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.

Design of substituted tetrahydrofuran derivatives for HIV-1 protease inhibitors: synthesis, biological evaluation, and X-ray structural studies.

Substituted tetrahydrofuran derivatives were designed and synthesized to serve as the P2 ligand for a series of potent HIV-1 protease inhibitors. Both enantiomers of the tetrahydrofuran derivatives were synthesized stereoselectivity in optically active forms using lipase-PS catalyzed enzymatic resolution as the key step. These tetrahydrofuran derivatives are designed to promote hydrogen bonding and van der Waals interactions with the backbone atoms in the S2 subsite of the HIV-1 protease active site. Several inhibitors displayed very potent HIV-1 protease inhibitory activity. A high-resolution X-ray crystal structure of an inhibitor-bound HIV-1 protease provided important insight into the ligand binding site interactions in the active site.

Enthalpic Classification of Water Molecules in Target-Ligand Binding.

Water molecules play various roles in target-ligand binding. For example, they can be replaced by the ligand and leave the surface of the binding pocket or stay conserved in the interface and form bridges with the target. While experimental techniques supply target-ligand complex structures at an increasing rate, they often have limitations in the measurement of a detailed water structure. Moreover, measurements of binding thermodynamics cannot distinguish between the different roles of individual water molecules. However, such a distinction and classification of the role of individual water molecules would be key to their application in drug design at atomic resolution. In this study, we investigate a quantitative approach for the description of the role of water molecules during ligand binding. Starting from complete hydration structures of the free and ligand-bound target molecules, binding enthalpy scores are calculated for each water molecule using quantum mechanical calculations. A statistical evaluation showed that the scores can distinguish between conserved and displaced classes of water molecules. The classification system was calibrated and tested on more than 1000 individual water positions. The practical tests of the enthalpic classification included important cases of antiviral drug research on HIV-1 protease inhibitors and the Influenza A ion channel. The methodology of classification is based on open source program packages, Gromacs, Mopac, and MobyWat, freely available to the scientific community.

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.

Identification of steroidal cardenolides from Calotropis procera as novel HIV-1 PR inhibitors: A molecular docking & molecular dynamics simulation study.

Background & objectives Despite advancements in antiretroviral therapy, drug-resistant strains of HIV (human immunodeficiency virus) remain a global health concern. Natural compounds from medicinal plants offer a promising avenue for developing new HIV-1 PR (protease) inhibitors. This study aimed to explore the potential of compounds derived from Calotropis procera, a medicinal plant, as inhibitors of HIV-1 PR. Methods This in silico study utilized natural compound information and the crystal structure of HIV-1 PR. Molecular docking of 17 steroidal cardenolides from Calotropis procera against HIV-1 PR was performed using AutoDock 4.2 to identify compounds with higher antiviral potential. A dynamic simulation study was performed to provide insights into the stability, binding dynamics, and potential efficacy of the top potential antiviral compound as an HIV-1 therapeutic. Results We found that all tested cardenolides had higher binding affinities than Amprenavir, indicating their potential as potent HIV-1 PR inhibitors. Voruscharin and uscharidin displayed the strongest interactions, forming hydrogen bonds and hydrophobic interactions with HIV-1 PR. Voruscharin showed improved stability with lower RMSD (Root Mean Square Deviation) values and reduced fluctuations in binding site residues but increased flexibility in certain regions. The radius of gyration analysis confirmed a stable binding pose between HIV-1 PR and Voruscharin. Interpretation & conclusions These findings suggest that Calotropis procera could potentially be a source of compounds for developing novel HIV-1 PR inhibitors, contributing to the efforts to combat HIV. Further studies and clinical trials are needed to evaluate the safety and efficacy of these compounds as potential drug candidates for the treatment of HIV-1 infection.

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.

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.

A kind of HIV-1 protease inhibitors containing phenols with antiviral activity against DRV-resistant variants.

Based upon the preliminary design of enhancing genetic barrier to drug-resistant viral mutants by maximizing hydrogen-bonding or other van der Waals contacts, we have designed, synthesized and biologically evaluated a new class of HIV-1 protease inhibitors with phenol derived P2 ligands and nitro or halogens in P2' ligands. Results indicate that a majority of inhibitors exhibit robust enzyme inhibitory with IC50 values in picomolar or single digit nanomolar ranges. Among which, compound 17d displays potency with IC50 value of 21 pM and high protease selectivity. Of note, 17d exhibits greater antiviral activity against the DRV-resistant variant than the efficacy against the wild type virus. Furthermore, the molecular modeling studies demonstrate important interactions between 17d and the active sites of both the wild-type and DRV-resistant HIV-1 protease, as well as furnish insights for further optimization of new inhibitors.

Joint neutron/molecular dynamics vibrational spectroscopy reveals softening of HIV-1 protease upon binding of a tight inhibitor.

Biomacromolecules are inherently dynamic, and their dynamics are interwoven into function. The fast collective vibrational dynamics in proteins occurs in the low picosecond timescale corresponding to frequencies of ∼5-50 cm-1. This sub-to-low THz frequency regime covers the low-amplitude collective breathing motions of a whole protein and vibrations of the constituent secondary structure elements, such as α-helices, ß-sheets and loops. We have used inelastic neutron scattering experiments in combination with molecular dynamics simulations to demonstrate the vibrational dynamics softening of HIV-1 protease, a target of HIV/AIDS antivirals, upon binding of a tight clinical inhibitor darunavir. Changes in the vibrational density of states of matching structural elements in the two monomers of the homodimeric protein are not identical, indicating asymmetric effects of darunavir on the vibrational dynamics. Three of the 11 major secondary structure elements contribute over 40% to the overall changes in the vibrational density of states upon darunavir binding. Molecular dynamics simulations informed by experiments allowed us to estimate that the altered vibrational dynamics of the protease would contribute -3.6 kcal mol-1 at 300 K, or 25%, to the free energy of darunavir binding. As HIV-1 protease drug resistance remains a concern, our results open a new avenue to help establish a direct quantitative link between protein vibrational dynamics and drug resistance.

Mechanism of darunavir binding to monomeric HIV-1 protease: a step forward in the rational design of dimerization inhibitors.

HIV protease (HIVPR) is a key target in AIDS therapeutics. All ten FDA-approved drugs that compete with substrates in binding to this dimeric enzyme's active site have become ineffective due to the emergence of drug resistant mutants. Blocking the dimerization interface of HIVPR is thus being explored as an alternate strategy. The latest drug, darunavir (DRV), which exhibited a high genetic barrier to viral resistance, is said to have a dual mode of action - (i) binding to the dimeric active site, and (ii) preventing the dimerization by binding to the HIVPR monomer. Despite several reports on DRV complexation with dimeric HIVPR, the mode and mechanism of the binding of DRV to the HIVPR monomer are poorly understood. In this study, we utilized all-atomic MD simulations and umbrella sampling techniques to identify the best possible binding mode of DRV to the monomeric HIVPR and its mechanism of association. The results suggest that DRV binds between the active site and the flap of the monomer, and the flap plays a crucial role in directing the drug to bind and driving the other protein domains to undergo induced fit changes for stronger complexation. The obtained binding mode of DRV was validated by comparing with various mutational data from clinical isolates to reported in vitro mutations. The identified binding pose was also able to successfully reproduce the experimental Ki value in the picomolar range. The residue-level information extracted from this study could accelerate the structure-based drug designing approaches targeting HIVPR dimerization.

Elasticity-Associated Functionality and Inhibition of the HIV Protease.

HIV protease plays a critical role in the life cycle of the virus through the generation of mature and infectious virions. Detailed knowledge of the structure of the enzyme and its substrate has led to the development of protease inhibitors. However, the development of resistance to all currently available protease inhibitors has contributed greatly to the decreased success of antiretroviral therapy. When therapy failure occurs, multiple mutations are found within the protease sequence starting with primary mutations, which directly impact inhibitor binding, which can also negatively impact viral fitness and replicative capacity by decreasing the binding affinity of the natural substrates to the protease. As such, secondary mutations which are located outside of the active site region accumulate to compensate for the recurrently deleterious effects of primary mutations. However, the resistance mechanism of these secondary mutations is not well understood, but what is known is that these secondary mutations contribute to resistance in one of two ways, either through increasing the energetic penalty associated with bringing the protease into the closed conformation, or, through decreasing the stability of the protein/drug complex in a manner that increases the dissociation rate of the drug, leading to diminished inhibition. As a result, the elasticity of the enzyme-substrate complex has been implicated in the successful recognition and catalysis of the substrates which may be inferred to suggest that the elasticity of the enzyme/drug complex plays a role in resistance. A realistic representation of the dynamic nature of the protease may provide a more powerful tool in structure-based drug design algorithms.

Discovery of Novel HIV Protease Inhibitors Using Modern Computational Techniques.

The human immunodeficiency virus type 1 (HIV-1) has continued to be a global concern. With the new HIV incidence, the emergence of multi-drug resistance and the untoward side effects of currently used anti-HIV drugs, there is an urgent need to discover more efficient anti-HIV drugs. Modern computational tools have played vital roles in facilitating the drug discovery process. This research focuses on a pharmacophore-based similarity search to screen 111,566,735 unique compounds in the PubChem database to discover novel HIV-1 protease inhibitors (PIs). We used an in silico approach involving a 3D-similarity search, physicochemical and ADMET evaluations, HIV protease-inhibitor prediction (IC50/percent inhibition), rigid receptor-molecular docking studies, binding free energy calculations and molecular dynamics (MD) simulations. The 10 FDA-approved HIV PIs (saquinavir, lopinavir, ritonavir, amprenavir, fosamprenavir, atazanavir, nelfinavir, darunavir, tipranavir and indinavir) were used as reference. The in silico analysis revealed that fourteen out of the twenty-eight selected optimized hit molecules were within the acceptable range of all the parameters investigated. The hit molecules demonstrated significant binding affinity to the HIV protease (PR) when compared to the reference drugs. The important amino acid residues involved in hydrogen bonding and п-п stacked interactions include ASP25, GLY27, ASP29, ASP30 and ILE50. These interactions help to stabilize the optimized hit molecules in the active binding site of the HIV-1 PR (PDB ID: 2Q5K). HPS/002 and HPS/004 have been found to be most promising in terms of IC50/percent inhibition (90.15%) of HIV-1 PR, in addition to their drug metabolism and safety profile. These hit candidates should be investigated further as possible HIV-1 PIs with improved efficacy and low toxicity through in vitro experiments and clinical trial investigations.

Novel HIV PR inhibitors with C4-substituted bis-THF and bis-fluoro-benzyl target the two active site mutations of highly drug resistant mutant PRS17.

The emergence of multidrug resistant (MDR) HIV strains severely reduces the effectiveness of antiretroviral therapy. Clinical inhibitor darunavir (1) has picomolar binding affinity for HIV-1 protease (PR), however, drug resistant variants like PRS17 show poor inhibition by 1, despite the presence of only two mutated residues in the inhibitor-binding site. Antiviral inhibitors that target MDR proteases like PRS17 would be valuable as therapeutic agents. Inhibitors 2 and 3 derived from 1 through substitutions at P1, P2 and P2' positions exhibit 3.4- to 500-fold better inhibition than clinical inhibitors for PRS17 with the exception of amprenavir. Crystal structures of PRS17/2 and PRS17/3 reveal how these inhibitors target the two active site mutations of PRS17. The substituted methoxy P2 group of 2 forms new interactions with G48V mutation, while the modified bis-fluoro-benzyl P1 group of 3 forms a halogen interaction with V82S mutation, contributing to improved inhibition of PRS17.

Off-Target-Based Design of Selective HIV-1 PROTEASE Inhibitors.

The approval of the first HIV-1 protease inhibitors (HIV-1 PRIs) marked a fundamental step in the control of AIDS, and this class of agents still represents the mainstay therapy for this illness. Despite the undisputed benefits, the necessary lifelong treatment led to numerous severe side-effects (metabolic syndrome, hepatotoxicity, diabetes, etc.). The HIV-1 PRIs are capable of interacting with "secondary" targets (off-targets) characterized by different biological activities from that of HIV-1 protease. In this scenario, the in-silico techniques undoubtedly contributed to the design of new small molecules with well-fitting selectivity against the main target, analyzing possible undesirable interactions that are already in the early stages of the research process. The present work is focused on a new mixed-hierarchical, ligand-structure-based protocol, which is centered on an on/off-target approach, to identify the new selective inhibitors of HIV-1 PR. The use of the well-established, ligand-based tools available in the DRUDIT web platform, in combination with a conventional, structure-based molecular docking process, permitted to fast screen a large database of active molecules and to select a set of structure with optimal on/off-target profiles. Therefore, the method exposed herein, could represent a reliable help in the research of new selective targeted small molecules, permitting to design new agents without undesirable interactions.

Combining Molecular Dynamic Information and an Aspherical-Atom Data Bank in the Evaluation of the Electrostatic Interaction Energy in Multimeric Protein-Ligand Complex: A Case Study for HIV-1 Protease.

Computational analysis of protein-ligand interactions is of crucial importance for drug discovery. Assessment of ligand binding energy allows us to have a glimpse of the potential of a small organic molecule to be a ligand to the binding site of a protein target. Available scoring functions, such as in docking programs, all rely on equations that sum each type of protein-ligand interactions in order to predict the binding affinity. Most of the scoring functions consider electrostatic interactions involving the protein and the ligand. Electrostatic interactions constitute one of the most important part of total interactions between macromolecules. Unlike dispersion forces, they are highly directional and therefore dominate the nature of molecular packing in crystals and in biological complexes and contribute significantly to differences in inhibition strength among related enzyme inhibitors. In this study, complexes of HIV-1 protease with inhibitor molecules (JE-2147 and darunavir) were analyzed by using charge densities from the transferable aspherical-atom University at Buffalo Databank (UBDB). Moreover, we analyzed the electrostatic interaction energy for an ensemble of structures, using molecular dynamic simulations to highlight the main features of electrostatic interactions important for binding affinity.

Electrostatics Plays a Crucial Role in HIV-1 Protease Substrate Binding, Drugs Fail to Take Advantage.

HIV-1 protease (HIVPR) is an important drug target for combating AIDS. This enzyme is an aspartyl protease that is functionally active in its dimeric form. Nuclear magnetic resonance reports have convincingly shown that a pseudosymmetry exists at the HIVPR active site, where only one of the two aspartates remains protonated over the pH range of 2.5-7.0. To date, all HIVPR-targeted drug design strategies focused on maximizing the size-shape complementarity and van der Waals interactions of the small molecule drugs with the deprotonated, symmetric active site envelope of crystallized HIVPR. However, these strategies were ineffective with the emergence of drug resistant protease variants, primarily due to the steric clashes at the active site. In this study, we traced a specificity in the substrate binding motif that emerges primarily from the asymmetrical electrostatic potential present in the protease active site due to the uneven protonation. Our detailed results from atomistic molecular dynamics simulations show that while such a specific mode of substrate binding involves significant electrostatic interactions, none of the existing drugs or inhibitors could utilize this electrostatic hot spot. As the electrostatic is long-range interaction, it can provide sufficient binding strength without the necessity of increasing the bulkiness of the inhibitors. We propose that introducing the electrostatic component along with optimal fitting at the binding pocket could pave the way for promising designs that might be more effective against both wild type and HIVPR resistant variants.

Accessory mutations balance the marginal stability of the HIV-1 protease in drug resistance.

The HIV-1 protease is a major target of inhibitor drugs in AIDS therapies. The therapies are impaired by mutations of the HIV-1 protease that can lead to resistance to protease inhibitors. These mutations are classified into major mutations, which usually occur first and clearly reduce the susceptibility to protease inhibitors, and minor, accessory mutations that occur later and individually do not substantially affect the susceptibility to inhibitors. Major mutations are predominantly located in the active site of the HIV-1 protease and can directly interfere with inhibitor binding. Minor mutations, in contrast, are typically located distal to the active site. A central question is how these distal mutations contribute to resistance development. In this article, we present a systematic computational investigation of stability changes caused by major and minor mutations of the HIV-1 protease. As most small single-domain proteins, the HIV-1 protease is only marginally stable. Mutations that destabilize the folded, active state of the protease therefore can shift the conformational equilibrium towards the unfolded, inactive state. We find that the most frequent major mutations destabilize the HIV-1 protease, whereas roughly half of the frequent minor mutations are stabilizing. An analysis of protease sequences from patients in treatment indicates that the stabilizing minor mutations are frequently correlated with destabilizing major mutations, and that highly resistant HIV-1 proteases exhibit significant fractions of stabilizing mutations. Our results thus indicate a central role of minor mutations in balancing the marginal stability of the protease against the destabilization induced by the most frequent major mutations.

Conformational landscape of non-B variants of HIV-1 protease: A pulsed EPR study.

HIV infection is a global health epidemic with current FDA-approved HIV-1 Protease inhibitors (PIs) designed against subtype B protease, yet they are used in HIV treatment world-wide regardless of patient HIV classification. In this study, double electron-electron resonance (DEER) electron paramagnetic resonance (EPR) spectroscopy was utilized to gain insights in how natural polymorphisms in several African and Brazilian protease (PR) variants affect the conformational landscape both in the absence and presence of inhibitors. Findings show that Subtypes F and H HIV-1 PR adopt a primarily closed conformation in the unbound state with two secondary mutations, D60E and I62V, postulated to be responsible for the increased probability for closed conformation. In contrast, subtype D, CRF_AG, and CRF_BF HIV-1 PR adopt a primarily semi-open conformation, as observed for PI-naïve-subtype B when unbound by substrate or inhibitor. The impact that inhibitor binding has on shifting the conformational land scape of these variants is also characterized, where analysis provides classification of inhibitor induced shifts away from the semi-open state into weak, moderate and strong effects. The findings are compared to those for prior studies of inhibitor induced conformational shifts in PI-naïve Subtype B, C and CRF_AE.