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.
Highlights
Systems exist that can uniquely measure the confidence of protein interactions
We show, how an assessment of the knowledge quality or the confidence behind a protein interaction network representing a disease mechanism, can serve as a rational to assist decision making for emerging targets and the selection of biomarker candidates
We show that application of this methodology to Alzheimer’s disease (AD)-specific protein-protein interactions (PPIs), in the first place, provides further support for the existing disease targets or known, valid, targeted interactions
Summary
Systems exist that can uniquely measure the confidence of protein interactions. For instance, PSISCORE3 is a framework for qualitative assessment of molecular interaction data that makes use of multiple confidence scoring servers to provide and compare scores on interaction data. We introduce a novel scoring strategy, which incorporates knowledge quality measurement parameters into protein interaction networks in a way that enables us to reliably distinguish established (well known) from emerging (speculative or surging) knowledge This is useful for investigating molecular mechanisms underlying complex and idiopathic diseases such as AD where the focus on integrating both established and emerging knowledge into integrative models is more likely to generate novel insights rather than considering only established, well-known information. We show that application of this methodology to AD-specific PPIs, in the first place, provides further support for the existing disease targets or known, valid, targeted interactions This assessment highlights regions of the interaction network where high-score interactions are in vicinity to low-score interactions, leading to a sudden decline of score. How mechanistic modelling and identification of reliable and novel findings in a network context can increase chances to identify new targets causally involved in the disease mechanism
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