A shape-based 3-D scaffold hopping method and its application to a bacterial protein-protein interaction

PLS and shape-based similarity analysis of maleimides--GSK-3 inhibitors

CONTEXT

Glycogen synthase kinase-3 (GSK-3) overactivity was correlated with several pathologies including type 2 diabetes mellitus, Alzheimer's disease, cancer, inflammation, obesity, etc.

OBJECTIVE

The aim of the current investigation was to model the inhibitory activity of maleimide derivatives--inhibitors of GSK-3, to evaluate the impact of alignment on statistical performances of the Quantitative Structure-Activity Relationship (QSAR) and the effect of the template on shape-similarity--binding affinity relationship.

MATERIALS AND METHODS

Dragon descriptors were used to generate Projection to Latent Structures (PLS) models in order to identify the structural prerequisites of maleimides to inhibit GSK-3. Additionally, shape/volume structural analysis of binding site interactions was evaluated.

RESULTS

Reliable statistics R(2)(Y(CUM)) = 0.938/0.920, Q((2)(Y)(CUM)) = 0.866/0.838 for aligned and alignment free QSAR models and significant (Pearson, Kendall and Spearman) correlations between shape/volume similarity and affinities were obtained.

DISCUSSION AND CONCLUSIONS

The crucial structural features modulating the activity of maleimides include topology, charge, geometry, 2D autocorrelations, 3D-MoRSE as well as shape/volume and molecular flexibility.

In silico methods in the discovery of endocrine disrupting chemicals

The prevalence of sex hormone-dependent cancers, reproductive problems, obesity, and cardiovascular complications has risen especially in the Western world. It has been suggested, that the exposure to various endocrine disrupting chemicals (EDCs) contributes to the development and progression of these diseases. EDCs can interfere with various proteins: nuclear steroid hormone receptors, such as estrogen-, androgen-, glucocorticoid- and mineralocorticoid receptors (ER, AR, GR, MR), and enzymes that are involved in steroid hormone synthesis and metabolism, for example hydroxysteroid dehydrogenases (HSDs). Numerous chemicals are known as endocrine disruptors. However, the mechanism of action for most of these EDCs is still unknown. It is exhaustive and time consuming to test in vitro all chemicals - potential EDCs - used in industry, agriculture or as food preservatives against their effects on the endocrine system. Computational methods, such as virtual screening, quantitative structure activity relationships and docking, are already well recognized and used in drug development. The same methods could also aid the research on EDCs. So far, the computational methods in the search of EDCs have been retrospective. There are, however, some prospective studies reporting the use of in silico methods: five studies reporting the identification of previously unknown 17β-HSD3 inhibitors, MR agonists, and ER antagonists/agonists. This review provides an overview of case studies and in silico methods that are used in the search of EDCs. This article is part of a Special Issue entitled 'CSR 2013'.

Discovery and design of tricyclic scaffolds as protein kinase CK2 (CK2) inhibitors through a combination of shape-based virtual screening and structure-based molecular modification

Protein kinase CK2 (CK2), a ubiquitous serine/threonine protein kinase for hundreds of endogenous substrates, serves as an attractive anticancer target. One of its most potent inhibitors, CX-4945, has entered a phase I clinical trial. Herein we present an integrated workflow combining shape-based virtual screening for the identification of novel CK2 inhibitors. A shape-based model derived from CX-4945 was built, and the subsequent virtual screening led to the identification of several novel scaffolds with high shape similarity to that of CX-4945. Among them two tricyclic scaffolds named [1,2,4]triazolo[4,3-c]quinazolin and [1,2,4]triazolo[4,3-a]quinoxalin attracted us the most. Combining strictly chemical similarity analysis, a second-round shape-based screening was performed based on the two tricyclic scaffolds, leading to 28 derivatives. These compounds not only targeted CK2 with potent and dose-dependent activities but also showed acceptable antiproliferative effects against a series of cancer cell lines. Our workflow supplies a high efficient strategy in the identification of novel CK2 inhibitors. Compounds reported here can serve as ideal leads for further modifications.

Structural biology approaches to antibacterial drug discovery

Antibacterial drug discovery has undertaken a major experiment in the 12 years since the first bacterial genomes were sequenced. Genome mining has identified hundreds of potential targets that have been distilled to a relatively small number of broad-spectrum targets ('low-hanging fruit') using the genetics tools of modern microbiology. Prosecuting these targets with high-throughput screens has led to a disappointingly small number of lead series that have mostly evaporated under closer scrutiny. In the meantime, multi-drug resistant pathogens are becoming a serious challenge in the clinic and the community and the number of pharmaceutical firms pursuing antibacterial discovery has declined. Filling the antibacterial development pipeline with novel chemical series is a significant challenge that will require the collaboration of scientists from many disciplines. Fortunately, advancements in the tools of structural biology and of in silico modeling are opening up new avenues of research that may help deal with the problems associated with discovering novel antibiotics.

3-substituted indole inhibitors against Francisella tularensis FabI identified by structure-based virtual screening

In this study, we describe novel inhibitors against Francisella tularensis SchuS4 FabI identified from structure-based in silico screening with integrated molecular dynamics simulations to account for induced fit of a flexible loop crucial for inhibitor binding. Two 3-substituted indoles, 54 and 57, preferentially bound the NAD(+) form of the enzyme and inhibited growth of F. tularensis SchuS4 at concentrations near that of their measured Ki. While 57 was species-specific, 54 showed a broader spectrum of growth inhibition against F. tularensis , Bacillus anthracis , and Staphylococcus aureus . Binding interaction analysis in conjunction with site-directed mutagenesis revealed key residues and elements that contribute to inhibitor binding and species specificity. Mutation of Arg-96, a poorly conserved residue opposite the loop, was unexpectedly found to enhance inhibitor binding in the R96G and R96M variants. This residue may affect the stability and closure of the flexible loop to enhance inhibitor (or substrate) binding.

Pharmacophore Alignment Search Tool (PhAST): Significance Assessment of Chemical Similarity

Previously, we proposed a ligand-based virtual screening technique (PhAST) based on global alignment of linearized interaction patterns. Here, we applied techniques developed for similarity assessment in local sequence alignments to our method resulting in p-values for chemical similarity. We compared two sampling strategies, a simple sampling strategy and a Markov Chain Monte Carlo (MCMC) method, and investigated the similarity of sampled distributions to Gaussian, Gumbel, modified Gumbel, and Gamma distributions. The Gumbel distribution with a Gaussian correction term was identified as the most similar to the observed empirical distributions. These techniques were applied in retrospective screenings on a drug-like dataset. Obtained p-values were adjusted to the size of the screening library with four different methods. Evaluation of E-value thresholds corroborated the Bonferroni correction as a preferred means to identify significant chemical similarity with PhAST. An online version of PhAST with significance estimation is available at http://modlab-cadd.ethz.ch/.

Protein pocket and ligand shape comparison and its application in virtual screening

Understanding molecular recognition is one major requirement for drug discovery and design. Physicochemical and shape complementarity between two binding partners is the driving force during complex formation. In this study, the impact of shape within this process is analyzed. Protein binding pockets and co-crystallized ligands are represented by normalized principal moments of inertia ratios (NPRs). The corresponding descriptor space is triangular, with its corners occupied by spherical, discoid, and elongated shapes. An analysis of a selected set of sc-PDB complexes suggests that pockets and bound ligands avoid spherical shapes, which are, however, prevalent in small unoccupied pockets. Furthermore, a direct shape comparison confirms previous studies that on average only one third of a pocket is filled by its bound ligand, supplemented by a 50 % subpocket coverage. In this study, we found that shape complementary is expressed by low pairwise shape distances in NPR space, short distances between the centers-of-mass, and small deviations in the angle between the first principal ellipsoid axes. Furthermore, it is assessed how different binding pocket parameters are related to bioactivity and binding efficiency of the co-crystallized ligand. In addition, the performance of different shape and size parameters of pockets and ligands is evaluated in a virtual screening scenario performed on four representative targets.

Scaffold-focused virtual screening: prospective application to the discovery of TTK inhibitors

We describe and apply a scaffold-focused virtual screen based upon scaffold trees to the mitotic kinase TTK (MPS1). Using level 1 of the scaffold tree, we perform both 2D and 3D similarity searches between a query scaffold and a level 1 scaffold library derived from a 2 million compound library; 98 compounds from 27 unique top-ranked level 1 scaffolds are selected for biochemical screening. We show that this scaffold-focused virtual screen prospectively identifies eight confirmed active compounds that are structurally differentiated from the query compound. In comparison, 100 compounds were selected for biochemical screening using a virtual screen based upon whole molecule similarity resulting in 12 confirmed active compounds that are structurally similar to the query compound. We elucidated the binding mode for four of the eight confirmed scaffold hops to TTK by determining their protein-ligand crystal structures; each represents a ligand-efficient scaffold for inhibitor design.

Cheminformatics at the interface of medicinal chemistry and proteomics

Multiple factors have to be optimized in the course of a drug discovery project. Traditionally this includes potency on a single target, eventually specificity as well as the pharmacokinetic, physicochemical and the safety profile. Recently an additional dimension has been added by realizing that the therapeutic outcome of a drug is often determined not only by its activity on a single target but also by its activity profile across a variety of biological targets. To address the polypharmacology of drug candidates many compounds are tested on a set of targets or in phenotypic screens generating a tremendous amount of data. To extract useful information computational methods at the interface of proteomics and cheminformatics are indispensable. This review will focus on some recent developments in this field. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.

Elaboration of thorough simplified vinca alkaloids as antimitotic agents based on pharmacophore similarity

Thorough simplification of vinca alkaloids based on pharmacophore similarity has been conducted. A concise process for the syntheses of target compounds was successfully developed with yields from poor to excellent (19-98%). Cell growth inhibitory activities of these synthesized compounds were evaluated in five cancer cell lines including MCF-7, MDA-MB-231, HepG2, HepG2/ADM and K562. Almost all compounds exhibited moderate antitumor activity with optimal IC50 value of 0.89 ± 0.07 μM in MCF-7 cells. Investigation of structure-activity relationship (SAR) indicates that electron-withdraw substituents on the ring contribute to the enhancement of the antitumor activities. The simplified vinca alkaloids are confirmed as antimitotic agents, which inhibit the polymerization of tubulin just like vinblastine.

Shape and pharmacophore-based virtual screening to identify potential cytochrome P450 sterol 14α-demethylase inhibitors

Sterol 14α-demethylase (CYP51) is a cytochrome P450 heme thiolate containing enzyme involved in biosynthesis of membrane sterols, including sterol in animals, ergosterol in fungi, and a variety of C24-modified sterols in plants and protozoa. Several clinical drugs have been developed to reduce the impact of fungal diseases, but their clinical uses have been limited by the emergence of drug resistance and insufficiencies in their antifungal activity. Therefore, in order to identify potential CYP51 inhibitors, we have implemented a virtual screening (VS) protocol by using both phase shape and pharmacophore model (AHHRR) against Asinex, ChemBridge and Maybridge databases. A filtering protocol, including Lipinski filter, number of rotatable bonds and different precisions of molecular docking was applied in hits selection. The results indicated that both shape-based and pharmacophore-based screening yielded the best result with potential inhibitors. The searched compounds were also evaluated with ADME properties, which show excellent pharmacokinetic properties under the acceptable range. We identified potential CYP51 inhibitors for further investigation, they could also be employed to design ligands with enhanced inhibitory potencies and to predict the potencies of analogs to guide synthesis/or prepare synthetic antifungal analogs against CYP51.

3D shape-based analysis of cell line-specific compound response in cancers

The rapid increase in the volume of high-throughput anticancer chemical screening data requires a better interpretation of the relationships between diverse chemical structures and their varied effects in distinct cancer subtypes. Unexpected compound efficacy or resistance in cancer cells has been difficult to explain, in part because there has been no systematic analysis of compound response profiles in cancer cells with different genotypic backgrounds. In this study, we compared 2D chemical- and 3D shape-based similarity search methods to study the structure-activity relationships of anticancer compounds in a collection of heterogeneous cancer cell lines. The 3D shape-based metric provided better resolution than the 2D chemical topology-based method for identifying compound pairs with similar cellular response profiles. We confirmed that the 3D method exclusively identified compound pairs with different chemical scaffolds that stimulated highly similar cellular responses. The present analyses provide useful guidelines for investigating the lineage- and genotype-specific activities of diverse compounds and their mechanisms of action.

Druggable protein interaction sites are more predisposed to surface pocket formation than the rest of the protein surface

Despite intense interest and considerable effort via high-throughput screening, there are few examples of small molecules that directly inhibit protein-protein interactions. This suggests that many protein interaction surfaces may not be intrinsically “druggable” by small molecules, and elevates in importance the few successful examples as model systems for improving our fundamental understanding of druggability. Here we describe an approach for exploring protein fluctuations enriched in conformations containing surface pockets suitable for small molecule binding. Starting from a set of seven unbound protein structures, we find that the presence of low-energy pocket-containing conformations is indeed a signature of druggable protein interaction sites and that analogous surface pockets are not formed elsewhere on the protein. We further find that ensembles of conformations generated with this biased approach structurally resemble known inhibitor-bound structures more closely than equivalent ensembles of unbiased conformations. Collectively these results suggest that “druggability” is a property encoded on a protein surface through its propensity to form pockets, and inspire a model in which the crude features of the predisposed pocket(s) restrict the range of complementary ligands; additional smaller conformational changes then respond to details of a particular ligand. We anticipate that the insights described here will prove useful in selecting protein targets for therapeutic intervention.

Identifying small-molecule inhibitors of protein interactions has traditionally presented a challenge for modern screening methods, despite interest stemming from the fact that such interactions comprise the underlying mechanisms for cell proliferation, differentiation, and survival. This suggests that many protein interaction surfaces may not be intrinsically “druggable” by small molecules, and elevates in importance the few successful examples as model systems for improving our understanding of factors contributing to druggability. Here we describe a new approach for exploring protein fluctuations leading to surface pockets suitable for small molecule binding. We find that the presence of such pockets is indeed a signature of druggable protein interaction sites, suggesting that “druggability” is a property encoded on a protein surface through its propensity to form pockets. We anticipate that the insights described here will prove useful in selecting protein targets for therapeutic intervention.

Torsion angle preferences in druglike chemical space: a comprehensive guide

Crystal structure databases offer ample opportunities to derive small molecule conformation preferences, but the derived knowledge is not systematically applied in drug discovery research. We address this gap by a comprehensive and extendable expert system enabling quick assessment of the probability of a given conformation to occur. It is based on a hierarchical system of torsion patterns that cover a large part of druglike chemical space. Each torsion pattern has associated frequency histograms generated from CSD and PDB data and, derived from the histograms, traffic-light rules for frequently observed, rare, and highly unlikely torsion ranges. Structures imported into the corresponding software are annotated according to these rules. We present the concept behind the tree of torsion patterns, the design of an intuitive user interface for the management and usage of the torsion library, and we illustrate how the system helps analyze and understand conformation properties of substructures widely used in medicinal chemistry.

Physicochemical and DMPK in silico models: facilitating their use by medicinal chemists

It is known that the developability of drugs is related to their physicochemical and DMPK properties. Given the time and expense involved in discovering and developing new drugs, maximizing the chance of success by calculating properties ahead of chemical synthesis and testing, and only acting on those candidates whose properties fall into a desired range, would seem to make sense. This paper provides an overview of calculable physicochemical and DMPK properties, an assessment of their relative difficulty of their calculation and accuracy, and available software. Methods companies have employed to communicate results will be discussed, including the use of composite scoring functions and ranking schemes. Calculations do no good if chemists will not use them to prioritize synthesis decisions. Strategies are presented for facilitating model usage. An approach adopted at Genentech for presenting results that involves the close coupling of property calculations with 3D structure based drug design is described.

MQN-mapplet: visualization of chemical space with interactive maps of DrugBank, ChEMBL, PubChem, GDB-11, and GDB-13

The MQN-mapplet is a Java application giving access to the structure of small molecules in large databases via color-coded maps of their chemical space. These maps are projections from a 42-dimensional property space defined by 42 integer value descriptors called molecular quantum numbers (MQN), which count different categories of atoms, bonds, polar groups, and topological features and categorize molecules by size, rigidity, and polarity. Despite its simplicity, MQN-space is relevant to biological activities. The MQN-mapplet allows localization of any molecule on the color-coded images, visualization of the molecules, and identification of analogs as neighbors on the MQN-map or in the original 42-dimensional MQN-space. No query molecule is necessary to start the exploration, which may be particularly attractive for nonchemists. To our knowledge, this type of interactive exploration tool is unprecedented for very large databases such as PubChem and GDB-13 (almost one billion molecules). The application is freely available for download at www.gdb.unibe.ch.

Visualization and virtual screening of the chemical universe database GDB-17

The chemical universe database GDB-17 contains 166.4 billion molecules of up to 17 atoms of C, N, O, S, and halogens obeying rules for chemical stability, synthetic feasibility, and medicinal chemistry. GDB-17 was analyzed using 42 integer value descriptors of molecular structure which we term "Molecular Quantum Numbers" (MQN). Principal component analysis and representation of the (PC1, PC2)-plane provided a graphical overview of the GDB-17 chemical space. Rapid ligand-based virtual screening (LBVS) of GDB-17 using the city-block distance CBD(MQN) as a similarity search measure was enabled by a hashed MQN-fingerprint. LBVS of the entire GDB-17 and of selected subsets identified shape similar, scaffold hopping analogs (ROCS > 1.6 and T(SF) < 0.5) of 15 drugs. Over 97% of these analogs occurred within CBD(MQN) ≤ 12 from each drug, a constraint which might help focus advanced virtual screening. An MQN-searchable 50 million subset of GDB-17 is publicly available at www.gdb.unibe.ch .

PubChem3D: conformer ensemble accuracy

BACKGROUND

PubChem is a free and publicly available resource containing substance descriptions and their associated biological activity information. PubChem3D is an extension to PubChem containing computationally-derived three-dimensional (3-D) structures of small molecules. All the tools and services that are a part of PubChem3D rely upon the quality of the 3-D conformer models. Construction of the conformer models currently available in PubChem3D involves a clustering stage to sample the conformational space spanned by the molecule. While this stage allows one to downsize the conformer models to more manageable size, it may result in a loss of the ability to reproduce experimentally determined "bioactive" conformations, for example, found for PDB ligands. This study examines the extent of this accuracy loss and considers its effect on the 3-D similarity analysis of molecules.

RESULTS

The conformer models consisting of up to 100,000 conformers per compound were generated for 47,123 small molecules whose structures were experimentally determined, and the conformers in each conformer model were clustered to reduce the size of the conformer model to a maximum of 500 conformers per molecule. The accuracy of the conformer models before and after clustering was evaluated using five different measures: root-mean-square distance (RMSD), shape-optimized shape-Tanimoto (STST-opt) and combo-Tanimoto (ComboTST-opt), and color-optimized color-Tanimoto (CTCT-opt) and combo-Tanimoto (ComboTCT-opt). On average, the effect of clustering decreased the conformer model accuracy, increasing the conformer ensemble's RMSD to the bioactive conformer (by 0.18 ± 0.12 Å), and decreasing the STST-opt, ComboTST-opt, CTCT-opt, and ComboTCT-opt scores (by 0.04 ± 0.03, 0.16 ± 0.09, 0.09 ± 0.05, and 0.15 ± 0.09, respectively).

CONCLUSION

This study shows the RMSD accuracy performance of the PubChem3D conformer models is operating as designed. In addition, the effect of PubChem3D sampling on 3-D similarity measures shows that there is a linear degradation of average accuracy with respect to molecular size and flexibility. Generally speaking, one can likely expect the worst-case minimum accuracy of 90% or more of the PubChem3D ensembles to be 0.75, 1.09, 0.43, and 1.13, in terms of STST-opt, ComboTST-opt, CTCT-opt, and ComboTCT-opt, respectively. This expected accuracy improves linearly as the molecule becomes smaller or less flexible.

Virtual screening in drug design

Virtual screening has become a standard tool in drug discovery to identify novel lead compounds that target a biomolecule of interest. I present several concepts in ligand-based and structure-based virtual screening and discuss some of the current shortcomings and new developments. I also highlight approaches that combine concepts from structure- and ligand-based design.

Fluorescence Observables and Enzyme Kinetics in the Investigation of PPI Modulation by Small Molecules: Detection, Mechanistic Insight, and Functional Consequences

The potential of fluorescence-based methods and kinetic analysis in the screening and molecular-scale mechanistic investigation of PPI modulation by small molecules is discussed through several representative examples collected and commented. These experimental approaches take advantage of a variety of observables. Changes in the protein aggregation pattern have been monitored through fluorescence properties such as spectra, intensities (related to quantum yields), time-decays, and anisotropies of intrinsic protein fluorophores, of extrinsic fluorescent tags and, even, of the same small molecules added to modulate PPIs, as well as through bimolecular excited-state processes such as static and collisional quenching, including electron and excitation-energy transfer, or exciton interaction, whose efficiencies are crucially structure dependent. Besides allowing for qualitative and quantitative information on the small-molecule induced PPI modulation, these approaches can take advantage from the sensitivity of fluorescence observables on fine structural details to shed light on the molecular-scale mechanisms of action and their functional consequences. Direct investigation of the latter by kinetic inhibition analysis represents a useful change in perspective whenever PPI are relevant for enzyme activity. Dissociative inhibition, that is, the ability of some small molecules to inhibit enzymes by disrupting their active oligomeric assembly is shortly reviewed.

What can we learn from the evolution of protein-ligand interactions to aid the design of new therapeutics?

Efforts to increase affinity in the design of new therapeutic molecules have tended to lead to greater lipophilicity, a factor that is generally agreed to be contributing to the low success rate of new drug candidates. Our aim is to provide a structural perspective to the study of lipophilic efficiency and to compare molecular interactions created over evolutionary time with those designed by humans. We show that natural complexes typically engage in more polar contacts than synthetic molecules bound to proteins. The synthetic molecules also have a higher proportion of unmatched heteroatoms at the interface than the natural sets. These observations suggest that there are lessons to be learnt from Nature, which could help us to improve the characteristics of man-made molecules. In particular, it is possible to increase the density of polar contacts without increasing lipophilicity and this is best achieved early in discovery while molecules remain relatively small.

Effects of multiple conformers per compound upon 3-D similarity search and bioassay data analysis

Background

To improve the utility of PubChem, a public repository containing biological activities of small molecules, the PubChem3D project adds computationally-derived three-dimensional (3-D) descriptions to the small-molecule records contained in the PubChem Compound database and provides various search and analysis tools that exploit 3-D molecular similarity. Therefore, the efficient use of PubChem3D resources requires an understanding of the statistical and biological meaning of computed 3-D molecular similarity scores between molecules.

Results

The present study investigated effects of employing multiple conformers per compound upon the 3-D similarity scores between ten thousand randomly selected biologically-tested compounds (10-K set) and between non-inactive compounds in a given biological assay (156-K set). When the “best-conformer-pair” approach, in which a 3-D similarity score between two compounds is represented by the greatest similarity score among all possible conformer pairs arising from a compound pair, was employed with ten diverse conformers per compound, the average 3-D similarity scores for the 10-K set increased by 0.11, 0.09, 0.15, 0.16, 0.07, and 0.18 for ST^ST-opt, CT^ST-opt, ComboT^ST-opt, ST^CT-opt, CT^CT-opt, and ComboT^CT-opt, respectively, relative to the corresponding averages computed using a single conformer per compound. Interestingly, the best-conformer-pair approach also increased the average 3-D similarity scores for the non-inactive–non-inactive (NN) pairs for a given assay, by comparable amounts to those for the random compound pairs, although some assays showed a pronounced increase in the per-assay NN-pair 3-D similarity scores, compared to the average increase for the random compound pairs.

Conclusion

These results suggest that the use of ten diverse conformers per compound in PubChem bioassay data analysis using 3-D molecular similarity is not expected to increase the separation of non-inactive from random and inactive spaces “on average”, although some assays show a noticeable separation between the non-inactive and random spaces when multiple conformers are used for each compound. The present study is a critical next step to understand effects of conformational diversity of the molecules upon the 3-D molecular similarity and its application to biological activity data analysis in PubChem. The results of this study may be helpful to build search and analysis tools that exploit 3-D molecular similarity between compounds archived in PubChem and other molecular libraries in a more efficient way.

Integrating ligand-based and protein-centric virtual screening of kinase inhibitors using ensembles of multiple protein kinase genes and conformations

The rapidly growing wealth of structural and functional information about kinase genes and kinase inhibitors that is fueled by a significant therapeutic role of this protein family provides a significant impetus for development of targeted computational screening approaches. In this work, we explore an ensemble-based, protein-centric approach that allows for simultaneous virtual ligand screening against multiple kinase genes and multiple kinase receptor conformations. We systematically analyze and compare the results of ligand-based and protein-centric screening approaches using both single-receptor and ensemble-based docking protocols. A panel of protein kinase targets that includes ABL, EGFR, P38, CDK2, TK, and VEGFR2 kinases is used in this comparative analysis. By applying various performance metrics we have shown that ligand-centric shape matching can provide an effective enrichment of active compounds outperforming single-receptor docking screening. However, ligand-based approaches can be highly sensitive to the choice of inhibitor queries. Employment of multiple inhibitor queries combined with parallel selection ranking criteria can improve the performance and efficiency of ligand-based virtual screening. We also demonstrated that replica-exchange Monte Carlo docking with kinome-based ensembles of multiple crystal structures can provide a superior early enrichment on the kinase targets. The central finding of this study is that incorporation of the template-based structural information about kinase inhibitors and protein kinase structures in diverse functional states can significantly enhance the overall performance and robustness of both ligand and protein-centric screening strategies. The results of this study may be useful in virtual screening of kinase inhibitors potentially offering a beneficial spectrum of therapeutic activities across multiple disease states.

Biophysical and computational fragment-based approaches to targeting protein-protein interactions: applications in structure-guided drug discovery

Drug discovery has classically targeted the active sites of enzymes or ligand-binding sites of receptors and ion channels. In an attempt to improve selectivity of drug candidates, modulation of protein-protein interfaces (PPIs) of multiprotein complexes that mediate conformation or colocation of components of cell-regulatory pathways has become a focus of interest. However, PPIs in multiprotein systems continue to pose significant challenges, as they are generally large, flat and poor in distinguishing features, making the design of small molecule antagonists a difficult task. Nevertheless, encouragement has come from the recognition that a few amino acids - so-called hotspots - may contribute the majority of interaction-free energy. The challenges posed by protein-protein interactions have led to a wellspring of creative approaches, including proteomimetics, stapled α-helical peptides and a plethora of antibody inspired molecular designs. Here, we review a more generic approach: fragment-based drug discovery. Fragments allow novel areas of chemical space to be explored more efficiently, but the initial hits have low affinity. This means that they will not normally disrupt PPIs, unless they are tethered, an approach that has been pioneered by Wells and co-workers. An alternative fragment-based approach is to stabilise the uncomplexed components of the multiprotein system in solution and employ conventional fragment-based screening. Here, we describe the current knowledge of the structures and properties of protein-protein interactions and the small molecules that can modulate them. We then describe the use of sensitive biophysical methods - nuclear magnetic resonance, X-ray crystallography, surface plasmon resonance, differential scanning fluorimetry or isothermal calorimetry - to screen and validate fragment binding. Fragment hits can subsequently be evolved into larger molecules with higher affinity and potency. These may provide new leads for drug candidates that target protein-protein interactions and have therapeutic value.

Identification of novel human dipeptidyl peptidase-IV inhibitors of natural origin (part I): virtual screening and activity assays

Background

There has been great interest in determining whether natural products show biological activity toward protein targets of pharmacological relevance. One target of particular interest is DPP-IV whose most important substrates are incretins that, among other beneficial effects, stimulates insulin biosynthesis and secretion. Incretins have very short half-lives because of their rapid degradation by DPP-IV and, therefore, inhibiting this enzyme improves glucose homeostasis. As a result, DPP-IV inhibitors are of considerable interest to the pharmaceutical industry. The main goals of this study were (a) to develop a virtual screening process to identify potential DPP-IV inhibitors of natural origin; (b) to evaluate the reliability of our virtual-screening protocol by experimentally testing the in vitro activity of selected natural-product hits; and (c) to use the most active hit for predicting derivatives with higher binding affinities for the DPP-IV binding site.

Methodology/Principal Findings

We predicted that 446 out of the 89,165 molecules present in the natural products subset of the ZINC database would inhibit DPP-IV with good ADMET properties. Notably, when these 446 molecules were merged with 2,342 known DPP-IV inhibitors and the resulting set was classified into 50 clusters according to chemical similarity, there were 12 clusters that contained only natural products for which no DPP-IV inhibitory activity has been previously reported. Nine molecules from 7 of these 12 clusters were then selected for in vitro activity testing and 7 out of the 9 molecules were shown to inhibit DPP-IV (where the remaining two molecules could not be solubilized, preventing the evaluation of their DPP-IV inhibitory activity). Then, the hit with the highest activity was used as a lead compound in the prediction of more potent derivatives.

Conclusions/Significance

We have demonstrated that our virtual-screening protocol was successful in identifying novel lead compounds for developing more potent DPP-IV inhibitors.

Shape-based reprofiling of FDA-approved drugs for the H₁ histamine receptor

Reprofiling of existing drugs to treat conditions not originally targeted is an attractive means of addressing the problem of a decreasing stream of approved drugs. To determine if 3D shape similarity can be used to rationalize an otherwise serendipitous process, we employed 3D shape-based virtual screening to reprofile existing FDA-approved drugs. The study was conducted in two phases. First, multiple histamine H(1) receptor antagonists were identified to be used as query molecules, and these were compared to a database of approved drugs. Second, the hits were ranked according to 3D similarity and the top drugs evaluated in a cell-based assay. The virtual screening methodology proved highly successful, as 13 of 23 top drugs tested selectively inhibited histamine-induced calcium release with the best being chlorprothixene (IC(50) 1 nM). Finally, we confirmed that the drugs identified using the cell-based assay were all acting at the receptor level by conducting a radioligand-binding assay using rat membrane.

Features of protein-protein interactions that translate into potent inhibitors: topology, surface area and affinity

Protein-protein interactions (PPIs) control the assembly of multi-protein complexes and, thus, these contacts have enormous potential as drug targets. However, the field has produced a mix of both exciting success stories and frustrating challenges. Here, we review known examples and explore how the physical features of a PPI, such as its affinity, hotspots, off-rates, buried surface area and topology, might influence the chances of success in finding inhibitors. This analysis suggests that concise, tight binding PPIs are most amenable to inhibition. However, it is also clear that emerging technical methods are expanding the repertoire of 'druggable' protein contacts and increasing the odds against difficult targets. In particular, natural product-like compound libraries, high throughput screens specifically designed for PPIs and approaches that favour discovery of allosteric inhibitors appear to be attractive routes. The first group of PPI inhibitors has entered clinical trials, further motivating the need to understand the challenges and opportunities in pursuing these types of targets.

Identification of FDA-approved drugs that computationally bind to MDM2

The integrity of the p53 tumor suppressor pathway is compromised in the majority of cancers. In 7% of cancers p53 is inactivated by abnormally high levels of MDM2--an E3 ubiquitin ligase that polyubiquitinates p53, marking it for degradation. MDM2 engages p53 through its hydrophobic cleft, and blockage of that cleft by small molecules can re-establish p53 activity. Small molecule MDM2 inhibitors have been developed, but there is likely to be a high cost and long time period before effective drugs reach the market. An alternative is to repurpose FDA-approved drugs. This report describes a new approach, called Computational Conformer Selection, to screen for compounds that potentially inhibit MDM2. This screen was used to computationally generate up to 600 conformers of 3244 FDA-approved drugs. Drug conformer similarities to 41 computationally-generated conformers of MDM2 inhibitor nutlin 3a were ranked by shape and charge distribution. Quantification of similarities by Tanimoto combo scoring resulted in scores that ranged from 0.142 to 0.802. In silico docking of drugs to MDM2 was used to calculate binding energies and to visualize contacts between the top-ranking drugs and the MDM2 hydrophobic cleft. We present 15 FDA-approved drugs predicted to inhibit p53/MDM2 interaction.

Parallel-ProBiS: fast parallel algorithm for local structural comparison of protein structures and binding sites

The ProBiS algorithm performs a local structural comparison of the query protein surface against the nonredundant database of protein structures. It finds proteins that have binding sites in common with the query protein. Here, we present a new parallelized algorithm, Parallel-ProBiS, for detecting similar binding sites on clusters of computers. The obtained speedups of the parallel ProBiS scale almost ideally with the number of computing cores up to about 64 computing cores. Scaling is better for larger than for smaller query proteins. For a protein with almost 600 amino acids, the maximum speedup of 180 was achieved on two interconnected clusters with 248 computing cores. Source code of Parallel-ProBiS is available for download free for academic users at http://probis.cmm.ki.si/download.

Established and emerging trends in computational drug discovery in the structural genomics era

Bioinformatics and chemoinformatics approaches contribute to hit discovery, hit-to-lead optimization, safety profiling, and target identification and enhance our overall understanding of the health and disease states. A vast repertoire of computational methods has been reported and increasingly combined in order to address more and more challenging targets or complex molecular mechanisms in the context of large-scale integration of structure and bioactivity data produced by private and public drug research. This review explores some key computational methods directly linked to drug discovery and chemical biology with a special emphasis on compound collection preparation, virtual screening, protein docking, and systems pharmacology. A list of generally freely available software packages and online resources is provided, and examples of successful applications are briefly commented upon.

Structure-based design, synthesis, and characterization of dual hotspot small-molecule HIV-1 entry inhibitors

Cellular infection by HIV-1 is initiated with a binding event between the viral envelope glycoprotein gp120 and the cellular receptor protein CD4. The CD4-gp120 interface is dominated by two hotspots: a hydrophobic gp120 cavity capped by Phe43(CD4) and an electrostatic interaction between residues Arg59(CD4) and Asp368(gp120). The CD4 mimetic small-molecule NBD-556 (1) binds within the gp120 cavity; however, 1 and related congeners demonstrate limited viral neutralization breadth. Herein, we report the design, synthesis, characterization, and X-ray structures of gp120 in complex with small molecules that simultaneously engage both binding hotspots. The compounds specifically inhibit viral infection of 42 tier 2 clades B and C viruses and are shown to be antagonists of entry into CD4-negative cells. Dual hotspot design thus provides both a means to enhance neutralization potency of HIV-1 entry inhibitors and a novel structural paradigm for inhibiting the CD4-gp120 protein-protein interaction.

Wrapping mimicking in drug-like small molecules disruptive of protein-protein interfaces

The discovery of small-molecule drugs aimed at disrupting protein-protein associations is expected to lead to promising therapeutic strategies. The small molecule binds to the target protein thus replacing its natural protein partner. Noteworthy, structural analysis of complexes between successful disruptive small molecules and their target proteins has suggested the possibility that such ligands might somehow mimic the binding behavior of the protein they replace. In these cases, the molecules show a spatial and "chemical" (i.e., hydrophobicity) similarity with the residues of the partner protein involved in the protein-protein complex interface. However, other disruptive small molecules do not seem to show such spatial and chemical correspondence with the replaced protein. In turn, recent progress in the understanding of protein-protein interactions and binding hot spots has revealed the main role of intermolecular wrapping interactions: three-body cooperative correlations in which nonpolar groups in the partner protein promote dehydration of a two-body electrostatic interaction of the other protein. Hence, in the present work, we study some successful complexes between already discovered small disruptive drug-like molecules and their target proteins already reported in the literature and we compare them with the complexes between such proteins and their natural protein partners. Our results show that the small molecules do in fact mimic to a great extent the wrapping behavior of the protein they replace. Thus, by revealing the replacement the small molecule performs of relevant wrapping interactions, we convey precise physical meaning to the mimicking concept, a knowledge that might be exploited in future drug-design endeavors.

Ligand-based virtual screening approach using a new scoring function

In this study, we aimed to develop a new ligand-based virtual screening approach using an effective shape-overlapping procedure and a more robust scoring function (denoted by the HWZ score for convenience). The HWZ score-based virtual screening approach was tested against the compounds for 40 protein targets available in the Database of Useful Decoys (DUD; dud.docking.org/jahn/ ), and the virtual screening performance was evaluated in terms of the area under the receiver operator characteristic (ROC) curve (AUC), enrichment factor (EF), and hit rate (HR), demonstrating an improved overall performance compared to other popularly used approaches examined. In particular, the HWZ score-based virtual screening led to an average AUC value of 0.84 ± 0.02 (95% confidence interval) for the 40 targets. The average HR values at the top 1% and 10% of the active compounds for the 40 targets were 46.3% ± 6.7% and 59.2% ± 4.7%, respectively. In addition, the performance of the HWZ score-based virtual screening approach is less sensitive to the choice of the target.