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Protein and peptide aggregation in amyloid fibrils has been associated with several clinical disorders, including Alzheimer’s, Diabetes-II, and prion disease. Despite several studies, molecular mechanisms of fibril formation initiation are still unknown. Some studies showed short amino-acid segments located at amyloid precursor proteins can be a trigger to fibril development, making these regions promising targets for further studies. A current challenge in bioinformatics is the development of methods to accurately predict such regions aggregation prone. In our previous work we employed machine learning to develop a method called Magre-I, which predicts aggregation propensity regions based on primary structure. We evaluated the performances of Magre-II with proteins with experimental results available in the database Amypro. The predictor was in agreement with experimental data and displayed good/relevant performance to training and testing sets, and also for additional proteins and different intermediate structures of Prion. The Magre-II 3D approach showed to be able to distinguish between soluble and fibrillar forms considering Transferethyn, Alpha-synuclein, Insulin and Beta-amyloid protein. Our approach was also able to capture different secondary structure content present in transient prion protein conformations generated through in silico simulations. Another advantage of Magre-II is that we used experimental data, deposited in the Amypro data to train and validate the machine learning model of the predictor. Magre-II is open to improving the model including more experimental data from literature. We started to develop a web server where the user can submit a primary structure or a 3D coordinate structure file (PDB format) of the protein to predict aggregation prone regions.

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1	Carlos Moreira(calvesmor001@gmail.com)
2	Eric Philot(ericphilot@gmail.com)
3	Ana Scott(analigiascott@gmail.com)

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