Search

Cryptococcosis is an invasive infection that accounts for 15% of AIDS-related fatalities. Still, treating cryptococcosis remains a significant challenge due to the poor availability of effective antifungal therapies and emergence of drug resistance. Interestingly, protease inhibitor components of antiretroviral therapy regimens have shown some clinical benefits in these opportunistic infections. We investigated Major aspartyl peptidase 1 (May1), a secreted Cryptococcus neoformans protease, as a possible target for the development of drugs that act against both fungal and retroviral aspartyl proteases. Here, we describe the biochemical characterization of May1, present its high-resolution X-ray structure, and provide its substrate specificity analysis. Through combinatorial screening of 11,520 compounds, we identified a potent inhibitor of May1 and HIV protease. This dual-specificity inhibitor exhibits antifungal activity in yeast culture, low cytotoxicity, and low off-target activity against host proteases and could thus serve as a lead compound for further development of May1 and HIV protease inhibitors.

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

We introduce a new heterogeneous CPU+GPU-enhanced DFTB approach for the routine and efficient simulation of large chemical and biological systems. Compared to homogeneous computing with conventional CPUs, heterogeneous computing approaches exhibit substantial performance with only a modest increase in power consumption, both of which are essential to upcoming exascale computing initiatives. We show that DFTB-based molecular dynamics is a natural candidate for heterogeneous computing, since the computational bottleneck in these simulations is the diagonalization of the Hamiltonian matrix, which is performed several times during a single molecular dynamics trajectory. To thoroughly test and understand the performance of our heterogeneous CPU+GPU approach, we examine a variety of algorithmic implementations, benchmarks of different hardware configurations, and applications of this methodology on several large chemical and biological systems. Finally, to demonstrate the capability of our implementation, we conclude with a large-scale DFTB MD simulation of explicitly solvated HIV protease (3974 atoms total) as a proof-of-concept example of an extremely large/complex system which, to the best of our knowledge, is the first time that an entire explicitly solvated protein has been treated at a quantum-based MD level of detail.

BackgroundTransgender women are among the groups at highest risk for HIV infection, with a prevalence of 27·7% in the USA; and despite this known high risk, undiagnosed infection is common in this population. We set out to identify transgender women and their partners in a molecular transmission network to prioritise public health activities.MethodsSince 2006, HIV protease and reverse transcriptase gene (pol) sequences from drug resistance testing have been reported to the Los Angeles County Department of Public Health and linked to demographic data, gender, and HIV transmission risk factor data for each case in the enhanced HIV/AIDS Reporting System. We reconstructed a molecular transmission network by use of HIV-TRAnsmission Cluster Engine (with a pairwise genetic distance threshold of 0·015 substitutions per site) from the earliest pol sequences from 22 398 unique individuals, including 412 (2%) self-identified transgender women. We examined the possible predictors of clustering with multivariate logistic regression. We characterised the genetically linked partners of transgender women and calculated assortativity (the tendency for people to link to other people with the same attributes) for each transmission risk group.Findings8133 (36·3%) of 22 398 individuals clustered in the network across 1722 molecular transmission clusters. Transgender women who indicated a sexual risk factor clustered at the highest frequency in the network, with 147 (43%) of 345 being linked to at least one other person (adjusted odds ratio [aOR] 2·0, p=0·0002). Transgender women were assortative in the network (assortativity 0·06, p

The primary goal of protein science is to understand how proteins function, which requires understanding the functional dynamics responsible for transitions between different functional structures of a protein. A central concept is the exact reaction coordinates that can determine the value of committor for any protein configuration, which provide the optimal description of functional dynamics. Despite intensive efforts, identifying the exact reaction coordinates (RCs) in complex molecules remains a formidable challenge. Using the recently developed generalized work functional, we report the discovery of the exact RCs for an important functional process--the flap opening of HIV-1 protease. Our results show that this process has six RCs, each one is a linear combination of ~240 backbone dihedrals, providing the precise definition of collectivity and cooperativity in the functional dynamics of a protein. Applying bias potentials along each RC can accelerate flap opening by 103 to 104 folds. The success in identifying the RCs of a protein with 198 residues represents a significant progress beyond that of the alanine dipeptide, currently the only other complex molecule for which the exact RCs for its conformational changes are known. Our results suggest that the generalized work functional (GWF) might be the fundamental operator of mechanics that controls protein dynamics. [ABSTRACT FROM AUTHOR]

Molecular motors are thought to generate force and directional motion via nonequilibrium switching between energy surfaces. Because all enzymes can undergo such switching, we hypothesized that the ability to generate rotary motion and torque is not unique to highly adapted biological motor proteins but is instead a common feature of enzymes. We used molecular dynamics simulations to compute energy surfaces for hundreds of torsions in three enzymes-adenosine kinase, protein kinase A, and HIV-1 protease-and used these energy surfaces within a kinetic model that accounts for intersurface switching and intrasurface probability flows. When substrate is out of equilibrium with product, we find computed torsion rotation rates up ∼140 cycles s-1, with stall torques up to ∼2 kcal mol-1 cycle-1, and power outputs up to ∼50 kcal mol-1 s-1. We argue that these enzymes are instances of a general phenomenon of directional probability flows on asymmetric energy surfaces for systems out of equilibrium. Thus, we conjecture that cyclic probability fluxes, corresponding to rotations of torsions and higher-order collective variables, exist in any chiral molecule driven between states in a nonequilibrium manner; we call this the "Asymmetry-Directionality" conjecture. This is expected to apply as well to synthetic chiral molecules switched in a nonequilibrium manner between energy surfaces by light, redox chemistry, or catalysis.

Through adding a harmonic boost potential to smooth the system potential energy surface, Gaussian accelerated molecular dynamics (GaMD) provides enhanced sampling and free energy calculation of biomolecules without the need of predefined reaction coordinates. This work continues to improve the acceleration power and energy reweighting of the GaMD by combining the GaMD with replica exchange algorithms. Two versions of replica exchange GaMD (rex-GaMD) are presented: force constant rex-GaMD and threshold energy rex-GaMD. During simulations of force constant rex-GaMD, the boost potential can be exchanged between replicas of different harmonic force constants with fixed threshold energy. However, the algorithm of threshold energy rex-GaMD tends to switch the threshold energy between lower and upper bounds for generating different levels of boost potential. Testing simulations on three model systems, including the alanine dipeptide, chignolin, and HIV protease, demonstrate that through continuous exchanges of the boost potential, the rex-GaMD simulations not only enhance the conformational transitions of the systems but also narrow down the distribution width of the applied boost potential for accurate energetic reweighting to recover biomolecular free energy profiles.

It is important to determine the binding pathways and mechanisms of ligand molecules to target proteins to effectively design therapeutic drugs. Molecular dynamics (MD) is a promising computational tool that allows us to simulate protein-drug binding at an atomistic level. However, the gap between the time scales of current simulations and those of many drug binding processes has limited the usage of conventional MD, which has been reflected in studies of the HIV protease. Here, we have applied a robust enhanced simulation method, Gaussian accelerated molecular dynamics (GaMD), to sample binding pathways of the XK263 ligand and associated protein conformational changes in the HIV protease. During two of 10 independent GaMD simulations performed over 500-2500 ns, the ligand was observed to successfully bind to the protein active site. Although GaMD-derived free energy profiles were not fully converged because of insufficient sampling of the complex system, the simulations still allowed us to identify relatively low-energy intermediate conformational states during binding of the ligand to the HIV protease. Relative to the X-ray crystal structure, the XK263 ligand reached a minimum root-mean-square deviation (RMSD) of 2.26 Å during 2.5 μs of GaMD simulation. In comparison, the ligand RMSD reached a minimum of only ∼5.73 Å during an earlier 14 μs conventional MD simulation. This work highlights the enhanced sampling power of the GaMD approach and demonstrates its wide applicability to studies of drug-receptor interactions for the HIV protease and by extension many other target proteins.

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. [ABSTRACT FROM AUTHOR]

The infectivity of HIV‐1 requires its protease (PR) cleave multiple cut‐sites with low sequence similarity. The diversity of cleavage sites has made it challenging to investigate the underlying sequence properties that determine binding and turnover of substrates by PR. We engineered a mutational scanning approach utilizing yeast display, flow cytometry, and deep sequencing to systematically measure the impacts of all individual amino acid changes at 12 positions in three different cut‐sites (MA/CA, NC/p1, and p1/p6). The resulting fitness landscapes revealed common physical features that underlie cutting of all three cut‐sites at the amino acid positions closest to the scissile bond. In contrast, positions more than two amino acids away from the scissile bond exhibited a strong dependence on the sequence background of the rest of the cut‐site. We observed multiple amino acid changes in cut‐sites that led to faster cleavage rates, including a preference for negative charge five and six amino acids away from the scissile bond at locations where the surface of protease is positively charged. Analysis of individual cut sites using full‐length matrix‐capsid proteins indicate that long‐distance sequence context can contribute to cutting efficiency such that analyses of peptides or shorter engineered constructs including those in this work should be considered carefully. This work provides a framework for understanding how diverse substrates interact with HIV‐1 PR and can be extended to investigate other viral PRs with similar properties. [ABSTRACT FROM AUTHOR]

Equilibrium constants, together with kinetic rate constants of binding, are key factors in the efficacy and safety of drug compounds, informing drug design. However, the association pathways of protein-ligand binding, which contribute to their kinetic behaviors, are little understood. In this work, we used unbiased all-atom molecular dynamics (MD) simulations with an explicit solvent model to study the association processes of protein-ligand binding. Using the HIV protease (HIVp)-xk263 and HIVp-ritonavir protein-ligand systems as cases, we observed that ligand association is a multistep process involving diffusion, localization, and conformational rearrangements of the protein, ligand, and water molecules. Moreover, these two ligands preferred different routes of binding, which reflect two well-known binding mechanisms: induced-fit and conformation selection models. Our study shows that xk263 has a stronger capacity for desolvating surrounding water molecules, thereby inducing a semiopen conformation of the HIVp flaps (induced-fit model). In contrast, the slow dehydration characteristic of ritonavir allows for gradual association with the binding pocket of HIVp when the protein's flap conformation is fully open (conformation selection model). By studying the mechanism of ligand association and understanding the role of solvent molecules during the binding event, we can obtain a different perspective on the mechanism of macromolecule recognition, providing insights into drug discovery.

Kinetic properties may serve as critical differentiators and predictors of drug efficacy and safety, in addition to the traditionally focused binding affinity. However the quantitative structure-kinetics relationship (QSKR) for modeling and ligand design is still poorly understood. This review provides an introduction to the kinetics of drug binding from a fundamental chemistry perspective. We focus on recent developments of computational tools and their applications to non-covalent binding kinetics.

BackgroundThe p6 region of the HIV-1 structural precursor polyprotein, Gag, contains two motifs, P7TAP11 and L35YPLXSL41, designated as late (L) domain-1 and -2, respectively. These motifs bind the ESCRT-I factor Tsg101 and the ESCRT adaptor Alix, respectively, and are critical for efficient budding of virus particles from the plasma membrane. L domain-2 is thought to be functionally redundant to PTAP. To identify possible other functions of L domain-2, we examined this motif in dominant viruses that emerged in a group of 14 women who had detectable levels of HIV-1 in both plasma and genital tract despite a history of current or previous antiretroviral therapy.ResultsRemarkably, variants possessing mutations or rare polymorphisms in the highly conserved L domain-2 were identified in seven of these women. A mutation in a conserved residue (S40A) that does not reduce Gag interaction with Alix and therefore did not reduce budding efficiency was further investigated. This mutation causes a simultaneous change in the Pol reading frame but exhibits little deficiency in Gag processing and virion maturation. Whether introduced into the HIV-1 NL4-3 strain genome or a model protease (PR) precursor, S40A reduced production of mature PR. This same mutation also led to high level detection of two extended forms of PR that were fairly stable compared to the WT in the presence of IDV at various concentrations; one of the extended forms was effective in trans processing even at micromolar IDV.ConclusionsOur results indicate that L domain-2, considered redundant in vitro, can undergo mutations in vivo that significantly alter PR function. These may contribute fitness benefits in both the absence and presence of PR inhibitor.

Abstract Background Drug resistance is a critical problem limiting effective antiviral therapy for HIV/AIDS. Computational techniques for predicting drug resistance profiles from genomic data can accelerate the appropriate choice of therapy. These techniques can also be used to identify protease mutants for experimental studies of resistance and thereby assist in the development of next-generation therapies. Few studies, however, have assessed the evolution of resistance from genotype–phenotype data. Results The machine learning produced highly accurate and robust classification of resistance to HIV protease inhibitors. Genotype data were mapped to the enzyme structure and encoded using Delaunay triangulation. Estimates of evolutionary relationships, based on this encoding, and using Minimum Spanning Trees, showed clusters of mutations that closely resemble the wild type. These clusters appear to evolve uniquely to more resistant phenotypes. Conclusions Using the triangulation metric and spanning trees results in paths that are consistent with evolutionary theory. The majority of the paths show bifurcation, namely they switch once from non-resistant to resistant or from resistant to non-resistant. Paths that lose resistance almost uniformly have far lower levels of resistance than those which either gain resistance or are stable. This strongly suggests that selection for stability in the face of a rapid rate of mutation is as important as selection for resistance in retroviral systems.

Anti-retroviral drugs suppress HIV-1 plasma viremia to undetectable levels; however, latent HIV-1 persists in reservoirs within HIV-1-infected patients. The silent provirus can be activated through the use of drugs, including protein kinase C activators and histone deacetylase inhibitors. This "shock" approach is then followed by "kill" of the producing cells either through direct HIV-1-induced cell death or natural immune mechanisms. However, these mechanisms are relatively slow and effectiveness is unclear. Here, we develop an approach to specifically target and kill cells that are activated early in the process of virus production. We utilize a novel nanocapsule technology whereby the ricin A chain is encapsulated in an inactive form within a polymer shell. Specificity for release of the ricin A toxin is conferred by peptide crosslinkers that are sensitive to cleavage by HIV-1 protease. By using well-established latent infection models, J-Lat and U1 cells, we demonstrate that only within an HIV-1-producing cell expressing functional HIV-1 protease will the nanocapsule release its ricin A cargo, shutting down viral and cellular protein synthesis, and ultimately leading to rapid death of the producer cell. Thus, we provide proof of principle for a novel technology to kill HIV-1-producing cells without effects on non-target cells.

The HIV-1 protease is critical for the process of viral maturation and as such, it is one of the most well characterized proteins in the Protein Data Bank. There is some evidence to suggest that the HIV-1 protease is capable of accommodating small molecule fragments at several locations on its surface outside of the active site. However, some pockets on the surface of proteins remain unformed in the apo structure and are termed "cryptic sites." To date, no cryptic sites have been identified in the structure of HIV-1 protease. Here, we characterize a novel cryptic cantilever pocket on the surface of the HIV-1 protease through mixed-solvent molecular dynamics simulations using several probes. Interestingly, we noted that several homologous retroviral proteases exhibit evolutionarily conserved dynamics in the cantilever region and possess a conserved pocket in the cantilever region. Immobilization of the cantilever region of the HIV-1 protease via disulfide cross-linking resulted in curling-in of the flap tips and the propensity for the protease to adopt a semi-open flap conformation. Structure-based analysis and fragment-based screening of the cryptic cantilever pocket suggested that the pocket may be capable of accommodating ligand structures. Furthermore, molecular dynamics simulations of a top scoring fragment bound to the cryptic pocket illustrated altered flap dynamics of the fragment-bound enzyme. Together, these results suggest that the mobility of the cantilever region plays a key role in the global dynamics of retroviral proteases. Therefore, the cryptic cantilever pocket of the HIV-1 protease may represent an interesting target for future in vitro studies., (© 2024 The Author(s). PROTEINS: Structure, Function, and Bioinformatics published by Wiley Periodicals LLC.)

In drug discovery, small molecules must often discriminate between healthy and diseased cells. This feat is usually accomplished by binding to a protein that is preferentially expressed in the target cell or on its surface. However, in many cases, the expression of an individual protein may not generate sufficient cyto-selectivity. Here, we demonstrate that bispecific molecules can better discriminate between similar cell types by exploiting their simultaneous affinity for two proteins. Inspired by the natural product FK506, we designed molecules that have affinity for both FKBP12 and HIV protease. Using cell-based reporters and live virus assays, we observed that these compounds preferentially accumulated in cells that express both targets, mimicking an infected lymphocyte. Treatment with FKBP12 inhibitors reversed this partitioning, while overexpression of FKBP12 protein further promoted it. The partitioning into the target cell type could be tuned by controlling the properties of the linker and the affinities for the two proteins. These results show that bispecific molecules create significantly better potential for cyto-selectivity, which might be especially important in the development of safe and effective antivirals and anticancer compounds.

ObjectivesThe objectives of this study were to determine the prevalence and patterns of resistance to integrase strand transfer inhibitors (INSTIs) in patients experiencing virological failure on raltegravir-based ART and the impact on susceptibility to INSTIs (raltegravir, elvitegravir and dolutegravir).Patients and methodsData were collected from 502 treatment-experienced patients failing a raltegravir-containing regimen in a multicentre study. Reverse transcriptase, protease and integrase were sequenced at failure for each patient. INSTI resistance-associated mutations investigated were those included in the last ANRS genotypic algorithm (v23).ResultsAmong the 502 patients, at failure, median baseline HIV-1 RNA (viral load) was 2.9 log10 copies/mL. Patients had been previously exposed to a median of five NRTIs, one NNRTI and three PIs. Seventy-one percent harboured HIV-1 subtype B and the most frequent non-B subtype was CRF02_AG (13.3%). The most frequent mutations observed were N155H/S (19.1%), Q148G/H/K/R (15.4%) and Y143C/G/H/R/S (6.7%). At failure, viruses were considered as fully susceptible to all INSTIs in 61.0% of cases, whilst 38.6% were considered as resistant to raltegravir, 34.9% to elvitegravir and 13.9% to dolutegravir. In the case of resistance to raltegravir, viruses were considered as susceptible to elvitegravir in 11% and to dolutegravir in 64% of cases. High HIV-1 viral load at failure (P

Traditional Chinese Medicines (TCMs) have been the sole source of therapeutics in China for two millennia. In recent drug discovery efforts, purified components of TCM formulations have shown activity in many in vitro assays, raising concerns of promiscuity. Here, we investigated 14 bioactive small molecules isolated from TCMs for colloidal aggregation. At concentrations commonly used in cell-based or biochemical assay conditions, eight of these compounds formed particles detectable by dynamic light scattering and showed detergent-reversible inhibition against β-lactamase and malate dehydrogenase, two counter-screening enzymes. When three of these compounds were tested against their literature-reported molecular targets, they showed similar reversal of their inhibitory activity in the presence of detergent. For three of the most potent aggregators, contributions to promiscuity via oxidative cycling were investigated; addition of 1 mM DTT had no effect on their activity, which is inconsistent with an oxidative mechanism. TCMs are often active at micromolar concentrations; this study suggests that care must be taken to control for artifactual activity when seeking their primary targets. Implications for the formulation of these molecules are considered.

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.

The emergence of multidrug resistant (MDR) HIV strains severely reduces the effectiveness of antiretroviral therapy. Clinical inhibitor darunavir (1) has picomolar binding affinity for HIV-1 protease (PR), however, drug resistant variants like PRS17 show poor inhibition by 1 , despite the presence of only two mutated residues in the inhibitor-binding site. Antiviral inhibitors that target MDR proteases like PRS17 would be valuable as therapeutic agents. Inhibitors 2 and 3 derived from 1 through substitutions at P1, P2 and P2ʹ positions exhibit 3.4- to 500-fold better inhibition than clinical inhibitors for PRS17 with the exception of amprenavir. Crystal structures of PRS17/ 2 and PRS17/ 3 reveal how these inhibitors target the two active site mutations of PRS17. The substituted methoxy P2 group of 2 forms new interactions with G48V mutation, while the modified bis -fluoro-benzyl P1 group of 3 forms a halogen interaction with V82S mutation, contributing to improved inhibition of PRS17. • Clinical inhibitors of HIV-1 protease are ineffective against drug-resistant mutant PRS17. • Two new antiviral compounds derived from darunavir show better inhibition of PRS17. • Structural analysis reveals new interactions of inhibitors with mutated amino acids in PRS17. [ABSTRACT FROM AUTHOR]

Revealing the processes of ligand-protein associations deepens our understanding of molecular recognition and binding kinetics. Hydrogen bonds (H-bonds) play a crucial role in optimizing ligand-protein interactions and ligand specificity. In addition to the formation of stable H-bonds in the final bound state, the formation of transient H-bonds during binding processes contributes binding kinetics that define a ligand as a fast or slow binder, which also affects drug action. However, the effect of forming the transient H-bonds on the kinetic properties is little understood. Guided by results from coarse-grained Brownian dynamics simulations, we used classical molecular dynamics simulations in an implicit solvent model and accelerated molecular dynamics simulations in explicit waters to show that the position and distribution of the H-bond donor or acceptor of a drug result in switching intermolecular and intramolecular H-bond pairs during ligand recognition processes. We studied two major types of HIV-1 protease ligands: a fast binder, xk263, and a slow binder, ritonavir. The slow association rate in ritonavir can be attributed to increased flexibility of ritonavir, which yields multistep transitions and stepwise entering patterns and the formation and breaking of complex H-bond pairs during the binding process. This model suggests the importance of conversions of spatiotemporal H-bonds during the association of ligands and proteins, which helps in designing inhibitors with preferred binding kinetics.

ObjectivesAs community viral load (CVL) measurements are associated with the incidence of new HIV-1 infections in a population, we hypothesized that similarly measured community drug resistance (CDR) could predict the prevalence of transmitted drug resistance (TDR).MethodsBetween 2001 and 2011, the prevalences of HIV-1 drug resistance for patients with established infection receiving HIV care (i.e. CDR) and TDR in recently infected patients were determined in San Diego. At each position in HIV-1 reverse transcriptase (RT) and protease (pro), drug resistance was evaluated both as the overall prevalence of resistance-associated mutations and by weighting each resistance position to the concurrent viral load of the patient and its proportion to the total viral load of the clinic (CVL). The weighting was the proportion of the CVL associated with patients identified with resistance at each residue. Spearman ranked correlation coefficients were used to determine associations between CDR and TDR.ResultsWe analysed 1088 resistance tests for 971 clinic patients and baseline resistance tests for 542 recently infected patients. CDR at positions 30, 46, and 88 in pro was associated with TDR between 2001 and 2011. When CDR was weighted by the viral load of patients, CDR was associated with TDR at position 103 in RT. Each of these associations was corroborated at least once using shorter measurement intervals.ConclusionsDespite evaluation of a limited percentage of chronically infected patients in San Diego, CDR correlated with TDR at key resistance positions and therefore may be a useful tool with which to predict the prevalence of TDR.

The process of iterative structure-based drug design involves the X-ray crystal structure determination of upwards of 100 ligands with the same general scaffold (i.e. chemotype) complexed with very similar, if not identical, protein targets. In conjunction with insights from computational models and assays, this collection of crystal structures is analyzed to improve potency, to achieve better selectivity and to reduce liabilities such as absorption, distribution, metabolism, excretion and toxicology. Current methods for modeling ligands into electron-density maps typically do not utilize information on how similar ligands bound in related structures. Even if the electron density is of sufficient quality and resolution to allow de novo placement, the process can take considerable time as the size, complexity and torsional degrees of freedom of the ligands increase. A new module, Guided Ligand Replacement (GLR), was developed in Phenix to increase the ease and success rate of ligand placement when prior protein-ligand complexes are available. At the heart of GLR is an algorithm based on graph theory that associates atoms in the target ligand with analogous atoms in the reference ligand. Based on this correspondence, a set of coordinates is generated for the target ligand. GLR is especially useful in two situations: (i) modeling a series of large, flexible, complicated or macrocyclic ligands in successive structures and (ii) modeling ligands as part of a refinement pipeline that can automatically select a reference structure. Even in those cases for which no reference structure is available, if there are multiple copies of the bound ligand per asymmetric unit GLR offers an efficient way to complete the model after the first ligand has been placed. In all of these applications, GLR leverages prior knowledge from earlier structures to facilitate ligand placement in the current structure.

High-throughput drug-discovery and mechanistic studies often require the determination of multiple related crystal structures that only differ in the bound ligands, point mutations in the protein sequence and minor conformational changes. If performed manually, solution and refinement requires extensive repetition of the same tasks for each structure. To accelerate this process and minimize manual effort, a pipeline encompassing all stages of ligand building and refinement, starting from integrated and scaled diffraction intensities, has been implemented in Phenix. The resulting system is able to successfully solve and refine large collections of structures in parallel without extensive user intervention prior to the final stages of model completion and validation.

ObjectivesTo determine protease mutations that develop at viral failure for protease inhibitor (PI)-naive patients on a regimen containing the PI atazanavir.MethodsResistance tests on patients failing atazanavir, conducted as part of routine clinical care in a multicentre observational study, were randomly matched by subtype to resistance tests from PI-naive controls to account for natural polymorphisms. Mutations from the consensus B sequence across the protease region were analysed for association and defined using the IAS-USA 2011 classification list.ResultsFour hundred and five of 2528 (16%) patients failed therapy containing atazanavir as a first PI over a median (IQR) follow-up of 1.76 (0.84-3.15) years and 322 resistance tests were available for analysis. Recognized major atazanavir mutations were found in six atazanavir-experienced patients (P < 0.001), including I50L and N88S. The minor mutations most strongly associated with atazanavir experience were M36I, M46I, F53L, A71V, V82T and I85V (P < 0.05). Multiple novel mutations, I15S, L19T, K43T, L63P/V, K70Q, V77I and L89I/T/V, were also associated with atazanavir experience.ConclusionsViral failure on atazanavir-containing regimens was not common and major resistance mutations were rare, suggesting that adherence may be a major contributor to viral failure. Novel mutations were described that have not been previously documented.

Fullerenes, particularly C 60 , exhibit unique properties that make them promising candidates for various applications, including drug delivery and nanomedicine. However, their interactions with biomolecules, especially proteins, remain not fully understood. This study implements both explicit and implicit C 60 models into the UNRES coarse-grained force field, enabling the investigation of fullerene-protein interactions without the need for restraints to stabilize protein structures. The UNRES force field offers computational efficiency, allowing for longer timescale simulations while maintaining accuracy. Five model proteins were studied: FK506 binding protein, HIV-1 protease, intestinal fatty acid binding protein, PCB-binding protein, and hen egg-white lysozyme. Molecular dynamics simulations were performed with and without C 60 to assess protein stability and investigate the impact of fullerene interactions. Analysis of contact probabilities reveals distinct interaction patterns for each protein. FK506 binding protein (1FKF) shows specific binding sites, while intestinal fatty acid binding protein (1ICN) and uteroglobin (1UTR) exhibit more generalized interactions. The explicit C 60 model shows good agreement with all-atom simulations in predicting protein flexibility, the position of C 60 in the binding pocket, and the estimation of effective binding energies. The integration of explicit and implicit C 60 models into the UNRES force field, coupled with recent advances in coarse-grained modeling and multiscale approaches, provides a powerful framework for investigating protein-nanoparticle interactions at biologically relevant scales without the need to use restraints stabilizing the protein, thus allowing for large conformational changes to occur. These computational tools, in synergy with experimental techniques, can aid in understanding the mechanisms and consequences of nanoparticle-biomolecule interactions, guiding the design of nanomaterials for biomedical applications.

Background: Drug resistance is a critical problem limiting effective antiviral therapy for HIV/AIDS. Computational techniques for predicting drug resistance profiles from genomic data can accelerate the appropriate choice of therapy. These techniques can also be used to identify protease mutants for experimental studies of resistance and thereby assist in the development of next-generation therapies. Few studies, however, have assessed the evolution of resistance from genotype–phenotype data. Results: The machine learning produced highly accurate and robust classification of resistance to HIV protease inhibitors. Genotype data were mapped to the enzyme structure and encoded using Delaunay triangulation. Estimates of evolutionary relationships, based on this encoding, and using Minimum Spanning Trees, showed clusters of mutations that closely resemble the wild type. These clusters appear to evolve uniquely to more resistant phenotypes. Conclusions: Using the triangulation metric and spanning trees results in paths that are consistent with evolutionary theory. The majority of the paths show bifurcation, namely they switch once from non-resistant to resistant or from resistant to non-resistant. Paths that lose resistance almost uniformly have far lower levels of resistance than those which either gain resistance or are stable. This strongly suggests that selection for stability in the face of a rapid rate of mutation is as important as selection for resistance in retroviral systems. [ABSTRACT FROM AUTHOR]

Predicting which mutations proteins tolerate while maintaining their structure and function has important applications for modeling fundamental properties of proteins and their evolution; it also drives progress in protein design. Here we develop a computational model to predict the tolerated sequence space of HIV-1 protease reachable by single mutations. We assess the model by comparison to the observed variability in more than 50,000 HIV-1 protease sequences, one of the most comprehensive datasets on tolerated sequence space. We then extend the model to a second protein, reverse transcriptase. The model integrates multiple structural and functional constraints acting on a protein and uses ensembles of protein conformations. We find the model correctly captures a considerable fraction of protease and reverse-transcriptase mutational tolerance and shows comparable accuracy using either experimentally determined or computationally generated structural ensembles. Predictions of tolerated sequence space afforded by the model provide insights into stability-function tradeoffs in the emergence of resistance mutations and into strengths and limitations of the computational model.

Abstract Background Drug resistance in HIV is the major problem limiting effective antiviral therapy. Computational techniques for predicting drug resistance profiles from genomic data can accelerate the appropriate choice of therapy. These techniques can also be used to select protease mutants for experimental studies of resistance and thereby assist in the development of next-generation therapies. Results The machine learning produced highly accurate and robust classification of HIV protease resistance. Genotype data were mapped to the enzyme structure and encoded using Delaunay triangulation. Generative machine learning models trained on one inhibitor could classify resistance from other inhibitors with varying levels of accuracy. Generally, the accuracy was best when the inhibitors were chemically similar. Conclusions Restricted Boltzmann Machines are an effective machine learning tool for classification of genomic and structural data. They can also be used to compare resistance profiles of different protease inhibitors.

T-Analyst is a user-friendly computer program for analyzing trajectories from molecular modeling. Instead of using Cartesian coordinates for protein conformational analysis, T-Analyst is based on internal bond-angle-torsion coordinates in which internal torsion angle movements, such as side-chain rotations, can be easily detected. The program computes entropy and automatically detects and corrects angle periodicity to produce accurate rotameric states of dihedrals. It also clusters multiple conformations and detects dihedral rotations that contribute hinge-like motions. Correlated motions between selected dihedrals can also be observed from the correlation map. T-Analyst focuses on showing changes in protein flexibility between different states and selecting representative protein conformations for molecular docking studies. The program is provided with instructions and full source code in Perl.

Drug‐resistance is a serious problem for treatment of the HIV/AIDS pandemic. Potent clinical inhibitors of HIV‐1 protease show several orders of magnitude worse inhibition of highly drug‐resistant variants. Hence, the structure and enzyme activities were analyzed for HIV protease mutant HIV‐1 protease (EC 3.4.23.16) (PR) with 22 mutations (PRS5B) from a clinical isolate that was selected by machine learning to represent high‐level drug‐resistance. PRS5B has 22 mutations including only one (I84V) in the inhibitor binding site; however, clinical inhibitors had poor inhibition of PRS5B activity with kinetic inhibition value (Ki) values of 4–1000 nm or 18‐ to 8000‐fold worse than for wild‐type PR. High‐resolution crystal structures of PRS5B complexes with the best inhibitors, amprenavir (APV) and darunavir (DRV) (Ki ~ 4 nm), revealed only minor changes in protease‐inhibitor interactions. Instead, two distinct clusters of mutations in distal regions induce coordinated conformational changes that decrease favorable internal interactions across the entire protein subunit. The largest structural rearrangements are described and compared to other characterized resistant mutants. In the protease hinge region, the N83D mutation eliminates a hydrogen bond connecting the hinge and core of the protease and increases disorder compared to highly resistant mutants PR with 17 mutations and PR with 20 mutations with similar hinge mutations. In a distal β‐sheet, mutations G73T and A71V coordinate with accessory mutations to bring about shifts that propagate throughout the subunit. Molecular dynamics simulations of ligand‐free dimers show differences consistent with loss of interactions in mutant compared to wild‐type PR. Clusters of mutations exhibit both coordinated and antagonistic effects, suggesting PRS5B may represent an intermediate stage in the evolution of more highly resistant variants. Databases: Structural data are available in Protein Data Bank under the accession codes 6P9A and 6P9B for PRS5B/DRV and PRS5B/APV, respectively. [ABSTRACT FROM AUTHOR]

Abstract Background Drug resistance in HIV treatment is still a worldwide problem. Predicting resistance to antiretrovirals (ARVs) before starting any treatment is important. Prediction accuracy is essential, as low-accuracy predictions increase the risk of prescribing sub-optimal drug regimens leading to patients developing resistance sooner. Artificial Neural Networks (ANNs) are a powerful tool that would be able to assist in drug resistance prediction. In this study, we constrained the dataset to subtype B, sacrificing generalizability for a higher predictive performance, and demonstrated that the predictive quality of the ANN regression models have definite improvement for most ARVs. Results Trained regression ANNs were optimized for eight protease inhibitors, six nucleoside reverse transcriptase (RT) inhibitors and four non-nucleoside RT inhibitors by experimenting combinations of rare variant filtering (none versus 1 residue occurrence) and ANN topologies (1–3 hidden layers with 2, 4, 6, 8 and 10 nodes per layer). Single hidden layers (5–20 nodes) were used for training where overfitting was detected. 5-fold cross-validation produced mean R2 values over 0.95 and standard deviations lower than 0.04 for all but two antiretrovirals. Conclusions Overall, higher accuracies and lower variances (compared to results published in 2016) were obtained by experimenting with various preprocessing methods, while focusing on the most prevalent subtype in the raw dataset (subtype B).We thus highlight the need to develop and make available subtype-specific datasets for developing higher accuracy in drug-resistance prediction methods.

Acquired Immune Deficiency Syndrome is the most severe phase of Human Immunodeficiency Virus (HIV) infection. Recent studies have seen an effort to isolate phytochemicals from plants to repress HIV, but less studies have focused on the effects of these phytochemicals on the activities of enzymes/transporters involved in the metabolism of these drugs, which is one of the aims of this study and, to examine the antiviral activity of these compounds against HIV-1 protease enzyme using computational tools. Centre of Awareness-Food Supplement (COA®-FS) herbal medicine, has been said to have potential anti-HIV features. SWISSTARGETPREDICTION and SWISSADME servers were used for determination of the enzymes/transporters involved in the metabolism of these protease inhibitor drugs, (PIs) (Atazanavir, Lopinavir, Darunavir, Saquinavir) and the effects of the selected phytochemicals on the enzymes/transporters involved in the metabolism of these PIs. Using Computational tools, potential structural inhibitory activities of these phytochemicals were explored. Two sub-families of Cytochrome P450 enzymes (CYP3A4 and CYP2C19) and Permeability glycoprotein (P-gp) were predicted to be involved in metabolism of the PIs. Six phytochemicals (Geranin, Apigenin, Fisetin, Luteolin, Phthalic acid and Gallic acid) were predicted to be inhibitors of CYP3A4 and, may slowdown elimination of PIs thereby maintain optimal PIs concentrations. Free binding energy analysis for antiviral activities identified four phytochemicals with favourable binding landscapes with HIV-1 protease enzyme. Epigallocatechin gallate and Kaempferol-7-glucoside exhibited pronounced structural evidence as potential HIV-1 protease enzyme inhibitors. This study acts as a steppingstone toward the use of natural products against diseases that are plagued with adverse drug-interactions.

Drug-resistance threatens effective treatment of HIV/AIDS. Clinical inhibitors, including darunavir (1), are ineffective for highly resistant protease mutant PR20, however, antiviral compound 2 derived from 1 with fused tricyclic group at P2, extended amino-benzothiazole P2' ligand and two fluorine atoms on P1 shows 16-fold better inhibition of PR20 enzyme activity. Crystal structures of PR20 and wild-type PR complexes reveal how the extra groups of 2 counteract the expanded ligand-binding pocket, dynamic flaps, and faster dimer dissociation of PR20. • Clinical inhibitors of HIV-1 protease are ineffective against drug-resistant mutant PR20. • PR20 is an excellent model for evaluating new inhibitors for drug-resistant HIV protease. • New antiviral compound derived from darunavir scaffold has better inhibition of PR20. • Two fluorine atoms of inhibitor introduce new halogen bonds and stabilize PR20. [ABSTRACT FROM AUTHOR]

A helical epitope-peptide (lle85-Gly94) was selected from the α-helix structure of the HIV protease (PR) as the template, which represents an intricate interplay between structure conformation and dimerization. The peptide template was mixed with water, trifluoroethanol (TFE), and acetonitrile (ACN) at a certain ratio to enlarge the helical conformation in the solution for the fabrication of helical epitope-mediated molecularly imprinted polymers (HEMIPs) on a quartz crystal microbalance (QCM) chip. The template molecules were then removed under equilibrium batch rebinding conditions involving 5% acetic acid/water. The resulting HEMIPs chip exhibited a high affinity toward template peptide HIV PR85–94, His-tagged HIV PR, and HIV PR, with dissociation constants (Kd) as 160, 43.3, and 78.5 pM, respectively. The detection limit of the developed HIV PR85–94 QCM sensor is 0.1 ng/mL. The HEMIPs chip exhibited a high affinity and selectivity to bind HIV PR and subsequently to an inhibitor of HIV PR (nelfinavir). The HIV PR binding site was properly oriented on the HEMIPs-chip to develop a HIV PR/HEMIPs chip, which can effectively bind nelfinavir to establish a sandwich assay. The nelfinavir then attached to the HIV PR/HEMIPs chip, which can be easily removed involving 0.8% acetic acid/water. Therefore, HIV PR/HEMIPs chip can be useful to screen for other HIV PR inhibitors. This technique may improve drug targeting for HIV therapy and also strengthen investigations into other virus assays.

In this study, the differences of binding patterns between two type HIV (HIV-1 and HIV-2) protease and two inhibitors (darunavir and amprenavir) are analyzed and compared using the newly developed interaction entropy (IE) method for the entropy change calculation combined with the polarized force field. The functional role of protonation states in the two HIV-2 complexes is investigated and our study finds that the protonated OD1 atom of Asp25′ in B chain is the optimal choice. Those calculated binding free energies obtained from the polarized force field combined with IE method are significantly consistent with the experimental observed. The bridging water W301 is favorable to the binding of HIV-1 complexes; however, it is unfavorable to the HIV-2 complexes in current study. The volume of pocket, B-factor of Cα atoms and the distance of flap tip in HIV-2 complexes are smaller than that of HIV-1 consistently. These changes may cause localized rearrangement of residues lining their surface and finally result in the different binding mode for the two types HIV. Predicated hot-spot residues (Ala28/Ala28′, Ile50/Ile50′, and Ile84/Ile84′) are nearly same in the four systems. However, the contribution to the free energy of Asp30 residue is more favorable in HIV-1 system than in HIV-2 system. Current study, to some extent, reveals the origin for the decrease in binding affinity of inhibitors against HIV-2 compared with HIV-1 and will provides theoretical guidance for future design of potent dual inhibitors targeting two type HIV protease.