Atomic Force Microscopy Studies of the Amyloidogenic Processes of Intrinsically Unstructured Proteins related to Neurodegenerative Diseases

Protein aggregation and formation of insoluble aggregates in central nervous system is the main cause of neurodegenerative disease. Parkinson’s disease is associated with the appearance of spherical masses of aggregated proteins inside nerve cells called Lewy bodies. α-Synuclein is the main component of Lewy bodies. In addition to α-synuclein, there are more than a hundred of other proteins co-localized in Lewy bodies: 14-3-3η protein is one of them. In order to increase our understanding on the aggregation mechanism of α-synuclein and to study the effect of 14-3-3η on it, I addressed the following questions. (i) How α-synuclein monomers pack each other during aggregation? (ii) Which is the role of 14-3-3η on α-synuclein packing during its aggregation? (iii) Which is the role of 14-3-3η on an aggregation of α-synuclein “seeded” by fragments of its fibrils? In order to answer these questions, I used different biophysical techniques (e.g., Atomic force microscope (AFM), Nuclear magnetic resonance (NMR), Surface plasmon resonance (SPR) and Fluorescence spectroscopy (FS)).

Machine-learning methods for structure prediction of β-barrel membrane proteins

Different types of proteins exist with diverse functions that are essential for living organisms. An important class of proteins is represented by transmembrane proteins which are specifically designed to be inserted into biological membranes and devised to perform very important functions in the cell such as cell communication and active transport across the membrane. Transmembrane β-barrels (TMBBs) are a sub-class of membrane proteins largely under-represented in structure databases because of the extreme difficulty in experimental structure determination. For this reason, computational tools that are able to predict the structure of TMBBs are needed. In this thesis, two computational problems related to TMBBs were addressed: the detection of TMBBs in large datasets of proteins and the prediction of the topology of TMBB proteins. Firstly, a method for TMBB detection was presented based on a novel neural network framework for variable-length sequence classification. The proposed approach was validated on a non-redundant dataset of proteins. Furthermore, we carried-out genome-wide detection using the entire Escherichia coli proteome. In both experiments, the method significantly outperformed other existing state-of-the-art approaches, reaching very high PPV (92%) and MCC (0.82). Secondly, a method was also introduced for TMBB topology prediction. The proposed approach is based on grammatical modelling and probabilistic discriminative models for sequence data labeling. The method was evaluated using a newly generated dataset of 38 TMBB proteins obtained from high-resolution data in the PDB. Results have shown that the model is able to correctly predict topologies of 25 out of 38 protein chains in the dataset. When tested on previously released datasets, the performances of the proposed approach were measured as comparable or superior to the current state-of-the-art of TMBB topology prediction.