Improved cyclic urea inhibitors of the HIV-1 protease: synthesis, potency, resistance profile, human pharmacokinetics and X-ray crystal structure of DMP 450

Preventing lipophilic aggregation in cosolvent molecular dynamics simulations with hydrophobic probes using Plumed Automatic Restraining Tool (PART)

Cosolvent molecular dynamics (MD) simulations are molecular dynamics simulations used to identify preferable locations of small organic fragments on a protein target. Most cosolvent molecular dynamics workflows make use of only water-soluble fragments, as hydrophobic fragments would cause lipophilic aggregation. To date the two approaches that allow usage of hydrophobic cosolvent molecules are to use a low (0.2 M) concentration of hydrophobic probes, with the disadvantage of a lower sampling speed, or to use force field modifications, with the disadvantage of a difficult and inflexible setup procedure. Here we present a third alternative, that does not suffer from low sampling speed nor from cumbersome preparation procedures. We have built an easy-to-use open source command line tool PART (Plumed Automatic Restraining Tool) to generate a PLUMED file handling all intermolecular restraints to prevent lipophilic aggregation. We have compared restrained and unrestrained cosolvent MD simulations, showing that restraints are necessary to prevent lipophilic aggregation at hydrophobic probe concentrations of 0.5 M. Furthermore, we benchmarked PART generated restraints on a test set of four proteins (Factor-Xa, HIV protease, P38 MAP kinase and RNase A), showing that cosolvent MD with PART generated restraints qualitatively reproduces binding features of cocrystallised ligands.

Introducing the Automated Ligand Searcher

The Automated Ligand Searcher (ALISE) is designed as an automated computational drug discovery tool. To approximate the binding free energy of ligands to a receptor, ALISE includes a three-stage workflow, with each stage involving an increasingly sophisticated computational method: molecular docking, molecular dynamics, and free energy perturbation, respectively. To narrow the number of potential ligands, poorly performing ligands are gradually segregated out. The performance and usability of ALISE are benchmarked for a case study containing known active ligands and decoys for the HIV protease. The example illustrates that ALISE filters the decoys successfully and demonstrates that the automation, comprehensiveness, and user-friendliness of the software make it a valuable tool for improved and faster drug development workflows.

Flagellin outer domain dimerization modulates motility in pathogenic and soil bacteria from viscous environments

Flagellar filaments function as the propellers of the bacterial flagellum and their supercoiling is key to motility. The outer domains on the surface of the filament are non-critical for motility in many bacteria and their structures and functions are not conserved. Here, we show the atomic cryo-electron microscopy structures for flagellar filaments from enterohemorrhagic Escherichia coli O157:H7, enteropathogenic E. coli O127:H6, Achromobacter, and Sinorhizobium meliloti, where the outer domains dimerize or tetramerize to form either a sheath or a screw-like surface. These dimers are formed by 180° rotations of half of the outer domains. The outer domain sheath (ODS) plays a role in bacterial motility by stabilizing an intermediate waveform and prolonging the tumbling of E. coli cells. Bacteria with these ODS and screw-like flagellar filaments are commonly found in soil and human intestinal environments of relatively high viscosity suggesting a role for the dimerization in these environments.

It has been suggested that the outer domains of bacterial flagellins are not needed for motility. Here, the authors show that flagellar filament outer domains from some bacteria have unique structures which can alter the motility of the bacteria.

Recent advances in urea- and thiourea-containing compounds: focus on innovative approaches in medicinal chemistry and organic synthesis

Urea and thiourea represent privileged structures in medicinal chemistry. Indeed, these moieties constitute a common framework of a variety of drugs and bioactive compounds endowed with a broad range of therapeutic and pharmacological properties. Herein, we provide an overview of the state-of-the-art of urea and thiourea-containing pharmaceuticals. We also review the diverse approaches pursued for (thio)urea bioisosteric replacements in medicinal chemistry applications. Finally, representative examples of recent advances in the synthesis of urea- and thiourea-based compounds by enabling chemical tools are discussed.

In search of therapeutic candidates for HIV/AIDS: rational approaches, design strategies, structure-activity relationship and mechanistic insights

The HIV/AIDS pandemic is a serious threat to the health and development of mankind, which has affected about 37.9 million people worldwide. The increasing negative health, economic and social impacts of this disease have led to the search for new therapeutic candidates for the mitigation of AIDS/HIV. However, to date, there is still no treatment that can cure this disease. Furthermore, the clinically available drugs have numerous severe side effects. Hence, the synthesis of novel agents from natural leads is one of the rational approaches to obtain new drugs in modern medicinal chemistry. This review article is an effort to summarize recent developments with regards to the discovery of novel analogs with promising biological potential against HIV/AIDS. Herein, we also aim to discuss prospective directions on the progress of more credible and specific analogues. Besides presenting design strategies, the present communication also highlights the structure-activity relationship together with the structural features of the most promising molecules, their IC50 values, mechanistic insights and some interesting key findings revealed during their biological evaluation. The interactions with the amino acid residues of the enzymes responsible for HIV-1 inhibition are also discussed. This collection will be of great interest for researchers working in this area.

1,3-Diazepine: A privileged scaffold in medicinal chemistry

Privileged structures have been widely used as effective templates for drug discovery. While benzo-1,4-diazepine constitutes the first historical example of such a structure, the 1,3 analogue is just as rich in terms of applications in medicinal chemistry. The 1,3-diazepine moiety is present in numerous biological active compounds including natural products, and is used to design compounds displaying a large range of biological activities. It is present in the clinically used anticancer compound pentostatin, in several recent FDA approved β-lactamase inhibitors (e.g., avibactam) and also in coformycin, a natural product known as a ring-expanded purine analogue displaying antiviral and anticancer activities. Several other 1,3-diazepine containing compounds have entered into clinical trials. This heterocyclic structure has been and is still widely used in medicinal chemistry to design enzyme inhibitors, GPCR ligands, and so forth. This review endeavours to highlight the main use of the 1,3-diazepine scaffold and its derivatives, and their applications in medicinal chemistry, drug design, and therapy. We will focus more particularly on the development of enzyme inhibitors incorporating this scaffold, with a strong emphasis on the molecular interactions involved in the inhibition mechanism.

aquaPELE: A Monte Carlo-Based Algorithm to Sample the Effects of Buried Water Molecules in Proteins

Water is frequently found inside proteins, carrying out important roles in catalytic reactions or molecular recognition tasks. Therefore, computational models that aim to study protein-ligand interactions usually have to include water effects through explicit or implicit approaches to obtain reliable results. While full explicit models might be too computationally daunting for some applications, implicit models are normally faster but omit some of the most important contributions of water. This is the case of our in-house software, called protein energy landscape exploration (PELE), which uses implicit models to speed up conformational explorations as much as possible; the lack of explicit water sampling, however, limits its model. In this work, we confront this problem with the development of aquaPELE. It is a new algorithm that extends the exploration capabilities while keeping efficiency as it employs a mixed implicit/explicit approach to also take into account the effects of buried water molecules. With an additional Monte Carlo (MC) routine, a set of explicit water molecules is perturbed inside protein cavities and their effects are dynamically adjusted to the current state of the system. As a result, this implementation can be used to predict the principal hydration sites or the rearrangement and displacement of conserved water molecules upon the binding of a ligand. We benchmarked this new tool focusing on estimating ligand binding modes and hydration sites in cavities with important interfacial water molecules, according to crystallographic structures. Results suggest that aquaPELE sets a fast and reliable alternative for molecular recognition studies in systems with a strong water-dependency.

A novel one-pot method for the stereoselective synthesis of tetrahydropyrimidinones in a low melting mixture

A direct and metal free one-pot method has been developed for the stereoselective synthesis of tetrahydropyrimidinone derivatives from a vinyl arene and formaldehyde using a tartaric acid-dimethylurea (TA : DMU) melt as a green reaction medium. The substrate scope of this method is very general and the tetrahydropyrimidinone (THPM) derivatives are synthesized in good yields with a high degree of diastereoselectivity. In this reaction, the melt plays a triple role as the solvent, catalyst and reagent.

Urea Derivatives in Modern Drug Discovery and Medicinal Chemistry

The urea functionality is inherent to numerous bioactive compounds, including a variety of clinically approved therapies. Urea containing compounds are increasingly used in medicinal chemistry and drug design in order to establish key drug-target interactions and fine-tune crucial drug-like properties. In this perspective, we highlight physicochemical and conformational properties of urea derivatives. We provide outlines of traditional reagents and chemical procedures for the preparation of ureas. Also, we discuss newly developed methodologies mainly aimed at overcoming safety issues associated with traditional synthesis. Finally, we provide a broad overview of urea-based medicinally relevant compounds, ranging from approved drugs to recent medicinal chemistry developments.

An insight to the molecular interactions of the FDA approved HIV PR drugs against L38L↑N↑L PR mutant

The aspartate protease of the human immune deficiency type-1 virus (HIV-1) has become a crucial antiviral target in which many useful antiretroviral inhibitors have been developed. However, it seems the emergence of new HIV-1 PR mutations enhances drug resistance, hence, the available FDA approved drugs show less activity towards the protease. A mutation and insertion designated L38L↑N↑L PR was recently reported from subtype of C-SA HIV-1. An integrated two-layered ONIOM (QM:MM) method was employed in this study to examine the binding affinities of the nine HIV PR inhibitors against this mutant. The computed binding free energies as well as experimental data revealed a reduced inhibitory activity towards the L38L↑N↑L PR in comparison with subtype C-SA HIV-1 PR. This observation suggests that the insertion and mutations significantly affect the binding affinities or characteristics of the HIV PIs and/or parent PR. The same trend for the computational binding free energies was observed for eight of the nine inhibitors with respect to the experimental binding free energies. The outcome of this study shows that ONIOM method can be used as a reliable computational approach to rationalize lead compounds against specific targets. The nature of the intermolecular interactions in terms of the host-guest hydrogen bond interactions is discussed using the atoms in molecules (AIM) analysis. Natural bond orbital analysis was also used to determine the extent of charge transfer between the QM region of the L38L↑N↑L PR enzyme and FDA approved drugs. AIM analysis showed that the interaction between the QM region of the L38L↑N↑L PR and FDA approved drugs are electrostatic dominant, the bond stability computed from the NBO analysis supports the results from the AIM application. Future studies will focus on the improvement of the computational model by considering explicit water molecules in the active pocket. We believe that this approach has the potential to provide information that will aid in the design of much improved HIV-1 PR antiviral drugs.

The Roles of Water in the Protein Matrix: A Largely Untapped Resource for Drug Discovery

The value of thoroughly understanding the thermodynamics specific to a drug discovery/design study is well known. Over the past decade, the crucial roles of water molecules in protein structure, function, and dynamics have also become increasingly appreciated. This Perspective explores water in the biological environment by adopting its point of view in such phenomena. The prevailing thermodynamic models of the past, where water was seen largely in terms of an entropic gain after its displacement by a ligand, are now known to be much too simplistic. We adopt a set of terminology that describes water molecules as being "hot" and "cold", which we have defined as being easy and difficult to displace, respectively. The basis of these designations, which involve both enthalpic and entropic water contributions, are explored in several classes of biomolecules and structural motifs. The hallmarks for characterizing water molecules are examined, and computational tools for evaluating water-centric thermodynamics are reviewed. This Perspective's summary features guidelines for exploiting water molecules in drug discovery.

Discovery of HIV Type 1 Aspartic Protease Hit Compounds through Combined Computational Approaches

A combination of computational techniques and inhibition assay experiments was employed to identify hit compounds from commercial libraries with enhanced inhibitory potency against HIV type 1 aspartic protease (HIV PR). Extensive virtual screening with the aid of reliable pharmacophore models yielded five candidate protease inhibitors. Subsequent molecular dynamics and molecular mechanics Poisson-Boltzmann surface area free-energy calculations for the five ligand-HIV PR complexes suggested a high stability of the systems through hydrogen-bond interactions between the ligands and the protease's flaps (Ile50/50'), as well as interactions with residues of the active site (Asp25/25'/29/29'/30/30'). Binding-energy calculations for the three most promising compounds yielded values between -5 and -10 kcal mol(-1) and suggested that van der Waals interactions contribute most favorably to the total energy. The predicted binding-energy values were verified by in vitro inhibition assays, which showed promising results in the high nanomolar range. These results provide structural considerations that may guide further hit-to-lead optimization toward improved anti-HIV drugs.

Computational challenges of structure-based approaches applied to HIV

Here, we review some of the opportunities and challenges that we face in computational modeling of HIV therapeutic targets and structural biology, both in terms of methodology development and structure-based drug design (SBDD). Computational methods have provided fundamental support to HIV research since the initial structural studies, helping to unravel details of HIV biology. Computational models have proved to be a powerful tool to analyze and understand the impact of mutations and to overcome their structural and functional influence in drug resistance. With the availability of structural data, in silico experiments have been instrumental in exploiting and improving interactions between drugs and viral targets, such as HIV protease, reverse transcriptase, and integrase. Issues such as viral target dynamics and mutational variability, as well as the role of water and estimates of binding free energy in characterizing ligand interactions, are areas of active computational research. Ever-increasing computational resources and theoretical and algorithmic advances have played a significant role in progress to date, and we envision a continually expanding role for computational methods in our understanding of HIV biology and SBDD in the future.

Inhibiting Helicobacter pylori HtrA protease by addressing a computationally predicted allosteric ligand binding site

Helicobacter pylori is associated with inflammatory diseases and can cause gastric cancer and mucosa-associated lymphoma. One of the bacterium's key proteins is high temperature requirement A (HpHtrA) protein, an extracellular serine protease that cleaves E-cadherin of gastric epithelial cells, which leads to loss of cell-cell adhesion. Inhibition of HpHtrA may constitute an intervention strategy against H. pylori infection. Guided by the computational prediction of hypothetical ligand binding sites on the surface of HpHtrA, we performed residue mutation experiments that confirmed the functional relevance of an allosteric region. We virtually screened for potential ligands addressing this surface cleft located between the catalytic and PDZ1 domains. Our receptor-based computational method represents protein surface pockets in terms of graph frameworks and retrieves small molecules that satisfy the constraints given by the pocket framework. A new chemical entity was identified that blocked E-cadherin cleavage in vitro by direct binding to HpHtrA, and efficiently blocked pathogen transmigration across the gastric epithelial barrier. A preliminary crystal structure of HpHtrA confirms the validity of a comparative "homology" model of the enzyme, which we used for the computational study. The results of this study demonstrate that addressing orphan protein surface cavities of target macromolecules can lead to new bioactive ligands.

Inclusion of multiple fragment types in the site identification by ligand competitive saturation (SILCS) approach

The site identification by ligand competitive saturation (SILCS) method identifies the location and approximate affinities of small molecular fragments on a target macromolecular surface by performing molecular dynamics (MD) simulations of the target in an aqueous solution of small molecules representative of different chemical functional groups. In this study, we introduce a set of small molecules to map potential interactions made by neutral hydrogen bond donors and acceptors and charged donor and acceptor fragments in addition to nonpolar fragments. The affinity pattern is obtained in the form of discretized probability or, equivalently, free energy maps, called FragMaps, which can be visualized with the target surface. We performed SILCS simulations for four proteins for which structural and thermodynamic data is available for multiple diverse ligands. Good overlap is shown between high affinity regions identified by the FragMaps and the crystallographic positions of ligand functional groups with similar chemical functionality, thus demonstrating the validity of the qualitative information obtained from the simulations. To test the ability of FragMaps in providing quantitative predictions, we calculate the previously introduced ligand grid free energy (LGFE) metric and observe its correspondence with experimentally measured binding affinity. LGFE is computed for different conformational ensembles and improvement in prediction is shown with increasing ligand conformational sampling. Ensemble generation includes a Monte Carlo sampling approach that uses the GFE FragMaps directly as the energy function. The results show that some but not all experimental trends are predicted and warrant improvements in the scoring methodology. In addition, the potential utility of atom-based free energy contributions to the LGFE scores and the use of multiple ligands in SILCS to identify displaceable water molecules during ligand design are discussed.

Second generation HIV protease inhibitors against resistant virus

BACKGROUND

The AIDS epidemic has spread around the world at an alarming rate. Although the first generation of HIV protease inhibitors, including indinavir, nelfinavir, saquinavir, ritonavir and amprenavir, were initially effective against HIV infection, the fast emerging resistance to these agents has been a substantial and persistent problem in the treatment of AIDS. Attempts to address the resistance issue with 'salvage therapy' consisting of high doses of multiple protease inhibitors have only been moderately successful owing to the high level of cross-resistance and toxicities associated with the protease inhibitors.

OBJECTIVE

To study the second generation HIV protease inhibitors against resistant virus.

METHOD

This review highlights new developments achieved by various organizations to address the challenge of high level resistance of current therapies since 2000.

CONCLUSION

All second generation protease inhibitors used in patients who experienced extensive treatment require ritonavir as a pharmacological boosting agent to increase the drug level in the plasma, but there is toxicity associated with such a practice. Accordingly, there remains a need for new protease inhibitors with improved effectiveness against the resistant viral variants. A third generation protease inhibitor will require no boosting agent while maintaining high potency against resistant virus.

A force field with discrete displaceable waters and desolvation entropy for hydrated ligand docking

In modeling ligand-protein interactions, the representation and role of water are of great importance. We introduce a force field and hydration docking method that enables the automated prediction of waters mediating the binding of ligands with target proteins. The method presumes no prior knowledge of the apo or holo protein hydration state and is potentially useful in the process of structure-based drug discovery. The hydration force field accounts for the entropic and enthalpic contributions of discrete waters to ligand binding, improving energy estimation accuracy and docking performance. The force field has been calibrated and validated on a total of 417 complexes (197 training set; 220 test set), then tested in cross-docking experiments, for a total of 1649 ligand-protein complexes evaluated. The method is computationally efficient and was used to model up to 35 waters during docking. The method was implemented and tested using unaltered AutoDock4 with new force field tables.

Multiple receptor conformation docking and dock pose clustering as tool for CoMFA and CoMSIA analysis - a case study on HIV-1 protease inhibitors

Multiple receptors conformation docking (MRCD) and clustering of dock poses allows seamless incorporation of receptor binding conformation of the molecules on wide range of ligands with varied structural scaffold. The accuracy of the approach was tested on a set of 120 cyclic urea molecules having HIV-1 protease inhibitory activity using 12 high resolution X-ray crystal structures and one NMR resolved conformation of HIV-1 protease extracted from protein data bank. A cross validation was performed on 25 non-cyclic urea HIV-1 protease inhibitor having varied structures. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were generated using 60 molecules in the training set by applying leave one out cross validation method, r (loo) (2) values of 0.598 and 0.674 for CoMFA and CoMSIA respectively and non-cross validated regression coefficient r(2) values of 0.983 and 0.985 were obtained for CoMFA and CoMSIA respectively. The predictive ability of these models was determined using a test set of 60 cyclic urea molecules that gave predictive correlation (r (pred) (2) ) of 0.684 and 0.64 respectively for CoMFA and CoMSIA indicating good internal predictive ability. Based on this information 25 non-cyclic urea molecules were taken as a test set to check the external predictive ability of these models. This gave remarkable out come with r (pred) (2) of 0.61 and 0.53 for CoMFA and CoMSIA respectively. The results invariably show that this method is useful for performing 3D QSAR analysis on molecules having different structural motifs.

Accounting for water molecules in drug design

IMPORTANCE OF THE FIELD

Water molecules often appear around ligands in protein crystal structures. Reliable prediction of the effects of water on ligand binding remains a challenge. Solvation effects are crucial for lead optimization where a 100-fold difference in binding affinity is significant but correspond to only ∼3 kcal/mol in binding free energy. Well-known examples, such as nonpeptidic urea inhibitors of HIV protease, prove that careful examination of water molecules and their energetics can contribute significantly to a successful drug design campaign.

AREAS COVERED IN THIS REVIEW

In this review, we examine methods to account for the effect of water in ligand binding at two stages of drug discovery: lead identification via docking calculations and lead optimization. We provide a survey of the models and techniques available to account for water in drug design.

WHAT THE READER WILL GAIN

The reader will become aware of common practices and pitfalls in dealing with water molecules in structure-based drug design.

TAKE HOME MESSAGE

Although solvation effects are not fully understood, some pragmatic recommendations at the end of the article provide guidance for modelers in this area as well as new practitioners.

Modeling Protein-Ligand Binding by Mining Minima

We present the first application of the mining minima algorithm to protein-small molecule binding. This end-point approach use an empirical force field and implicit solvent models, treats the protein binding-site as fully flexible and estimates free energies as sums over local energy wells. The calculations are found to yield encouraging agreement with experiment for three sets of HIV-1protease inhibitors and a set of phosphodiesterase 10a inhibitors. The contributions of various aspects of the model to its accuracy are examined, and the Poisson-Boltzmann correction is found to be the most critical. Interestingly, the computed changes in configurational entropy upon binding fall roughly along the same entropy-energy correlation previously observed for smaller host-guest systems. Strengths and weaknesses of the method are discussed, as are the prospects for enhancing accuracy and speed.

Impact of amino acid variations in Gag and protease of HIV type 1 CRF01_AE strains on drug susceptibility of virus to protease inhibitors

BACKGROUND

Protease (PR) inhibitors (PIs) were designed against subtype B virus of human immunodeficiency virus type 1 (HIV-1), but believed to retain its activity against most of the other subtypes. CRF01_AE PR (AE-PR) contains background mutations that are presumed to alter the drug susceptibility of PR. In addition, amino acid variations found in HIV-1 Gag potentially affect the drug susceptibility or catalytic efficiency of PR.

METHODS

We studied the impact of naturally occurring amino acid substitutions found in AE-PR and CRF01_AE Gag (AE-Gag) on the drug susceptibility of PR to 9 currently available PIs, using the pNL4-3-derived luciferase reporter virus containing AE-Gag and/or AE-PR genes derived from drug treatment-naïve, HIV-1-infected Thai patients.

RESULTS

Sequencing analysis revealed that several mutations were detected in deduced amino acid sequences of AE-PR and AE-Gag genes, as compared to these genes of pNL4-3. Drug susceptibility tests revealed that AE-PR showed a variety of susceptibilities to 9 PIs compared with pNL4-3 PR. In addition, AE-Gag significantly reduced the drug susceptibility of AE-PR and pNL4-3 PR.

CONCLUSION

Our results suggest that amino acid variations in AE-PR and AE-Gag play roles in determining the drug susceptibility of CRF01_AE viruses to PIs.

Assessment of QM/MM scoring functions for molecular docking to HIV-1 protease

We explore the ability of four quantum mechanical (QM)/molecular mechanical (MM) models to accurately identify the native pose of six HIV-1 protease inhibitors and compare them with the AMBER force field and ChemScore and GoldScore scoring functions. Three QM/MM scoring functions treated the ligand at the HF/6-31G*, AM1d, and PM3 levels; the fourth QM/MM function modeled the ligand and active site at the PM3-D level. For the discrimination of native from non-native poses, solvent-corrected HF/6-31G*:AMBER and AMBER functions exhibited the best overall performance. While the electrostatic component of the MM and QM/MM functions appears important for discriminating the native pose of the ligand, the polarization contribution in the QM/MM functions was relatively insensitive to a ligand's binding mode and, for one ligand, actually hindered discrimination. The inclusion of a desolvation penalty, here using a generalized Born solvent model, improved discrimination for the MM and QM/MM methods. There appeared to be no advantage to binding mode prediction by incorporating active site polarization at the PM3-D level. Finally, we found that choice of the protonation state of the aspartyl dyad in the HIV-1 protease active site influenced the ability of scoring methods to determine the native binding pose.

Complexity in modeling and understanding protonation states: computational titration of HIV-1-protease-inhibitor complexes

The computational-titration (CT) algorithm based on the 'natural' Hydropathic INTeractions (HINT) force field is described. The HINT software model is an empirical, non-Newtonian force field derived from experimentally measured partition coefficients for solvent transfer between octanol and H(2)O (log P(o/w)). The CT algorithm allows the identification, modeling, and optimization of multiple protonation states of residues and ligand functional groups at the protein-ligand active site. The importance of taking into account pH and ionization states of residues, which strongly affect the process of ligand binding, for correctly predicting binding free energies is discussed. The application of the CT protocol to a set of six cyclic inhibitors in their complexes with HIV-1 protease is presented, and the advance of HINT as a virtual-screening tool is outlined.

Potent new antiviral compound shows similar inhibition and structural interactions with drug resistant mutants and wild type HIV-1 protease

The potent new antiviral inhibitor GRL-98065 (1) of HIV-1 protease (PR) has been studied with PR variants containing the single mutations D30N, I50V, V82A, and I84V that provide resistance to the major clinical inhibitors. Compound 1 had inhibition constants of 17-fold, 8-fold, 3-fold, and 3-fold, respectively, for PR(D30N), PR(I50V), PR(V82A), and PR(I84V) relative to wild type PR. The chemically related darunavir had similar relative inhibition, except for PR(D30N), where inhibitor 1 was approximately 3-fold less potent. The high resolution (1.11-1.60 Angstrom) crystal structures of PR mutant complexes with inhibitor 1 showed small changes relative to the wild type enzyme. PR(D30N) and PR(V82A) showed compensating interactions with inhibitor 1 relative to those of PR, while reduced hydrophobic contacts were observed with PR(I50V) and PR(I84V). Importantly, inhibitor 1 complexes showed fewer changes relative to wild type enzyme than reported for darunavir complexes. Therefore, inhibitor 1 is a valuable addition to the antiviral inhibitors with high potency against resistant strains of HIV.

Exploring Experimental Sources of Multiple Protein Conformations in Structure-Based Drug Design

Receptor flexibility must be incorporated into structure-based drug design in order to portray a more accurate representation of a protein in solution. Our approach is to generate pharmacophore models based on multiple conformations of a protein and is very similar to solvent mapping of hot spots. Previously, we had success using computer-generated conformations of apo human immunodeficiency virus-1 protease (HIV-1p). Here, we examine the use of an NMR ensemble versus a collection of crystal structures, and we compare back to our previous study based on computer-generated conformations. To our knowledge, this is the first direct comparison of an NMR ensemble and a collection of crystal structures to incorporate protein flexibility in structure-based drug design. To provide an accurate comparison between the experimental sources, we used bound structures for our multiple protein structure (MPS) pharmacophore models. The models from an NMR ensemble and a collection of crystal structures were both able to discriminate known HIV-1p inhibitors from decoy molecules and displayed superior performance over models created from single conformations of the protein. Although the active-site conformations were already predefined by bound ligands, the use of MPS allows us to overcome the cross-docking problem and generate a model that does not simply reproduce the chemical characteristics of a specific ligand class. We show that there is more structural variation between 28 structures in an NMR ensemble than 90 crystal structures bound to a variety of ligands. MPS models from both sources performed well, but the model determined using the NMR ensemble appeared to be the most general yet accurate representation of the active site. This work encourages the use of NMR models in structure-based design.

Water at biomolecular binding interfaces

Water molecules are often found at the binding interface of biomolecular complexes mediating the interaction between polar groups via hydrogen bonds, or simply filling space providing van der Waals interactions. Recent studies have demonstrated the importance of taking such water molecules into account in docking and binding affinity prediction. Here, we review the recent experimental and theoretical work aimed at quantifying the influence of interfacial water on the thermodynamic properties of binding. We highlight especially our recent results obtained by inhomogeneous fluid solvation theory in several systems and the prediction of the thermodynamic consequences of displacement of the bound water molecule by ligand modification. Finally, we discuss possible directions for further progress in this field.

Effectiveness of nonpeptide clinical inhibitor TMC-114 on HIV-1 protease with highly drug resistant mutations D30N, I50V, and L90M

The potent new antiviral inhibitor TMC-114 (UIC-94017) of HIV-1 protease (PR) has been studied with three PR variants containing single mutations D30N, I50V, and L90M, which provide resistance to the major clinical inhibitors. The inhibition constants (K(i)) of TMC-114 for mutants PR(D30N), PR(I50V), and PR(L90M) were 30-, 9-, and 0.14-fold, respectively, relative to wild-type PR. The molecular basis for the inhibition was analyzed using high-resolution (1.22-1.45 A) crystal structures of PR mutant complexes with TMC-114. In PR(D30N), the inhibitor has a water-mediated interaction with the side chain of Asn30 rather than the direct interaction observed in PR, which is consistent with the relative inhibition. Similarly, in PR(I50V) the inhibitor loses favorable hydrophobic interactions with the side chain of Val50. TMC-114 has additional van der Waals contacts in PR(L90M) structure compared to the PR structure, leading to a tighter binding of the inhibitor. The observed changes in PR structure and activity are discussed in relation to the potential for development of resistant mutants on exposure to TMC-114.

De novo ligand design to an ensemble of protein structures

We describe a combinatorial method for de novo ligand design to an ensemble of receptor structures. Receptor conformations, protonation states, and structural water molecules are considered consistently within the framework of de novo ligand design. The method relies on Monte Carlo optimization to search the space of ligand structures, conformations, and rigid-body movements as well as receptor models. The method is applied to an ensemble of HIV protease and human collagenase receptor models. Ligand structures generated de novo exhibit the correct hydrogen-bonding pattern in the core of the active site, with hydrophobic groups extending into the receptor S1 and S1' pocket space. Furthermore, it is shown that known ligands are recovered in the correct binding mode and in the native, most tightly binding receptor model.

Modeling of activity of cyclic urea HIV-1 protease inhibitors using regularized-artificial neural networks

Artificial neural networks (ANNs) were used to model both inhibition of HIV-1 protease (K(i)) and inhibition of HIV replication (IC90) for 55 cyclic urea derivatives using constitutional and 2D descriptors. As a preliminary step, linear dependences were established by multiple linear regression (MLR) approaches, selecting the relevant descriptors by genetic algorithm (GA) feature selection. For ANN models non-linear GA feature selection was also applied. Non-linear modeling of K(i) overcame the results of the linear one using four properties, keeping in mind standard Pearson R correlation coefficients (0.931 vs. 0.862) and leave one out (LOO) cross-validation analysis (Q(LOO)2 = 0.703 vs. 0.510). On the other hand, IC90 modeling was insoluble by a linear approach: no predictive model was achieved; however, a non-linear relation was encountered according to statistic results (R = 0.891; Q(LOO)2 = 0.568). The best non-linear models suggested the influence of the presence of nitrogen atoms and the molecular volume distribution in the inhibitor structures on the HIV-1 protease inhibition as well as that the inhibition of HIV replication was dependent on the occurrence of five-member rings. Finally, inhibitors were well distributed regarding its activity levels in a Kohonen self-organizing map built using the input variables of the best non-linear models.

Combinatorial design of nonsymmetrical cyclic urea inhibitors of aspartic protease of HIV-1

The aspartic protease (PR) of the human immunodeficiency virus type 1 (HIV-1) is an important target for the design of specific antiviral agents dedicated to treatment of HIV-1 infection. We have employed computer-assisted combinatorial chemistry methods to design a small focused virtual library of nonsymmetrically substituted cyclic urea inhibitors of the PR. Nonsymmetrical compounds with decreased peptidic character were namely found to inhibit the PR with comparable inhibition potencies as their C2-pseudosymmetric counterparts and to possess superior pharmacokinetic properties. To generate the virtual library of fully nonsymmetrical cyclic urea analogs, diverse reagents were selected from databases of available chemicals with characteristics similar to those of the building blocks of known potent PR inhibitors. The X-ray structure of the protease-inhibitor complex PR-XV-638 was used as the receptor model in the structure-based focusing and in silico screening of the virtual library. A target-specific LUDI-type scoring function, parameterized for a QSAR training set of known cyclic urea inhibitors and validated on a set of compounds not included into the training set, was used to predict the inhibition constants (Ki) of the generated analogs toward the HIV-1 PR. The fragments most frequently occurring in the analogs with the highest predicted inhibition potencies (Ki*<10 pM) were then selected to constitute a highly focused library subset containing novel nonsymmetrical cyclic ureas with predicted Ki*s 1 order of magnitude lower than the most potent known cyclic urea inhibitors. ADME properties calculated for the most promising analogs suggested that the cyclic ureas are endowed with a wide range of favorable pharmacokinetic properties, which may favor the discovery of a potent orally administrable antiviral drug.