Advanced Protein Modeling Method: Benchmarking and Applications in Computer-Aided Drug Discovery

Development of homology modeling methods will remain an area of active research. These methods aim to develop and model increasingly accurate three-dimensional structures of yet uncrystallized therapeutically relevant proteins e.g. Class A G-Protein Coupled Receptors. Incorporating protein flexibility is one way to achieve this goal. Here, I will discuss the enhancement and validation of the ligand-steered modeling, originally developed by Dr. Claudio Cavasotto, via cross modeling of the newly crystallized GPCR structures. This method uses known ligands and known experimental information to optimize relevant protein binding sites by incorporating protein flexibility. The ligand-steered models were able to model, reasonably reproduce binding sites and the co-crystallized native ligand poses of the β2 adrenergic and Adenosine 2A receptors using a single template structure. They also performed better than the choice of template, and crude models in a small scale high-throughput docking experiments and compound selectivity studies. Next, the application of this method to develop high-quality homology models of Cannabinoid Receptor 2, an emerging non-psychotic pain management target, is discussed. These models were validated by their ability to rationalize structure activity relationship data of two, inverse agonist and agonist, series of compounds. The method was also applied to improve the virtual screening performance of the β2 adrenergic crystal structure by optimizing the binding site using β2 specific compounds. These results show the feasibility of optimizing only the pharmacologically relevant protein binding sites and applicability to structure-based drug design projects.

Bacteria lacking a multidrug pump: A sensitive tool for drug discovery

Microorganisms express multidrug resistance pumps (MDRs) that can confound antibiotic discovery. We propose the use of mutants deficient in MDRs to overcome this problem. Sensitivity to quinolones and to amphipathic cations (norfloxacin, benzalkonium chloride, cetrimide, pentamidine, etc.) was increased 5- to 30-fold in a Staphylococcus aureus mutant with a disrupted chromosomal copy of the NorA MDR. NorA was required both for increased sensitivity to drugs in the presence of an MDR inhibitor and for increased rate of cation efflux. This requirement suggests that NorA is the major MDR protecting S. aureus from the antimicrobials studied. A 15- to 60-fold increase in sensitivity to antimicrobials also was observed in wild-type cells at an alkaline pH that favors accumulation of cations and weak bases. This effect was synergistic with a norA mutation, resulting in an increase up to 1,000-fold in sensitivity to antimicrobials. The usefulness of applying MDR mutants for natural product screening was demonstrated further by increased sensitivity of the norA− strain to plant alkaloid antimicrobials, which might be natural MDR substrates.

Betidamino acids: versatile and constrained scaffolds for drug discovery.

Betidamino acids (a contraction of "beta" position and "amide") are N'-monoacylated (optionally, N'-monoacylated and N-mono- or N,N'-dialkylated) aminoglycine derivatives in which each N'acyl/alkyl group may mimic naturally occurring amino acid side chains or introduce novel functionalities. Betidamino acids are most conveniently generated on solid supports used for the synthesis of peptides by selective acylation of one of the two amino functions of orthogonally protected aminoglycine(s) to generate the side chain either prior to or after the elongation of the main chain. We have used unresolved Nalpha-tert-butyloxycarbonyl-N'alpha-fluorenylmethoxycarbonyl++ + aminoglycine, and Nalpha-(Nalpha-methyl)-tert-butyloxycarbonyl-N'alpha-fluo renylmethoxycarbonyl aminoglycine as the templates for the introduction of betidamino acids in Acyline [Ac-D2Nal-D4Cpa-D3Pal-Ser-4Aph(Ac)-D4Aph(A c)-Leu-Ilys-Pro-DAla-NH2, where 2Nal is 2-naphthylalanine, 4Cpa is 4-chlorophenylalanine, 3Pal is 3-pyridylalanine, Aph is 4-aminophenylalanine, and Ilys is Nepsilon-isopropyllysine], a potent gonadotropin-releasing hormone antagonist, in order to test biocompatibility of these derivatives. Diasteremneric peptides could be separated in most cases by reverse-phase HPLC. Biological results indicated small differences in relative potencies (<5-fold) between the D and L nonalkylated betidamino acid-containing Acyline derivatives. Importantly, most betide diastereomers were equipotent with Acyline. In an attempt to correlate structure and observed potency, Ramachandran-type plots were calculated for a series of betidamino acids and their methylated homologs. According to these calculations, betidamino acids have access to a more limited and distinct number of conformational states (including those associated with alpha-helices, beta-sheets, or turn structures), with deeper minima than those observed for natural amino acids.

A paradigm for drug discovery employing encoded combinatorial libraries.

Very large combinatorial libraries of small molecules on solid supports can now be synthesized and each library element can be identified after synthesis by using chemical tags. These tag-encoded libraries are potentially useful in drug discovery, and, to test this utility directly, we have targeted carbonic anhydrase (carbonate dehydratase; carbonate hydro-lyase, EC 4.2.1.1) as a model. Two libraries consisting of a total of 7870 members were synthesized, and structure-activity relationships based on the structures predicted by the tags were derived. Subsequently, an active representative of each library was resynthesized (2-[N-(4-sulfamoylbenzoyl)-4'-aminocyclohexanespiro]-4-oxo-7 -hydroxy- 2,3-dihydrobenzopyran and [N-(4-sulfamoylbenzoyl)-L-leucyl]piperidine-3-carboxylic acid) and these compounds were shown to have nanomolar dissociation constants (15 and 4 nM, respectively). In addition, a focused sublibrary of 217 sulfamoylbenzamides was synthesized and revealed a clear, testable structure-activity relationship describing isozyme-selective carbonic anhydrase inhibitors.

Carbohydrate-based scaffolds in drug discovery

Carbohydrates have been proven as valuable scaffolds to display pharmocophores and the resulting molecules have demonstrated useful biological activity towards various targets including the somatostatin receptors (SSTR), integrins, HIV-1 protease, matrix metalloproteinases (MMP), multidrug resistance-associated protein (MRP), and as RNA binders. Carbohydrate-based compounds have also shown antibacterial and herbicidal activity.

S. cerevisiae as a model for studying mutations in the human gene OPA1 associated with dominant optic atrophy and for drug discovery

L’atrofia ottica dominante (ADOA) è una malattia mitocondriale caratterizzata da difetti visivi, che si manifestano durante l’infanzia, causati da progressiva degenerazione delle cellule gangliari della retina (RGC). ADOA è una malattia genetica associata, nella maggior parte dei casi, a mutazioni nel gene OPA1 che codifica per la GTPasi mitocondriale OPA1, appartenente alla famiglia delle dinamine, principalmente coinvolta nel processo di fusione mitocondriale e nel mantenimento del mtDNA. Finora sono state identificate più di 300 mutazioni patologiche nel gene OPA1. Circa il 50% di queste sono mutazioni missenso, localizzate nel dominio GTPasico, che si pensa agiscano come dominanti negative. Questa classe di mutazioni è associata ad una sindrome più grave nota come “ADOA-plus”. Nel lievito Saccharomyces cerevisiae MGM1 è l’ortologo del gene OPA1: nonostante i due geni abbiano domini funzionali identici le sequenze amminoacidiche sono scarsamente conservate. Questo costituisce una limitazione all’uso del lievito per lo studio e la validazione di mutazioni patologiche nel gene OPA1, infatti solo poche sostituzioni possono essere introdotte e studiate nelle corrispettive posizioni del gene di lievito. Per superare questo ostacolo è stato pertanto costruito un nuovo modello di S. cerevisiae, contenente il gene chimerico MGM1/OPA1, in grado di complementare i difetti OXPHOS del mutante mgm1Δ. Questo gene di fusione contiene una larga parte di sequenza corrispondente al gene OPA1, nella quale è stato inserito un set di nuove mutazioni trovate in pazienti affetti da ADOA e ADOA-plus. La patogenicità di queste mutazioni è stata validata sia caratterizzando i difetti fenotipici associati agli alleli mutati, sia la loro dominanza/recessività nel modello di lievito. A tutt’oggi non è stato identificato alcun trattamento farmacologico per la cura di ADOA e ADOA-plus. Per questa ragione abbiamo utilizzato il nostro modello di lievito per la ricerca di molecole che agiscono come soppressori chimici, ossia composti in grado di ripristinare i difetti fenotipici indotti da mutazioni nel gene OPA1. Attraverso uno screening fenotipico high throughput sono state testate due differenti librerie di composti chimici. Questo approccio, noto con il nome di drug discovery, ha permesso l’identificazione di 23 potenziali molecole attive.

Data visualization during the early stages of drug discovery

Multidimensional compound optimization is a new paradigm in the drug discovery process, yielding efficiencies during early stages and reducing attrition in the later stages of drug development. The success of this strategy relies heavily on understanding this multidimensional data and extracting useful information from it. This paper demonstrates how principled visualization algorithms can be used to understand and explore a large data set created in the early stages of drug discovery. The experiments presented are performed on a real-world data set comprising biological activity data and some whole-molecular physicochemical properties. Data visualization is a popular way of presenting complex data in a simpler form. We have applied powerful principled visualization methods, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), to help the domain experts (screening scientists, chemists, biologists, etc.) understand and draw meaningful decisions. We also benchmark these principled methods against relatively better known visualization approaches, principal component analysis (PCA), Sammon's mapping, and self-organizing maps (SOMs), to demonstrate their enhanced power to help the user visualize the large multidimensional data sets one has to deal with during the early stages of the drug discovery process. The results reported clearly show that the GTM and HGTM algorithms allow the user to cluster active compounds for different targets and understand them better than the benchmarks. An interactive software tool supporting these visualization algorithms was provided to the domain experts. The tool facilitates the domain experts by exploration of the projection obtained from the visualization algorithms providing facilities such as parallel coordinate plots, magnification factors, directional curvatures, and integration with industry standard software. © 2006 American Chemical Society.

Molecularly imprinted polymers in the drug discovery process

Since molecularly imprinted polymers (MIPs) are designed to have a memory for their molecular templates it is easy to draw parallels with the affinity between biological receptors and their substrates. Could MIPs take the place of natural receptors in the selection of potential drug molecules from synthetic compound libraries? To answer that question this review discusses the results of MIP studies which attempt to emulate natural receptors. In addition the possible use of MIPs to guide a compound library synthesis towards a desired biological activity is highlighted. © 2005 Elsevier B.V. All rights reserved.

Probabilistic methods for drug discovery

This thesis introduces a flexible visual data exploration framework which combines advanced projection algorithms from the machine learning domain with visual representation techniques developed in the information visualisation domain to help a user to explore and understand effectively large multi-dimensional datasets. The advantage of such a framework to other techniques currently available to the domain experts is that the user is directly involved in the data mining process and advanced machine learning algorithms are employed for better projection. A hierarchical visualisation model guided by a domain expert allows them to obtain an informed segmentation of the input space. Two other components of this thesis exploit properties of these principled probabilistic projection algorithms to develop a guided mixture of local experts algorithm which provides robust prediction and a model to estimate feature saliency simultaneously with the training of a projection algorithm.Local models are useful since a single global model cannot capture the full variability of a heterogeneous data space such as the chemical space. Probabilistic hierarchical visualisation techniques provide an effective soft segmentation of an input space by a visualisation hierarchy whose leaf nodes represent different regions of the input space. We use this soft segmentation to develop a guided mixture of local experts (GME) algorithm which is appropriate for the heterogeneous datasets found in chemoinformatics problems. Moreover, in this approach the domain experts are more involved in the model development process which is suitable for an intuition and domain knowledge driven task such as drug discovery. We also derive a generative topographic mapping (GTM) based data visualisation approach which estimates feature saliency simultaneously with the training of a visualisation model.

Relieving the first bottleneck in the drug discovery pipeline:using array technologies to rationalize membrane protein production

The slow down in the drug discovery pipeline is, in part, owing to a lack of structural and functional information available for new drug targets. Membrane proteins, the targets of well over 50% of marketed pharmaceuticals, present a particular challenge. As they are not naturally abundant, they must be produced recombinantly for the structural biology that is a prerequisite to structure-based drug design. Unfortunately, however, obtaining high yields of functional, recombinant membrane proteins remains a major bottleneck in contemporary bioscience. While repeated rounds of trial-and-error optimization have not (and cannot) reveal mechanistic details of the biology of recombinant protein production, examination of the host response has provided new insights. To this end, we published an early transcriptome analysis that identified genes implicated in high-yielding yeast cell factories, which has enabled the engineering of improved production strains. These advances offer hope that the bottleneck of membrane protein production can be relieved rationally.