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.

Multi-level models and infrastructures for simulating biological system development

The hierarchical organisation of biological systems plays a crucial role in the pattern formation of gene expression resulting from the morphogenetic processes, where autonomous internal dynamics of cells, as well as cell-to-cell interactions through membranes, are responsible for the emergent peculiar structures of the individual phenotype. Being able to reproduce the systems dynamics at different levels of such a hierarchy might be very useful for studying such a complex phenomenon of self-organisation. The idea is to model the phenomenon in terms of a large and dynamic network of compartments, where the interplay between inter-compartment and intra-compartment events determines the emergent behaviour resulting in the formation of spatial patterns. According to these premises the thesis proposes a review of the different approaches already developed in modelling developmental biology problems, as well as the main models and infrastructures available in literature for modelling biological systems, analysing their capabilities in tackling multi-compartment / multi-level models. The thesis then introduces a practical framework, MS-BioNET, for modelling and simulating these scenarios exploiting the potential of multi-level dynamics. This is based on (i) a computational model featuring networks of compartments and an enhanced model of chemical reaction addressing molecule transfer, (ii) a logic-oriented language to flexibly specify complex simulation scenarios, and (iii) a simulation engine based on the many-species/many-channels optimised version of Gillespie’s direct method. The thesis finally proposes the adoption of the agent-based model as an approach capable of capture multi-level dynamics. To overcome the problem of parameter tuning in the model, the simulators are supplied with a module for parameter optimisation. The task is defined as an optimisation problem over the parameter space in which the objective function to be minimised is the distance between the output of the simulator and a target one. The problem is tackled with a metaheuristic algorithm. As an example of application of the MS-BioNET framework and of the agent-based model, a model of the first stages of Drosophila Melanogaster development is realised. The model goal is to generate the early spatial pattern of gap gene expression. The correctness of the models is shown comparing the simulation results with real data of gene expression with spatial and temporal resolution, acquired in free on-line sources.

The biological basis of autism spectrum disorders: evaluation of oxidative stress and erytrocyte membrane alterations

This case-control study involved a total of 29 autistic children (Au) aged 6 to 12 years, and 28 gender and age-matched typically developing children (TD). We evaluated a high number of peripheral oxidative stress parameters, erythrocyte and lymphocyte membrane functional features and membrane lipid composition of erythrocyte. Erythrocyte TBARS, Peroxiredoxin II, Protein Carbonyl Groups and urinary HEL and isoprostane levels were elevated in AU (confirming an imbalance of the redox status of Au); other oxidative stress markers or associated parameters (urinary 8-oxo-dG, plasma Total antioxidant capacity and plasma carbonyl groups, erythrocyte SOD and catalase activities) were unchanged, whilst peroxiredoxin I showed a trend of elevated levels in red blood cells of Au children. A very significant reduction of both erythrocyte and lymphocyte Na+, K+-ATPase activity (NKA), a reduction of erythrocyte membrane fluidity, a reduction of phospatydyl serine exposition on erythrocyte membranes, an alteration in erythrocyte fatty acid membrane profile (increase in MUFA and in ω6/ω3 ratio due to decrease in EPA and DHA) and a reduction of cholesterol content of erythrocyte membrane were found in Au compared to TD, without change in erythrocyte membrane sialic acid content and in lymphocyte membrane fluidity. Some Au clinical features appear to be correlated with these findings; in particular, hyperactivity score appears to be related with some parameters of the lipidomic profile and membrane fluidity, and ADOS and CARS score are inversely related to peroxiredoxin II levels. Oxidative stress and erythrocyte structural and functional alterations may play a role in the pathogenesis of Autism Spectrum Disorders and could be potentially utilized as peripheral biomarkers.