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.

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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

Here, we present a GPU-accelerated OpenCL implementation of a back-propagation artificial neural network for the creation of QSAR models for drug discovery and virtual high-throughput screening. A QSAR model for HSD achieved an enrichment of 5.9 and area under the curve of 0.83 on an independent data set which signifies sufficient predictive ability for virtual high-throughput screening efforts. The speed-up demonstrated on this data set allows for the complete cross-validated feature optimization of QSAR models based on ANNs within 24 hours on a workstation equipped with 4 consumer GPUs of $260 each (GTX 470), achieving performance equal to that of ∼340 cores. This GPU-accelerated ANN framework for the creation of optimized QSAR models from biological data will be available free of charge for academic users at http://www.meilerlab.org through a server interface.

Poster: GPU-accelerated artificial neural network for QSAR modeling

Three initial fits of 1ubi in a 6.6A resolution synthesized density map had backbone RMSDs to the correct placement of 2.7, 2.9 and 6.6 A. They have been refined with a Powell optimizer [5] in 10 iterations using 6 directions, 3 rotations a, β with 0.15 radians and γ with 0.075 radians starting direction to cover the entire euler sphere and translation with 1.1 A search directions in x, y and z. Each line search was terminated after 10 iterations or if dCCC \lg 0.001. TABLE I. summarizes timings on CPU and 4 different GPU architectures in double floating point precision.

http://www.meilerlab.org/index.php/publications/showPublication/pub_id/104

Poster: GPU-accelerated rigid body fitting of atomic structures into electron density maps

Peptidylglycine α-amidating monooxygenase (PAM) is a bifunctional enzyme that catalyzes the final reaction in the maturation of α-amidated peptide hormones. Peptidylglycine α-hydroxylating monooxygenase (PHM) is the PAM domain responsible for the copper-, ascorbate- and O2-dependent hydroxylation of a glycine-extended peptide. Peptidylamidoglycolate lyase is the PAM domain responsible for the Zn(II)-dependent dealkylation of the α-hydroxyglycine-containing precursor to the final α-amidated peptide. We report herein that cinnamic acid and cinnamic acid analogs are inhibitors or inactivators of PHM. The inactivation chemistry exhibited by the cinnamates exhibits all the attributes of a suicide-substrate. However, we find no evidence for the formation of an irreversible linkage between cinnamate and PHM in the inactivated enzyme. Our data support the reversible formation of a Michael adduct between an active site nucleophile and cinnamate that leads to inactive enzyme. Our data are of significance given that cinnamates are found in foods, perfumes, cosmetics and pharmaceuticals.

/pdf/inactivation-of-peptidylglycine-a-hydroxylating-213s8ng5ce.pdf

Inactivation of peptidylglycine α-hydroxylating monooxygenase by cinnamic acid analogs.

Molecular dynamics simulations of myoglobin and aspartate aminotransferase, with explicit solvent, are shown to accurately reproduce the experimentally measured molar volumes. Single amino-acid substitution at VAL39 of aspartate aminotransferase is known to produce large volumetric changes in the enzyme, and this effect is demonstrated in simulation as well. This molecular dynamics approach, while more computationally expensive that extant computational methods of determining the apparent volume of biological systems, is quite feasible with modern computer hardware and is shown to yield accurate volumetric data with as little as several nanoseconds of dynamics.

Edward W. Lowe

Papers

Poster: GPU-accelerated artificial neural network for QSAR modeling

Poster: GPU-accelerated rigid body fitting of atomic structures into electron density maps

Inactivation of peptidylglycine α-hydroxylating monooxygenase by cinnamic acid analogs.

Volume Determination of Globular Proteins by Molecular Dynamics

Computational fragment-based drug design