Investigating interactions between HIV-1 gp41 and inhibitors by molecular dynamics simulation and MM–PBSA/GBSA calculations

Molecular dynamics simulations show how antibodies may rescue HIV-1 mutants incapable of infecting host cells

High mutation and replication rates of HIV-1 result in the continuous generation of variants, allowing it to adapt to changing host environments. Mutations often have deleterious effects, but variants carrying them are rapidly purged. Surprisingly, a particular variant incapable of entering host cells was found to be rescued by host antibodies targeting HIV-1. Understanding the molecular mechanism of this rescue is important to develop and improve antibody-based therapies. To unravel the underlying mechanisms, we performed fully atomistic molecular dynamics simulations of the HIV-1 gp41 trimer responsible for viral entry into host cells, its entry-deficient variant, and its complex with the rescuing antibody. We find that the Q563R mutation, which the entry-deficient variant carries, prevents the native conformation of the gp41 6-helix bundle required for entry and stabilizes an alternative conformation instead. This is the consequence of substantial changes in the secondary structure and interactions between the domains of gp41. Binding of the antibody F240 to gp41 reverses these changes and re-establishes the native conformation, resulting in rescue. To test the generality of this mechanism, we performed simulations with the entry-deficient L565A variant and antibody 3D6. We find that 3D6 binding was able to reverse structural and interaction changes introduced by the mutation and restore the native gp41 conformation. Viral variants may not only escape antibodies but be aided by them in their survival, potentially compromising antibody-based therapies, including vaccination and passive immunization. Our simulation framework could serve as a tool to assess the likelihood of such resistance against specific antibodies.Communicated by Ramaswamy H. SarmaCommunicated by Ramaswamy H. Sarma.

Species Differences in Response to Binding Interactions of Bisphenol A and its Analogs with the Modeled Estrogen Receptor 1 and In Vitro Reporter Gene Assay in Human and Zebrafish

Adverse impacts associated with the interactions of numerous endocrine-disruptor chemicals (EDCs) with estrogen receptor 1 play a pivotal role in reproductive dysfunction. The predictive studies on these interactions thus are crucial in the risk assessment of EDCs but rely heavily on the accuracy of specific protein structure in three dimensions. As the three-dimensional (3D) structure of zebrafish estrogen receptor 1 (zEsr1) is not available, the 3D structure of zEsr1 ligand-binding domain (zEsr1-LBD) was generated using MODELLER and its quality was assessed by the PROCHECK, ERRAT, ProSA, and Verify-3D tools. After the generated model was verified as reliable, bisphenol A and its analogs were docked on the zEsr1-LBD and human estrogen receptor 1 ligand-binding domain (hESR1-LBD) using the Discovery Studio and Autodock Vina programs. The molecular dynamics followed by molecular docking were simulated using the Nanoscale Molecular Dynamics program and compared to those of the in vitro reporter gene assays. Some chemicals were bound with an orientation similar to that of 17β-estradiol in both models and in silico binding energies showed moderate or high correlations with in vitro results (0.33 ≤ r²  ≤ 0.71). Notably, hydrogen bond occupancy during molecular dynamics simulations exhibited a high correlation with in vitro results (r²  ≥ 0.81) in both complexes. These results show that the combined in silico and in vitro approaches is a valuable tool for identifying EDCs in different species, facilitating the assessment of EDC-induced reproductive toxicity. Environ Toxicol Chem 2022;41:2431-2443. © 2022 SETAC.

The discovery of Zika virus NS2B-NS3 inhibitors with antiviral activity via an integrated virtual screening approach

With expanding recent outbreaks and a lack of treatment options, the Zika virus (ZIKV) poses a severe health concern. The availability of ZIKV NS2B-NS3 co-crystallized structures paved the way for rational drug discovery. A computer-aided structure-based approach was used to screen a diverse library of compounds against ZIKV NS2B-NS3 protease. The top hits were selected based on various binding free energy calculations followed by per-residue decomposition analysis. The selected hits were then evaluated for their biological potential with ZIKV protease inhibition assay and antiviral activity. Among 26 selected compounds, 8 compounds showed promising activity against ZIKV protease with a percentage inhibition of greater than 25 and 3 compounds displayed ∼50% at 10 µM, which indicates an enrichment rate of approximately 36% (threshold IC₅₀ < 10 µM) in the ZIKV-NS2B-NS3 protease inhibition assay. Of these, only one compound (23) produced whole-cell anti-ZIKV activity, and the binding mode of 23 was extensively analyzed through long-run molecular dynamics simulations. The current study provides a promising starting point for the further development of novel compounds against ZIKV.

Discovery of Novel HCV NS5B polymerase inhibitor, 2-(3,4-dimethyl-5,5-dioxidobenzo[e]pyrazolo[4,3-c][1,2]thiazin-2(4H)-yl)-N-(2-fluorobenzyl)acetamide via molecular docking and experimental approach

Hepatitis C Virus (HCV) is a viral infection posing a severe global threat that left untreated progresses to end-stage liver disease, including cirrhosis and hepatocellular carcinoma (HCC). Moreover, no prophylactic approach exists so far enabling its prevention. The NS5B polymerase holds special significance as the target of intervention against HCV infection. The current study kindles benzothiazine derivatives against HCV NS5B polymerase through in silico and experimental approaches. Following docking, the compound 2-(3,4-dimethyl-5,5-dioxidobenzo[e]pyrazolo[4,3-c][1,2]thiazin-2(4H)-yl)-N-(2-fluorobenzyl)acetamide was revealed to form effective binding interaction in the proposed site of HCV NS5B with a score of -10 kcal/mol and subsequently was deciphered through molecular dynamics (MD) simulation study which indicated interaction of residues TYR_382, VAL_381 and HIS_467 through hydrophobic interaction and two residues such as GLU_202 and LYS_209 contributed in the formation of water bridges. The subsequent in silico pharmacological analysis revealed its safe drug profile. The cytotoxicity activity of compound 6c indicated to be non-toxic in HepG2 cells at concentration ranges from 0.001-1.0 µmol/L with >80% cell viability and diminished expression of the HCV NS5B to 98% at the dose of 1.0 µmol/L and 90% at 0.5µmol/L. Thus the hit compound 6c might be a potent NS5B polymerase inhibitor required to be validated further through in vivo and preclinical studies.

Pharmacoinformatics and molecular dynamics simulation studies reveal potential covalent and FDA-approved inhibitors of SARS-CoV-2 main protease 3CLpro

The SARS-CoV-2 was confirmed to cause the global pandemic of coronavirus disease 2019 (COVID-19). The 3-chymotrypsin-like protease (3CLpro), an essential enzyme for viral replication, is a valid target to combat SARS-CoV and MERS-CoV. In this work, we present a structure-based study to identify potential covalent inhibitors containing a variety of chemical warheads. The targeted Asinex Focused Covalent (AFCL) library was screened based on different reaction types and potential covalent inhibitors were identified. In addition, we screened FDA-approved protease inhibitors to find candidates to be repurposed against SARS-CoV-2 3CLpro. A number of compounds with significant covalent docking scores were identified. These compounds were able to establish a covalent bond (C-S) with the reactive thiol group of Cys145 and to form favorable interactions with residues lining the substrate-binding site. Moreover, paritaprevir and simeprevir from FDA-approved protease inhibitors were identified as potential inhibitors of SARS-CoV-2 3CLpro. The mechanism and dynamic stability of binding between the identified compounds and SARS-CoV-2 3CLpro were characterized by molecular dynamics (MD) simulations. The identified compounds are potential inhibitors worthy of further development as COVID-19 drugs. Importantly, the identified FDA-approved anti-hepatitis-C virus (HCV) drugs paritaprevir and simeprevir could be ready for clinical trials to treat infected patients and help curb COVID-19. Communicated by Ramaswamy H. Sarma.

Promising terpenes as SARS-CoV-2 spike receptor-binding domain (RBD) attachment inhibitors to the human ACE2 receptor: Integrated computational approach

The spike protein receptor binding domain (S-RBD) is a necessary corona-viral protein for binding and entry of coronaviruses (COVs) into the host cells. Hence, it has emerged as an attractive antiviral drug target. Therefore, present study was aimed to target severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) S-RBD with novel bioactive compounds to retrieve potential candidates that could serve as anti-coronavirus disease 2019 (COVID-19) drugs. In this paper, computational approaches were employed, especially the structure-based virtual screening followed by molecular dynamics (MD) simulation as well as binding energy analysis for the computational identification of specific terpenes from the medicinal plants, which can block SARS-CoV-2 S-RBD binding to Human angiotensin-converting enzyme 2 (H-ACE2) and can act as potent anti-COVID-19 drugs after further advancements. The screening of focused terpenes inhibitors database composed of ~1000 compounds with reported therapeutic potential resulted in the identification of three candidate compounds, NPACT01552, NPACT01557 and NPACT00631. These three compounds established conserved interactions, which were further explored through all-atom MD simulations, free energy calculations, and a residual energy contribution estimated by MM-PB(GB)SA method. All these compounds showed stable conformation and interacted well with the hot-spot residues of SARS-CoV-2 S-RBD. Conclusively, the reported SARS-CoV-2 S-RBD specific terpenes could serve as seeds for developing potent anti-COVID-19 drugs. Importantly, the experimentally tested glycyrrhizin (NPACT00631) against SARS-CoV could be used further in the fast-track drug development process to help curb COVID-19.

Discovery of human coronaviruses pan-papain-like protease inhibitors using computational approaches

The papain-like protease (PLpro) is vital for the replication of coronaviruses (CoVs), as well as for escaping innate-immune responses of the host. Hence, it has emerged as an attractive antiviral drug-target. In this study, computational approaches were employed, mainly the structure-based virtual screening coupled with all-atom molecular dynamics (MD) simulations to computationally identify specific inhibitors of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) PLpro, which can be further developed as potential pan-PLpro based broad-spectrum antiviral drugs. The sequence, structure, and functional conserveness of most deadly human CoVs PLpro were explored, and it was revealed that functionally important catalytic triad residues are well conserved among SARS-CoV, SARS-CoV-2, and middle east respiratory syndrome coronavirus (MERS-CoV). The subsequent screening of a focused protease inhibitors database composed of ∼7,000 compounds resulted in the identification of three candidate compounds, ADM_13083841, LMG_15521745, and SYN_15517940. These three compounds established conserved interactions which were further explored through MD simulations, free energy calculations, and residual energy contribution estimated by MM-PB(GB)SA method. All these compounds showed stable conformation and interacted well with the active residues of SARS-CoV-2 PLpro, and showed consistent interaction profile with SARS-CoV PLpro and MERS-CoV PLpro as well. Conclusively, the reported SARS-CoV-2 PLpro specific compounds could serve as seeds for developing potent pan-PLpro based broad-spectrum antiviral drugs against deadly human coronaviruses. Moreover, the presented information related to binding site residual energy contribution could lead to further optimization of these compounds.

Structural elucidation of SARS-CoV-2 vital proteins: Computational methods reveal potential drug candidates against main protease, Nsp12 polymerase and Nsp13 helicase

Recently emerged SARS-CoV-2 caused a major outbreak of coronavirus disease 2019 (COVID-19) and instigated a widespread fear, threatening global health safety. To date, no licensed antiviral drugs or vaccines are available against COVID-19 although several clinical trials are under way to test possible therapies. During this urgent situation, computational drug discovery methods provide an alternative to tiresome high-throughput screening, particularly in the hit-to-lead-optimization stage. Identification of small molecules that specifically target viral replication apparatus has indicated the highest potential towards antiviral drug discovery. In this work, we present potential compounds that specifically target SARS-CoV-2 vital proteins, including the main protease, Nsp12 RNA polymerase and Nsp13 helicase. An integrative virtual screening and molecular dynamics simulations approach has facilitated the identification of potential binding modes and favourable molecular interaction profile of corresponding compounds. Moreover, the identification of structurally important binding site residues in conserved motifs located inside the active site highlights relative importance of ligand binding based on residual energy decomposition analysis. Although the current study lacks experimental validation, the structural information obtained from this computational study has paved way for the design of targeted inhibitors to combat COVID-19 outbreak.

Discovery of novel Hepatitis C virus inhibitor targeting multiple allosteric sites of NS5B polymerase

HCV is a viral infection posing a severe global threat when left untreated progress to end-stage liver disease, including cirrhosis and HCC. The NS5B polymerase of HCV is the most potent target that harbors four allosteric binding sites that could interfere with the HCV infection. We present the discovery of a novel synthetic compound that harbors the potential of NS5B polymerase inhibition. All eight compounds belonging to the benzothiazine family of heterocycles displayed no cellular cytotoxicity in HepG2 cells at nontoxic dose concentration (200 μM). Subsequently, among eight compounds of the series, merely compound 5b exhibited significant inhibition of the expression of the HCV NS5B gene as compared to DMSO control in semi-quantitative PCR. Based on our western blot result, 5b at the range of 50, 100 and 200 μM induced 20, 40, and 70% inhibition of NS5B protein respectively. To estimate the binding potential, 5b was docked at respective allosteric sites followed by molecular dynamics (MD) simulations for a period of 20 ns. In addition, binding free energy calculation by MM-GB/PBSA method revealed a conserved interaction profile of residues lining the allosteric sites in agreement with the reported NS5B co-crystallized inhibitors. The presented results provide important information about a novel compound 5b which may facilitate the the discovery of novel inhibitors that tends to target multiple sites on NS5B polymerase.

In silico structural elucidation of RNA-dependent RNA polymerase towards the identification of potential Crimean-Congo Hemorrhagic Fever Virus inhibitors

The Crimean-Congo Hemorrhagic Fever virus (CCHFV) is a segmented negative single-stranded RNA virus (-ssRNA) which causes severe hemorrhagic fever in humans with a mortality rate of ~50%. To date, no vaccine has been approved. Treatment is limited to supportive care with few investigational drugs in practice. Previous studies have identified viral RNA dependent RNA Polymerase (RdRp) as a potential drug target due to its significant role in viral replication and transcription. Since no crystal structure is available yet, we report the structural elucidation of CCHFV-RdRp by in-depth homology modeling. Even with low sequence identity, the generated model suggests a similar overall structure as previously reported RdRps. More specifically, the model suggests the presence of structural/functional conserved RdRp motifs for polymerase function, the configuration of uniform spatial arrangement of core RdRp sub-domains, and predicted positively charged entry/exit tunnels, as seen in sNSV polymerases. Extensive pharmacophore modeling based on per-residue energy contribution with investigational drugs allowed the concise mapping of pharmacophoric features and identified potential hits. The combination of pharmacophoric features with interaction energy analysis revealed functionally important residues in the conserved motifs together with in silico predicted common inhibitory binding modes with highly potent reference compounds.

DG-GL: Differential geometry-based geometric learning of molecular datasets

MOTIVATION

Despite its great success in various physical modeling, differential geometry (DG) has rarely been devised as a versatile tool for analyzing large, diverse, and complex molecular and biomolecular datasets because of the limited understanding of its potential power in dimensionality reduction and its ability to encode essential chemical and biological information in differentiable manifolds.

RESULTS

We put forward a differential geometry-based geometric learning (DG-GL) hypothesis that the intrinsic physics of three-dimensional (3D) molecular structures lies on a family of low-dimensional manifolds embedded in a high-dimensional data space. We encode crucial chemical, physical, and biological information into 2D element interactive manifolds, extracted from a high-dimensional structural data space via a multiscale discrete-to-continuum mapping using differentiable density estimators. Differential geometry apparatuses are utilized to construct element interactive curvatures in analytical forms for certain analytically differentiable density estimators. These low-dimensional differential geometry representations are paired with a robust machine learning algorithm to showcase their descriptive and predictive powers for large, diverse, and complex molecular and biomolecular datasets. Extensive numerical experiments are carried out to demonstrate that the proposed DG-GL strategy outperforms other advanced methods in the predictions of drug discovery-related protein-ligand binding affinity, drug toxicity, and molecular solvation free energy.

AVAILABILITY AND IMPLEMENTATION

http://weilab.math.msu.edu/DG-GL/ Contact: wei@math.msu.edu.

Discover potential inhibitors for PFKFB3 using 3D-QSAR, virtual screening, molecular docking and molecular dynamics simulation

The 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase-3 (PFKFB3) is a master regulator of glycolysis in cancer cells by synthesizing fructose-2,6-bisphosphate (F-2,6-BP), a potent allosteric activator of phosphofructokinase-1 (PFK-1), which is a rate-limiting enzyme of glycolysis. PFKFB3 is an attractive target for cancer treatment. It is valuable to discover promising inhibitors by using 3D-QSAR pharmacophore modeling, virtual screening, molecular docking and molecular dynamics simulation. Twenty molecules with known activity were used to build 3D-QSAR pharmacophore models. The best pharmacophore model was ADHR called Hypo1, which had the highest correlation value of 0.98 and the lowest RMSD of 0.82. Then, the Hypo1 was validated by cost value method, test set method and decoy set validation method. Next, the Hypo1 combined with Lipinski's rule of five and ADMET properties were employed to screen databases including Asinex and Specs, total of 1,048,159 molecules. The hits retrieved from screening were docked into protein by different procedures including HTVS, SP and XP. Finally, nine molecules were picked out as potential PFKFB3 inhibitors. The stability of PFKFB3-lead complexes was verified by 40 ns molecular dynamics simulation. The binding free energy and the energy contribution of per residue to the binding energy were calculated by MM-PBSA based on molecular dynamics simulation.

Pharmacophore modeling, multiple docking, and molecular dynamics studies on Wee1 kinase inhibitors

Wee1-like protein kinase (Wee1) is a tyrosine kinase that regulates the G₂ checkpoint and prevents entry into mitosis in response to DNA damage. Based on a series of signaling pathways initiated by Wee1, Wee1 has been recognized as a potential target for cancer therapy. To discover potent Wee1 inhibitors with novel scaffolds, ligand-based pharmacophore model has been built based on 101 known Wee1 inhibitors. Then the best pharmacophore model, AADRRR.340, with good partial least square (PLS) statistics (R² = 0.9212, Q² = 0.7457), was selected and validated. The validated model was used as a three-dimensional (3D) search query for databases virtual screening. The filtered molecules were further analyzed and refined by Lipinski's rule of 5, multiple docking procedures (high throughput virtual screening (HTVS), standard precision (SP), genetic optimization for ligand docking (GOLD), extra precision (XP), and unique quantum polarized ligand docking (QPLD)); absorption, distribution, metabolism, excretion, and toxicity (ADMET) screening; and the Prime/molecular mechanics generalized born surface area (MM-GBSA) method binding free energy calculations. Eight leads were identified as potential Wee1 inhibitors, and a 50 ns molecular dynamics (MD) simulation was carried out for top four inhibitors to predict the stability of ligand-protein complex. Molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) based on MD simulation and the energy contribution per residue to the binding energy were calculated. In the end, three hits with good stabilization and affinity to protein were identified. Communicated by Ramaswamy H. Sarma.

Computer-Aided Approaches for Targeting HIVgp41

Virus-cell fusion is the primary means by which the human immunodeficiency virus-1 (HIV) delivers its genetic material into the human T-cell host. Fusion is mediated in large part by the viral glycoprotein 41 (gp41) which advances through four distinct conformational states: (i) native, (ii) pre-hairpin intermediate, (iii) fusion active (fusogenic), and (iv) post-fusion. The pre-hairpin intermediate is a particularly attractive step for therapeutic intervention given that gp41 N-terminal heptad repeat (NHR) and C-terminal heptad repeat (CHR) domains are transiently exposed prior to the formation of a six-helix bundle required for fusion. Most peptide-based inhibitors, including the FDA-approved drug T20, target the intermediate and there are significant efforts to develop small molecule alternatives. Here, we review current approaches to studying interactions of inhibitors with gp41 with an emphasis on atomic-level computer modeling methods including molecular dynamics, free energy analysis, and docking. Atomistic modeling yields a unique level of structural and energetic detail, complementary to experimental approaches, which will be important for the design of improved next generation anti-HIV drugs.

Differential geometry based solvation model II: Lagrangian formulation

Solvation is an elementary process in nature and is of paramount importance to more sophisticated chemical, biological and biomolecular processes. The understanding of solvation is an essential prerequisite for the quantitative description and analysis of biomolecular systems. This work presents a Lagrangian formulation of our differential geometry based solvation models. The Lagrangian representation of biomolecular surfaces has a few utilities/advantages. First, it provides an essential basis for biomolecular visualization, surface electrostatic potential map and visual perception of biomolecules. Additionally, it is consistent with the conventional setting of implicit solvent theories and thus, many existing theoretical algorithms and computational software packages can be directly employed. Finally, the Lagrangian representation does not need to resort to artificially enlarged van der Waals radii as often required by the Eulerian representation in solvation analysis. The main goal of the present work is to analyze the connection, similarity and difference between the Eulerian and Lagrangian formalisms of the solvation model. Such analysis is important to the understanding of the differential geometry based solvation model. The present model extends the scaled particle theory of nonpolar solvation model with a solvent-solute interaction potential. The nonpolar solvation model is completed with a Poisson-Boltzmann (PB) theory based polar solvation model. The differential geometry theory of surfaces is employed to provide a natural description of solvent-solute interfaces. The optimization of the total free energy functional, which encompasses the polar and nonpolar contributions, leads to coupled potential driven geometric flow and PB equations. Due to the development of singularities and nonsmooth manifolds in the Lagrangian representation, the resulting potential-driven geometric flow equation is embedded into the Eulerian representation for the purpose of computation, thanks to the equivalence of the Laplace-Beltrami operator in the two representations. The coupled partial differential equations (PDEs) are solved with an iterative procedure to reach a steady state, which delivers desired solvent-solute interface and electrostatic potential for problems of interest. These quantities are utilized to evaluate the solvation free energies and protein-protein binding affinities. A number of computational methods and algorithms are described for the interconversion of Lagrangian and Eulerian representations, and for the solution of the coupled PDE system. The proposed approaches have been extensively validated. We also verify that the mean curvature flow indeed gives rise to the minimal molecular surface and the proposed variational procedure indeed offers minimal total free energy. Solvation analysis and applications are considered for a set of 17 small compounds and a set of 23 proteins. The salt effect on protein-protein binding affinity is investigated with two protein complexes by using the present model. Numerical results are compared to the experimental measurements and to those obtained by using other theoretical methods in the literature.

Differential geometry based solvation model I: Eulerian formulation

This paper presents a differential geometry based model for the analysis and computation of the equilibrium property of solvation. Differential geometry theory of surfaces is utilized to define and construct smooth interfaces with good stability and differentiability for use in characterizing the solvent-solute boundaries and in generating continuous dielectric functions across the computational domain. A total free energy functional is constructed to couple polar and nonpolar contributions to the salvation process. Geometric measure theory is employed to rigorously convert a Lagrangian formulation of the surface energy into an Eulerian formulation so as to bring all energy terms into an equal footing. By minimizing the total free energy functional, we derive coupled generalized Poisson-Boltzmann equation (GPBE) and generalized geometric flow equation (GGFE) for the electrostatic potential and the construction of realistic solvent-solute boundaries, respectively. By solving the coupled GPBE and GGFE, we obtain the electrostatic potential, the solvent-solute boundary profile, and the smooth dielectric function, and thereby improve the accuracy and stability of implicit solvation calculations. We also design efficient second order numerical schemes for the solution of the GPBE and GGFE. Matrix resulted from the discretization of the GPBE is accelerated with appropriate preconditioners. An alternative direct implicit (ADI) scheme is designed to improve the stability of solving the GGFE. Two iterative approaches are designed to solve the coupled system of nonlinear partial differential equations. Extensive numerical experiments are designed to validate the present theoretical model, test computational methods, and optimize numerical algorithms. Example solvation analysis of both small compounds and proteins are carried out to further demonstrate the accuracy, stability, efficiency and robustness of the present new model and numerical approaches. Comparison is given to both experimental and theoretical results in the literature.

AIDS treatment with novel anti-HIV compounds improved by nanotechnology

The first International Symposium of Nanomedicine on AIDS "AIDS Treatment with Novel Anti-HIV compounds Improved by Nanotechnology" was held November 19-20, 2009 in Beijing, China. This symposium provided an international forum for presentation and discussion of exciting new advances in the emerging research area of nanobiomedical research on AIDS treatment as the focus point, as well as some issues in relevant fields such as nanobiomedical research on tumor treatment and safety evaluation of nanomedicines. Key highlights of the symposium include (1) reviewing current status of nanobiotechnology programs and their relations, more or less, with AIDS treatment; (2) reviewing current AIDS epidemiology in China and examining effectiveness and efficiency of current prevention and treatment strategies; (3) highlighting the obstacles to improve AIDS prevention and treatment, and (4) exploring innovative ways for nanotechnology to advance AIDS treatment, especially to combat HIV resistance to drugs.