Steven W. Muchmore

Crystal structure of the biphenyl-cleaving extradiol dioxygenase from a PCB-degrading pseudomonad

Polychlorinated biphenyls (PCBs) typify a class of stable aromatic pollutants that are targeted by bioremediation strategies. In the aerobic degradation of biphenyl by bacteria, the key step of ring cleavage is catalyzed by an Fe(II)-dependent extradiol dioxygenase. The crystal structure of 2,3-dihydroxybiphenyl 1,2-dioxygenase from a PCB-degrading strain of Pseudomonas cepacia has been determined at 1.9 angstrom resolution. The monomer comprises amino- and carboxyl-terminal domains. Structural homology between and within the domains reveals evolutionary relationships within the extradiol dioxygenase family. The iron atom has five ligands in square pyramidal geometry: one glutamate and two histidine side chains, and two water molecules.

Application of belief theory to similarity data fusion for use in analog searching and lead hopping.

A wide variety of computational algorithms have been developed that strive to capture the chemical similarity between two compounds for use in virtual screening and lead discovery. One limitation of such approaches is that, while a returned similarity value reflects the perceived degree of relatedness between any two compounds, there is no direct correlation between this value and the expectation or confidence that any two molecules will in fact be equally active. A lack of a common framework for interpretation of similarity measures also confounds the reliable fusion of information from different algorithms. Here, we present a probabilistic framework for interpreting similarity measures that directly correlates the similarity value to a quantitative expectation that two molecules will in fact be equipotent. The approach is based on extensive benchmarking of 10 different similarity methods (MACCS keys, Daylight fingerprints, maximum common subgraphs, rapid overlay of chemical structures (ROCS) shape similarity, and six connectivity-based fingerprints) against a database of more than 150,000 compounds with activity data against 23 protein targets. Given this unified and probabilistic framework for interpreting chemical similarity, principles derived from decision theory can then be applied to combine the evidence from different similarity measures in such a way that both capitalizes on the strengths of the individual approaches and maintains a quantitative estimate of the likelihood that any two molecules will exhibit similar biological activity.

Synthesis and Evaluation of Benzothiazole-Based Analogues as Novel, Potent, and Selective Fatty Acid Amide Hydrolase Inhibitors

High-throughput screening (HTS) identified benzothiazole analogue 3 as a potent fatty acid amide hydrolase (FAAH) inhibitor. Structure-activity relationship (SAR) studies indicated that the sulfonyl group, the piperidine ring and benzothiazole were the key components to their activity, with 16j being the most potent analogue in this series. Time-dependent preincubation study of compound 3 was consistent with it being a reversible inhibitor. Activity-based protein-profiling (ABPP) evaluation of 3 in rat tissues revealed that it had exceptional selectivity and no off-target activity with respect to other serine hydrolases. Molecular shape overlay of 3 with a known FAAH inhibitor indicated that these compounds might act as transition-state analogues, forming putative hydrogen bonds with catalytic residues and mimicking the charge distribution of the tetrahedral transition state. The modeling study also indicated that hydrophobic interactions of the benzothiazole ring with the enzyme contributed to its extraordinary potency. These compounds may provide useful tools for the study of FAAH and the endocannabinoid system.

Cheminformatic Tools for Medicinal Chemists

IntroductionCheminformatics can be broadly described as any attempttousechemicalinformationtoinfertherelationshipsbetweenor attributes of chemical structures. From a drug discoveryperspective, cheminformatic principles can be applied fromthe earliest stages of lead discovery (e.g., chemical similarityand library design) to lead optimization (e.g., QSAR studies)through to preclinical and clinical development (e.g., predic-tive toxicology). The popularity of cheminformatics and itsuse in academia and the pharmaceutical industry can beappreciated from the fact that at least five scientific journalsexist almost exclusively dedicated to the field (The Journal ofCheminformatics, The Journal of Chemical Information andModeling, The Journal of Computer-Aided Molecular Design,Molecular Bioinformatics,andQSAR and CombinatorialScience), and more than 15000 scientific journal articles havebeen published during just the last 5 years that describecheminformatic research. This intense interest in cheminfor-matics stems from the promise that, if underlying relation-ships between a given chemical structure and a host ofbiological end points exist and can be elucidated, drug dis-covery timelines can be significantly reduced. Given thepressure on the pharmaceutical industry to increase produc-tivity while decreasing costs, prior knowledge of which mole-cules have the highest probability of success (or at leastknowing which molecules are likely to fail) is worthy ofvigorous pursuit.Over the past decade there have been several significantadvancements in our understanding and application of che-minformatic principles. Approaches to measuring and com-paring chemical information have become both moresophisticated and accessible. For example, two of the mostpowerfulchemicalsimilaritymeasures(two-dimensional(2D)extended connectivity fingerprints and three-dimensional(3D) shape and electrostatic overlays) are available in user-friendly software packages from Scitegic (Accelrys) andOpeneye Scientific Software. Multiple methods for under-standing and predictingbioactivity have proven their robust-ness,includingpartialleast-squares(PLS),geneticalgorithms,Bayesian analyses, and Random Forest analyses. Our under-standing of molecular features or properties associated withcertain pharmacological end points has also dramaticallyincreased. For example, it has been widely recognized thatcertain structural features can be associated with toxicity,while other molecular properties (such as ClogP, molecularweight, and polar surface area) can be associated with oralbioavailability

A unified, probabilistic framework for structure- and ligand-based virtual screening.

We present a probabilistic framework for interpreting structure-based virtual screening that returns a quantitative likelihood of observing bioactivity and can be quantitatively combined with ligand-based screening methods to yield a cumulative prediction that consistently outperforms any single screening metric. The approach has been developed and validated on more than 30 different protein targets. Transforming structure-based in silico screening results into robust probabilities of activity enables the general fusion of multiple structure- and ligand-based approaches and returns a quantitative expectation of success that can be used to prioritize (or deprioritize) further discovery activities. This unified probabilistic framework offers a paradigm shift in how docking and scoring results are interpreted, which can enhance early lead-finding efforts by maximizing the value of in silico computational tools.

Large-scale application of high-throughput molecular mechanics with Poisson-Boltzmann surface area for routine physics-based scoring of protein-ligand complexes.

We apply a high-throughput formulation of the molecular mechanics with Poisson-Boltzmann surface area (htMM-PBSA) to estimate relative binding potencies on a set of 308 small-molecule ligands in complex with the proteins urokinase, PTP-1B, and Chk-1. We observe statistically significant correlation to experimentally measured potencies and report correlation coefficients for the three proteins in the range 0.72-0.83. The htMM-PBSA calculations illustrate the feasibility of procedural automation of physics-based scoring calculations to produce rank-ordered binding-potency estimates for protein-ligand complexes, with sufficient throughput for realization of practical implementation into scientist workflows in an industrial drug discovery research setting.

Automated crystal mounting and data collection for protein crystallography.

To increase the efficiency of diffraction data collection for protein crystallographic studies, an automated system designed to store frozen protein crystals, mount them sequentially, align them to the X-ray beam, collect complete data sets, and return the crystals to storage has been developed. Advances in X-ray data collection technology including more brilliant X-ray sources, improved focusing optics, and faster-readout detectors have reduced diffraction data acquisition times from days to hours at a typical protein crystallography laboratory [1,2]. In addition, the number of high-brilliance synchrotron X-ray beam lines dedicated to macromolecular crystallography has increased significantly, and data collection times at these facilities can be routinely less than an hour per crystal. Because the number of protein crystals that may be collected in a 24 hr period has substantially increased, unattended X-ray data acquisition, including automated crystal mounting and alignment, is a desirable goal for protein crystallography. The ability to complete X-ray data collection more efficiently should impact a number of fields, including the emerging structural genomics field [3], structure-directed drug design, and the newly developed screening by X-ray crystallography [4], as well as small molecule applications.

POSIT: Flexible Shape-Guided Docking For Pose Prediction

We present a new approach to structure-based drug design (POSIT) rigorously built on the simple concept that pose prediction is intimately coupled to the quality and availability of experimental structural data. We demonstrate the feasibility of the approach by performing retrospective analyses on three data sets designed to explore the strengths and weaknesses of POSIT relative to existing methods. We then present results documenting 2.5 years of prospective use of POSIT across a variety of structure-based industrial drug-discovery research projects. We find that POSIT is well-suited to guiding research decision making for structure-based design and, in particular, excels at enabling lead-optimization campaigns. We show that the POSIT framework can drive superior pose-prediction performance and generate results that naturally lend themselves to prospective decision making during lead optimization. We believe the results presented here are (1) the largest prospective validation of a pose prediction method reported to date (71 crystal structures); (2) provide an unprecedented look at the scope of impact of a computational tool; and (3) represent a first-of-its-kind analysis. We hope that this work inspires additional studies that look at the real impact and performance of computational research tools on prospective drug design.

Structure of Secreted Aspartic Proteinases from Candida

Pathogens of the genus Candida can cause life threatening infections in immunocompromised patients. The three–dimensional structures of two closely related secreted aspartic proteinases from C. albicans complexed with a potent (Ki=0.17 nM) inhibitor, and an analogous enzyme from C. tropicalis reveal variations on the classical aspartic proteinase theme that dramatically alter the specificity of this class of enzymes. The novel fungal proteases present: i) an 8 residue insertion near the first disulfide (Cys45–Cys50, pepsin numbering) that results in a broad flap extending towards the active site; ii) a seven residue deletion replacing helix hN2 (Serll0–Tyrll4), which enlarges the S3 pocket; iii) a short polar connection between the two rigid body domains that alters their relative orientation and provides certain specificity; and iv) an ordered 12 residue addition at the car–boxy terminus. The same inhibitor (A–70450) binds in an extended conformation in the two variants of C albicans protease, and presents a branched structure at the P3 position. However, the conformation of the terminal methylpiperazine ring is different in the two crystals structures. The implications of these findings for the design of potent antifungal agents are discussed.

A highly potent and selective farnesyltransferase inhibitor ABT-100 in preclinical studies.

Ras mutation has been detected in approximately 20–30% of all human carcinomas, primarily in pancreatic, colorectal, lung and bladder carcinomas. The indirect inhibition of Ras activity by inhibiting farnesyltransferase (FTase) function is one therapeutic intervention to control tumor growth. Here we report the preclinical anti-tumor activity of our most advanced FTase inhibitor (FTI), ABT-100, and a direct comparison with the current clinical candidates. ABT-100 is a highly selective, potent and orally bioavailable FTI. It broadly inhibits the growth of solid tumors in preclinical animal models. Thus, ABT-100 is an attractive candidate for further clinical evaluation. In addition, our results provide plausible insights to explain the impressive potency and selectivity of ABT-100. Finally, we have demonstrated that ABT-100 significantly suppresses the expression of vascular endothelial growth factor (VEGF) mRNA and secretion of VEGF protein, as well as inhibiting angiogenesis in the animal model.