Recent applications of computational methods to allosteric drug discovery.

Interest in exploiting allosteric sites for the development of new therapeutics has grown considerably over the last two decades. The chief driving force behind the interest in allostery for drug discovery stems from the fact that in comparison to orthosteric sites, allosteric sites are less conserved across a protein family, thereby offering greater opportunity for selectivity and ultimately tolerability. While there is significant overlap between structure-based drug design for orthosteric and allosteric sites, allosteric sites offer additional challenges mostly involving the need to better understand protein flexibility and its relationship to protein function. Here we examine the extent to which structure-based drug design is impacting allosteric drug design by highlighting several targets across a variety of target classes.

Rigorous Computational and Experimental Investigations on MDM2/MDMX-Targeted Linear and Macrocyclic Peptides.

There is interest in peptide drug design, especially for targeting intracellular protein-protein interactions. Therefore, the experimental validation of a computational platform for enabling peptide drug design is of interest. Here, we describe our peptide drug design platform (CMDInventus) and demonstrate its use in modeling and predicting the structural and binding aspects of diverse peptides that interact with oncology targets MDM2/MDMX in comparison to both retrospective (pre-prediction) and prospective (post-prediction) data. In the retrospective study, CMDInventus modules (CMDpeptide, CMDboltzmann, CMDescore and CMDyscore) were used to accurately reproduce structural and binding data across multiple MDM2/MDMX data sets. In the prospective study, CMDescore, CMDyscore and CMDboltzmann were used to accurately predict binding affinities for an Ala-scan of the stapled α-helical peptide ATSP-7041. Remarkably, CMDboltzmann was used to accurately predict the results of a novel D-amino acid scan of ATSP-7041. Our investigations rigorously validate CMDInventus and support its utility for enabling peptide drug design.

Identification and initial optimization of inhibitors of Clostridium difficile (C. difficile) toxin B (TcdB).

The discovery, synthesis and preliminary structure-activity relationship (SAR) of a novel class of inhibitors of Clostridium difficile (C. difficile) toxin B (TcdB) is described. A high throughput screening (HTS) campaign resulted in the identification of moderately active screening hits 1-5 the most potent of which was compound 1 (IC50 = 0.77 µM). In silico docking of an early analog offered suggestions for structural modification which resulted in the design and synthesis of highly potent analogs 13j(IC50 = 1 nM) and 13 l(IC50 = 7 nM) which were chosen as leads for further optimization.

PeptideNavigator: An interactive tool for exploring large and complex data sets generated during peptide-based drug design projects.

There is growing interest in peptide-based drug design and discovery. Due to their relatively large size, polymeric nature, and chemical complexity, the design of peptide-based drugs presents an interesting "big data" challenge. Here, we describe an interactive computational environment, PeptideNavigator, for naturally exploring the tremendous amount of information generated during a peptide drug design project. The purpose of PeptideNavigator is the presentation of large and complex experimental and computational data sets, particularly 3D data, so as to enable multidisciplinary scientists to make optimal decisions during a peptide drug discovery project. PeptideNavigator provides users with numerous viewing options, such as scatter plots, sequence views, and sequence frequency diagrams. These views allow for the collective visualization and exploration of many peptides and their properties, ultimately enabling the user to focus on a small number of peptides of interest. To drill down into the details of individual peptides, PeptideNavigator provides users with a Ramachandran plot viewer and a fully featured 3D visualization tool. Each view is linked, allowing the user to seamlessly navigate from collective views of large peptide data sets to the details of individual peptides with promising property profiles. Two case studies, based on MHC-1A activating peptides and MDM2 scaffold design, are presented to demonstrate the utility of PeptideNavigator in the context of disparate peptide-design projects.

Development of a Simple Electron Transfer and Polarization Model and Its Application to Biological Systems.

Here we present a new method for point charge calculation which we call QET (charges by electron transfer). The intent of this work is to develop a method that can be useful for studying charge transfer in large biological systems. It is based on the intuitive framework of the QEQ method with the key difference being that the QET method tracks all pairwise electron transfers by augmenting the QEQ pseudoenergy function with a distance dependent cost function for each electron transfer. This approach solves the key limitation of the QEQ method which is its handling of formally charged groups. First, we parametrize the QET method by fitting to electrostatic potentials calculated using ab initio quantum mechanics on over 11,000 small molecules. On an external test set of over 2500 small molecules the QET method achieves a mean absolute error of 1.37 kcal/mol/electron when compared to the ab initio electrostatic potentials. Second, we examine the conformational dependence of the charges on over 2700 tripeptides. With the tripeptide data set, we show that the conformational effects account for approximately 0.4 kcal/mol/electron on the electrostatic potentials. Third, we test the QET method for its ability to reproduce the effects of polarization and electron transfer on 1000 water clusters. For the water clusters, we show that the QET method captures about 50% of the polarization and electron transfer effects. Finally, we examine the effects of electron transfer and polarizability on the electrostatic interaction between p38 and 94 small molecule ligands. When used in conjunction with the Generalized-Born continuum solvent model, polarization and electron transfer with the QET model lead to an average change of 17 kcal/mol on the calculated electrostatic component of ΔG.

Rational, computer-enabled peptide drug design: principles, methods, applications and future directions.

Peptides provide promising templates for developing drugs to occupy a middle space between small molecules and antibodies and for targeting 'undruggable' intracellular protein-protein interactions. Importantly, rational or in cerebro design, especially when coupled with validated in silico tools, can be used to efficiently explore chemical space and identify islands of 'drug-like' peptides to satisfy diverse drug discovery program objectives. Here, we consider the underlying principles of and recent advances in rational, computer-enabled peptide drug design. In particular, we consider the impact of basic physicochemical properties, potency and ADME/Tox opportunities and challenges, and recently developed computational tools for enabling rational peptide drug design. Key principles and practices are spotlighted by recent case studies. We close with a hypothetical future case study.

Avalanche for shape and feature-based virtual screening with 3D alignment.

This report introduces a new ligand-based virtual screening tool called Avalanche that incorporates both shape- and feature-based comparison with three-dimensional (3D) alignment between the query molecule and test compounds residing in a chemical database. Avalanche proceeds in two steps. The first step is an extremely rapid shape/feature based comparison which is used to narrow the focus from potentially millions or billions of candidate molecules and conformations to a more manageable number that are then passed to the second step. The second step is a detailed yet still rapid 3D alignment of the remaining candidate conformations to the query conformation. Using the 3D alignment, these remaining candidate conformations are scored, re-ranked and presented to the user as the top hits for further visualization and evaluation. To provide further insight into the method, the results from two prospective virtual screens are presented which show the ability of Avalanche to identify hits from chemical databases that would likely be missed by common substructure-based or fingerprint-based search methods. The Avalanche method is extended to enable patent landscaping, i.e., structural refinements to improve the patentability of hits for deployment in drug discovery campaigns.

A combined cheminformatic and bioinformatic approach to address the proteolytic stability challenge in peptide-based drug discovery.

We have created models to predict cleavage sites for several human proteases including caspase-1, caspase-3, caspase-6, caspase-7, cathepsin B, cathepsin D, cathepsin G, cathepsin K, cathepsin L, elastase-2, granzyme A, granzyme B, matrix metallopeptidase-2 (MMP2), MMP7, MMP9, thrombin, and trypsin-1. Rather than representing the sequence pattern around the potential cleavage site through a series of flags with each flag representing one of the 20 standard amino acids, we first represent each amino acid by its calculated properties. For these calculated properties, we use validated cheminformatic descriptors, such as molecular weight, logP, and polar surface area, of the individual amino acids. Finally, the cleavage site-specific descriptors are calculated through various combinations of the individual amino acid descriptors for the residues surrounding the cleavage site. Some of these combinations do not take into account the location of the residue, as long as it is in a prescribed neighborhood of the potential cleavage site, whereas others are sensitive to the precise order of the residues in the sequence. The key advantage of this approach is that it allows one to perform meaningful calculations with nonstandard amino acids for which little or no data exists. Finally, using both docking and molecular dynamics simulations, we examine the potential for and limitations of protease crystal structures to impact the design of proteolytically stable peptides.

VSViewer3D: a tool for interactive data mining of three-dimensional virtual screening data.

The VSviewer3D is a simple Java tool for visual exploration of three-dimensional (3D) virtual screening data. The VSviewer3D brings together the ability to explore numerical data, such as calculated properties and virtual screening scores, structure depiction, interactive topological and 3D similarity searching, and 3D visualization. By doing so the user is better able to quickly identify outliers, assess tractability of large numbers of compounds, visualize hits of interest, annotate hits, and mix and match interesting scaffolds. We demonstrate the utility of the VSviewer3D by describing a use case in a docking based virtual screen.

Insights for predicting blood-brain barrier penetration of CNS targeted molecules using QSPR approaches.

Due to the high attrition rate of central nervous system drug candidates during clinical trials, the assessment of blood-brain barrier (BBB) penetration in early research is particularly important. A genetic approximation (GA)-based regression model was developed for predicting in vivo blood-brain partitioning data, expressed as logBB (log[brain]/[blood]). The model was built using an in-house data set of 193 compounds assembled from 22 different therapeutic projects. The final model (cross-validated r(2) = 0.72) with five molecular descriptors was selected based on validation using several large internal and external test sets. We demonstrate the potential utility of the model by applying it to a set of literature reported secretase inhibitors. In addition, we describe a rule-based approach for rapid assessment of brain penetration with several simple molecular descriptors.

Computational alanine scanning with linear scaling semiempirical quantum mechanical methods.

Alanine scanning is a powerful experimental tool for understanding the key interactions in protein-protein interfaces. Linear scaling semiempirical quantum mechanical calculations are now sufficiently fast and robust to allow meaningful calculations on large systems such as proteins, RNA and DNA. In particular, they have proven useful in understanding protein-ligand interactions. Here we ask the question: can these linear scaling quantum mechanical methods developed for protein-ligand scoring be useful for computational alanine scanning? To answer this question, we assembled 15 protein-protein complexes with available crystal structures and sufficient alanine scanning data. In all, the data set contains Delta Delta Gs for 400 single point alanine mutations of these 15 complexes. We show that with only one adjusted parameter the quantum mechanics-based methods outperform both buried accessible surface area and a potential of mean force and compare favorably to a variety of published empirical methods. Finally, we closely examined the outliers in the data set and discuss some of the challenges that arise from this examination.

Ion channel screening plates: design, construction, and maintenance.

Ion channels have provided a diverse set of therapeutic targets across all areas of the pharmaceutical industry. Many companies are pursuing this unique class of targets for areas of unmet medical need such as neuropathic and inflammatory pains. In the past, focused library screening sets had been designed for CNS and kinase targets. Our investigations were aimed at creating a similar dynamic screening set enriched for compounds targeting ion channels to aid screening efforts of this important class of targets. The key advantages of this approach for ion channel targets would be: (1) to identify tool compounds for novel targets and assist in assay validation, (2) to serve as a focused screen for non-384-well adaptable targets, and (3) to jump start a particular program, that is, catch-up to competition for validated, well-known targets.

2-Benzimidazolyl-9-(chroman-4-yl)-purinone derivatives as JAK3 inhibitors.

A novel class of Janus tyrosine kinase 3 (JAK3) inhibitors based on a 2-benzimidazoylpurinone core structure is described. Through substitution of the benzimidazoyl moiety and optimization of the N-9 substituent of the purinone, compound 24 was identified incorporating a chroman-based functional group. Compound 24 shows excellent kinase activity, good oral bioavailability and demonstrates efficacy in an acute mechanistic mouse model through inhibition of interleukin-2 (IL-2) induced interferon-gamma (INF-gamma) production.

The synergy between combinatorial chemistry and high-throughput screening.

Despite the initial promise of combinatorial chemistry, particularly large library combinatorial chemistry, to greatly accelerate drug discovery, this approach has not been fully utilized as a means to build the compound collections of pharmaceutical and biotechnology companies. This review highlights some of the strengths of large library combinatorial chemistry as a means of generating molecules for lead discovery, such as providing rich and robust structure-activity relationships around each hit series. The challenges and concepts emerging from traditional high-throughput screening and fragment-based drug design, how these methods influence the design of large combinatorial libraries and the interpretation of the ensuing high-throughput screening data are also highlighted.

Technique for generating three-dimensional alignments of multiple ligands from one-dimensional alignments.

We describe and demonstrate a method for the simultaneous, fully flexible alignment of multiple molecules with a common biological activity. The key aspect of the algorithm is that the alignment problem is first solved in a lower dimensional space, in this case using the one-dimensional representations of the molecules. The three-dimensional alignment is then guided by constraints derived from the one-dimensional alignment. We demonstrate using 10 hERG channel blockers, with a total of 72 rotatable bonds, that the one-dimensional alignment is able to effectively isolate key conserved pharmacophoric features and that these conserved features can effectively guide the three-dimensional alignment. Further using 10 estrogen receptor agonists and 5 estrogen receptor antagonists with publicly available cocrystal structures we show that the method is able to produce superpositions comparable to those derived from crystal structures. Finally, we demonstrate, using examples from peptidic CXCR3 agonists, that the method is able to generate reasonable binding hypotheses.

Characterization of chemical libraries for luciferase inhibitory activity.

To aid in the interpretation of high-throughput screening (HTS) results derived from luciferase-based assays, we used quantitative HTS, an approach that defines the concentration-response behavior of each library sample, to profile the ATP-dependent luciferase from Photinus pyralis against more than 70,000 samples. We found that approximately 3% of the library was active, containing only compounds with inhibitory concentration-responses, of which 681 (0.9%) exhibited IC 50 < 10 microM. Representative compounds were shown to inhibit purified P. pyralis as well as several commercial luciferase-based detection reagents but were found to be largely inactive against Renilla reniformis luciferase. Light attenuation by the samples was also examined and found to be more prominent in the blue-shifted bioluminescence produced by R. reniformis luciferase than in the bioluminescence produced by P. pyralis luciferase. We describe the structure-activity relationship of the luciferase inhibitors and discuss the use of this data in the interpretation of HTS results and configuration of luciferase-based assays.

Library fingerprints: a novel approach to the screening of virtual libraries.

We propose a novel method to prioritize libraries for combinatorial synthesis and high-throughput screening that assesses the viability of a particular library on the basis of the aggregate physical-chemical properties of the compounds using a naïve Bayesian classifier. This approach prioritizes collections of related compounds according to the aggregate values of their physical-chemical parameters in contrast to single-compound screening. The method is also shown to be useful in screening existing noncombinatorial libraries when the compounds in these libraries have been previously clustered according to their molecular graphs. We show that the method used here is comparable or superior to the single-compound virtual screening of combinatorial libraries and noncombinatorial libraries and is superior to the pairwise Tanimoto similarity searching of a collection of combinatorial libraries.

Understanding hERG inhibition with QSAR models based on a one-dimensional molecular representation.

Blockage of the potassium channel encoded by the human ether-a-go-go related gene (hERG) is well understood to be the root cause of the cardio-toxicity of numerous approved and investigational drugs. As such, a cascade of in vitro and in vivo assays have been developed to filter compounds with hERG inhibitory activity. Quantitative structure activity relationship (QSAR) models are used at the very earliest part of this cascade to eliminate compounds that are likely to have this undesirable activity prior to synthesis. Here a new QSAR technique based on the one-dimensional representation is described in the context of the development of a model to predict hERG inhibition. The model is shown to perform close to the limits of the quality of the data used for model building. In order to make optimal use of the available data, a general robust mathematical scheme was developed and is described to simultaneously incorporate quantitative data, such as IC50 = 50 nM, and qualitative data, such as inactive or IC50 > 30 microM into QSAR models without discarding any experimental information.

Improved naïve Bayesian modeling of numerical data for absorption, distribution, metabolism and excretion (ADME) property prediction.

We have implemented a naïve Bayesian classifier which models continuous numerical data using a Gaussian distribution. Several cases of interest in the area of absorption, distribution, metabolism, and excretion prediction are presented which demonstrate that this approach is superior to the implementation of naïve Bayesian classifiers in which continuous chemical descriptors are modeled as binary data. We demonstrate that this enhanced performance, upon comparison with other implementations, is independent of the descriptor sets chosen. We also compare the performance of three implementations of naïve Bayesian classifiers with other previously described models.

Successful screening of large encoded combinatorial libraries leading to the discovery of novel p38 MAP kinase inhibitors.

Screening of more than 2 million compounds comprising 41 distinct encoded combinatorial libraries revealed a novel structural class of p38 mitogen-activated protein (MAP) kinase inhibitors. The methodology used for screening large encoded combinatorial libraries combined with the statistical interpretation of screening results is described. A strong preference for a particular triaminotriazine aniline amide was discovered based on biological activity observed in the screening campaign. Additional screening of a focused follow-up combinatorial library yielded data expanding the unique combinatorial SAR and emphasizing an extraordinary preference for this particular building block and structural class. The preference is further highlighted when the p38 inhibitor data set is compared to data obtained for a panel of other kinases.