Each one teaches one: characterizing active forms of proteins by single molecule force spectroscopy

This Ph.D. candidate thesis collects the research work I conducted under the supervision of Prof.Bruno Samor´ı in 2005,2006 and 2007. Some parts of this work included in the Part III have been begun by myself during my undergraduate thesis in the same laboratory and then completed during the initial part of my Ph.D. thesis: the whole results have been included for the sake of understanding and completeness. During my graduate studies I worked on two very different protein systems. The theorical trait d’union between these studies, at the biological level, is the acknowledgement that protein biophysical and structural studies must, in many cases, take into account the dynamical states of protein conformational equilibria and of local physico-chemical conditions where the system studied actually performs its function. This is introducted in the introductory part in Chapter 2. Two different examples of this are presented: the structural significance deriving from the action of mechanical forces in vivo (Chapter 3) and the complexity of conformational equilibria in intrinsically unstructured proteins and amyloid formation (Chapter 4). My experimental work investigated both these examples by using in both cases the single molecule force spectroscopy technique (described in Chapter 5 and Chapter 6). The work conducted on angiostatin focused on the characterization of the relationships between the mechanochemical properties and the mechanism of action of the angiostatin protein, and most importantly their intertwining with the further layer of complexity due to disulfide redox equilibria (Part III). These studies were accompanied concurrently by the elaboration of a theorical model for a novel signalling pathway that may be relevant in the extracellular space, detailed in Chapter 7.2. The work conducted on -synuclein (Part IV) instead brought a whole new twist to the single molecule force spectroscopy methodology, applying it as a structural technique to elucidate the conformational equilibria present in intrinsically unstructured proteins. These equilibria are of utmost interest from a biophysical point of view, but most importantly because of their direct relationship with amyloid aggregation and, consequently, the aetiology of relevant pathologies like Parkinson’s disease. The work characterized, for the first time, conformational equilibria in an intrinsically unstructured protein at the single molecule level and, again for the first time, identified a monomeric folded conformation that is correlated with conditions leading to -synuclein and, ultimately, Parkinson’s disease. Also, during the research work, I found myself in the need of a generalpurpose data analysis application for single molecule force spectroscopy data analysis that could solve some common logistic and data analysis problems that are common in this technique. I developed an application that addresses some of these problems, herein presented (Part V), and that aims to be publicly released soon.

Machine learning methods for prediction of disulphide bonding states of cysteine residues in proteins

The goal of this thesis work is to develop a computational method based on machine learning techniques for predicting disulfide-bonding states of cysteine residues in proteins, which is a sub-problem of a bigger and yet unsolved problem of protein structure prediction. Improvement in the prediction of disulfide bonding states of cysteine residues will help in putting a constraint in the three dimensional (3D) space of the respective protein structure, and thus will eventually help in the prediction of 3D structure of proteins. Results of this work will have direct implications in site-directed mutational studies of proteins, proteins engineering and the problem of protein folding. We have used a combination of Artificial Neural Network (ANN) and Hidden Markov Model (HMM), the so-called Hidden Neural Network (HNN) as a machine learning technique to develop our prediction method. By using different global and local features of proteins (specifically profiles, parity of cysteine residues, average cysteine conservation, correlated mutation, sub-cellular localization, and signal peptide) as inputs and considering Eukaryotes and Prokaryotes separately we have reached to a remarkable accuracy of 94% on cysteine basis for both Eukaryotic and Prokaryotic datasets, and an accuracy of 90% and 93% on protein basis for Eukaryotic dataset and Prokaryotic dataset respectively. These accuracies are best so far ever reached by any existing prediction methods, and thus our prediction method has outperformed all the previously developed approaches and therefore is more reliable. Most interesting part of this thesis work is the differences in the prediction performances of Eukaryotes and Prokaryotes at the basic level of input coding when ‘profile’ information was given as input to our prediction method. And one of the reasons for this we discover is the difference in the amino acid composition of the local environment of bonded and free cysteine residues in Eukaryotes and Prokaryotes. Eukaryotic bonded cysteine examples have a ‘symmetric-cysteine-rich’ environment, where as Prokaryotic bonded examples lack it.