In silico approaches for biomolecule-driven disease diagnosis and therapy

Abstract

Developments in computational biology have facilitated the systematic study of biomolecules including peptides, proteins or nucleic acids. Such advancements have provided disease-oriented research and therapeutic discovery opportunities. The thesis deals with two broad disciplines in this field: Disease Diagnosis and Biomolecule-Based Therapeutics, with a particular focus on integrating curated data resources and machine-learning-based predictive systems. The initial part is aimed at enhancing the molecular knowledge and diagnostic studies of mucormycosis, a serious and rapidly spreading fungal infestation also referred to as “Black Fungus”. Here, we have a developed a web-repository titled as “MucormyDB”. The repository is a compilation of genomic, proteomic, virulence and therapeutic information. This repository enables researchers to easily perform comparative analyses and allows the identification of possible genetic biomarkers. With such capabilities MucormyDB is a valuable resource to study the molecular basis of mucormycosis. The second part of the thesis provides computational models for peptide-based therapeutics prediction. This involves IL4pred2, a machine-learning model that predicts peptides that can induce interleukin-4, and the AntiCP4, which predicts anticancer peptides with enhanced predictive capability. In addition to therapeutic prediction, identification of safety of peptide candidates is also taken into account in the thesis. In order to deal with this aspect, we have designed RAIpred to detect peptides that could trigger rheumatoid arthritis. We have also developed CDpred to predict peptides related to the celiac disease. These tests provide a valuable adjunct for immunological risk determination. They help researchers to select safer peptide candidates in the early phase of drug discovery early. Together, the resources developed in this thesis present high-quality molecular data, powerful predictive algorithms, and easily available computing platforms that can be used to study mucormycosis and complementarily improve the systematic evaluation of therapeutic peptides and the potential immunological consequences of peptides.