Protein-protein interactions prediction, drug repurposing and molecular docking for emerging viral threats

Abstract

PPIs are fundamental to the functioning of biological systems, regulating a myriad of cellular processes. Therefore, accurate prediction of PPIs is crucial for disease diagnosis and the development of novel or repurposed therapeutic interventions. Computational techniques streamline the forecast of PPIs and prove to be more cost-effective in terms of resources and time than experimental approaches. In this thesis, initially, a CNN-based novel methodology EnMAS has been proposed to predict PPIs by transforming amino acid sequences of interacting proteins into 2D images using a colour encoding scheme derived from evolutionary data such as PAM and BLOSUM matrices. Additionally, EnMAS has been applied to human- SARS-COV-2 PPIN to predict interactions between human hosts and the COVID- 19 virus. This is followed by another GNN-based study GSPPI where a topological feature of PPIN called graphlet (up to 5 nodes) has been leveraged to predict PPIs. Subsequently, in the next study, this graphlet feature and various amino acid composition-based features have been integrated to predict PPIs. This ensemble characteristic has demonstrated excellent prediction performance for PPI on the HIPPIE dataset. Owing to this performance, an illustrative case study has been conducted to predict the PHIs on the interacting protein network of MPXV and Humans. Mpox, a zoonotic illness triggered by MPXV presents a substantial risk to public health since it may be transmitted from person to person and there are no particular treatments available. Investigating how viral proteins interact with host proteins provides valuable insights into the dynamics of PHIs which is essential for designing effective vaccines. Utilizing the human protein targets for MPXV identified accurately, a drug repurposing study is undertaken to discover potential candidate drugs capable of curing Mpox. Additionally, validation has been conducted through subsequent docking studies on these promising drugs for MPXV using the prominent MPXV crystal structures currently available. The results of our study emphasize the effectiveness of using EnMAS and ensemble feature-based PPI prediction to understand the molecular processes involved in viral infection and to aid in the development of drugs for emerging viral threats such as Mpox and COVID-19. Though we have shown the efficacy of our method on Mpox as a case study, the approach is generalizable to a broad range of new emerging viral diseases with potential epidemic or pandemic threats, where the proposed method can reduce the risks and cost of developing new compounds and can offer a viable answer to the time constraints of traditional drug development.