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Identifying the optimal model to represent biochemical systems
Mochamad Apri, Research Scientist, Wageningen University and Research Centre
The complexity of biochemical systems due to the number of involved components and intricate interaction, often gives rise to a very large nonlinear mathematical model. Although a large model can describe detail mechanisms of the system and thus may become a reliable model, severe problems remain. For example, a high level of details can obscure the viewer on the main characteristics; the evaluation of the model may require long computing times; and the model parameters may be hardly identifiable, especially in view of a limited data availability.
In this work, we present a novel method to extract an optimal model from the full model which typically contains many parameters. Our method combines a reduction method and a model discrimination method, which are applied iteratively. This gives us a smaller model that contains only the important components and parameters that are necessary to govern the output of the system. Yet, the model has the same capability as the full model to represent the biochemical system in any allowed conditions. In the resulting model, parameter identification can be carried out more efficaciously. We show the effectiveness of our method by applying it to an EGFR model from (Kholodenko, 1999).
Molecular Docking Study of Calmodulin (CAM) in complex with small molecule ligands based on TFP and Phenothiazine
Asif Naqvi, CEO & Founder, BioDiscovery Group Life Sciences
Calmodulin (CaM) is a ubiquitous, calcium-binding protein that can bind to and regulate a multitude of different protein targets, thereby affecting many different cellular functions. It is a CALcium MODULated protein and is capable of regulating biological activities of many cellular proteins and transmembrane ion transporters mainly in a Ca2+-dependent manner. Small, hydrophobic molecules bind to CAM modify its function by inhibiting or modifying the interaction with other proteins. Triflouperazine is a phenothiazine derivative and a dopamine antagonist with antipsychotic and antiemetic activities. Trifluoperazine exerts its antipsychotic effect by blocking central dopamine receptors, thereby preventing effects such as delusions and hallucinations caused by an excess of dopamine. This agent also functions as a calmodulin inhibitor, thereby leading to elevation of cytosolic calcium. We have attempted with the help of virtual screening on the basis of structural similarity of TFP & Phenothiazine and molecular docking approach using Lamarckian Genetic Algorithm to elucidate the extent of specificity of CaM towards different classes of TFP & Phenothiazine. Total no of molecules were 3000 in number with the minimum binding energy of -11.50 kcal/mol. All the selected 3000 inhibitors were taken from different databases on the basis of the structural similarity of TFP & Phenothiazine.
The docking result of the study of 3000 molecules demonstrated that the binding energies were in the range of
-11.50 kcal/mol to -4.51 kcal/mol, with 8 molecules showing hydrogen bonds with the active site residue Met 124.
Further in-vitro and in-vivo study is required on these molecules as the binding mode provided hints for the future design of new derivatives with higher potency and specificity.
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