Interplay between protease and reverse transcriptase dimerization in a model HIV-1 polyprotein.

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.

LSTM-driven drug design using SELFIES for target-focused de novo generation of HIV-1 protease inhibitor candidates for AIDS treatment.

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.

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.

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.

Towards designing of a potential new HIV-1 protease inhibitor using QSAR study in combination with Molecular docking and Molecular dynamics simulations.

Human Immunodeficiency Virus type 1 protease (HIV-1 PR) is one of the most challenging targets of antiretroviral therapy used in the treatment of AIDS-infected people. The performance of protease inhibitors (PIs) is limited by the development of protease mutations that can promote resistance to the treatment. The current study was carried out using statistics and bioinformatics tools. A series of thirty-three compounds with known enzymatic inhibitory activities against HIV-1 protease was used in this paper to build a mathematical model relating the structure to the biological activity. These compounds were designed by software; their descriptors were computed using various tools, such as Gaussian, Chem3D, ChemSketch and MarvinSketch. Computational methods generated the best model based on its statistical parameters. The model's applicability domain (AD) was elaborated. Furthermore, one compound has been proposed as efficient against HIV-1 protease with comparable biological activity to the existing ones; this drug candidate was evaluated using ADMET properties and Lipinski's rule. Molecular Docking performed on Wild Type, and Mutant Type HIV-1 proteases allowed the investigation of the interaction types displayed between the proteases and the ligands, Darunavir (DRV) and the new drug (ND). Molecular dynamics simulation was also used in order to investigate the complexes' stability allowing a comparative study on the performance of both ligands (DRV & ND). Our study suggested that the new molecule showed comparable results to that of darunavir and maybe used for further experimental studies. Our study may also be used as pipeline to search and design new potential inhibitors of HIV-1 proteases.

Understanding the impact of halogen functional group (Br, Cl, F, OH) in amprenavir ligand of the HIV protease.

We focused our attention towards the most dreadful disease that threatens the mankind of 20th century - Acquired immunodeficiency syndrome (AIDS), caused through the human immunodeficiency virus (HIV) and a sexually transmitted infection (STI). In this study, our foremost interest was to identify the potency and stability of HIV ligand- Amprenavir (APV) and its modelled functional group (Br, Cl, F, CF3, CH3, NH2) ligands through halogen and hydrogen bond contact, which will have a clear portrait on the structure activity of protein ligand interactions. This will assist chemist in synthesizing novel APV ligands, which are expected to inhibit the activity of HIV-1 protease enzyme. The binding strength of Amprenavir ligand with interacting hinge region amino acid side chains: Isoleucine (ILE 147, 150, 184), Valine (VAL 82), Alanine (ALA 28), Aspartic acid (25, 30, 125, 130) and Glycine (GLY 127, 149) were understood through interaction energy calculations at HF, B3LYP, M052X, MP2 level of theories for different basis set (6-311 G**, LANL2DZ). The present work will reveal an understandable picture about the halogen and hydrogen bond interaction that grip the contact of ligand and amino acids in the hinge region. Overall the Halogen atom (Br, Cl, F) functional groups improved the binding strength of APV in HIV protease; which provide a new novel path for the functional group preference on the ligand that enclose perfectly with the amino acid in the hinge region.Communicated by Ramaswamy H. Sarma.

Revealing the drug resistance mechanism of saquinavir due to G48V and V82F mutations in subtype CRF01_AE HIV-1 protease: molecular dynamics simulation and binding free energy calculations.

Human immunodeficiency virus-1 (HIV-1) protease is one of the important targets in AIDS therapy. The majority of HIV infections are caused due to non-B subtypes in developing countries. The co-occurrence of mutations along with naturally occurring polymorphisms in HIV-1 protease cause resistance to the FDA approved drugs, thereby posing a major challenge in the treatment of antiretroviral therapy. In this work, the resistance mechanism against SQV due to active site mutations G48V and V82F in CRF01_AE (AE) protease was explored. The binding free energy calculations showed that the direct substitution of valine at position 48 introduces a bulkier side chain, directly impairing the interaction with SQV in the binding pocket. Also, the intramolecular hydrogen bonding network of the neighboring residues is altered, indirectly affecting the binding of SQV. Interestingly, the substitution of phenylalanine at position 82 induces conformational changes in the 80's loop and the flap region, thereby favoring the binding of SQV. The V82F mutant structure also maintains similar intramolecular hydrogen bond interactions as observed in AE-WT.Communicated by Ramaswamy H. Sarma.

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.

Beyond darunavir: recent development of next generation HIV-1 protease inhibitors to combat drug resistance.

We report our recent development of a conceptually new generation of exceptionally potent non-peptidic HIV-1 protease inhibitors that displayed excellent pharmacological and drug-resistance profiles. Our X-ray structural studies of darunavir and other designed inhibitors from our laboratories led us to create a variety of inhibitors incorporating fused ring polycyclic ethers and aromatic heterocycles to promote hydrogen bonding interactions with the backbone atoms of HIV-1 protease as well as van der Waals interactions with residues in the S2 and S2' subsites. We have also incorporated specific functionalities to enhance van der Waals interactions in the S1 and S1' subsites. The combined effects of these structural templates are critical to the inhibitors' exceptional potency and drug-like properties. We highlight here our molecular design strategies to promote backbone hydrogen bonding interactions to combat drug-resistance and specific design of polycyclic ether templates to mimic peptide-like bonds in the HIV-1 protease active site. Our medicinal chemistry and drug development efforts led to the development of new generation inhibitors significantly improved over darunavir and displaying unprecedented antiviral activity against multidrug-resistant HIV-1 variants.

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.

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.

HIV-1 protease with 10 lopinavir and darunavir resistance mutations exhibits altered inhibition, structural rearrangements and extreme dynamics.

Antiretroviral drug resistance is a therapeutic obstacle for people with HIV. HIV protease inhibitors darunavir and lopinavir are recommended for resistant infections. We characterized a protease mutant (PR10x) derived from a highly resistant clinical isolate including 10 mutations associated with resistance to lopinavir and darunavir. Compared to the wild-type protease, PR10x exhibits ∼3-fold decrease in catalytic efficiency and Ki values of 2-3 orders of magnitude worse for darunavir, lopinavir, and potent investigational inhibitor GRL-519. Crystal structures of the mutant were solved in a ligand-free form and in complex with GRL-519. The structures show altered interactions in the active site, flap-core interface, hydrophobic core, hinge region, and 80s loop compared to the corresponding wild-type protease structures. The ligand-free crystal structure exhibits a highly curled flap conformation which may amplify drug resistance. Molecular dynamics simulations performed for 1 µs on ligand-free dimers showed extremely large fluctuations in the flaps for PR10x compared to equivalent simulations on PR with a single L76V mutation or wild-type protease. This analysis offers insight about the synergistic effects of mutations in highly resistant variants.

Functionalized carbon nanotubes as an alternative to traditional anti-HIV-1 protease inhibitors: An understanding towards Nano-medicine development through MD simulations.

The Human Immunodeficiency Virus (HIV) has been the source of epidemic infection of AIDS for a longer period. One of the most difficult tasks is identifying novel medications that can help to decrease or control this global health hazard by overcoming drug resistance. In recent decades' nanoparticles are emerging as extremely relevant in drug delivery platforms. In the current study, the pristine (SWCNT) and hydroxyl functionalized (SWCNT-OH) versions of the SWCNT were investigated as inhibitors against the wild-type (WT) and three key mutants of HIV-1 protease (HIV-pr) (I50V, V82A, and I84V). Molecular docking of SWCNT in the catalytic domain and running all-atom MD simulations of all complexes are also part of this project. A thorough inspection of conformational dynamics from 50 ns trajectories reveals that both the pristine and SWCNT-OH can fit right to the pocket region of HIV-pr and govern flap dynamics. The binding affinity of the four HIV-pr-SWCNT/SWCNT-OH complexes was further investigated using MM-PBSA-dependent binding free energy studies. In most mutants and WT systems, SWCNT-OH was reported to bind proportionately many folds (kcal/mol) more than pristine SWCNTs. Hence, SWCNTs are possible HIV-pr inhibitors in terms of their stable existence in the pocket area, stronger binding to the protease, and regulation of flap dynamics in controlling the active site volume, which have vast potential for applications against drug resistance.

Non-active site mutations in the HIV protease: Diminished drug binding affinity is achieved through modulating the hydrophobic sliding mechanism.

The global HIV/AIDS epidemic still currently affects approximately 38 million individuals globally. The protease enzyme of the human immunodeficiency virus is a major drug target in antiviral therapy, however, under the influence of reverse transcriptase and in the context of drug pressure, the rapid PR mutation rate contributes significantly to clinical failure. The set of cooperative non-active site mutations, I13V/I62V/V77I, have been associated with reduced inhibitor susceptibility and are the focus of the current study. When compared to the wild-type protease the mutant protease exhibited decreased binding affinities towards ATV and DRV by 64- and 12-fold, respectively, and decreased the overall favourable Gibbs free energy for ATV, DRV, RTV and SQV. Moreover, these mutations decreased the thermal stability of the protease when in complex with ATV and DRV by approximately 6.4 and 4.2 °C, respectively. The crystal structure of the mutant protease revealed that the location of these mutations and their effect on the hydrophobic sliding mechanism may be crucial in their role in resistance.

In silico evaluation of atazanavir as a potential HIV main protease inhibitor and its comparison with new designed analogs.

Starting three decades ago and spreading rapidly around the world, acquired immunodeficiency syndrome (AIDS) is an infectious disease distinct from other contagious diseases by its unique ways of transmission. Over the past few decades, research into new drug compounds has been accompanied by extensive advances, and the design and manufacture of drugs that inhibit virus enzymes is one way to combat the AIDS virus. Since blocking enzyme activity can kill a pathogen or correct a metabolic imbalance, the design and use of enzyme inhibitors is a new approach against viruses. We carried out an in-depth analysis of the efficacy of atazanavir and its newly designed analogs as human immunodeficiency virus (HIV) protease inhibitors using molecular docking. The best-designed analogs were then compared with atazanavir by the molecular dynamics simulation. The most promising results were ultimately found based on the docking analysis for HIV protease. Several exhibited an estimated free binding energy lower than -9.45 kcal/mol, indicating better prediction results than the atazanavir. ATV7 inhibitor with antiviral action may be more beneficial for infected patients with HIV. Molecular dynamics analysis and binding energy also showed that the ATV7 drug had more inhibitory ability than the atazanavir drug.

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.

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.

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.

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.

Exploring the concerted mechanistic pathway for HIV-1 PR-substrate revealed by umbrella sampling simulation.

HIV-1 protease (HIV-1 PR) is an essential enzyme for the replication process of its virus, and therefore considered an important target for the development of drugs against the acquired immunodeficiency syndrome (AIDS). Our previous study shows that the catalytic mechanism of subtype B/C-SA HIV-1 PR follows a one-step concerted acyclic hydrolysis reaction process using a two-layered ONIOM B3LYP/6-31++G(d,p) method. This present work is aimed at exploring the proposed mechanism of the proteolysis catalyzed by HIV-1 PR and to ensure our proposed mechanism is not an artefact of a single theoretical technique. Hence, we present umbrella sampling method that is suitable for calculating potential mean force (PMF) for non-covalent ligand/substrate-enzyme association/dissociation interactions which provide thermodynamic details for molecular recognition. The free activation energy results were computed in terms of PMF analysis within the hybrid QM(DFTB)/MM approach. The theoretical findings suggest that the proposed mechanism corresponds in principle with experimental data. Given our observations, we suggest that the QM/MM MD method can be used as a reliable computational technique to rationalize lead compounds against specific targets such as the HIV-1 protease.