Protein biomarker druggability profiling

Abstract

Developing automated and interactive methods for building a model by incorporating mechanistic and potentially causal annotations of ranked biomarkers of a disease or clinical condition followed by a mapping into a contextual framework in disease-linked biochemical pathways can be used for potential drug-target evaluation and for proposing new drug targets. We demonstrate the potential of this approach using ranked protein biomarkers obtained in neonatal sepsis by enrolling 127 infants (39 infants with late onset neonatal sepsis and 88 control infants) and by performing a focused proteomic profile of the sera and by applying the interactive druggability profiling algorithm (DPA) developed by us.

Keywords: Druggability profiling; Machine learning; Mechanistic annotation; Neonatal sepsis; Pathway analysis; Protein biomarkers.

Figure 1

ML Framework

Figure 2

The NOD-like receptor signaling pathway from KEGG shown with drugs, targets and known mechanism of action (→ denotes activation, ---> and ---> denote indirect effect and — denotes binding/association).

Figure 3

Expression across samples of selected sepsis proteomic biomarkers. Only six fetal tissues are represented. White squares denote absence of any expression, faded red denotes some level of expression and bright red denotes high levels of expression in the specific tissue for the particular protein biomarker providing a relative measure of tissue target localization.