Improving drug discovery using hybrid softcomputing methods

Virtual screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface in order to find new hotspots, where ligands might potentially interact with, and which is implemented in last generation massively parallel GPU hardware, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods and concretely BINDSURF is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to improve accuracy of the scoring functions used in BINDSURF we propose a hybrid novel approach where neural networks (NNET) and support vector machines (SVM) methods are trained with databases of known active (drugs) and inactive compounds, being this information exploited afterwards to improve BINDSURF VS predictions.

Improving drug discovery using a neural networks based parallel scoring function

Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface to find new hotspots, where ligands might potentially interact with, and which is implemented in massively parallel Graphics Processing Units, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to solve this problem, we propose a novel approach where neural networks are trained with databases of known active (drugs) and inactive compounds, and later used to improve VS predictions.

Design and validation of a novel generic platform for the production of tetravalent IgG1-like bispecific antibodies

International audience

Natural and engineered kallikrein inhibitors: an emerging pharmacopoeia

The kallikreins and kallikrein-related peptidases are serine proteases that control a plethora of developmental and homeostatic phenomena, ranging from semen liquefaction to skin desquamation and blood pressure. The diversity of roles played by kallikreins has stimulated considerable interest in these enzymes from the perspective of diagnostics and drug design. Kallikreins already have well-established credentials as targets for therapeutic intervention and there is increasing appreciation of their potential both as biomarkers and as targets for inhibitor design. Here, we explore the current status of naturally occurring kallikrein protease-inhibitor complexes and illustrate how this knowledge can interface with strategies for rational re-engineering of bioscaffolds and design of small-molecule inhibitors.

Advances in the systemic therapy of malignant pleural mesothelioma

Malignant pleural mesothelioma is an aggressive thoracic malignancy associated with exposure to asbestos, and its incidence is anticipated to increase during the first half of this century. Chemotherapy is the mainstay of treatment, yet sufficiently robust evidence to substantiate the current standard of care has emerged only in the past 5 years. This Review summarizes the evidence supporting the clinical activity of chemotherapy, discusses the use of end points for its assessment and examines the influence of clinical and biochemical prognostic factors on the natural history of malignant pleural mesothelioma. Early-phase clinical trials of second-line and novel agents are emerging from an increased understanding of mesothelioma cell biology. Coupled with high-quality translational research, such developments have real potential to improve the outlook of patients at a time of increasing incidence.

Targeting oxidative stress in cancer

Importance of the field: Reactive oxygen species (ROS) occur as natural by-products of oxygen metabolism and have important cellular functions. Normally, the cell is able to maintain an adequate balance between the formation and removal of ROS either via anti-oxidants or through the use specific enzymatic pathways. However, if this balance is disturbed, oxidative stress may occur in the cell, a situation linked to the pathogenesis of many diseases, including cancer. Areas covered in this review: HDACs are important regulators of many oxidative stress pathways including those involved with both sensing and coordinating the cellular response to oxidative stress. In particular aberrant regulation of these pathways by histone deacetylases may play critical roles in cancer progression. What the reader will gain: In this review we discuss the notion that targeting HDACs may be a useful therapeutic avenue in the treatment of oxidative stress in cancer, using chronic obstructive pulmonary disease (COPD), NSCLC and hepatocellular carcinoma (HCC) as examples to illustrate this possibility. Take home message: Epigenetic mechanisms may be an important new therapeutic avenue for targeting oxidative stress in cancer. © 2010 Informa UK, Ltd.

Interpreting scratch assays using pair density dynamics and approximate Bayesian computation

Quantifying the impact of biochemical compounds on collective cell spreading is an essential element of drug design, with various applications including developing treatments for chronic wounds and cancer. Scratch assays are a technically simple and inexpensive method used to study collective cell spreading; however, most previous interpretations of scratch assays are qualitative and do not provide estimates of the cell diffusivity, D, or the cell proliferation rate,l. Estimating D and l is important for investigating the efficacy of a potential treatment and provides insight into the mechanism through which the potential treatment acts. While a few methods for estimating D and l have been proposed, these previous methods lead to point estimates of D and l, and provide no insight into the uncertainty in these estimates. Here, we compare various types of information that can be extracted from images of a scratch assay, and quantify D and l using discrete computational simulations and approximate Bayesian computation. We show that it is possible to robustly recover estimates of D and l from synthetic data, as well as a new set of experimental data. For the first time, our approach also provides a method to estimate the uncertainty in our estimates of D and l. We anticipate that our approach can be generalized to deal with more realistic experimental scenarios in which we are interested in estimating D and l, as well as additional relevant parameters such as the strength of cell-to-cell adhesion or the strength of cell-to-substrate adhesion.

Microtubulin binding sites as target for developing anticancer agents

Microtubules (MTs) play important and diverse roles in eukaryotic cells. Their function and biophysical properties have made α−and β−tubulin, the main components of MTs, the subject of intense study. Interfering with normal MT dynamics, for example, by the addition of tubulin ligands, can cause the cell great distress and affect MT stability and functions, including mitosis, cell motion and intracellular organelle transport. It has been shown in the literature that tubulin is an important target molecule for developing anticancer drugs. Tubulin binding molecules have generated considerable interest after the successful introduction of the taxanes into clinical oncology and the widespread use of the vinca alkaloids vincristine and vinblastine. These compounds inhibit cell mitosis by binding to the protein tubulin in the mitotic spindle and preventing polymerization into the MTs. This mode of action is also shared with other natural agents eg colchicine and podophyllotoxin. However various tubulin isotypes have shown resistance to taxanes and other MT agents. Therefore, there is a strong need to design and develop new natural analogs as antimitotic agents to interact with tubulin at sites different from those of vinca alkaloids and taxanes. This minireview provides SAR on several classes of antimitotic agents reported in the literature. The structures and data given are essential to the scientists who are involved in drug design and development in the field of anticancer drugs.

Timing the Emergence of Resistance to Anti-HIV Drugs with Large Genetic Barriers

New antiretroviral drugs that offer large genetic barriers to resistance, such as the recently approved inhibitors of HIV-1 protease, tipranavir and darunavir, present promising weapons to avert the failure of current therapies for HIV infection. Optimal treatment strategies with the new drugs, however, are yet to be established. A key limitation is the poor understanding of the process by which HIV surmounts large genetic barriers to resistance. Extant models of HIV dynamics are predicated on the predominance of deterministic forces underlying the emergence of resistant genomes. In contrast, stochastic forces may dominate, especially when the genetic barrier is large, and delay the emergence of resistant genomes. We develop a mathematical model of HIV dynamics under the influence of an antiretroviral drug to predict the waiting time for the emergence of genomes that carry the requisite mutations to overcome the genetic barrier of the drug. We apply our model to describe the development of resistance to tipranavir in in vitro serial passage experiments. Model predictions of the times of emergence of different mutant genomes with increasing resistance to tipranavir are in quantitative agreement with experiments, indicating that our model captures the dynamics of the development of resistance to antiretroviral drugs accurately. Further, model predictions provide insights into the influence of underlying evolutionary processes such as recombination on the development of resistance, and suggest guidelines for drug design: drugs that offer large genetic barriers to resistance with resistance sites tightly localized on the viral genome and exhibiting positive epistatic interactions maximally inhibit the emergence of resistant genomes.

Molecular Modelling of Estrogen-Producing Enzymes CYP450 Aromatase and 17beta-Hydroxysteroid Dehydrogenase Type 1

Breast cancer is the most common cancer in women in the western countries. Approximately two-thirds of breast cancer tumours are hormone dependent, requiring estrogens to grow. Estrogens are formed in the human body via a multistep route starting from cholesterol. The final steps in the biosynthesis include the CYP450 aromatase enzyme, converting the male hormones androgens (preferred substrate androstenedione ASD) into estrogens(estrone E1), and the 17beta-HSD1 enzyme, converting the biologically less active E1 into the active hormone 17beta-hydroxyestradiol E2. E2 is bound to the nuclear estrogen receptors causing a cascade of biochemical reactions leading to cell proliferation in normal tissue, and to tumour growth in cancer tissue. Aromatase and 17beta-HSD1 are expressed in or near the breast tumour, locally providing the tissue with estrogens. One approach in treating hormone dependent breast tumours is to block the local estrogen production by inhibiting these two enzymes. Aromatase inhibitors are already on the market in treating breast cancer, despite the lack of an experimentally solved structure. The structure of 17beta-HSD1, on the other hand, has been solved, but no commercial drugs have emerged from the drug discovery projects reported in the literature. Computer-assisted molecular modelling is an invaluable tool in modern drug design projects. Modelling techniques can be used to generate a model of the target protein and to design novel inhibitors for them even if the target protein structure is unknown. Molecular modelling has applications in predicting the activities of theoretical inhibitors and in finding possible active inhibitors from a compound database. Inhibitor binding at atomic level can also be studied with molecular modelling. To clarify the interactions between the aromatase enzyme and its substrate and inhibitors, we generated a homology model based on a mammalian CYP450 enzyme, rabbit progesterone 21-hydroxylase CYP2C5. The model was carefully validated using molecular dynamics simulations (MDS) with and without the natural substrate ASD. Binding orientation of the inhibitors was based on the hypothesis that the inhibitors coordinate to the heme iron, and were studied using MDS. The inhibitors were dietary phytoestrogens, which have been shown to reduce the risk for breast cancer. To further validate the model, the interactions of a commercial breast cancer drug were studied with MDS and ligand–protein docking. In the case of 17beta-HSD1, a 3D QSAR model was generated on the basis of MDS of an enzyme complex with active inhibitor and ligand–protein docking, employing a compound library synthesised in our laboratory. Furthermore, four pharmacophore hypotheses with and without a bound substrate or an inhibitor were developed and used in screening a commercial database of drug-like compounds. The homology model of aromatase showed stable behaviour in MDS and was capable of explaining most of the results from mutagenesis studies. We were able to identify the active site residues contributing to the inhibitor binding, and explain differences in coordination geometry corresponding to the inhibitory activity. Interactions between the inhibitors and aromatase were in agreement with the mutagenesis studies reported for aromatase. Simulations of 17beta-HSD1 with inhibitors revealed an inhibitor binding mode with hydrogen bond interactions previously not reported, and a hydrophobic pocket capable of accommodating a bulky side chain. Pharmacophore hypothesis generation, followed by virtual screening, was able to identify several compounds that can be used in lead compound generation. The visualisation of the interaction fields from the QSAR model and the pharmacophores provided us with novel ideas for inhibitor development in our drug discovery project.

The structure of glycosaminoglycans and their interactions with proteins

Glycosaminoglycans (GAGs) are important complex carbohydrates that participate in many biological processes through the regulation of their various protein partners. Biochemical, structural biology and molecular modelling approaches have assisted in understanding the molecular basis of such interactions, creating an opportunity to capitalize on the large structural diversity of GAGs in the discovery of new drugs. The complexity of GAG–protein interactions is in part due to the conformational flexibility and underlying sulphation patterns of GAGs, the role of metal ions and the effect of pH on the affinity of binding. Current understanding of the structure of GAGs and their interactions with proteins is here reviewed: the basic structures and functions of GAGs and their proteoglycans, their clinical significance, the three-dimensional features of GAGs, their interactions with proteins and the molecular modelling of heparin binding sites and GAG–protein interactions. This review focuses on some key aspects of GAG structure–function relationships using classical examples that illustrate the specificity of GAG–protein interactions, such as growth factors, anti-thrombin, cytokines and cell adhesion molecules. New approaches to the development of GAG mimetics as possible new glycotherapeutics are also briefly covered.

On the relationship of thermodynamic parameters with the buried surface area in protein-ligand complex formation

Prediction of thermodynamic parameters of protein-protein and antigen-antibody complex formation from high resolution structural parameters has recently received much attention, since an understanding of the contributions of different fundamental processes like hydrophobic interactions, hydrogen bonding, salt bridge formation, solvent reorganization etc. to the overall thermodynamic parameters and their relations with the structural parameters would lead to rational drug design. Using the results of the dissolution of hydrocarbons and other model compounds the changes in heat capacity (DeltaCp), enthalpy (DeltaH) and entropy (DeltaS) have been empirically correlated with the polar and apolar surface areas buried during the process of protein folding/unfolding and protein-ligand complex formation. In this regard, the polar and apolar surfaces removed from the solvent in a protein-ligand complex have been calculated from the experimentally observed values of changes in heat capacity (DeltaCp) and enthalpy (DeltaH) for protein-ligand complexes for which accurate thermodynamic and high resolution structural data are available, and the results have been compared with the x-ray crystallographic observations. Analyses of the available results show poor correlation between the thermodynamic and structural parameters. Probable reasons for this discrepancy are mostly related with the reorganization of water accompanying the reaction which is indeed proven by the analyses of the energetics of the binding of the wheat germ agglutinin to oligosaccharides.

Crystal Structures of a New Polymorphic Form of Gabapentin Monohydrate and the E and Z Isomers of 4-Tertiarybutylgabapentin

Gabapentin, a widely used antiepileptic drug, crystallizes in multiple polymorphic forms. A new crystal form of gabapentin monohydrate in the space group Pbca is reported and the packing arrangement compared with that of a previously reported polymorph in the space group P2(1)/c [Ibers, J.A. (2001) Acta Crystallogr; C57:641]. Gabapentin polymorphs can also occur from a selection of one of the two distinct chair forms of the 1,1-disubstituted cyclohexane. Crystal structures of the E and Z isomers of 4-tert-butylgabapentin provide models for analyzing anticipated packing modes in the conformational isomers of gabapentin. The E isomer crystallized in the space group Pca2(1), while the Z isomer crystallized in the space group P2(1)/c. The crystal structure of E-4-tert-butylgabapentin provides the only example of a structure in a non-centrosymmetric space group. Crystal structures of the hydrochloride and hydrobromide salts of 4-tert-butyl derivatives are reported. The results suggest that for gabapentin, a large 'polymorph-space' may be anticipated, in view of the multiple conformational states that are accessible to the molecule.

Engineered protease inhibitors based on sunflower trypsin inhibitor-1 (SFTI-1) provide insights into the role of sequence and conformation in Laskowski mechanism inhibition

Laskowski inhibitors regulate serine proteases by an intriguing mode of action that involves deceiving the protease into synthesizing a peptide bond. Studies exploring naturally occurring Laskowski inhibitors have uncovered several structural features that convey the inhibitor's resistance to hydrolysis and exceptional binding affinity. However, in the context of Laskowski inhibitor engineering, the way that various modifications intended to fine-tune an inhibitor's potency and selectivity impact on its association and dissociation rates remains unclear. This information is important as Laskowski inhibitors are becoming increasingly used as design templates to develop new protease inhibitors for pharmaceutical applications. In this study, we used the cyclic peptide, sunflower trypsin inhibitor-1 (SFTI-1), as a model system to explore how the inhibitor's sequence and structure relate to its binding kinetics and function. Using enzyme assays, MD simulations and NMR spectroscopy to study SFTI variants with diverse sequence and backbone modifications, we show that the geometry of the binding loop mainly influences the inhibitor's potency by modulating the association rate, such that variants lacking a favourable conformation show dramatic losses in activity. Additionally, we show that the inhibitor's sequence (including both the binding loop and its scaffolding) influences its potency and selectivity by modulating both the association and the dissociation rates. These findings provide new insights into protease inhibitor function and design that we apply by engineering novel inhibitors for classical serine proteases, trypsin and chymotrypsin and two kallikrein-related peptidases (KLK5 and KLK14) that are implicated in various cancers and skin diseases.

Parallel implementation of AutoDock

Computational docking of ligands to protein structures is a key step in structure-based drug design. Currently, the time required for each docking run is high and thus limits the use of docking in a high-throughput manner, warranting parallelization of docking algorithms. AutoDock, a widely used tool, has been chosen for parallelization. Near-linear increases in speed were observed with 96 processors, reducing the time required for docking ligands to HIV-protease from 81 min, as an example, on a single IBM Power-5 processor ( 1.65 GHz), to about 1 min on an IBM cluster, with 96 such processors. This implementation would make it feasible to perform virtual ligand screening using AutoDock.