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Long S.M.,University of Queensland | Tran T.T.,Protagonist Pty Ltd | Adams P.,University of Queensland | Darwen P.,Protagonist Pty Ltd | Smythe M.L.,University of Queensland
Journal of Computational Chemistry | Year: 2011

A new population-based incremental learning algorithm for conformational searching of molecules is presented. This algorithm is particularly effective at determining, by relatively small number of energy minimizations, global energy minima of large flexible molecules. The algorithm is also able to find a large set of low energy conformations of more rigid small molecules. The performance of the algorithm is relation to other algorithm is examined via the test molecules: C18H38, C39H80, cycloheptadecane and a set of five drug-like molecules. © 2011 Wiley Periodicals, Inc. Source

Tran T.T.,Protagonist Pty Ltd | Tran T.T.,University of Queensland | Kulis C.,University of Queensland | Long S.M.,Protagonist Pty Ltd | And 5 more authors.
Journal of Computer-Aided Molecular Design | Year: 2010

Medicinal chemists synthesize arrays of molecules by attaching functional groups to scaffolds. There is evidence suggesting that some scaffolds yield biologically active molecules more than others, these are termed privileged substructures. One role of the scaffold is to present its side-chains for molecular recognition, and biologically relevant scaffolds may present side-chains in biologically relevant geometries or shapes. Since drug discovery is primarily focused on the discovery of compounds that bind to proteinaceous targets, we have been deciphering the scaffold shapes that are used for binding proteins as they reflect biologically relevant shapes. To decipher the scaffold architecture that is important for binding protein surfaces, we have analyzed the scaffold architecture of protein loops, which are defined in this context as continuous four residue segments of a protein chain that are not part of an α-helix or β-strand secondary structure. Loops are an important molecular recognition motif of proteins. We have found that 39 clusters reflect the scaffold architecture of 89% of the 23,331 loops in the dataset, with average intra-cluster and inter-cluster RMSD of 0.47 and 1.91, respectively. These protein loop scaffolds all have distinct shapes. We have used these 39 clusters that reflect the scaffold architecture of protein loops as biological descriptors. This involved generation of a small dataset of scaffold-based peptidomimetics. We found that peptidomimetic scaffolds with reported biological activities matched loop scaffold geometries and those peptidomimetic scaffolds with no reported biologically activities did not. This preliminary evidence suggests that organic scaffolds with tight matches to the preferred loop scaffolds of proteins, implies the likelihood of the scaffold to be biologically relevant. © 2010 Springer Science+Business Media B.V. Source

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