A Computational Framework to Identify Cross Association Between Complex Disorders by Protein-protein Interaction Network Analysis

Abstract

Objective: It is a known fact that numerous complex disorders do not happen in isolation indicating the plausible set of shared causes common to several different sicknesses. Hence, analysis of comorbidity can be utilized to explore association between several disorders. In this study, we have proposed a network-based computational approach, in which genes are organized based on the topological characteristics of the constructed Protein-Protein Interaction Network (PPIN) followed by a network prioritization scheme, to identify distinctive key genes and biological pathways shared among diseases. Methods: The proposed approach is initiated from constructed PPIN of any randomly chosen disease genes in order to infer its associations with other diseases in terms of shared pathways, co-expression, co-occurrence etc. For this, initially proteins associated to any disease based on random choice were identified. Secondly, PPIN is organized through topological analysis to define hub genes. Finally, using a prioritization algorithm a ranked list of newly predicted multimorbidity-associated proteins is generated. Using Gene Ontology (GO), cellular pathways involved in multimorbidity-associated proteins are mined. Result and Conclusion: The proposed methodology is tested using three disorders namely Diabetes, Obesity and blood pressure at an atomic level and the results suggest the comorbidity of other complex diseases that have associations with the proteins included in disease of present study through shared proteins and pathways. For diabetes, we have obtained key genes like GAPDH, TNF, IL6, AKT1, ALB, TP53, IL10, MAPK3, TLR4 and EGF with key pathways like P53 pathway, VEGF signaling pathway, Ras Pathway, Interleukin signaling pathway, Endothelin signaling pathway, Huntington disease etc. Study on other disorders such as obesity and blood pressure also revealed promising results.

Quercetin loaded and alginate sealed β-Glucan particles-based drug delivery system against DU145 a prostate cancer cell line: Integrating network pharmacology, molecular docking and in vitro studies

“…Protein-Protein Interaction (PPI) network was analyzed using STRING 11.0 database (https://stringdb.org/) (Suresh et al, 2021). These common potential target genes were merged into the STRING 11.0 database to create a PPI network, species keeping to Homo sapiens.…”

iThermo: A Sequence-Based Model for Identifying Thermophilic Proteins Using a Multi-Feature Fusion Strategy

“…Thermophiles can produce thermally stable proteins and even effectively resist high temperatures of up to 120°C ( Fan et al, 2016 ; Tang et al, 2017 ). Therefore, the study of proteins produced by thermophilic organisms is significant for the development of enzyme engineering ( Huang and Gong, 2020 ; Wang et al, 2020 ; Alim et al, 2021 ; Suresh et al, 2021 ; Zou et al, 2021 ).…”

MoRF-FUNCpred: Molecular Recognition Feature Function Prediction Based on Multi-Label Learning and Ensemble Learning

“…IDPs are widely found in eukaryotes, and traditional protein annotations do not consider disordered regions. Recent studies summarized the IDRs as seven functions: entropic chain, biological condensation, molecular recognition assembler (MoR_assembler), molecular recognition chaperone (MoR_chaperone), molecular recognition display sites (MoR_display_sites), molecular recognition effector (MoR_effector) and molecular recognition scavenger (MoR_scavenger), 1) entropic chain carries out some specific functions, these functions are generated by their conformational disorder, 2) molecular recognition assembler brings together multiple binding partners, promoting the formation of higher-order protein complexes, 3) molecular recognition scavenger can store and neutralizes some small ligands, 4) molecular recognition effector interacts with other proteins and to some extent influences their activity, 5) molecular recognition display sites is beneficial to deposition of post-translational modification, 6) molecular recognition chaperone can support RNA and protein to achieve functionally folded states, 7) biological condensation causes proteins to undergo transition from solution to condensed phase ( Mohan et al, 2006 ; Van Der Lee et al, 2014 ; Hwang Fu et al, 2019 ; Canzhuang and Yonge, 2021 ; Gao et al, 2021 ; Luo et al, 2021 ; Qian D. et al, 2021 ; Qian L. et al, 2021 ; Sharma and Srivastava, 2021 ; Suresh et al, 2021 ; Wu et al, 2021 ). The MoR_assembler, MoR_chaperone, MoR_display_sites, MoR_effector, and MoR_scavenger of the 7 IDRs functions are MoRF functions ( Mohan et al, 2006 ; Van Der Lee et al, 2014 ; Hwang Fu et al, 2019 ; Lv et al, 2019 ; Lv et al, 2020a ; Kanathezath et al, 2021 ; Peng et al, 2021 ; Rives et al, 2021 ; Szklarczyk et al, 2021 ; Villegas-Morcillo et al, 2021 ).…”