StemCellCKB: An Integrated Stem Cell-Specific Chemogenomics KnowledgeBase for Target Identification and Systems-Pharmacology Research

Abstract

Given the capacity of self-renewal and multilineage differentiation, stem cells are promising sources for use in regenerative medicines as well as in the clinical treatment of certain hematological malignancies and degenerative diseases. Complex networks of cellular signaling pathways largely determine stem cell fate and function. Small molecules that modulate these pathways can provide important biological and pharmacological insights. However, it is still challenging to identify the specific protein targets of these compounds, to explore the changes in stem cell phenotypes induced by compound treatment and to ascertain compound mechanisms of action. To facilitate stem cell related small molecule study and provide a better understanding of the associated signaling pathways, we have constructed a comprehensive domain-specific chemogenomics resource, called StemCellCKB (http://www.cbligand.org/StemCellCKB/). This new cloud-computing platform describes the chemical molecules, genes, proteins, and signaling pathways implicated in stem cell regulation. StemCellCKB is also implemented with web applications designed specifically to aid in the identification of stem cell relevant protein targets, including TargetHunter, a machine-learning algorithm for predicting small molecule targets based on molecular fingerprints, and HTDocking, a high-throughput docking module for target prediction and systems-pharmacology analyses. We have systematically tested StemCellCKB to verify data integrity. Target-prediction accuracy has also been validated against the reported known target/compound associations. This proof-of-concept example demonstrates that StemCellCKB can (1) accurately predict the macromolecular targets of existing stem cell modulators and (2) identify novel small molecules capable of probing stem cell signaling mechanisms, for use in systems-pharmacology studies. StemCellCKB facilitates the exploration and exchange of stem cell chemogenomics data among members of the broader research community.

Computational Systems Pharmacology-Target Mapping for Fentanyl-Laced Cocaine Overdose

“…Each knowledgebase adopts our established high-throughput protein–ligand docking technique, HTDocking, ,, for the identification of possible interactions between ligands and predicted protein targets. HTDocking generates docking scores by automatically docking each query compound into a 3D structure.…”

The efficacy and safety of cilostazol as an alternative to aspirin in Chinese patients with aspirin intolerance after coronary stent implantation: a combined clinical study and computational system pharmacology analysis

“…We have constructed a cardiovascular disease (CVD)-specific chemogenomics database [19] that can be used for target/offtarget (or additional) identification and network system pharmacology analysis of small molecules and their potential targets. Several in-house chemoinformatics tools were also used, including TargetHunter, HTDocking, Blood−Brain Barrier (BBB) Predictor (developed by Xie's laboratory, Pittsburgh, PA, USA), etc [20][21][22][23][24][25][26] . CVDPlatform (www.cbligand.org/CVD) archived 984 CVD-related target proteins, 924 approved CVD drugs in clinical trials, 2080 active chemical compounds associated with the therapeutic targets of CVD, 276 cardiovascularrelated pathways, and 350 765 references.…”

MCCS: a novel recognition pattern-based method for fast track discovery of anti-SARS-CoV-2 drugs

“…On the basis of our established domain specific chemogenomics databases [ 32 , 35 ] and our novel computational techniques, we constructed a virus-associated disease-specific chemogenomics knowledgebase (Virus-CKB, https://www.cbligand.org/g/virus-ckb ) [ 27 ] and applied our computational systems pharmacology-target mapping (CSP-Target Mapping) to accelerate drug combinational therapy to meet the urgent demand due to the COVID-19 outbreak.…”