Biomedical subjects

Matthew N Davies

Publications and source records attributed to Matthew N Davies.

5 recordsLinked to original sources

PubMed2006

Benchmarking pK(a) prediction.

BACKGROUND: pKa values are a measure of the protonation of ionizable groups in proteins. Ionizable groups are involved in intra-protein, protein-solvent and protein-ligand interactions as well as solubility, protein folding and catalytic activity. The pKa shift of a group from its intrinsic value is determined by the perturbation of the residue by the environment and can be calculated from three-dimensional structural data. RESULTS: Here we use a large dataset of experimentally-determined pKas to analyse the performance of different prediction techniques. Our work provides a benchmark of available software implementations: MCCE, MEAD, PROPKA and UHBD. Combinatorial and regression analysis is also used in an attempt to find a consensus approach towards pKa prediction. The tendency of individual programs to over- or underpredict the pKa value is related to the underlying methodology of the individual programs. CONCLUSION: Overall, PROPKA is more accurate than the other three programs. Key to developing accurate predictive software will be a complete sampling of conformations accessible to protein structures.

Catalysis↗

PubMed2006

Statistical deconvolution of enthalpic energetic contributions to MHC-peptide binding affinity.

BACKGROUND: MHC Class I molecules present antigenic peptides to cytotoxic T cells, which forms an integral part of the adaptive immune response. Peptides are bound within a groove formed by the MHC heavy chain. Previous approaches to MHC Class I-peptide binding prediction have largely concentrated on the peptide anchor residues located at the P2 and C-terminus positions. RESULTS: A large dataset comprising MHC-peptide structural complexes was created by re-modelling pre-determined x-ray crystallographic structures. Static energetic analysis, following energy minimisation, was performed on the dataset in order to characterise interactions between bound peptides and the MHC Class I molecule, partitioning the interactions within the groove into van der Waals, electrostatic and total non-bonded energy contributions. CONCLUSION: The QSAR techniques of Genetic Function Approximation (GFA) and Genetic Partial Least Squares (G/PLS) algorithms were used to identify key interactions between the two molecules by comparing the calculated energy values with experimentally-determined BL50 data. Although the peptide termini binding interactions help ensure the stability of the MHC Class I-peptide complex, the central region of the peptide is also important in defining the specificity of the interaction. As thermodynamic studies indicate that peptide association and dissociation may be driven entropically, it may be necessary to incorporate entropic contributions into future calculations.

Binding Sites↗

PubMed2006

PPD v1.0--an integrated, web-accessible database of experimentally determined protein pKa values.

The Protein pK(a) Database (PPD) v1.0 provides a compendium of protein residue-specific ionization equilibria (pK(a) values), as collated from the primary literature, in the form of a web-accessible postgreSQL relational database. Ionizable residues play key roles in the molecular mechanisms that underlie many biological phenomena, including protein folding and enzyme catalysis. The PPD serves as a general protein pK(a) archive and as a source of data that allows for the development and improvement of pK(a) prediction systems. The database is accessed through an HTML interface, which offers two fast, efficient search methods: an amino acid-based query and a Basic Local Alignment Search Tool search. Entries also give details of experimental techniques and links to other key databases, such as National Center for Biotechnology Information and the Protein Data Bank, providing the user with considerable background information. The database can be found at the following URL: http://www.jenner.ac.uk/PPD.

Amino Acids↗

PubMed2006

Receptor-ligand binding sites and virtual screening.

Within the pharmaceutical industry, the ultimate source of continuing profitability is the unremitting process of drug discovery. To be profitable, drugs must be marketable: legally novel, safe and relatively free of side effects, efficacious, and ideally inexpensive to produce. While drug discovery was once typified by a haphazard and empirical process, it is now increasingly driven by both knowledge of the receptor-mediated basis of disease and how drug molecules interact with receptors and the wider physiome. Medicinal chemistry postulates that to understand a congeneric ligand series, or set thereof, is to understand the nature and requirements of a ligand binding site. Likewise, structural molecular biology posits that to understand a binding site is to understand the nature of ligands bound therein. Reality sits somewhere between these extremes, yet subsumes them both. Complementary to rules of ligand design, arising through decades of medicinal chemistry, structural biology and computational chemistry are able to elucidate the nature of binding site-ligand interactions, facilitating, at both pragmatic and conceptual levels, the drug discovery process.

Binding Sites↗

PubMedDate not supplied

A novel predictive technique for the MHC class II peptide-binding interaction.

Antigenic peptide is presented to a T-cell receptor through the formation of a stable complex with a Major Histocompatibility Complex (MHC) molecule. Various predictive algorithms have been developed to estimate a peptide's capacity to form a stable complex with a given MHC Class II allele, a technique integral to the strategy of vaccine design. These have previously incorporated such computational techniques as quantitative matrices and neural networks. We have developed a novel predictive technique that uses molecular modeling of predetermined crystal structures to estimate the stability of an MHC Class II peptide complex. This is the 1st structure-based technique, as previous methods have been based on binding data. ROC curves are used to quantify the accuracy of the molecular modeling technique. The novel predictive technique is found to be comparable with the best predictive software currently available.

Alleles↗