a 3-way data split, and then used a "stratified" 10-fold cross-validation technique alongside the testing set to evaluate the models. RESULTS: Among the 8 models evaluated in the "stratified" 10-fold CV experiment, logistic regression, multi-layer perceptron classifier, linear discriminant analysis, gradient boosting classifier, Naive Bayes classifier, and decision tree classifier with AUC, F-score, and B. Acc. scores in the ranges of 0.91-0.96, 0.81-0.88, and 80.1-86.4%, respectively, have the closest predictive performance to the state-of-the-art model (AUC 0.96, F-score 0.80 and B. Acc. ~ 80.0%). Whereas, the perceptron classifier and the K-nearest neighbors had statistically lower performance (AUC 0.77-0.82, F-score 0.53-0.69, and B. Acc. 60.0-68.5%) at p < 0.05. On the other hand, logistic regression, and multi-layer perceptron classifier (AUC of 0.97, F-score > 0.89, and B. Acc. > 90.0%) had the best performance on further evaluation on the testing set, though linear discriminant analysis, gradient boosting classifier, and Naive Bayes classifier equally performed well (AUC > 0.94, F-score > 0.87, and B. Acc. > 86.0%). CONCLUSIONS: Logistic regression and multi-layer perceptron classifiers have comparable predictive performances to the state-of-the-art model when octapeptide sequence descriptors consisting of AABP, bond composition and standard physicochemical properties are used as input variables. In our future work, we hope to develop a standalone software for HIV-1 protease cleavage site prediction utilizing the linear regression algorithm and the aforementioned octapeptide sequence descriptors.

The denatured state of HIV-1 protease under native conditions.

The denatured state of several proteins has been shown to display transient structures that are relevant for folding, stability, and aggregation. To detect them by nuclear magnetic resonance (NMR) spectroscopy, the denatured state must be stabilized by chemical agents or changes in temperature. This makes the environment different from that experienced in biologically relevant processes. Using high-resolution heteronuclear NMR spectroscopy, we have characterized several denatured states of a monomeric variant of HIV-1 protease, which is natively structured in water, induced by different concentrations of urea, guanidinium chloride, and acetic acid. We have extrapolated the chemical shifts and the relaxation parameters to the denaturant-free denatured state at native conditions, showing that they converge to the same values. Subsequently, we characterized the conformational properties of this biologically relevant denatured state under native conditions by advanced molecular dynamics simulations and validated the results by comparison to experimental data. We show that the denatured state of HIV-1 protease under native conditions displays rich patterns of transient native and non-native structures, which could be of relevance to its guidance through a complex folding process.