FMO-guided design of darunavir analogs as HIV-1 protease inhibitors.

The prevalence of HIV-1 infection continues to pose a significant global public health issue, highlighting the need for antiretroviral drugs that target viral proteins to reduce viral replication. One such target is HIV-1 protease (PR), responsible for cleaving viral polyproteins, leading to the maturation of viral proteins. While darunavir (DRV) is a potent HIV-1 PR inhibitor, drug resistance can arise due to mutations in HIV-1 PR. To address this issue, we developed a novel approach using the fragment molecular orbital (FMO) method and structure-based drug design to create DRV analogs. Using combinatorial programming, we generated novel analogs freely accessible via an on-the-cloud mode implemented in Google Colab, Combined Analog generator Tool (CAT). The designed analogs underwent cascade screening through molecular docking with HIV-1 PR wild-type and major mutations at the active site. Molecular dynamics (MD) simulations confirmed the assess ligand binding and susceptibility of screened designed analogs. Our findings indicate that the three designed analogs guided by FMO, 19-0-14-3, 19-8-10-0, and 19-8-14-3, are superior to DRV and have the potential to serve as efficient PR inhibitors. These findings demonstrate the effectiveness of our approach and its potential to be used in further studies for developing new antiretroviral drugs.

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.

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.

Exploration of imatinib and nilotinib-derived templates as the P2-Ligand for HIV-1 protease inhibitors: Design, synthesis, protein X-ray structural studies, and biological evaluation.

Structure-based design, synthesis, X-ray structural studies, and biological evaluation of a new series of potent HIV-1 protease inhibitors are described. These inhibitors contain various pyridyl-pyrimidine, aryl thiazole or alkylthiazole derivatives as the P2 ligands in combination with darunavir-like hydroxyethylamine sulfonamide isosteres. These heterocyclic ligands are inherent to kinase inhibitor drugs, such as nilotinib and imatinib. These ligands are designed to make hydrogen bonding interactions with the backbone atoms in the S2 subsite of HIV-1 protease. Various benzoic acid derivatives have been synthesized and incorporation of these ligands provided potent inhibitors that exhibited subnanomolar level protease inhibitory activity and low nanomolar level antiviral activity. Two high resolution X-ray structures of inhibitor-bound HIV-1 protease were determined. These structures provided important ligand-binding site interactions for further optimization of this class of protease inhibitors.

AI-Aided Search for New HIV-1 Protease Ligands.

The availability of drugs capable of blocking the replication of microorganisms has been one of the greatest triumphs in the history of medicine, but the emergence of an ever-increasing number of resistant strains poses a serious problem for the treatment of infectious diseases. The search for new potential ligands for proteins involved in the life cycle of pathogens is, therefore, an extremely important research field today. In this work, we have considered the HIV-1 protease, one of the main targets for AIDS therapy. Several drugs are used today in clinical practice whose mechanism of action is based on the inhibition of this enzyme, but after years of use, even these molecules are beginning to be interested by resistance phenomena. We used a simple artificial intelligence system for the initial screening of a data set of potential ligands. These results were validated by docking and molecular dynamics, leading to the identification of a potential new ligand of the enzyme which does not belong to any known class of HIV-1 protease inhibitors. The computational protocol used in this work is simple and does not require large computational power. Furthermore, the availability of a large number of structural information on viral proteins and the presence of numerous experimental data on their ligands, with which it is possible to compare the results obtained with computational methods, make this research field the ideal terrain for the application of these new computational techniques.

HIV-1 protease inhibitors with a P1 phosphonate modification maintain potency against drug-resistant variants by increased interactions with flap residues.

Protease inhibitors are the most potent antivirals against HIV-1, but they still lose efficacy against resistant variants. Improving the resistance profile is key to developing more robust inhibitors, which may be promising candidates for simplified next-generation antiretroviral therapies. In this study, we explored analogs of darunavir with a P1 phosphonate modification in combination with increasing size of the P1' hydrophobic group and various P2' moieties to improve potency against resistant variants. The phosphonate moiety substantially improved potency against highly mutated and resistant HIV-1 protease variants, but only when combined with more hydrophobic moieties at the P1' and P2' positions. Phosphonate analogs with a larger hydrophobic P1' moiety maintained excellent antiviral potency against a panel of highly resistant HIV-1 variants, with significantly improved resistance profiles. The cocrystal structures indicate that the phosphonate moiety makes extensive hydrophobic interactions with the protease, especially with the flap residues. Many residues involved in these protease-inhibitor interactions are conserved, enabling the inhibitors to maintain potency against highly resistant variants. These results highlight the need to balance inhibitor physicochemical properties by simultaneous modification of chemical groups to further improve resistance profiles.

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.

Selection of HIV-1 for resistance to fifth-generation protease inhibitors reveals two independent pathways to high-level resistance.

Darunavir (DRV) is exceptional among potent HIV-1 protease inhibitors (PIs) in high drug concentrations that are achieved in vivo. Little is known about the de novo resistance pathway for DRV. We selected for resistance to high drug concentrations against 10 PIs and their structural precursor DRV. Mutations accumulated through two pathways (anchored by protease mutations I50V or I84V). Small changes in the inhibitor P1'-equivalent position led to preferential use of one pathway over the other. Changes in the inhibitor P2'-equivalent position determined differences in potency that were retained in the resistant viruses and that impacted the selected mutations. Viral variants from the two pathways showed differential selection of compensatory mutations in Gag cleavage sites. These results reveal the high level of selective pressure that is attainable with fifth-generation PIs and how features of the inhibitor affect both the resistance pathway and the residual potency in the face of resistance.

Understanding Drug Resistance of Wild-Type and L38HL Insertion Mutant of HIV-1 C Protease to Saquinavir.

Acquired immunodeficiency syndrome (AIDS) is one of the most challenging infectious diseases to treat on a global scale. Understanding the mechanisms underlying the development of drug resistance is necessary for novel therapeutics. HIV subtype C is known to harbor mutations at critical positions of HIV aspartic protease compared to HIV subtype B, which affects the binding affinity. Recently, a novel double-insertion mutation at codon 38 (L38HL) was characterized in HIV subtype C protease, whose effects on the interaction with protease inhibitors are hitherto unknown. In this study, the potential of L38HL double-insertion in HIV subtype C protease to induce a drug resistance phenotype towards the protease inhibitor, Saquinavir (SQV), was probed using various computational techniques, such as molecular dynamics simulations, binding free energy calculations, local conformational changes and principal component analysis. The results indicate that the L38HL mutation exhibits an increase in flexibility at the hinge and flap regions with a decrease in the binding affinity of SQV in comparison with wild-type HIV protease C. Further, we observed a wide opening at the binding site in the L38HL variant due to an alteration in flap dynamics, leading to a decrease in interactions with the binding site of the mutant protease. It is supported by an altered direction of motion of flap residues in the L38HL variant compared with the wild-type. These results provide deep insights into understanding the potential drug resistance phenotype in infected individuals.

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.

Structure based design and evaluation of benzoheterocycle derivatives as potential dual HIV-1 protease and reverse transcriptase inhibitors.

The development of dual inhibitors of HIV-1 protease and reverse transcriptase is an attractive strategy for multi-target therapeutic of AIDS, which may be privileged in delaying the occurrence of drug resistance. We herein designed a novel kind of dual inhibitors with benzofuran or indole cores. Biological results showed that a number of inhibitors displayed significant activity against both HIV-1 protease and reverse transcriptase. Among which, inhibitor 10f exhibited a good correlation with an approximate ratio of 1: 2 between the two enzymes. Furthermore, the dual inhibitors illustrated similar potency against both the wild-type virus and drug-resistant mutant. In addition, the molecular dynamic simulation studies verified the dual actions of such inhibitors.

Decoding drug resistant mechanism of V32I, I50V and I84V mutations of HIV-1 protease on amprenavir binding by using molecular dynamics simulations and MM-GBSA calculations.

Mutations V32I, I50V and I84V in the HIV-1 protease (PR) induce drug resistance towards drug amprenavir (APV). Multiple short molecular dynamics (MSMD) simulations and molecular mechanics generalized Born surface area (MM-GBSA) method were utilized to investigate drug-resistant mechanism of V32I, I50V and I84V towards APV. Dynamic information arising from MSMD simulations suggest that V32I, I50V and I84V highly affect structural flexibility, motion modes and conformational behaviours of two flaps in the PR. Binding free energies calculated by MM-GBSA method suggest that the decrease in binding enthalpy and the increase in binding entropy induced by mutations V32I, I50V and I84V are responsible for drug resistance of the mutated PRs on APV. The energetic contributions of separate residues on binding of APV to the PR show that V32I, I50V and I84V highly disturb the interactions of two flaps with APV and mostly drive the decrease in binding ability of APV to the PR. Thus, the conformational changes of two flaps in the PR caused by V32I, I50V and I84V play key roles in drug resistance of three mutated PR towards APV. This study can provide useful dynamics information for the design of potent inhibitors relieving drug resistance.

Design and Evaluation of Novel HIV-1 Protease Inhibitors Containing Phenols or Polyphenols as P2 Ligands with High Activity against DRV-Resistant HIV-1 Variants.

With the increasing prevalence of drug-resistant variants, novel potent HIV-1 protease inhibitors with broad-spectrum antiviral activity against multidrug-resistant causative viruses are urgently needed. Herein, we designed and synthesized a new series of HIV-1 protease inhibitors with phenols or polyphenols as the P2 ligands and a variety of sulfonamide analogs as the P2' ligands. A number of these new inhibitors showed superb enzymatic inhibitory activity and antiviral activity. In particular, inhibitors 15d and 15f exhibited potent enzymatic inhibitory activity in the low picomolar range, and the latter showed excellent activity against the Darunavir-resistant HIV-1 variant. Furthermore, the molecular modeling studies provided insight into the ligand-binding site interactions between inhibitors and the enzyme cavity, and they sparked inspiration for the further optimization of potent inhibitors.

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.

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.