In silico identification of toxins and their effect on host pathways: feature extraction classification and pathway prediction/ Rishika Sen

Thesis (Ph.D.) - Indian Statistical Institute, 2021

Includes bibliography

Introduction and Scope of the Thesis -- A Review on Host–Pathogen Interactions: Classification and Prediction -- PyPredT6: An Ensemble Learning-based System for Identification of Type VI Effector Proteins -- Cluster Quality-based Non-Reductional (CQNR) Oversampling Technique and Effector Protein Predictor Based on 3D Structure (EPP3D) of Proteins -- DeepT7: A Deep Neural Network System for Identification of Type VII Effector Proteins -- DeepT7: A Deep Neural Network System for Identification of Type VII Effector Proteins -- Boolean Logic-based Network Robustness Analyzer (BNRA) and Its Application
to a System of Host-Pathogen Interactions -- Conclusions and Scope for Future Research

Guided by Prof. Rajat K. De

Identification of toxins, which are either proteins or small molecules, from pathogens is of paramount importance due to their crucial role as first-line invaders infiltrating a host, often leading to infection of the host. These toxins can affect specific proteins, like enzymes that catalyze metabolic pathways, affect metabolites that form the basis of metabolic reactions, and prevent the progression of those pathways, or more generally they may affect the regular functioning of other proteins in signaling pathways in the host. In this regard, the thesis addresses the problem of identification of toxins, and the effect of perturbations by toxins on the host pathways based on three tasks: feature extraction, classification and pathway prediction. The thesis starts with in silico identification of such toxins in pathogens. This is followed by the analysis of the effect of toxins on various metabolic and signaling pathways of the host.