Biomedical subjects

W Michael Brown

Publications and source records attributed to W Michael Brown.

7 recordsLinked to original sources

PubMed2006

Optimal neuronal tuning for finite stimulus spaces.

The efficiency of neuronal encoding in sensory and motor systems has been proposed as a first principle governing response properties within the central nervous system. We present a continuation of a theoretical study presented by Zhang and Sejnowski, where the influence of neuronal tuning properties on encoding accuracy is analyzed using information theory. When a finite stimulus space is considered, we show that the encoding accuracy improves with narrow tuning for one- and two-dimensional stimuli. For three dimensions and higher, there is an optimal tuning width.

Action Potentials↗

PubMed2005

Prediction of beta-strand packing interactions using the signature product.

The prediction of beta-sheet topology requires the consideration of long-range interactions between beta-strands that are not necessarily consecutive in sequence. Since these interactions are difficult to simulate using ab initio methods, we propose a supplementary method able to assign beta-sheet topology using only sequence information. We envision using the results of our method to reduce the three-dimensional search space of ab initio methods. Our method is based on the signature molecular descriptor, which has been used previously to predict protein-protein interactions successfully, and to develop quantitative structure-activity relationships for small organic drugs and peptide inhibitors. Here, we show how the signature descriptor can be used in a Support Vector Machine to predict whether or not two beta-strands will pack adjacently within a protein. We then show how these predictions can be used to order beta-strands within beta-sheets. Using the entire PDB database with ten-fold cross-validation, we have achieved 74.0% accuracy in packing prediction and 75.6% accuracy in the prediction of edge strands. For the case of beta-strand ordering, we are able to predict the correct ordering accurately for 51.3% of the beta-sheets. Furthermore, using a simple confidence metric, we can determine those sheets for which accurate predictions can be obtained. For the top 25% highest confidence predictions, we are able to achieve 95.7% accuracy in beta-strand ordering. [Figure: see text].

Amino Acid Sequence↗

PubMed2005

Reverse engineering chemical structures from molecular descriptors: how many solutions?

Physical, chemical and biological properties are the ultimate information of interest for chemical compounds. Molecular descriptors that map structural information to activities and properties are obvious candidates for information sharing. In this paper, we consider the feasibility of using molecular descriptors to safely exchange chemical information in such a way that the original chemical structures cannot be reverse engineered. To investigate the safety of sharing such descriptors, we compute the degeneracy (the number of structure matching a descriptor value) of several 2D descriptors, and use various methods to search for and reverse engineer structures. We examine degeneracy in the entire chemical space taking descriptors values from the alkane isomer series and the PubChem database. We further use a stochastic search to retrieve structures matching specific topological index values. Finally, we investigate the safety of exchanging of fragmental descriptors using deterministic enumeration.

Chemical Engineering↗

PubMed2005

A deterministic algorithm for constrained enumeration of transmembrane protein folds.

A deterministic algorithm for enumeration of transmembrane protein folds is presented. Using a set of sparse pairwise atomic distance constraints (such as those obtained from chemical cross-linking, FRET, or dipolar EPR experiments), the algorithm performs an exhaustive search of secondary structure element packing conformations distributed throughout the entire conformational space. The end result is a set of distinct protein conformations, which can be scored and refined as part of a process designed for computational elucidation of transmembrane protein structures.

Algorithms↗

PubMed2004

Comparative structural analysis and kinetic properties of lactate dehydrogenases from the four species of human malarial parasites.

Parasite lactate dehydrogenase (pLDH) is a potential drug target for new antimalarials owing to parasite dependence on glycolysis for ATP production. The pLDH from all four species of human malarial parasites were cloned, expressed, and analyzed for structural and kinetic properties that might be exploited for drug development. pLDH from Plasmodium vivax, malariae, and ovale exhibit 90-92% identity to pLDH from Plasmodium falciparum. Catalytic residues are identical. Resides I250 and T246, conserved in most LDH, are replaced by proline in all pLDH. The pLDH contain the same five-amino acid insert (DKEWN) in the substrate specificity loops. Within the cofactor site, pLDH from P. falciparum and P. malariae are identical, while pLDH from P. vivax and P. ovale have one substitution. Homology modeling of pLDH from P. vivax, ovale, and malariae with the crystal structure of pLDH from P. falciparum gave nearly identical structures. Nevertheless, the kinetic properties and sensitivities to inhibitors targeted to the cofactor binding site differ significantly. Michaelis constants for pyruvate and lactate differ 8-9-fold; Michaelis constants for NADH, NAD(+), and the NAD(+) analogue 3-acetylpyridine adenine dinucleotide differ up to 4-fold. Dissociation constants for the inhibitors differ up to 21-fold. Molecular docking studies of the binding of the inhibitors to the cofactor sites of all four pLDH predict similar orientations, with the docked ligands positioned at the nicotinamide end of the cofactor site. pH studies indicate that inhibitor binding is independent of pH in the pH 6-8 range, suggesting that differences in dissociation constants for a specific inhibitor are not due to altered active site pK values among the four pLDH.

Amino Acid Sequence↗

PubMedDate not supplied

Creating artificial binding pocket boundaries to improve the efficiency of flexible ligand docking.

Traditionally, algorithms for binding site characterization or identification focus on the problem of identifying atoms within a macromolecule that might be responsible for ligand binding. In this manuscript, we focus on the binding pocket problem from a different perspective as a challenge of calculating an artificial binding pocket boundary that is sufficient to isolate binding pocket volume. The approach involves the calculation of a macromolecule encapsulating surface (MES) that separates binding pocket volume from outside space. We show that the MES can be used to increase the efficiency of flexible docking as implemented in AutoDock 3.0. The most significant improvement in docking efficiency is seen when the entire protein is searched and results show additional support for the use of AutoDock, in and of itself, as a feasible tool for binding-site identification for cases in which a protein ligand is known.

Algorithms↗

PubMedDate not supplied

Designing novel polymers with targeted properties using the signature molecular descriptor.

A method for solving the inverse quantitative structure-property relationship (QSPR) problem is presented which facilitates the design of novel polymers with targeted properties. Here, we demonstrate the efficacy of the approach using the targeted design of polymers exhibiting a desired glass transition temperature, heat capacity, and density. We present novel QSPRs based on the signature molecular descriptor capable of predicting glass transition temperature, heat capacity, density, molar volume, and cohesive energies of linear homopolymers with cross-validation squared correlation coefficients ranging between 0.81 and 0.95. Using these QSPRs, we show how the inverse problem can be solved to design poly(N-methyl hexamethylene sebacamide) despite the fact that the polymer was used not used in the training of this model.

Algorithms↗