Biomedical subjects

Martyn G Ford

Publications and source records attributed to Martyn G Ford.

4 recordsLinked to original sources

PubMed2005

DASH: a novel analysis method for molecular dynamics simulation data. Analysis of ligands of PPAR-gamma.

A novel molecular dynamics (MD) analysis algorithm, DASH, is introduced in this paper. DASH has been developed to utilize the sequential nature of MD simulation data. By adjusting a set of parameters, the sensitivity of DASH can be controlled, allowing molecular motions of varying magnitudes to be detected or ignored as desired, with no knowledge of the number of conformations required being prerequisite. MD simulations of three synthetic ligands of the orphan nuclear receptor PPARgamma were generated in vacuo using Tripos's SYBYL and used as the training set for DASH. Two X-ray crystal structures of PPARgamma complexed with Rosiglitazone were compared to gain knowledge of the pharmacophoric conformation; this showed that the conformation of the ligand is significantly different between the two structures, indicating that there is no distinct conformation in which rosiglitazone binds to PPARgamma but multiple binding modes. An investigation into simulation length was carried out. A simulation of 5 ns was found to give highly variable results, whereas a simulation of 25 ns gave a representative window of motion for molecules of this size. DASH was compared with Ward's hierarchical cluster analysis method. The results show that DASH analysis is as good as Ward analysis in some areas (e.g. conformation identification) and is superior in others (e.g. speed and input size).

Algorithms↗

PubMed2002

QSAR studies of the pyrethroid insecticides. Part 3. A putative pharmacophore derived using methodology based on molecular dynamics and hierarchical cluster analysis.

Previous studies of the conformational behaviour of a group of synthetic pyrethroid insecticides have been extended to a more structurally diverse set. This includes compounds with different backbones and differing stereochemistry, with both Types I and II biological activity. These compounds also encompass a large range of biological activities. A parameterisation of the CHARMM force field for these compounds has been performed and the extra parameters are reported. Conformational sampling, using molecular dynamics (MD), has been performed for each of the 41 active structures. The accessible conformations of each have been characterised by the values of the common torsion angles using hierarchichal cluster analysis (HCA). A further CA, based on the centroids derived from the conformational sampling, identified a conformation common to at least 39 of the 41 structures. The critical torsion angles of this conformation lie at the centre of the molecule about the ester linkage and are defining an extended conformation, which differs from the minimum energy conformation of deltamethrin used previously. This may represent a putative pharmacophore for kill. The methods used here improve significantly on those used previously. The CHARMM force field was parameterised for the compounds and an improved method of conformational sampling, based on centroid clustering, has also been used.

Allethrins↗

PubMedDate not supplied

Selecting screening candidates for kinase and G protein-coupled receptor targets using neural networks.

A series of neural networks has been trained, using consensus methods, to recognize compounds that act at biological targets belonging to specific gene families. The MDDR database was used to provide compounds targeted against gene families and sets of randomly selected molecules. BCUT parameters were employed as input descriptors that encode structural properties and information relevant to ligand-receptor interactions. In each case, the networks identified over 80% of the compounds targeting a gene family. The technique was applied to purchasing compounds from external suppliers, and results from screening against one gene family demonstrated impressive abilities to predict the activity of the majority of known hit compounds.

Databases, Factual↗

PubMedDate not supplied

A consensus neural network-based technique for discriminating soluble and poorly soluble compounds.

BCUT [Burden, CAS, and University of Texas] descriptors, defined as eigenvalues of modified connectivity matrices, have traditionally been applied to drug design tasks such as defining receptor relevant subspaces to assist in compound selections. In this paper we present studies of consensus neural networks trained on BCUTs to discriminate compounds with poor aqueous solubility from those with reasonable solubility. This level was set at 0.1 mg/mL on advice from drug formulation and drug discovery scientists. By applying strict criteria to the insolubility predictions, approximately 95% of compounds are classified correctly. For compounds whose predictions have a lower level of confidence, further parameters are examined in order to flag those considered to possess unsuitable biopharmaceutical and physicochemical properties. This approach is not designed to be applied in isolation but is intended to be used as a filter in the selection of screening candidates, compound purchases, and the application of synthetic priorities to combinatorial libraries.

Journal Article↗