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Biomedical subjects

D Gorse

Publications and source records attributed to D Gorse.

4 recordsLinked to original sources

Global minimization of an off-lattice potential energy function using a chaperone-based refolding method.

A global energy minimization method based on what is known about the mechanisms of the GroEL/GroES chaperonin system is applied to two 22-mers of an off-lattice protein model whose native states are beta-hairpins and which have structural similarity to short peptides known to interact strongly with the GroEL substrate binding domain. These model substrates have been used by other workers to test the effectiveness of a number of global minimization techniques, and are regarded as providing a significant challenge. The minimization method developed here is progressively elaborated from an initial simple form that targets exposed hydrophobic regions for unfolding to include a refolding phase that encourages the later recompactification of partly unfolded substrate; this refolding phase is seen to be crucial in the successful application of the method. The optimal handling of hydrophilic monomers within the model is also systematically explored, and it is seen that the best interpretation of their role is one that allows the chaperonin model to operate in "proofreading" mode whereby misfolded substrates are recognized by their surface exposure of a large proportion of hydrophobic monomers. The final version of the model allows native-like structures to be found quickly, on average for the two 22-mer substrates after 6 or 7 chaperone contacts. These results compare very favorably with those that have been obtained elsewhere using generic global minimization methods such as those based on thermal annealing. The paper concludes with a discussion of the place of the technique within the general category of hypersurface deformation methods for global minimization, and with suggestions as to how the chaperone-based method developed here could be elaborated so as to be effective on longer substrate chains that give rise to more complex tertiary structures in their native states.

Biopolymers↗

Functional diversity of compound libraries.

The ideal designed screening library should contain compounds with a variety of structural shapes and molecular properties, while avoiding redundancies. Other requirements involve the need to find structurally distinct leads and to recognise drug-like molecules. Functional diversity analysis is one way in which these objectives can be achieved. For this, molecular descriptions that relate to both structure and properties of molecules are needed, as well as their evaluation in terms of biological relevance.

Combinatorial Chemistry Techniques↗

Molecular diversity and its analysis.

We have recently developed a novel strategy for the rational design of compounds. This 'in silico screening' approach is based on the design and screening of virtual combinatorial libraries. Screening is performed using defined rules derived from a comprehensive description of active and inactive molecules in a relevant learning set. This strategy allows the development of potential ligands without the necessity of any knowledge of the 3D-structure of the target receptor. Key to the success of such methods is the quality of the information being processed, in particular, the diversity of the data in the context of the molecular population in the libraries concerned. Here, we review the problem of data diversity, its definition and its analysis using a new software tool, named Diverser.

Journal Article↗

Prediction of the location and type of beta-turns in proteins using neural networks.

A neural network has been used to predict both the location and the type of beta-turns in a set of 300 nonhomologous protein domains. A substantial improvement in prediction accuracy compared with previous methods has been achieved by incorporating secondary structure information in the input data. The total percentage of residues correctly classified as beta-turn or not-beta-turn is around 75% with predicted secondary structure information. More significantly, the method gives a Matthews correlation coefficient (MCC) of around 0.35, compared with a typical MCC of around 0.20 using other beta-turn prediction methods. Our method also distinguishes the two most numerous and well-defined types of beta-turn, types I and II, with a significant level of accuracy (MCCs 0.22 and 0.26, respectively).

Algorithms↗