Exploring novel mechanistic insights in Alzheimer’s disease by assessing reliability of protein interactions

Abstract

Protein interaction networks are widely used in computational biology as a graphical means of representing higher-level systemic functions in a computable form. Although, many algorithms exist that seamlessly collect and measure protein interaction information in network models, they often do not provide novel mechanistic insights using quantitative criteria. Measuring information content and knowledge representation in network models about disease mechanisms becomes crucial particularly when exploring new target candidates in a well-defined functional context of a potential disease mechanism. To this end, we have developed a knowledge-based scoring approach that uses literature-derived protein interaction features to quantify protein interaction confidence. Thereby, we introduce the novel concept of knowledge cliffs, regions of the interaction network where a significant gap between high scoring and low scoring interactions is observed, representing a divide between established and emerging knowledge on disease mechanism. To show the application of this approach, we constructed and assessed reliability of a protein-protein interaction model specific to Alzheimer’s disease, which led to screening, and prioritization of four novel protein candidates. Evaluation of the identified candidates showed that two of them are already followed in clinical trials for testing potential AD drugs.

A Coordinated Approach by Public Domain Bioinformatics Resources to Aid the Fight Against Alzheimer’s Disease Through Expert Curation of Key Protein Targets

“…This network, and subsequent ongoing expansions to the dataset, is now freely available to the research community to enable network analysis of generated data and can easily be extended to encompass, for example, all the proteins known to be expressed in the human brain by performing the relevant queries on the IntAct website. This resource will facilitate interrogation of large-scale GWAS, transcriptome and proteomics clinical datasets, and allow users to explore novel biology and enhance our understanding of the disease process [ 70 ].…”

Network Analysis of a Comprehensive Knowledge Repository Reveals a Dual Role for Ceramide in Alzheimer’s Disease

“…Recently, a study with network analysis of knowledge driven protein-protein interaction (PPI) network has been reported [ 54 ]. In this study, they calculated reliability scores of PPI with knowledge and discovered ‘Knowledge cliff’ which includes new therapeutic target of AD.…”

NeuroRDF: semantic integration of highly curated data to prioritize biomarker candidates in Alzheimer's disease

“…Using the above-mentioned dictionaries, our group previously harvested AD specific PPIs from MEDLINE abstracts and full text articles [ 69 ]. Here we used the interaction terms compiled by Thomas et al [ 70 ].…”

“…Thus, the need for manual verification is unavoidable, especially when considering the full text articles. The previously published test corpus used for evaluating the constructed AD PPI network contained AD-specific PPIs extracted by the machine learning approach from 200 full text articles [ 69 ]. Manual inspection by the authors resulted in retaining PPIs from 38 articles that are truly specific to AD, thus discarding 81 % of the originally retrieved articles.…”