Analysis layered structure of proteins and their amino acid interactions by constructing residue networks

Determining the factors in protein stability and identifying how amino acid types contribute to the protein folding mechanism are hot topics in biomolecular sciences. By determining these factors and amino acid types, protein structures and their functions can be better understood. Our motivation is to detect the importance of the net charge distribution and the amino acid interactions in protein stability and to find important amino acid types that play key roles in protein folding. In the first part of this thesis, the stability of proteins is inspected by analyzing the charge distribution. In the second part, interactions in proteins are evaluated by constructing amino acid networks. Once generated, the redundant part of these networks is deleted by breaking the interactions between amino acids choosen by several sets of predetermined criteria. These subnetworks are compared to each other through analyzing their matrix structures by making use of singular value decomposition. In the first part of the thesis, the structure of the protein is examined according to the charge distribution at different accessible surface areas (ASAs) and depths of amino acids. ASA does not discriminate between atoms just below the protein surface and those in the core of the protein. In order to differentiate the location of such buried residues, the depths of amino acids from the protein surface are calculated. It is inferred from these calculations that there is a layer composition of protein structures. At the innermost and the outermost parts of the protein, there is a net negative charge, while the middle has nearly neutral. This layered composition gives stability to the protein. Also, the ASA and depths are evaluated based on subsets of proteins with different secondary structure types. It is inferred from these analyses that the layered structure does not display any tracktable differences for different protein secondary structure types (α, β, α/β, α+β); i.e. this distribution is universal to all proteins. In the second part of thesis, the amino acid network of all proteins are constructed using each amino acid as node and the interaction between them as an edge. The interactions between amino acids are deleted (either randomly, or according to predetermined rules) to identify the redundancies in the network. We conclude that when seventy percent of the edges is randomly deleted, the path length does not change more compared to the full amino acid network. In addition, the amino acid subnetworks which consist of specific type of amino acids are constructed and these subnetworks are compared according to their similarity to the whole amino acid network. The subnetworks which are composed of long-range interactions between the hydrophobic amino acids only are more similar to the amino acid networks constructed from using all interactions. This result supports a protein folding mechanism where a hydrophobic core is formed, followed by the rearrangement of the rest of the amino acids around this core. Also, all amino acid subnetworks and whole amino acid network are evaluated by projecting their contact maps onto a set of large eigenvectors that are related to the collective motions in proteins. The projection results show that the hydrophobic amino acid subnetwork is more similar to full amino acid network in this projected space. The amino acids in this subnetwork are also found to be more conserved than others

In silico identification of critical proteins associated with learning process and immune system for Down syndrome.

Understanding expression levels of proteins and their interactions is a key factor to diagnose and explain the Down syndrome which can be considered as the most prevalent reason of intellectual disability in human beings. In the previous studies, the expression levels of 77 proteins obtained from normal genotype control mice and from trisomic Ts65Dn mice have been analyzed after training in contextual fear conditioning with and without injection of the memantine drug using statistical methods and machine learning techniques. Recent studies have also pointed out that there may be a linkage between the Down syndrome and the immune system. Thus, the research presented in this paper aim at in silico identification of proteins which are significant to the learning process and the immune system and to derive the most accurate model for classification of mice. In this paper, the features are selected by implementing forward feature selection method after preprocessing step of the dataset. Later, deep neural network, gradient boosting tree, support vector machine and random forest classification methods are implemented to identify the accuracy. It is observed that the selected feature subsets not only yield higher accuracy classification results but also are composed of protein responses which are important for the learning and memory process and the immune system