Simple, intuitive calculations of free energy of binding for protein-ligand complexes. 3. The free energy contribution of structural water molecules in HIV-1 protease complexes

Merging cultures and disciplines to create a drug discovery ecosystem at Virginia commonwealth university: Medicinal chemistry, structural biology, molecular and behavioral pharmacology and computational chemistry

The Department of Medicinal Chemistry, together with the Institute for Structural Biology, Drug Discovery and Development, at Virginia Commonwealth University (VCU) has evolved, organically with quite a bit of bootstrapping, into a unique drug discovery ecosystem in response to the environment and culture of the university and the wider research enterprise. Each faculty member that joined the department and/or institute added a layer of expertise, technology and most importantly, innovation, that fertilized numerous collaborations within the University and with outside partners. Despite moderate institutional support with respect to a typical drug discovery enterprise, the VCU drug discovery ecosystem has built and maintained an impressive array of facilities and instrumentation for drug synthesis, drug characterization, biomolecular structural analysis and biophysical analysis, and pharmacological studies. Altogether, this ecosystem has had major impacts on numerous therapeutic areas, such as neurology, psychiatry, drugs of abuse, cancer, sickle cell disease, coagulopathy, inflammation, aging disorders and others. Novel tools and strategies for drug discovery, design and development have been developed at VCU in the last five decades; e.g., fundamental rational structure-activity relationship (SAR)-based drug design, structure-based drug design, orthosteric and allosteric drug design, design of multi-functional agents towards polypharmacy outcomes, principles on designing glycosaminoglycans as drugs, and computational tools and algorithms for quantitative SAR (QSAR) and understanding the roles of water and the hydrophobic effect.

HINT, a code for understanding the interaction between biomolecules: a tribute to Donald J. Abraham

A long-lasting goal of computational biochemists, medicinal chemists, and structural biologists has been the development of tools capable of deciphering the molecule-molecule interaction code that produces a rich variety of complex biomolecular assemblies comprised of the many different simple and biological molecules of life: water, small metabolites, cofactors, substrates, proteins, DNAs, and RNAs. Software applications that can mimic the interactions amongst all of these species, taking account of the laws of thermodynamics, would help gain information for understanding qualitatively and quantitatively key determinants contributing to the energetics of the bimolecular recognition process. This, in turn, would allow the design of novel compounds that might bind at the intermolecular interface by either preventing or reinforcing the recognition. HINT, hydropathic interaction, was a model and software code developed from a deceptively simple idea of Donald Abraham with the close collaboration with Glen Kellogg at Virginia Commonwealth University. HINT is based on a function that scores atom-atom interaction using LogP, the partition coefficient of any molecule between two phases; here, the solvents are water that mimics the cytoplasm milieu and octanol that mimics the protein internal hydropathic environment. This review summarizes the results of the extensive and successful collaboration between Abraham and Kellogg at VCU and the group at the University of Parma for testing HINT in a variety of different biomolecular interactions, from proteins with ligands to proteins with DNA.

Comprehending the Structure, Dynamics, and Mechanism of Action of Drug-Resistant HIV Protease

Since the emergence of the Human Immunodeficiency Virus (HIV) in the 1980s, strategies to combat HIV-AIDS are continuously evolving. Among the many tested targets to tackle this virus, its protease enzyme (PR) was proven to be an attractive option that brought about numerous research publications and ten FDA-approved drugs to inhibit the PR activity. However, the drug-induced mutations in the enzyme made these small molecule inhibitors ineffective with prolonged usage. The research on HIV PR, therefore, remains a thrust area even today. Through this review, we reiterate the importance of understanding the various structural and functional components of HIV PR in redesigning the structure-based small molecule inhibitors. We also discuss at length the currently available FDA-approved drugs and how these drug molecules induced mutations in the enzyme structure. We then recapitulate the reported mechanisms on how these drug-resistant variants remain sufficiently active to cleave the natural substrates. We end with the future scope covering the recently proposed strategies that show promise to deal with the mutations.

Outliers in SAR and QSAR: 3. Importance of considering the role of water molecules in protein-ligand interactions and quantitative structure-activity relationship studies

It is frequently mentioned that QSARs have not generally lived up to expectations, especially in cases where high predictability is expected yet failed to deliver satisfactory results. Even though outliers can provide an increased opportunity in drug discovery research, outliers in SAR and QSAR can contort predictions and affect the accuracy if proper attention is not given. The percentages of outliers in QSARs have not changed appreciably over the last decade. In our previous studies, we suggested two possible sources of outliers in SAR and QSAR. In this paper, we suggest an additional possible source of outliers in QSAR. We presented several literature examples that show one or more water molecules that play a critical role in protein-ligand binding interactions as observed in their crystal structures. These examples illustrate that failing to account for the effects of water molecules in protein-ligand interactions could mislead interpretation and possibly yield outliers in SAR and QSAR. Examples include cases where QSAR, considering the role of water molecules in protein-ligand crystal structures, provided deeper insight into the understanding and interpretation of the developed QSAR.

Identification of Potential Inhibitors of 3CL Protease of SARS-CoV-2 From ZINC Database by Molecular Docking-Based Virtual Screening

The rapid outbreak of Coronavirus Disease 2019 (COVID-19) that was first identified in Wuhan, China is caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The 3CL protease (3CLpro) is the main protease of the SARS-CoV-2, which is responsible for the viral replication and therefore considered as an attractive drug target since to date there is no specific and effective vaccine available against this virus. In this paper, we reported molecular docking-based virtual screening (VS) of 2000 compounds obtained from the ZINC database and 10 FDA-approved (antiviral and anti-malaria) on 3CLpro using AutoDock Vina to find potential inhibitors. The screening results showed that the top four compounds, namely ZINC32960814, ZINC12006217, ZINC03231196, and ZINC33173588 exhibited high affinity at the 3CLpro binding pocket. Their free energy of binding (FEB) were -12.3, -11.9, -11.7, and -11.2 kcal/mol while AutoDock Vina scores were -12.61, -12.32, -12.01, and -11.92 kcal/mol, respectively. These results were better than the co-crystallized ligand N3, whereby its FEB was -7.5 kcal/mol and FDA-approved drugs. Different but stable interactions were obtained between the four identified compounds with the catalytic dyad residues of the 3CLpro. In conclusion, novel 3CLpro inhibitors from the ZINC database were successfully identified using VS and molecular docking approach, fulfilling the Lipinski rule of five, and having low FEB and functional molecular interactions with the target protein. The findings suggests that the identified compounds may serve as potential leads that act as COVID-19 3CLpro inhibitors, worthy for further evaluation and development.

Novel and potent inhibitors for dihydropteroate synthase of Helicobacter pylori

An endless drug-resistant strains of Helicobacter pylori and multitudinous drug reactions are obstacles in the treatment of H. pylori infections, thereby ambitious novel proof-of-concept for inhibitor design was practiced in advancement of medication. Dihydropteroate synthase (DHPS) is an alluring target that plays a great role in folate synthesis pathway essential for amino acids biosynthesis was selected for designing novel drugs to prevent infections caused by pathogenic H. pylori. In the present study, a reliable tertiary structure of DHPS in complex with inhibitor 6MB was constructed by Modeler 9v19. DrugBank compounds of DHPS, published inhibitors, and co-crystal ligand (6MB) were docked against DHPS. The best docked compounds were screened against 28.5 million compounds resulted 1186 structural analogs. Virtual screening workflow and quantum polarized ligand dockings of these compounds against DHPS resulted three leads that showed better XP Gscores, ADME properties, and binding-free energies compared to 6MB, DrugBank compounds, and published inhibitors. The proposed leads were also validated by receiver operative characteristic (ROC) curve metrics in the presence of thousand decoys and the best docked existing compounds against DHPS. Long-range molecular dynamics (MD) simulations for 100 ns were executed after post-docking evaluations. Trajectory analysis showed the lead-DHPS docking complex's inter-molecular interactions were stable throughout the entire runtime of MD simulations than 6MB-DHPS complex and Eliglustat-DHPS complex. The study outcomes showed good competitive binding propensity and active-tunneling of leads over the existing inhibitors, thereby these leads could be ideal inhibitors against DHPS to target H. pylori.

Advances in the Treatment of Explicit Water Molecules in Docking and Binding Free Energy Calculations

Background:

The inclusion of direct effects mediated by water during the ligandreceptor recognition is a hot-topic of modern computational chemistry applied to drug discovery and development. Docking or virtual screening with explicit hydration is still debatable, despite the successful cases that have been presented in the last years. Indeed, how to select the water molecules that will be included in the docking process or how the included waters should be treated remain open questions.

Objective:

In this review, we will discuss some of the most recent methods that can be used in computational drug discovery and drug development when the effect of a single water, or of a small network of interacting waters, needs to be explicitly considered.

Results:

Here, we analyse the software to aid the selection, or to predict the position, of water molecules that are going to be explicitly considered in later docking studies. We also present software and protocols able to efficiently treat flexible water molecules during docking, including examples of applications. Finally, we discuss methods based on molecular dynamics simulations that can be used to integrate docking studies or to reliably and efficiently compute binding energies of ligands in presence of interfacial or bridging water molecules.

Conclusions:

Software applications aiding the design of new drugs that exploit water molecules, either as displaceable residues or as bridges to the receptor, are constantly being developed. Although further validation is needed, workflows that explicitly consider water will probably become a standard for computational drug discovery soon.

Overview of Recent Strategic Advances in Medicinal Chemistry

Introducing novel strategies, concepts, and technologies that speed up drug discovery and the drug development cycle is of great importance both in the highly competitive pharmaceutical industry as well as in academia. This Perspective aims to present a "big-picture" overview of recent strategic innovations in medicinal chemistry and drug discovery.

An Overview of Scoring Functions Used for Protein-Ligand Interactions in Molecular Docking

Currently, molecular docking is becoming a key tool in drug discovery and molecular modeling applications. The reliability of molecular docking depends on the accuracy of the adopted scoring function, which can guide and determine the ligand poses when thousands of possible poses of ligand are generated. The scoring function can be used to determine the binding mode and site of a ligand, predict binding affinity and identify the potential drug leads for a given protein target. Despite intensive research over the years, accurate and rapid prediction of protein-ligand interactions is still a challenge in molecular docking. For this reason, this study reviews four basic types of scoring functions, physics-based, empirical, knowledge-based, and machine learning-based scoring functions, based on an up-to-date classification scheme. We not only discuss the foundations of the four types scoring functions, suitable application areas and shortcomings, but also discuss challenges and potential future study directions.

Lipophilicity in drug design: an overview of lipophilicity descriptors in 3D-QSAR studies

The pharmacophore concept is a fundamental cornerstone in drug discovery, playing a critical role in determining the success of in silico techniques, such as virtual screening and 3D-QSAR studies. The reliability of these approaches is influenced by the quality of the physicochemical descriptors used to characterize the chemical entities. In this context, a pivotal role is exerted by lipophilicity, which is a major contribution to host-guest interaction and ligand binding affinity. Several approaches have been undertaken to account for the descriptive and predictive capabilities of lipophilicity in 3D-QSAR modeling. Recent efforts encode the use of quantum mechanical-based descriptors derived from continuum solvation models, which open novel avenues for gaining insight into structure-activity relationships studies.

Development and Validation of Molecular Overlays Derived from Three-Dimensional Hydrophobic Similarity with PharmScreen

Molecular alignment is a standard procedure for three-dimensional (3D) similarity measurements and pharmacophore elucidation. This process is influenced by several factors, such as the physicochemical descriptors utilized to account for the molecular determinants of biological activity and the reference templates. Relying on the hypothesis that the maximal achievable binding affinity for a drug-like molecule is largely due to desolvation, we explore a novel strategy for 3D molecular overlays that exploits the partitioning of molecular hydrophobicity into atomic contributions in conjunction with information about the distribution of hydrogen-bond (HB) donor/acceptor groups. A brief description of the method, as implemented in the software package PharmScreen, including the derivation of the fractional hydrophobic contributions within the quantum mechanical version of the Miertus-Scrocco-Tomasi (MST) continuum model, and the procedure utilized for the optimal superposition between molecules, is presented. The computational procedure is calibrated by using a data set of 402 molecules pertaining to 14 distinct targets taken from the literature and validated against the AstraZeneca test, which comprises 121 experimentally derived sets of molecular overlays. The results point out the suitability of the MST-based hydrophobic parameters for generating molecular overlays, as correct predictions were obtained for 94%, 79%, and 54% of the molecules classified into easy, moderate, and hard sets, respectively. Moreover, the results point out that this accuracy is attained at a much lower degree of identity between the templates used by hydrophobic/HB fields and electrostatic/steric ones. These findings support the usefulness of the hydrophobic/HB descriptors to generate complementary overlays that may be valuable to rationalize structure-activity relationships and for virtual screening campaigns.

Adamantyl Antiestrogens with Novel Side Chains Reveal a Spectrum of Activities in Suppressing Estrogen Receptor Mediated Activities in Breast Cancer Cells

To search for new antiestrogens more effective in treating breast cancers, we explored alternatives to the acrylic acid side chain used in many antiestrogens. To facilitate our search, we used a simple adamantyl ligand core that by avoiding stereochemical issues enabled rapid synthesis of acrylate ketone, ester, and amide analogs. All compounds were high affinity estrogen receptor α (ERα) ligands but displayed a range of efficacies and potencies as antiproliferative and ERα-downregulating agents. There were large differences in activity between compounds having minor structural changes, but antiproliferative and ERα-downregulating efficacies generally paralleled one another. Some compounds with side chain polar groups had particularly high affinities. The secondary carboxamides had the best cellular activities, and the 3-hydroxypropylamide was as efficacious as fulvestrant in suppressing cell proliferation and gene expression. This study has produced structurally novel antiestrogens based on a simple adamantyl core structure with acrylate side chains optimized for cellular antagonist activity.

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.

Molecular Docking: From Lock and Key to Combination Lock

Accurate modeling of protein ligand binding is an important step in structure-based drug design, is a useful starting point for finding new lead compounds or drug candidates. The 'Lock and Key' concept of protein-ligand binding has dominated descriptions of these interactions, and has been effectively translated to computational molecular docking approaches. In turn, molecular docking can reveal key elements in protein-ligand interactions-thereby enabling design of potent small molecule inhibitors directed against specific targets. However, accurate predictions of binding pose and energetic remain challenging problems. The last decade has witnessed more sophisticated molecular docking approaches to modeling protein-ligand binding and energetics. However, the complexities that confront accurate modeling of binding phenomena remain formidable. Subtle recognition and discrimination patterns governed by three-dimensional features and microenvironments of the active site play vital roles in consolidating the key intermolecular interactions that mediates ligand binding. Herein, we briefly review contemporary approaches and suggest that future approaches treat protein-ligand docking problems in the context of a 'combination lock' system.

Degradation of Aflatoxins by Means of Laccases from Trametes versicolor: An In Silico Insight

Mycotoxins are secondary metabolites of fungi that contaminate food and feed, and are involved in a series of foodborne illnesses and disorders in humans and animals. The mitigation of mycotoxin content via enzymatic degradation is a strategy to ensure safer food and feed, and to address the forthcoming issues in view of the global trade and sustainability. Nevertheless, the search for active enzymes is still challenging and time-consuming. The in silico analysis may strongly support the research by providing the evidence-based hierarchization of enzymes for a rational design of more effective experimental trials. The present work dealt with the degradation of aflatoxin B₁ and M₁ by laccase enzymes from Trametes versicolor. The enzymes–substrate interaction for various enzyme isoforms was investigated through 3D molecular modeling techniques. Structural differences among the isoforms have been pinpointed, which may cause different patterns of interaction between aflatoxin B₁ and M₁. The possible formation of different products of degradation can be argued accordingly. Moreover, the laccase gamma isoform was identified as the most suitable for protein engineering aimed at ameliorating the substrate specificity. Overall, 3D modeling proved to be an effective analytical tool to assess the enzyme–substrate interaction and provided a solid foothold for supporting the search of degrading enzyme at the early stage.

Hazard identification of cis/trans-zearalenone through the looking-glass

Among the food-related health issues, the presence of contaminants has a prominent role, due to the wide range of exogenous compounds that can occur in food commodities and to their large differences in structure and biological activity. A comprehensive assessment of the related risk is thus actually demanding in terms of time and facilities involved. In this context, the use of computational strategies can be an effective choice for supporting the hazard identification procedure at the early stage. In this work, we focused on the food contaminant zearalenone by comparing the trans and cis isomers, respectively the well-known mycoestrogen and its still largely understudied isomer. We estimated the possible effects exerted by human metabolism on the xenoestrogenicity of cis-ZEN by using a validated in silico strategy based on docking simulations and rescoring procedures. Similarly, the exploitation of the most promising enzymatic detoxifying routes designed for trans-ZEN - which relies on the enzyme lactono hydrolase from Clonostachys rosea - has been assessed for the cis-isomer as well. Our results showed that both isomers can act as functional analogues with respect to xenoestrogenic activity, and several cis-ZEN metabolites with high biological potential have been identified. On the contrary, in spite of the high degree of structural analogy, the cis isomer showed a pattern of interaction with the degrading enzyme in stark contrast with that observed for trans-ZEN. For these reasons, the outcomes presented herein strongly support the inclusion of cis-ZEN in further studies of occurrence, metabolism and bioactivity assessment, and suggest the need for a dedicated handling for the cis isomer in risk assessment studies.

Toward Improved Force-Field Accuracy through Sensitivity Analysis of Host-Guest Binding Thermodynamics

Improving the capability of atomistic computer models to predict the thermodynamics of noncovalent binding is critical for successful structure-based drug design, and the accuracy of such calculations remains limited by nonoptimal force field parameters. Ideally, one would incorporate protein-ligand affinity data into force field parametrization, but this would be inefficient and costly. We now demonstrate that sensitivity analysis can be used to efficiently tune Lennard-Jones parameters of aqueous host-guest systems for increasingly accurate calculations of binding enthalpy. These results highlight the promise of a comprehensive use of calorimetric host-guest binding data, along with existing validation data sets, to improve force field parameters for the simulation of noncovalent binding, with the ultimate goal of making protein-ligand modeling more accurate and hence speeding drug discovery.

Hybrid in Silico/in Vitro Approach for the Identification of Angiotensin I Converting Enzyme Inhibitory Peptides from Parma Dry-Cured Ham

The bioactivity assessment of foodborne peptides is currently a research area of great relevance, and, in particular, several studies are devoted to the antihypertensive effects through the inhibition of angiotensin I converting enzyme (ACE). In the present work, a straightforward workflow to identify inhibitory peptides from food matrices is proposed, which involves a hybrid in vitro/in silico tandem approach. Parma dry-cured ham was chosen as case study. In particular, the advantage of using the hybrid approach to identify active sequences (in comparison to the experimental trials alone) has been pointed out. Specifically, fractions obtained by in vitro gastrointestinal digestion of ham samples of 18 and 24 months of aging have been assessed for ACE inhibition. At the same time, the released peptidomic profiles, which cannot be entirely evaluated by using in vitro assays, have been screened for the inhibition by using an in silico model. Then, to identify novel inhibitory sequences, a series of strong candidates have been synthesized and assessed for their inhibitory activity through in vitro assay. On the one hand, the use of computational simulations appeared to be an effective strategy to find active sequences, as confirmed by in vitro analysis. On the other hand, strong inhibitory sequences were identified for the first time in Parma dry-cured ham (e.g., LGL and SFVTT with IC50 values of 145 and 395 μM, respectively), which is a product of international dietary and economic relevance. Therefore, these findings demonstrate the usefulness of in silico methodologies coupled to in vitro tests for the identification of potentially bioactive peptides, and they give an important contribution to the study of the overall nutritional value of Parma ham.

Ergot alkaloids: From witchcraft till in silico analysis. Multi-receptor analysis of ergotamine metabolites

The term Ergot is referred to the sclerotium of ascomycetes - a protective kernel produced during resting stage of some fungi - which replaces seeds of susceptible cereals and plants intended for human and animal diet. It contains various composition of tryptophan-derived toxins defined ergot alkaloids. Since sclerotia can be harvested and milled together with cereals, they represent a source of food and feed contamination after breakage and spreading of mycotoxins into the various milling fractions. The effects of ergot alkaloids, including those adverse for human health, have been known since the Middle Ages. Nevertheless, as recently stated by the European Food Safety Authority, further information is needed on metabolism and target receptors-binding of common alkaloids in food. Unfortunately, the experimental investigation is challenging due to the high costs in terms of time and money. This study was thus aimed at assessing whether the in silico modeling can be an effective tool to investigate the interaction between multiple serotonin receptors and a wide set of ergotamine metabolites, including experimentally detected molecules and predicted derivatives. Validated models provided precious insights about the effects exerted by metabolic modifications on the receptor-ligand interaction. Such structural information may be useful to support the design of further experimental analysis.

Modeling the effect of phase II conjugations on topoisomerase I poisoning: pilot study with luteolin and quercetin

Topoisomerases are targeted by several drugs in cancer chemotherapy acting as key enzymes in cell viability. Some flavonoids and their glycosides may exert health protective effects through the poisoning of topoisomerases. However, previous studies did not consider the substantial modifications taking place after ingestion neglecting that only metabolites can interact with the internal compartments of the human body. Since the high number of possible metabolites hinders their systematic analysis, an in silico approach can be a valuable tool to prioritize compounds by identifying candidates for further characterization. Specifically focusing on luteolin and quercetin, among the most ubiquitous flavonoids in the human diet, this work reports a computational procedure to model the effect of hepatic phase II conjugative metabolism on poisoning of human Topoisomerase I. As a general effect, glucuronidation and sulphation might enhance and quench poisoning activity, respectively. Among all, quercetin-3-O-glucuronide represents a promising candidate to be analyzed more thoroughly.

Computing Clinically Relevant Binding Free Energies of HIV-1 Protease Inhibitors

The use of molecular simulation to estimate the strength of macromolecular binding free energies is becoming increasingly widespread, with goals ranging from lead optimization and enrichment in drug discovery to personalizing or stratifying treatment regimes. In order to realize the potential of such approaches to predict new results, not merely to explain previous experimental findings, it is necessary that the methods used are reliable and accurate, and that their limitations are thoroughly understood. However, the computational cost of atomistic simulation techniques such as molecular dynamics (MD) has meant that until recently little work has focused on validating and verifying the available free energy methodologies, with the consequence that many of the results published in the literature are not reproducible. Here, we present a detailed analysis of two of the most popular approximate methods for calculating binding free energies from molecular simulations, molecular mechanics Poisson-Boltzmann surface area (MMPBSA) and molecular mechanics generalized Born surface area (MMGBSA), applied to the nine FDA-approved HIV-1 protease inhibitors. Our results show that the values obtained from replica simulations of the same protease-drug complex, differing only in initially assigned atom velocities, can vary by as much as 10 kcal mol-1, which is greater than the difference between the best and worst binding inhibitors under investigation. Despite this, analysis of ensembles of simulations producing 50 trajectories of 4 ns duration leads to well converged free energy estimates. For seven inhibitors, we find that with correctly converged normal mode estimates of the configurational entropy, we can correctly distinguish inhibitors in agreement with experimental data for both the MMPBSA and MMGBSA methods and thus have the ability to rank the efficacy of binding of this selection of drugs to the protease (no account is made for free energy penalties associated with protein distortion leading to the over estimation of the binding strength of the two largest inhibitors ritonavir and atazanavir). We obtain improved rankings and estimates of the relative binding strengths of the drugs by using a novel combination of MMPBSA/MMGBSA with normal mode entropy estimates and the free energy of association calculated directly from simulation trajectories. Our work provides a thorough assessment of what is required to produce converged and hence reliable free energies for protein-ligand binding.

Predicting the molecular interactions of CRIP1a-cannabinoid 1 receptor with integrated molecular modeling approaches

Cannabinoid receptors are a family of G-protein coupled receptors that are involved in a wide variety of physiological processes and diseases. One of the key regulators that are unique to cannabinoid receptors is the cannabinoid receptor interacting proteins (CRIPs). Among them CRIP1a was found to decrease the constitutive activity of the cannabinoid type-1 receptor (CB1R). The aim of this study is to gain an understanding of the interaction between CRIP1a and CB1R through using different computational techniques. The generated model demonstrated several key putative interactions between CRIP1a and CB1R, including the critical involvement of Lys130 in CRIP1a.

Intuitive, but not simple: including explicit water molecules in protein-protein docking simulations improves model quality

Characterizing the nature of interaction between proteins that have not been experimentally cocrystallized requires a computational docking approach that can successfully predict the spatial conformation adopted in the complex. In this work, the Hydropathic INTeractions (HINT) force field model was used for scoring docked models in a data set of 30 high-resolution crystallographically characterized "dry" protein-protein complexes and was shown to reliably identify native-like models. However, most current protein-protein docking algorithms fail to explicitly account for water molecules involved in bridging interactions that mediate and stabilize the association of the protein partners, so we used HINT to illuminate the physical and chemical properties of bridging waters and account for their energetic stabilizing contributions. The HINT water Relevance metric identified the "truly" bridging waters at the 30 protein-protein interfaces and we utilized them in "solvated" docking by manually inserting them into the input files for the rigid body ZDOCK program. By accounting for these interfacial waters, a statistically significant improvement of ∼24% in the average hit-count within the top-10 predictions the protein-protein dataset was seen, compared to standard "dry" docking. The results also show scoring improvement, with medium and high accuracy models ranking much better than incorrect ones. These improvements can be attributed to the physical presence of water molecules that alter surface properties and better represent native shape and hydropathic complementarity between interacting partners, with concomitantly more accurate native-like structure predictions.

Isozyme-specific ligands for O-acetylserine sulfhydrylase, a novel antibiotic target

The last step of cysteine biosynthesis in bacteria and plants is catalyzed by O-acetylserine sulfhydrylase. In bacteria, two isozymes, O-acetylserine sulfhydrylase-A and O-acetylserine sulfhydrylase-B, have been identified that share similar binding sites, although the respective specific functions are still debated. O-acetylserine sulfhydrylase plays a key role in the adaptation of bacteria to the host environment, in the defense mechanisms to oxidative stress and in antibiotic resistance. Because mammals synthesize cysteine from methionine and lack O-acetylserine sulfhydrylase, the enzyme is a potential target for antimicrobials. With this aim, we first identified potential inhibitors of the two isozymes via a ligand- and structure-based in silico screening of a subset of the ZINC library using FLAP. The binding affinities of the most promising candidates were measured in vitro on purified O-acetylserine sulfhydrylase-A and O-acetylserine sulfhydrylase-B from Salmonella typhimurium by a direct method that exploits the change in the cofactor fluorescence. Two molecules were identified with dissociation constants of 3.7 and 33 µM for O-acetylserine sulfhydrylase-A and O-acetylserine sulfhydrylase-B, respectively. Because GRID analysis of the two isoenzymes indicates the presence of a few common pharmacophoric features, cross binding titrations were carried out. It was found that the best binder for O-acetylserine sulfhydrylase-B exhibits a dissociation constant of 29 µM for O-acetylserine sulfhydrylase-A, thus displaying a limited selectivity, whereas the best binder for O-acetylserine sulfhydrylase-A exhibits a dissociation constant of 50 µM for O-acetylserine sulfhydrylase-B and is thus 8-fold selective towards the former isozyme. Therefore, isoform-specific and isoform-independent ligands allow to either selectively target the isozyme that predominantly supports bacteria during infection and long-term survival or to completely block bacterial cysteine biosynthesis.

Pharmacophore selection and redesign of non-nucleotide inhibitors of anthrax edema factor

Antibiotic treatment may fail to protect individuals, if not started early enough, after infection with Bacillus anthracis, due to the continuing activity of toxins that the bacterium produces. Stable and easily stored inhibitors of the edema factor toxin (EF), an adenylyl cyclase, could save lives in the event of an outbreak, due to natural causes or a bioweapon attack. The toxin’s basic activity is to convert ATP to cAMP, and it is thus in principle a simple phosphatase, which means that many mammalian enzymes, including intracellular adenylcyclases, may have a similar activity. While nucleotide based inhibitors, similar to its natural substrate, ATP, were identified early, these compounds had low activity and specificity for EF. We used a combined structural and computational approach to choose small organic molecules in large, web-based compound libraries that would, based on docking scores, bind to residues within the substrate binding pocket of EF. A family of fluorenone-based inhibitors was identified that inhibited the release of cAMP from cells treated with EF. The lead inhibitor was also shown to inhibit the diarrhea caused by enterotoxigenic E. coli (ETEC) in a murine model, perhaps by serving as a quorum sensor. These inhibitors are now being tested for their ability to inhibit Anthrax infection in animal models and may have use against other pathogens that produce toxins similar to EF, such as Bordetella pertussis or Vibrio cholera.

Placevent: an algorithm for prediction of explicit solvent atom distribution-application to HIV-1 protease and F-ATP synthase

We have created a simple algorithm for automatically predicting the explicit solvent atom distribution of biomolecules. The explicit distribution is coerced from the three-dimensional (3D) continuous distribution resulting from a 3D reference interaction site model (3D-RISM) calculation. This procedure predicts optimal location of solvent molecules and ions given a rigid biomolecular structure and the solvent composition. We show examples of predicting water molecules near the KNI-272 bound form of HIV-1 protease and predicting both sodium ions and water molecules near the rotor ring of F-adenosine triphosphate (ATP) synthase. Our results give excellent agreement with experimental structure with an average prediction error of 0.39-0.65 Å. Further, unlike experimental methods, this method does not suffer from the partial occupancy limit. Our method can be performed directly on 3D-RISM output within minutes. It is extremely useful for examining multiple specific solvent-solute interactions, as a convenient method for generating initial solvent structures for molecular dynamics calculations, and may assist in refinement of experimental structures. © 2012 Wiley Periodicals, Inc.

Bound water at protein-protein interfaces: partners, roles and hydrophobic bubbles as a conserved motif

Background

There is a great interest in understanding and exploiting protein-protein associations as new routes for treating human disease. However, these associations are difficult to structurally characterize or model although the number of X-ray structures for protein-protein complexes is expanding. One feature of these complexes that has received little attention is the role of water molecules in the interfacial region.

Methodology

A data set of 4741 water molecules abstracted from 179 high-resolution (≤ 2.30 Å) X-ray crystal structures of protein-protein complexes was analyzed with a suite of modeling tools based on the HINT forcefield and hydrogen-bonding geometry. A metric termed Relevance was used to classify the general roles of the water molecules.

Results

The water molecules were found to be involved in: a) (bridging) interactions with both proteins (21%), b) favorable interactions with only one protein (53%), and c) no interactions with either protein (26%). This trend is shown to be independent of the crystallographic resolution. Interactions with residue backbones are consistent for all classes and account for 21.5% of all interactions. Interactions with polar residues are significantly more common for the first group and interactions with non-polar residues dominate the last group. Waters interacting with both proteins stabilize on average the proteins' interaction (−0.46 kcal mol⁻¹), but the overall average contribution of a single water to the protein-protein interaction energy is unfavorable (+0.03 kcal mol⁻¹). Analysis of the waters without favorable interactions with either protein suggests that this is a conserved phenomenon: 42% of these waters have SASA ≤ 10 Ų and are thus largely buried, and 69% of these are within predominantly hydrophobic environments or “hydrophobic bubbles”. Such water molecules may have an important biological purpose in mediating protein-protein interactions.

Free Energy Calculations of Mutations Involving a Tightly Bound Water Molecule and Ligand Substitutions in a Ligand-Protein Complex

The accurate calculation of the free energy of interaction of protein-water-ligand systems has an important role in molecular recognition and drug design that is often not fully considered. We report free energy thermodynamic integration calculations used to evaluate the effects of inclusion, neglect, and targeting and removal (i.e., systematic substitution by ligand functional groups) of an important, tightly bound, water molecule in the SH3 domain of Abl tyrosine kinase. The effects of this water molecule on the free energies of interaction of several Abl-SH3 domain-ligand systems reveal that there is an unfavourable free energy change associated with its removal into the bulk solvent. Only three substitutions by an additional functional group (out of methyl, ethyl, hydroxyl, amino, and amide groups) in the phenyl ring of a tyrosine in the peptide ligand resulted in a favourable change in the free energy of binding upon replacement of the ordered water molecule. This computational approach provides a direct route to the systematic and rigorous prediction of the thermodynamic influence of ordered, structural water molecules on ligand modification and optimization in drug design by calculating free energy changes in protein-water-ligand systems.

Accurate ensemble molecular dynamics binding free energy ranking of multidrug-resistant HIV-1 proteases

Accurate calculation of important thermodynamic properties, such as macromolecular binding free energies, is one of the principal goals of molecular dynamics simulations. However, single long simulation frequently produces incorrectly converged quantitative results due to inadequate sampling of conformational space in a feasible wall-clock time. Multiple short (ensemble) simulations have been shown to explore conformational space more effectively than single long simulations, but the two methods have not yet been thermodynamically compared. Here we show that, for end-state binding free energy determination methods, ensemble simulations exhibit significantly enhanced thermodynamic sampling over single long simulations and result in accurate and converged relative binding free energies that are reproducible to within 0.5 kcal/mol. Completely correct ranking is obtained for six HIV-1 protease variants bound to lopinavir with a correlation coefficient of 0.89 and a mean relative deviation from experiment of 0.9 kcal/mol. Multidrug resistance to lopinavir is enthalpically driven and increases through a decrease in the protein-ligand van der Waals interaction, principally due to the V82A/I84V mutation, and an increase in net electrostatic repulsion due to water-mediated disruption of protein-ligand interactions in the catalytic region. Furthermore, we correctly rank, to within 1 kcal/mol of experiment, the substantially increased chemical potency of lopinavir binding to the wild-type protease compared to saquinavir and show that lopinavir takes advantage of a decreased net electrostatic repulsion to confer enhanced binding. Our approach is dependent on the combined use of petascale computing resources and on an automated simulation workflow to attain the required level of sampling and turn around time to obtain the results, which can be as little as three days. This level of performance promotes integration of such methodology with clinical decision support systems for the optimization of patient-specific therapy.

Ligand-protein cross-docking with water molecules

The accuracy of ligand-protein docking may be affected by the presence of water molecules on the surface of the protein. Cross-docking simulations have been performed on a number of ligand-protein complexes for various proteins whose crystal structures contain water molecules in their binding sites. Only common sets of water molecules found in the binding site of the proteins were considered. A statistically significant overall increase in accuracy was observed when water molecules were included in cross-docking simulations. These results confirm the importance of including water molecules whenever possible in ligand-protein docking simulations.

Analysis of structural water and CH···π interactions in HIV-1 protease and PTP1B complexes using a hydrogen bond prediction tool, HBPredicT

A hydrogen bond prediction tool HBPredicT is developed for detecting structural water molecules and CH···π interactions in PDB files of protein-ligand complexes. The program adds the missing hydrogen atoms to the protein, ligands, and oxygen atoms of water molecules and subsequently all the hydrogen bonds in the complex are located using specific geometrical criteria. Hydrogen bonds are classified into various types based on (i) donor and acceptor atoms, and interactions such as (ii) protein-protein, (iii) protein-ligand, (iv) protein-water, (v) ligand-water, (vi) water-water, and (vii) protein-water-ligand. Using the information in category (vii), the water molecules which form hydrogen bonds with the ligand and the protein simultaneously-the structural water-is identified and retrieved along with the associated ligand and protein residues. For CH···π interactions, the relevant portions of the corresponding structures are also extracted in the output. The application potential of this program is tested using 19 HIV-1 protease and 11 PTP1B inhibitor complexes. All the systems showed presence of structural water molecules and in several cases, the CH···π interaction between ligand and protein are detected. A rare occurrence of CH···π interactions emanating from both faces of a phenyl ring of the inhibitor is identified in HIV-1 protease 1D4L.

Prediction of potency of protease inhibitors using free energy simulations with polarizable quantum mechanics-based ligand charges and a hybrid water model

Reliable and robust prediction of the binding affinity for drug molecules continues to be a daunting challenge. We simulated the binding interactions and free energy of binding of nine protease inhibitors (PIs) with wild-type and various mutant proteases by performing GBSA simulations in which each PI's partial charge was determined by quantum mechanics (QM) and the partial charge accounts for the polarization induced by the protease environment. We employed a hybrid solvation model that retains selected explicit water molecules in the protein with surface-generalized Born (SGB) implicit solvent. We examined the correlation of the free energy with the antiviral potency of PIs with regard to amino acid substitutions in protease. The GBSA free energy thus simulated showed strong correlations (r > 0.75) with antiviral IC(50) values of PIs when amino acid substitutions were present in the protease active site. We also simulated the binding free energy of PIs with P2-bis-tetrahydrofuranylurethane (bis-THF) or related cores, utilizing a bis-THF-containing protease crystal structure as a template. The free energy showed a strong correlation (r = 0.93) with experimentally determined anti-HIV-1 potency. The present data suggest that the presence of selected explicit water in protein and protein polarization-induced quantum charges for the inhibitor, compared to lack of explicit water and a static force-field-based charge model, can serve as an improved lead optimization tool and warrants further exploration.

Design of O-acetylserine sulfhydrylase inhibitors by mimicking nature

The inhibition of cysteine biosynthesis in prokaryotes and protozoa has been proposed to be relevant for the development of antibiotics. Haemophilus influenzae O-acetylserine sulfhydrylase (OASS), catalyzing l-cysteine formation, is inhibited by the insertion of the C-terminal pentapeptide (MNLNI) of serine acetyltransferase into the active site. Four-hundred MNXXI pentapeptides were generated in silico, docked into OASS active site using GOLD, and scored with HINT. The terminal P5 Ile accounts for about 50% of the binding energy. Glu or Asp at position P4 and, to a lesser extent, at position P3 also significantly contribute to the binding interaction. The predicted affinity of 14 selected pentapeptides correlated well with the experimentally determined dissociation constants. The X-ray structure of three high affinity pentapeptide-OASS complexes were compared with the docked poses. These results, combined with a GRID analysis of the active site, allowed us to define a pharmacophoric scaffold for the design of peptidomimetic inhibitors.