Computational Prediction of Aggregated Structure of Denatured Lysozyme - CHOTIKASEMSRI, PONGSATHORN
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Mis-folded proteins and their associated aggregates are a contributing factor in some human diseases. In this study we used the protein lysozyme as a model to define aggregation structures under denaturing conditions. We used Rosetta++ protein folding and blind docking software to create in silico models of the protein at denaturing temperatures and subsequently docked them into aggregates. Here we compare those structures and select forms consistent with the fibril structure from the previous papers. The next step is to be able to use the predicted models of the fibrilar forms of denatured…mehr

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Produktbeschreibung
Mis-folded proteins and their associated aggregates are a contributing factor in some human diseases. In this study we used the protein lysozyme as a model to define aggregation structures under denaturing conditions. We used Rosetta++ protein folding and blind docking software to create in silico models of the protein at denaturing temperatures and subsequently docked them into aggregates. Here we compare those structures and select forms consistent with the fibril structure from the previous papers. The next step is to be able to use the predicted models of the fibrilar forms of denatured lysozyme to help us understand the exact conformation of fibril structures. This will let us confirm the docking interactions during the fibril aggregation process. The ultimate goal is to use the validated denatured structures to model interactions with heat shock proteins during the dis-aggregation process. By using this approach, we can further analyse other molecules to understand and solve the real problem of those diseases.
Autorenporträt
B.Sc.Genetics & M.Sc.Molecular Biology concentrated in Bioinformatics. Now, Pursuing Ph.D. in Protein folding prediction. My ultimate goal is to completely understand whole cell mechanisms, "System Biology", and able to definitely simulate every details of changes in order to predict the effects. This will facilitate many new discoveries.