Systematic, network-based characterization of therapeutic target inhibitors.

Yao Shen,Brygida Bisikirska,Andrea Califano,Ronald Realubit,Mariano J Alvarez,Sergey Pampou,Alexander Lachmann,Jorida Coku,Charles Karan,Elena Papaleo

doi:10.1371/journal.pcbi.1005599

Abstract

A large fraction of the proteins that are being identified as key tumor dependencies represent poor pharmacological targets or lack clinically-relevant small-molecule inhibitors. Availability of fully generalizable approaches for the systematic and efficient prioritization of tumor-context specific protein activity inhibitors would thus have significant translational value. Unfortunately, inhibitor effects on protein activity cannot be directly measured in systematic and proteome-wide fashion by conventional biochemical assays. We introduce OncoLead, a novel network based approach for the systematic prioritization of candidate inhibitors for arbitrary targets of therapeutic interest. In vitro and in vivo validation confirmed that OncoLead analysis can recapitulate known inhibitors as well as prioritize novel, context-specific inhibitors of difficult targets, such as MYC and STAT3. We used OncoLead to generate the first unbiased drug/regulator interaction map, representing compounds modulating the activity of cancer-relevant transcription factors, with potential in precision medicine.

Highlights

While the number of high-value, candidate therapeutic target proteins has increased dramatically over the past five years, most of them lack a corresponding FDA-approved or late-stage investigational small-molecule inhibitor
Context-specific mechanism of action for small molecule compounds can be described by network-based protein activity inferences
OncoLead assesses whether a compound is an effective inhibitor/activator of a given regulatory protein, based on its effect on the transcriptional level of the protein’s regulon—i.e. its set of direct and indirect transcriptional targets—to infer the regulatory protein’s differential activity; see Methods and [18]

Summary

Introduction

While the number of high-value, candidate therapeutic target proteins has increased dramatically over the past five years, most of them lack a corresponding FDA-approved or late-stage investigational (i.e., clinically relevant) small-molecule inhibitor. A key problem in addressing this challenge is the lack of generalizable methodologies for the efficient and systematic prioritization of small molecule compounds as direct or indirect inhibitors of an arbitrary protein of interest. Throughout this manuscript, we will use the word ‘compound’ for short to refer to small molecule compounds. Computational HTS approaches, such as quantitative structure activity relation (QSAR) analysis [3] and virtual screening [4], rely on availability of structural models for both the ligands and the target protein and on prior knowledge from related compound’s binding assays or from X-ray/NMR target structure elucidation [3]. Results completely depend on the availability of ligand analogs, whose structure has been previously elucidated

Methods

Results

Discussion

Conclusion