Data Mining - Concepts and Techniques.

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Based on its generally accessible I/II redox couple and bioavailability, copper plays a wide variety of roles in nature that mostly involve electron transfer (ET), O2 binding, activation and reduction, NO2− and N2O reduction and substrate activation. Copper sites that perform ET are the mononuclear blue Cu site that has a highly covalent CuII-S(Cys) bond and the binuclear CuA site that has a Cu2S(Cys)2 core with a Cu-Cu bond that keeps the site delocalized (Cu(1.5)2) in its oxidized state. In contrast to inorganic Cu complexes, these metalloprotein sites transfer electrons rapidly often over long distances, as has been previously reviewed.1–4 Blue Cu and CuA sites will only be considered here in their relation to intramolecular ET in multi-center enzymes. The focus of this review is on the Cu enzymes (Figure 1). Many are involved in O2 activation and reduction, which has mostly been thought to involve at least two electrons to overcome spin forbiddenness and the low potential of the one electron reduction to superoxide (Figure 2).5,6 Since the Cu(III) redox state has not been observed in biology, this requires either more than one Cu center or one copper and an additional redox active organic cofactor. The latter is formed in a biogenesis reaction of a residue (Tyr) that is also Cu catalyzed in the first turnover of the protein. Recently, however, there have been a number of enzymes suggested to utilize one Cu to activate O2 by 1e− reduction to form a Cu(II)-O2•− intermediate (an innersphere redox process) and it is important to understand the active site requirements to drive this reaction. The oxidases that catalyze the 4e−reduction of O2 to H2O are unique in that they effectively perform this reaction in one step indicating that the free energy barrier for the second two-electron reduction of the peroxide product of the first two-electron step is very low. In nature this requires either a trinuclear Cu cluster (in the multicopper oxidases) or a Cu/Tyr/Heme Fe cluster (in the cytochrome oxidases). The former accomplishes this with almost no overpotential maximizing its ability to oxidize substrates and its utility in biofuel cells, while the latter class of enzymes uses the excess energy to pump protons for ATP synthesis. In bacterial denitrification, a mononuclear Cu center catalyzes the 1e- reduction of nitrite to NO while a unique µ4S2−Cu4 cluster catalyzes the reduction of N2O to N2 and H2O, a 2e− process yet requiring 4Cu’s. Finally there are now several classes of enzymes that utilize an oxidized Cu(II) center to activate a covalently bound substrate to react with O2.



Figure 1

Copper active sites in biology.





Figure 2

Latimer Diagram for Oxygen Reduction at pH = 7.0 Adapted from References 5 and 6.



This review presents in depth discussions of all these classes of Cu enzymes and the correlations within and among these classes. For each class we review our present understanding of the enzymology, kinetics, geometric structures, electronic structures and the reaction mechanisms these have elucidated. While the emphasis here is on the enzymology, model studies have significantly contributed to our understanding of O2 activation by a number of Cu enzymes and are included in appropriate subsections of this review. In general we will consider how the covalency of a Cu(II)–substrate bond can activate the substrate for its spin forbidden reaction with O2, how in binuclear Cu enzymes the exchange coupling between Cu’s overcomes the spin forbiddenness of O2 binding and controls electron transfer to O2 to direct catalysis either to perform two e− electrophilic aromatic substitution or 1e− H-atom abstraction, the type of oxygen intermediate that is required for H-atom abstraction from the strong C-H bond of methane (104 kcal/mol) and how the trinuclear Cu cluster and the Cu/Tyr/Heme Fe cluster achieve their very low barriers for the reductive cleavage of the O-O bond.

Much of the insight available into these mechanisms in Cu biochemistry has come from the application of a wide range of spectroscopies and the correlation of spectroscopic results to electronic structure calculations. Thus we start with a tutorial on the different spectroscopic methods utilized to study mononuclear and multinuclear Cu enzymes and their correlations to different levels of electronic structure calculations.

/pdf/copper-active-sites-in-biology-lhhabv47if.pdf

Copper Active Sites in Biology

Pharmaceutical research has successfully incorporated a wealth of molecular modeling methods, within a variety of drug discovery programs, to study complex biological and chemical systems. The integration of computational and experimental strategies has been of great value in the identification and development of novel promising compounds. Broadly used in modern drug design, molecular docking methods explore the ligand conformations adopted within the binding sites of macromolecular targets. This approach also estimates the ligand-receptor binding free energy by evaluating critical phenomena involved in the intermolecular recognition process. Today, as a variety of docking algorithms are available, an understanding of the advantages and limitations of each method is of fundamental importance in the development of effective strategies and the generation of relevant results. The purpose of this review is to examine current molecular docking strategies used in drug discovery and medicinal chemistry, exploring the advances in the field and the role played by the integration of structure- and ligand-based methods.

Molecular Docking and Structure-Based Drug Design Strategies

Interactions between proteins and small molecules are an integral part of biological processes in living organisms. Information on these interactions is dispersed over many databases, texts and prediction methods, which makes it difficult to get a comprehensive overview of the available evidence. To address this, we have developed STITCH ('Search Tool for Interacting Chemicals') that integrates these disparate data sources for 430 000 chemicals into a single, easy-to-use resource. In addition to the increased scope of the database, we have implemented a new network view that gives the user the ability to view binding affinities of chemicals in the interaction network. This enables the user to get a quick overview of the potential effects of the chemical on its interaction partners. For each organism, STITCH provides a global network; however, not all proteins have the same pattern of spatial expression. Therefore, only a certain subset of interactions can occur simultaneously. In the new, fifth release of STITCH, we have implemented functionality to filter out the proteins and chemicals not associated with a given tissue. The STITCH database can be downloaded in full, accessed programmatically via an extensive API, or searched via a redesigned web interface at http://stitch.embl.de.

/pdf/stitch-5-augmenting-protein-chemical-interaction-networks-4vyzdhxjdt.pdf

STITCH 5: augmenting protein–chemical interaction networks with tissue and affinity data

Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.

/pdf/computational-methods-in-drug-discovery-3sww6kbvd5.pdf

Computational Methods in Drug Discovery

Manganese (Mn) is both an essential biological cofactor and neurotoxicant. Disruption of Mn biology in the basal ganglia has been implicated in the pathogenesis of neurodegenerative disorders, such as parkinsonism and Huntington's disease. Handling of other essential metals (e.g. iron and zinc) occurs via complex intracellular signaling networks that link metal detection and transport systems. However, beyond several non-selective transporters, little is known about the intracellular processes regulating neuronal Mn homeostasis. We hypothesized that small molecules that modulate intracellular Mn could provide insight into cell-level Mn regulatory mechanisms. We performed a high throughput screen of 40,167 small molecules for modifiers of cellular Mn content in a mouse striatal neuron cell line. Following stringent validation assays and chemical informatics, we obtained a chemical ‘toolbox' of 41 small molecules with diverse structure-activity relationships that can alter intracellular Mn levels under biologically relevant Mn exposures. We utilized this toolbox to test for differential regulation of Mn handling in human floor-plate lineage dopaminergic neurons, a lineage especially vulnerable to environmental Mn exposure. We report differential Mn accumulation between developmental stages and stage-specific differences in the Mn-altering activity of individual small molecules. This work demonstrates cell-level regulation of Mn content across neuronal differentiation.

/pdf/cellular-manganese-content-is-developmentally-regulated-in-vet6fsu4er.pdf

Cellular manganese content is developmentally regulated in human dopaminergic neurons

With the rapidly increasing availability of High-Throughput Screening (HTS) data in the public domain, such as the PubChem database, methods for ligand-based computer-aided drug discovery (LB-CADD) have the potential to accelerate and reduce the cost of probe development and drug discovery efforts in academia. We assemble nine data sets from realistic HTS campaigns representing major families of drug target proteins for benchmarking LB-CADD methods. Each data set is public domain through PubChem and carefully collated through confirmation screens validating active compounds. These data sets provide the foundation for benchmarking a new cheminformatics framework BCL::ChemInfo, which is freely available for non-commercial use. Quantitative structure activity relationship (QSAR) models are built using Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Decision Trees (DTs), and Kohonen networks (KNs). Problem-specific descriptor optimization protocols are assessed including Sequential Feature Forward Selection (SFFS) and various information content measures. Measures of predictive power and confidence are evaluated through cross-validation, and a consensus prediction scheme is tested that combines orthogonal machine learning algorithms into a single predictor. Enrichments ranging from 15 to 101 for a TPR cutoff of 25% are observed.

http://www.meilerlab.org/index.php/publications/showPublication/pub_id/141

Benchmarking ligand-based virtual High-Throughput Screening with the PubChem database.

http://www.meilerlab.org/index.php/publications/showPublication/pub_id/175

Discoidin domain receptor 1 (DDR1) kinase as target for structure-based drug discovery

Small angle X-ray scattering (SAXS) is used for low resolution structural characterization of proteins often in combination with other experimental techniques After briefly reviewing the theory of SAXS we discuss computational methods based on 1) the Debye equation and 2) Spherical Harmonics to compute intensity profiles from a particular macromolecular structure Further, we review how these formulas are parameterized for solvent density and hydration shell adjustment Finally we introduce our solution to compute SAXS profiles utilizing GPU acceleration

Edward W. Lowe

Papers

Computational Methods in Drug Discovery

Cellular manganese content is developmentally regulated in human dopaminergic neurons

Benchmarking ligand-based virtual High-Throughput Screening with the PubChem database.

Discoidin domain receptor 1 (DDR1) kinase as target for structure-based drug discovery

Reconstruction of saxs profiles from protein structures