The consequences of scoring docked ligand conformations using free energy correlations

Blocking potential metabolic sites on NAT to improve its safety profile while retaining the pharmacological profile

The number of opioid-related overdose deaths and individuals that have suffered from opioid use disorders have significantly increased over the last 30 years. FDA approved maintenance therapies to treat opioid use disorder may successfully curb drug craving and prevent relapse but harbor adverse effects that reduce patient compliance. This has created a need for new chemical entities with improved patient experience. Previously our group reported a novel lead compound, NAT, a mu-opioid receptor antagonist that potently antagonized the antinociception of morphine and showed significant blood-brain barrier permeability. However, NAT belongs to thiophene containing compounds which are known structural alerts for potential oxidative metabolism. To overcome this, 15 NAT derivatives with various substituents at the 5'-position of the thiophene ring were designed and their structure-activity relationships were studied. These derivatives were characterized for their binding affinity, selectivity, and functional activity at the mu opioid receptor and assessed for their ability to antagonize the antinociceptive effects of morphine in vivo. Compound 12 showed retention of the basic pharmacological attributes of NAT while improving the withdrawal effects that were experienced in opioid-dependent mice. Further studies will be conducted to fully characterize compound 12 to examine whether it would serve as a new lead for opioid use disorder treatment and management.

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.

Synthesis of New 3-Arylaminophthalides and 3-Indolyl-phthalides using Ammonium Chloride, Evaluation of their Anti-Mycobacterial Potential and Docking Study

OBJECTIVE

The study aims at the derivatization of "Phthalides" and synthesizes 3- arylaminophthalides & 3-indolyl-phthalides compounds, and evaluates their anti-tubercular and antioxidant activities. The study has also intended to employ the in silico methods for the identification of possible drug targets in Mycobacterium and evaluate the binding affinities of synthesized compounds.

METHODS

This report briefly explains the synthesis of phthalide derivatives using ammonium chloride. The synthesized compounds were characterized using spectral analysis. Resazurin Microtiter Assay (REMA) plate method was used to demonstrate the anti-mycobacterial activity of the synthesized compounds. An in-silico pharmacophore probing approach was used for target identification in Mycobacterium. The structural level interaction between the identified putative drug target and synthesized phthalides was studied using Lamarckian genetic algorithm-based software.

RESULTS AND DISCUSSION

In the present study, we report an effective, environmentally benign scheme for the synthesis of phthalide derivatives. Compounds 5c and 5d from the current series appear to possess good anti-mycobacterial activity. dCTP: deaminasedUTPase was identified as a putative drug target in Mycobacterium. The docking results clearly showed the interactive involvement of conserved residues of dCTP with the synthesized phthalide compounds.

CONCLUSION

On the eve of evolving anti-TB drug resistance, the data on anti-tubercular and allied activities of the compounds in the present study demonstrates the enormous significance of these newly synthesized derivatives as possible candidate leads in the development of novel anti-tubercular agents. The docking results from the current report provide a structural rationale for the promising anti-tubercular activity demonstrated by 3-arylaminophthalides and 3-indolyl-phthalides compounds.

Computational representations of protein-ligand interfaces for structure-based virtual screening

Introduction: Structure-based virtual screening (SBVS) is an essential strategy for hit identification. SBVS primarily uses molecular docking, which exploits the protein-ligand binding mode and associated affinity score for compound ranking. Previous studies have shown that computational representation of protein-ligand interfaces and the later establishment of machine learning models are efficacious in improving the accuracy of SBVS.Areas covered: The authors review the computational methods for representing protein-ligand interfaces, which include the traditional ones that use deliberately designed fingerprints and descriptors and the more recent methods that automatically extract features with deep learning. The effects of these methods on the performance of machine learning models are briefly discussed. Additionally, case studies that applied various computational representations to machine learning are cited with remarks.Expert opinion: It has become a trend to extract binding features automatically by deep learning, which uses a completely end-to-end representation. However, there is still plenty of scope for improvement . The interpretability of deep-learning models, the organization of data management, the quantity and quality of available data, and the optimization of hyperparameters could impact the accuracy of feature extraction. In addition, other important structural factors such as water molecules and protein flexibility should be considered.

Microwave-assisted synthesis of ruthenium(ii) complexes containing levofloxacin-induced G2/M phase arrest by triggering DNA damage

Ru(ii) complexes have attracted increasing attention as promising antitumor agents for their relatively low toxicity, high affinity to DNA molecules, and correlation with multiple targets. Meanwhile, quinolones are synthetic antibacterial agents widely used in the clinical practice. In this paper, two novel Ru(ii) complexes coordinated by levofloxacin (LOFLX), [Ru(bpy)₂(LOFLX)]·2ClO₄ (1), and [Ru(dmbpy)₂(LOFLX)]·2ClO₄ (2) (bpy = 2,2′-bipyridine, dmbpy = 4,4′-dimethyl-2,2′-bipyridine) were synthesized with high efficiency under microwave irradiation and characterized by ESI-MS, ¹H NMR, and ¹³C NMR. The binding behavior of these complexes with double-strand calf thymus DNA(CT-DNA) was investigated using spectroscopy, molecular docking, and density functional theory calculations. Results showed that 2 exhibited higher binding affinity than 1 and LOFLX. Further studies showed that 2 could induce the G2/M phase arrest of A549 cells via DNA damage. In summary, these results indicated that 2 could be developed as a potential anticancer agent in treatment of lung cancer through the induction of cell cycle arrest at G2/M phase by triggering DNA damage.

This study showed that levofloxacin-based ruthenium(ii) complex 2 effectively inhibited the growth of A549 cells by inducing G2/M phase arrest through triggering DNA damage.

Performance of machine-learning scoring functions in structure-based virtual screening

Classical scoring functions have reached a plateau in their performance in virtual screening and binding affinity prediction. Recently, machine-learning scoring functions trained on protein-ligand complexes have shown great promise in small tailored studies. They have also raised controversy, specifically concerning model overfitting and applicability to novel targets. Here we provide a new ready-to-use scoring function (RF-Score-VS) trained on 15 426 active and 893 897 inactive molecules docked to a set of 102 targets. We use the full DUD-E data sets along with three docking tools, five classical and three machine-learning scoring functions for model building and performance assessment. Our results show RF-Score-VS can substantially improve virtual screening performance: RF-Score-VS top 1% provides 55.6% hit rate, whereas that of Vina only 16.2% (for smaller percent the difference is even more encouraging: RF-Score-VS top 0.1% achieves 88.6% hit rate for 27.5% using Vina). In addition, RF-Score-VS provides much better prediction of measured binding affinity than Vina (Pearson correlation of 0.56 and −0.18, respectively). Lastly, we test RF-Score-VS on an independent test set from the DEKOIS benchmark and observed comparable results. We provide full data sets to facilitate further research in this area (http://github.com/oddt/rfscorevs) as well as ready-to-use RF-Score-VS (http://github.com/oddt/rfscorevs_binary).

Anti-Influenza Virus (H5N1) Activity Screening on the Phloroglucinols from Rhizomes of Dryopteris crassirhizoma

For screening the active phloroglucinols on influenza virus (H5N1) from Dryopteris crassirhizoma NaKai, a database was established including twenty-three phloroglucinols that had been isolated from Dryopteris crassirhizoma. Their inhibitory effect on the neuraminidase (NA) of influenza virus H5N1 was screened by molecular docking. As a result, three candidates were selected. The rhizomes of D. crassirhizoma were subjected to isolation and purification processes to obtain the inhibitor candidates. Thirteen phloroglucinols were obtained, including three selected candidates and two new phloroglucinols. The five phloroglucinols were investigated for their inhibitory activity on NA in vitro. The results showed that dryocrassin ABBA and filixic acid ABA exhibited inhibitory effects on NA with IC50 as 18.59 ± 4.53 and 29.57 ± 2.48 μM, respectively, and the other three phloroglucinols showed moderate inhibitory activity. Moreover, the anti-influenza virus (H5N1) activity and cytotoxicity of dryocrassin ABBA and filixic acid ABA were tested on Madin-Darby canine kidney (MDCK) cells with the cell counting kit-8 (CCK8) method. The results confirmed that dryocrassin ABBA exhibited an inhibitory activity with low cytotoxicity (TC50 > 400 μM) against influenza virus (H5N1) which will have to be investigated in further detail. In conclusion, phloroglucinols from D. crassirhizoma were shown to have anti-influenza virus activity, and especially dryocrassin ABBA, one of the phloroglucinols, may have the potential to control influenza virus (H5N1) infection.

Design, synthesis, and characterization of rhein analogs as novel inhibitors of scavenger receptor A

Scavenger receptor A (SRA) has been known as an immunosuppressive factor and therefore therapeutic inhibition of SRA may be potentially exploited for cancer immunotherapy. Our previously work suggested that rhein may act as an inhibitor of SRA in reversing immunosuppression of SRA during T cells activation. Herein, three deconstruction analogs of rhein, compound 1, 2, and 3, were further studied as inhibitors of SRA. These three compounds, particularly compound 1, also known as a natural product danthron, enhanced T cells activation, indicated by increased transcriptional activation of interleukin 2 (Il2) gene, production of IL-2 protein, and proliferation of T cells. Additionally, the interaction between these compounds and SRA was studied by molecular modeling. Compound 1 showed a favorable binding mode with the cysteine rich domain of SRA protein compared to compound 2 and 3. Collectively, those results would provide insight for future design and development of next generation rhein derivatives as SRA inhibitors.

Biological Characterization of an Improved Pyrrole-Based Colchicine Site Agent Identified through Structure-Based Design

A refined model of the colchicine site on tubulin was used to design an improved analog of the pyrrole parent compound, JG-03-14. The optimized compound, NT-7-16, was evaluated in biological assays that confirm that it has potent activities as a new colchicine site microtubule depolymerizer. NT-7-16 exhibits antiproliferative and cytotoxic activities against multiple cancer cell lines, with IC(50) values of 10-16 nM, and it is able to overcome drug resistance mediated by the expression of P-glycoprotein and the βIII isotype of tubulin. NT-7-16 initiated the concentration-dependent loss of cellular microtubules and caused the formation of abnormal mitotic spindles, leading to mitotic accumulation. The direct interaction of NT-7-16 with purified tubulin was confirmed, and it was more potent than combretastatin A-4 in these assays. Binding studies verified that NT-7-16 binds to tubulin within the colchicine site. The antitumor effects of NT-7-16 were evaluated in an MDA-MB-435 xenograft model and it had excellent activity at concentrations that were not toxic. A second compound, NT-9-21, which contains dichloro moieties in place of the 3,5-dibromo substituents of NT-7-16, had a poorer fit within the colchicine site as predicted by modeling and the Hydropathic INTeractions score. Biological evaluations showed that NT-9-21 has 10-fold lower potency than NT-7-16, confirming the modeling predictions. These studies highlight the value of the refined colchicine-site model and identify a new pyrrole-based colchicine-site agent with potent in vitro activities and promising in vivo antitumor actions.

Identification of Small-Molecule Inhibitors against Meso-2, 6-Diaminopimelate Dehydrogenase from Porphyromonas gingivalis

Species-specific antimicrobial therapy has the potential to combat the increasing threat of antibiotic resistance and alteration of the human microbiome. We therefore set out to demonstrate the beginning of a pathogen-selective drug discovery method using the periodontal pathogen Porphyromonas gingivalis as a model. Through our knowledge of metabolic networks and essential genes we identified a “druggable” essential target, meso-diaminopimelate dehydrogenase, which is found in a limited number of species. We adopted a high-throughput virtual screen method on the ZINC chemical library to select a group of potential small-molecule inhibitors. Meso-diaminopimelate dehydrogenase from P. gingivalis was first expressed and purified in Escherichia coli then characterized for enzymatic inhibitor screening studies. Several inhibitors with similar structural scaffolds containing a sulfonamide core and aromatic substituents showed dose-dependent inhibition. These compounds were further assayed showing reasonable whole-cell activity and the inhibition mechanism was determined. We conclude that the establishment of this target and screening strategy provides a model for the future development of new antimicrobials.

From protein structure to function via single crystal optical spectroscopy

The more than 100,000 protein structures determined by X-ray crystallography provide a wealth of information for the characterization of biological processes at the molecular level. However, several crystallographic “artifacts,” including conformational selection, crystallization conditions and radiation damages, may affect the quality and the interpretation of the electron density maps, thus limiting the relevance of structure determinations. Moreover, for most of these structures, no functional data have been obtained in the crystalline state, thus posing serious questions on their validity in infereing protein mechanisms. In order to solve these issues, spectroscopic methods have been applied for the determination of equilibrium and kinetic properties of proteins in the crystalline state. These methods are UV-vis spectrophotometry, spectrofluorimetry, IR, EPR, Raman, and resonance Raman spectroscopy. Some of these approaches have been implemented with on-line instruments at X-ray synchrotron beamlines. Here, we provide an overview of investigations predominantly carried out in our laboratory by single crystal polarized absorption UV-vis microspectrophotometry, the most applied technique for the functional characterization of proteins in the crystalline state. Studies on hemoglobins, pyridoxal 5′-phosphate dependent enzymes and green fluorescent protein in the crystalline state have addressed key biological issues, leading to either straightforward structure-function correlations or limitations to structure-based mechanisms.

Targeting cystalysin, a virulence factor of treponema denticola-supported periodontitis

Cystalysin from Treponema denticola is a pyridoxal 5'-phosphate dependent lyase that catalyzes the formation of pyruvate, ammonia, and sulfide from cysteine. It is a virulence factor in adult periodontitis because its reaction contributes to hemolysis, which sustains the pathogen. Therefore, it was proposed as a potential antimicrobial target. To identify specific inhibitors by structure-based in silico methods, we first validated the crystal structure of cystalysin as a reliable starting point for the design of ligands. By using single-crystal absorption microspectrophotometry, we found that the enzyme in the crystalline state, with respect to that in solution, exhibits: 1) the same absorption spectra for the catalytic intermediates, 2) a close pKa value for the residue controlling the keto enamine ionization, and 3) similar reactivity with glycine, L-serine, L-methionine, and the nonspecific irreversible inhibitor aminoethoxyvinylglycine. Next, we screened in silico a library of 9357 compounds with the Fingerprints for Ligands and Proteins (FLAP) software, by using the three-dimensional structure of cystalysin as a template. From the library, 17 compounds were selected and experimentally evaluated by enzyme assays and spectroscopic methods. Two compounds were found to competitively inhibit recombinant T. denticola cystalysin, with inhibition constant (Ki ) values of 25 and 37 μM. One of them exhibited a minimum inhibitory concentration (MIC) value of 64 μg mL(-1) on Moraxella catarrhalis ATCC 23246, which proves its ability to cross bacterial membranes.

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.

How to deal with low-resolution target structures: using SAR, ensemble docking, hydropathic analysis, and 3D-QSAR to definitively map the αβ-tubulin colchicine site

αβ-Tubulin colchicine site inhibitors (CSIs) from four scaffolds that we previously tested for antiproliferative activity were modeled to better understand their effect on microtubules. Docking models, constructed by exploiting the SAR of a pyrrole subset and HINT scoring, guided ensemble docking of all 59 compounds. This conformation set and two variants having progressively less structure knowledge were subjected to CoMFA, CoMFA+HINT, and CoMSIA 3D-QSAR analyses. The CoMFA+HINT model (docked alignment) showed the best statistics: leave-one-out q(2) of 0.616, r(2) of 0.949, and r(2)pred (internal test set) of 0.755. An external (tested in other laboratories) collection of 24 CSIs from eight scaffolds were evaluated with the 3D-QSAR models, which correctly ranked their activity trends in 7/8 scaffolds for CoMFA+HINT (8/8 for CoMFA). The combination of SAR, ensemble docking, hydropathic analysis, and 3D-QSAR provides an atomic-scale colchicine site model more consistent with a target structure resolution much higher than the ~3.6 Å available for αβ-tubulin.

Binding mode characterization of 6α- and 6β-N-heterocyclic substituted naltrexamine derivatives via docking in opioid receptor crystal structures and site-directed mutagenesis studies: application of the 'message-address' concept in development of mu opioid receptor selective antagonists

Highly selective opioid receptor antagonists are essential pharmacological probes in opioid receptor structural characterization and opioid agonist functional studies. Currently, there is no highly selective, nonpeptidyl and reversible mu opioid receptor antagonist available. Among a series of naltrexamine derivatives that have been designed and synthesized, two compounds, NAP and NAQ, were previously identified as novel leads for this purpose based on their in vitro and in vivo pharmacological profiles. Both compounds displayed high binding affinity and selectivity to the mu opioid receptor. To further study the interaction of these two ligands with the three opioid receptors, the recently released opioid receptor crystal structures were employed in docking studies to further test our original hypothesis that the ligands recognize a unique 'address' domain in the mu opioid receptor involving Trp318 that facilitates their selectivity. These modeling results were supported by site-directed mutagenesis studies on the mu opioid receptor, where the mutants Y210A and W318A confirmed the role of the latter in binding. Such work not only enriched the 'message-address' concept, also facilitated our next generation ligand design and development.

How to improve docking accuracy of AutoDock4.2: a case study using different electrostatic potentials

Molecular docking, which is the indispensable emphasis in predicting binding conformations and energies of ligands to receptors, constructs the high-throughput virtual screening available. So far, increasingly numerous molecular docking programs have been released, and among them, AutoDock 4.2 is a widely used docking program with exceptional accuracy. It has heretofore been substantiated that the calculation of partial charge is very fundamental for the accurate conformation search and binding energy estimation. However, no systematic comparison of the significances of electrostatic potentials on docking accuracy of AutoDock 4.2 has been determined. In this paper, nine different charge-assigning methods, including AM1-BCC, Del-Re, formal, Gasteiger-Hückel, Gasteiger-Marsili, Hückel, Merck molecular force field (MMFF), and Pullman, as well as the ab initio Hartree-Fock charge, were sufficiently explored for their molecular docking performance by using AutoDock4.2. The results clearly demonstrated that the empirical Gasteiger-Hückel charge is the most applicable in virtual screening for large database; meanwhile, the semiempirical AM1-BCC charge is practicable in lead compound optimization as well as accurate virtual screening for small databases.

Developing novel C-4 analogues of pyrrole-based antitubulin agents: weak but critical hydrogen bonding in the colchicine site

The synthesis, biological evaluation and molecular modeling of a series of pyrrole compounds related to 3,5-dibromo-4-(3,4-dimethoxyphenyl)-1H-pyrrole-2-carboxylic acid that evaluates and optimizes C-4 substituents are reported. The key factor for microtubule depolymerization activity appears to be the presence of an appropriately positioned acceptor for Cys241β in the otherwise hydrophobic subpocket A.

In silico-screening approaches for lead generation: identification of novel allosteric modulators of human-erythrocyte pyruvate kinase

Identification of allosteric binding site modulators have gained increased attention lately for their potential to be developed as selective agents with a novel chemotype and targeting perhaps a new and unique binding site with probable fewer side effects. Erythrocyte pyruvate kinase (R-PK) is an important glycolytic enzyme that can be pharmacologically modulated through its allosteric effectors for the treatment of hemolytic anemia, sickle-cell anemia, hypoxia-related diseases, and other disorders arising from erythrocyte PK malfunction. An in-silico screening approach was applied to identify novel allosteric modulators of pyruvate kinase. A small-molecules database of the National Cancer Institute (NCI), was virtually screened based on structure/ligand-based pharmacophore. The virtual screening campaign led to the identification of several compounds with similar pharmacophoric features as fructose-1,6-bisphosphate (FBP), the natural allosteric activator of the kinase. The compounds were subsequently docked into the FBP-binding site using the programs FlexX and GOLD, and their interactions with the protein were analyzed with the energy-scoring function of HINT. Seven promising candidates were obtained from the NCI and subjected to kinetics analysis, which revealed both activators and inhibitors of the R-isozyme of PK (R-PK).

Computational analysis of structure-based interactions and ligand properties can predict efflux effects on antibiotics

AcrA-AcrB-TolC efflux pumps extrude drugs of multiple classes from bacterial cells and are a leading cause for antimicrobial resistance. Thus, they are of paramount interest to those engaged in antibiotic discovery. Accurate prediction of antibiotic efflux has been elusive, despite several studies aimed at this purpose. Minimum inhibitory concentration (MIC) ratios of 32 β-lactam antibiotics were collected from literature. 3-Dimensional Quantitative Structure-Activity Relationship on the β-lactam antibiotic structures revealed seemingly predictive models (q(2)=0.53), but the lack of a general superposition rule does not allow its use on antibiotics that lack the β-lactam moiety. Since MIC ratios must depend on interactions of antibiotics with lipid membranes and transport proteins during influx, capture and extrusion of antibiotics from the bacterial cell, descriptors representing these factors were calculated and used in building mathematical models that quantitatively classify antibiotics as having high/low efflux (>93% accuracy). Our models provide preliminary evidence that it is possible to predict the effects of antibiotic efflux if the passage of antibiotics into, and out of, bacterial cells is taken into account--something descriptor and field-based QSAR models cannot do. While the paucity of data in the public domain remains the limiting factor in such studies, these models show significant improvements in predictions over simple LogP-based regression models and should pave the path toward further work in this field. This method should also be extensible to other pharmacologically and biologically relevant transport proteins.

Pyrrole-Based Antitubulin Agents: Two Distinct Binding Modalities are Predicted for C-2 Analogs in the Colchicine Site

3,5-dibromo-4-(3,4-dimethoxyphenyl)-1H-pyrrole-2-carboxylic acid ethyl ester is a promising antitubulin lead agent that targets the colchicine site of tubulin. C-2 analogs were synthesized and tested for microtubule depolymerizing and antiproliferative activity. Molecular modeling studies using both GOLD docking and HINT (Hydropathic INTeraction) scoring revealed two distinct binding modes that explain the structural-activity relationships and are in accord with the structural basis of colchicine binding to tubulin. The binding mode of higher activity compounds is buried deeper in the site and overlaps well with rings A and C of colchicine, while the lower activity binding mode shows fewer critical contacts with tubulin. The model distinguishes highly active compounds from those with weaker activities and provides novel insights into the colchicine site and compound design.

Using active site mapping and receptor-based pharmacophore tools: prelude to docking and de novo/fragment-based ligand design

Understanding the three-dimensional aspects of drug-receptor interactions and their specificity at the molecular level has become a focal point in modern drug discovery. Herein, we describe a set of methods by which the binding site on a protein can be located and mapped and the protein-ligand intermolecular interactions can be studied in the context of drug discovery. The methodology we describe is based on the empirical Hydropathic INTeraction (HINT) force field. Applications of the novel cavity detection algorithm, VICE, are demonstrated in delineating the binding pockets. The binding site environment is mapped using hydropathic "complementary map." The two binding sites are compared by calculating their 3D differences and the intermolecular interactions between a bound ligand and protein was further studied by HINT intermolecular maps. We illustrate the applications of these different types of HINT maps through an example from the development of selective COX-2 inhibitors.

Hydropathic analysis and biological evaluation of stilbene derivatives as colchicine site microtubule inhibitors with anti-leukemic activity

The crucial role of the microtubule in cell division has identified tubulin as a target for the development of therapeutics for cancer; in particular, tubulin is a target for antineoplastic agents that act by interfering with the dynamic stability of microtubules. A molecular modeling study was carried out to accurately represent the complex structure and the binding mode of a new class of stilbene-based tubulin inhibitors that bind at the alphabeta-tubulin colchicine site. Computational docking along with HINT (Hydropathic INTeractions) score analysis fitted these inhibitors into the colchicine site and revealed detailed structure-activity information useful for inhibitor design. Quantitative analysis of the results was in good agreement with the in vitro antiproliferative activity of these derivatives (ranging from 3 nM to 100 muM) such that calculated and measured free energies of binding correlate with an r(2) of 0.89 (standard error +/- 0.85 kcal mol(-1)). This correlation suggests that the activity of unknown compounds may be predicted.

A novel and efficient tool for locating and characterizing protein cavities and binding sites

Systematic investigation of a protein and its binding site characteristics are crucial for designing small molecules that modulate protein functions. However, fundamental uncertainties in binding site interactions and insufficient knowledge of the properties of even well-defined binding pockets can make it difficult to design optimal drugs. Herein, we report the development and implementation of a cavity detection algorithm built with HINT toolkit functions that we are naming Vectorial Identification of Cavity Extents (VICE). This very efficient algorithm is based on geometric criteria applied to simple integer grid maps. In testing, we carried out a systematic investigation on a very diverse data set of proteins and protein-protein/protein-polynucleotide complexes for locating and characterizing the indentations, cavities, pockets, grooves, channels, and surface regions. Additionally, we evaluated a curated data set of unbound proteins for which a ligand-bound protein structures are also known; here the VICE algorithm located the actual ligand in the largest cavity in 83% of the cases and in one of the three largest in 90% of the cases. An interactive front-end provides a quick and simple procedure for locating, displaying and manipulating cavities in these structures. Information describing the cavity, including its volume and surface area metrics, and lists of atoms, residues, and/or chains lining the binding pocket, can be easily obtained and analyzed. For example, the relative cross-sectional surface area (to total surface area) of cavity openings in well-enclosed cavities is 0.06 +/- 0.04 and in surface clefts or crevices is 0.25 +/- 0.09. Proteins 2010. (c) 2009 Wiley-Liss, Inc.

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.

Automated site preparation in physics-based rescoring of receptor ligand complexes

Hydrogen atoms are not typically observable in X-ray crystal structures, but inferring their locations is often important in structure-based drug design. In addition, protonation states of the protein can change in response to ligand binding, as can the orientations of OH groups, a subtle form of "induced fit." We implement and evaluate an automated procedure for optimizing polar hydrogens in protein-binding sites in complex with ligands. Specifically, we apply the previously described Independent Cluster Decomposition Algorithm (ICDA) algorithm (Li et al., Proteins 2007;66:824-837), which assigns the ionization states of titratable residues, the amide orientations of Asn/Gln side chains, the imidazole ring orientation in His, and the orientations of OH/SH groups, in a unified algorithm. We test the utility of this method for identifying nativelike ligand poses using 247 protein-ligand complexes from an established database of docked decoys. Pose selection is performed with a physics-based scoring function based on a molecular mechanics energy function and a Generalized Born implicit solvent model. The use of the ICDA receptor preparation protocol, implemented with no knowledge of the native ligand pose, increases the accuracy of pose selection significantly, with the average RMSD over all complexes decreasing from 2.7 to 1.5 A when applying ICDA. Large improvements are seen for specific classes of binding sites with titratable groups, such as aspartyl proteases.

Application of the PM6 semi-empirical method to modeling proteins enhances docking accuracy of AutoDock

BACKGROUND

Molecular docking methods are commonly used for predicting binding modes and energies of ligands to proteins. For accurate complex geometry and binding energy estimation, an appropriate method for calculating partial charges is essential. AutoDockTools software, the interface for preparing input files for one of the most widely used docking programs AutoDock 4, utilizes the Gasteiger partial charge calculation method for both protein and ligand charge calculation. However, it has already been shown that more accurate partial charge calculation - and as a consequence, more accurate docking- can be achieved by using quantum chemical methods. For docking calculations quantum chemical partial charge calculation as a routine was only used for ligands so far. The newly developed Mozyme function of MOPAC2009 allows fast partial charge calculation of proteins by quantum mechanical semi-empirical methods. Thus, in the current study, the effect of semi-empirical quantum-mechanical partial charge calculation on docking accuracy could be investigated.

RESULTS

The docking accuracy of AutoDock 4 using the original AutoDock scoring function was investigated on a set of 53 protein ligand complexes using Gasteiger and PM6 partial charge calculation methods. This has enabled us to compare the effect of the partial charge calculation method on docking accuracy utilizing AutoDock 4 software. Our results showed that the docking accuracy in regard to complex geometry (docking result defined as accurate when the RMSD of the first rank docking result complex is within 2 A of the experimentally determined X-ray structure) significantly increased when partial charges of the ligands and proteins were calculated with the semi-empirical PM6 method. Out of the 53 complexes analyzed in the course of our study, the geometry of 42 complexes were accurately calculated using PM6 partial charges, while the use of Gasteiger charges resulted in only 28 accurate geometries. The binding affinity estimation was not influenced by the partial charge calculation method - for more accurate binding affinity prediction development of a new scoring function for AutoDock is needed.

CONCLUSION

Our results demonstrate that the accuracy of determination of complex geometry using AutoDock 4 for docking calculation greatly increases with the use of quantum chemical partial charge calculation on both the ligands and proteins.

Web application for studying the free energy of binding and protonation states of protein-ligand complexes based on HINT

A public web server performing computational titration at the active site in a protein-ligand complex has been implemented. This calculation is based on the Hydropathic interaction noncovalent force field. From 3D coordinate data for the protein, ligand and bridging waters (if available), the server predicts the best combination of protonation states for each ionizable residue and/or ligand functional group as well as the Gibbs free energy of binding for the ionization-optimized protein-ligand complex. The 3D structure for the modified molecules is available as output. In addition, a graph depicting how this energy changes with acidity, i.e., as a function of added protons, can be obtained. This data may prove to be of use in preparing models for virtual screening and molecular docking. A few illustrative examples are presented. In beta secretase (2va7) computational titration flipped the amide groups of Gln12 and Asn37 and protonated a ligand amine yielding an improvement of 6.37 kcal mol(-1) in the protein-ligand binding score. Protonation of Glu139 in mutant HIV-1 reverse transcriptase (2opq) allows a water bridge between the protein and inhibitor that increases the protein-ligand interaction score by 0.16 kcal mol(-1). In human sialidase NEU2 complexed with an isobutyl ether mimetic inhibitor (2f11) computational titration suggested that protonating Glu218, deprotonating Arg237, flipping the amide bond on Tyr334, and optimizing the positions of several other polar protons would increase the protein-ligand interaction score by 0.71 kcal mol(-1).

Identification of xenoestrogens in food additives by an integrated in silico and in vitro approach

In the search for xenoestrogens within food additives, we have analyzed the Joint FAO-WHO expert committee database, containing 1500 compounds, using an integrated in silico and in vitro approach. This analysis identified 31 potential estrogen receptor alpha ligands that were reduced to 13 upon applying a stringent filter based on ligand volume and binding mode. Among the 13 potential xenoestrogens, four were already known to exhibit an estrogenic activity, and the other nine were assayed in vitro, determining the binding affinity to the receptor and biological effects. Propyl gallate was found to act as an antagonist, and 4-hexylresorcinol was found to act as a potent transactivator; both ligands were active at nanomolar concentrations, as predicted by the in silico analysis. Some caution should be issued for the use of propyl gallate and 4-hexylresorcinol as food additives.

Lead Discovery Using Virtual Screening

The practice of virtual screening (VS) to identify chemical leads to known or novel targets is becoming a core function of the computational chemist within industry. By employing a range of techniques, when attempting to identify compounds with activity against a biological target, a small focused subset of a larger collection of compounds can be identified and tested, often with results much better than selecting a similar number of compounds at random. We will review the key methods available, their relative success, and provide practical insights into best practices and key gaps. We will also argue that the capability of VS methods has grown to a point where fuller integration with experimental methods, including HTS, could increase the effectiveness of both.

Mycotoxin detection plays "cops and robbers": cyclodextrin chemosensors as specialized police?

As in a cops and robbers play we discover new mycotoxins and metabolites everyday and we are forced to develop new molecules quickly as chemo- or biosensors or to modify existing molecules able to recognize these new hazardous compounds. This will result in an enormous cost saving to agro-food industry through the prevention and reduction of product recalls and reduced treatment costs. Here we present a brief review of the rapid methods used to detect mycotoxins, considering usefulness and limits. Then we propose a new fast, efficient and cheap methodology, based on a combination of computer chemistry aided design and fluorescence, that can help to drive synthesis in a more efficient way.

Docking and hydropathic scoring of polysubstituted pyrrole compounds with antitubulin activity

Compounds that bind at the colchicine site of tubulin have drawn considerable attention with studies indicating that these agents suppress microtubule dynamics and inhibit tubulin polymerization. Data for 18 polysubstituted pyrrole compounds are reported, including antiproliferative activity against human MDA-MB-435 cells and calculated free energies of binding following docking the compounds into models of alphabeta-tubulin. These docking calculations coupled with HINT interaction analyses are able to represent the complex structures and the binding modes of inhibitors such that calculated and measured free energies of binding correlate with an r(2) of 0.76. Structural analysis of the binding pocket identifies important intermolecular contacts that mediate binding. As seen experimentally, the complex with JG-03-14 (3,5-dibromo-4-(3,4-dimethoxyphenyl)-1H-pyrrole-2-carboxylic acid ethyl ester) is the most stable. These results illuminate the binding process and should be valuable in the design of new pyrrole-based colchicine site inhibitors as these compounds have very accessible syntheses.