Abstract: Liver Receptor Homolog-1 (LRH-1) is a ligand-regulated nuclear receptor, activated by small molecules which bind the ligand binding domain (LBD) within full-length LRH- 1. We recently used the LBD in a wet-lab screen to identify 57 compounds that bind LRH-1, and unexpectedly found these compounds regulated the function of either the isolated LBD, or the full-length LRH-1 in cells, with little overlap. Here, we used rigid body docking to correlate compound binding energy with activity in cells. We show compounds that activated full-length LRH-1 in cells docked to the full-length LRH-1 model close to Helix 6, an important regulatory helix. Docked binding energies of the 57 compounds did not correlate with LRH-1 regulation in wet lab assays, however
we empirically derived a new metric of the docking scores we call "ΔΔG". Regressions, correlations and contingency analyses all suggest compounds with high ΔΔG values more frequently regulated LRH-1 in wet lab assays. Docking all 57 compounds to 18 crystal structures of LRH-1 to obtain averaged ΔΔG values robustly associated with compound-induced full-length LRH-1 regulation in cells. Network analyses suggest unique communication paths exist between subsets of LRH-1 crystal structures that produced high ΔΔG values, identifying a relationship between ΔΔG and the position of Helix 6, important for LRH-1 regulation. Together, these data suggest
ΔΔG correlates with the ability of 57 hit compounds to regulate full-length LRH-1 in cell-based assays. We propose ΔΔG as a novel tool that can be applied to LRH-1 drug screens to prioritize compounds for secondary wet lab assays.
Drug screening project
00-Drug Screening Summary and Results
00-Drug Screening Summary and Results
00-Drug Screening Summary and Results
This is a brief summary of the challenge, the solution, and the implications from the drug screening project.
01-Drug Screening with Graph Theory
00-Drug Screening Summary and Results
00-Drug Screening Summary and Results
This comprehensive notebook provides an in-depth look at the process of performing network analysis on LRH-1 project.
02-Network Analysis Review
00-Drug Screening Summary and Results
03-Using the RING Server to Create PSNs
Networkx: This section reviews basic concepts in network analysis in general, why these concepts are of interest, and how they are applied to computational structural biology using the Python Networkx package.
03-Using the RING Server to Create PSNs
04-Visualizing PSNs within the 3D Protein structure
03-Using the RING Server to Create PSNs
The Residue Interaction Network Generator (RING): This online resource generates residue-residue interactions from PDB structure files. This section reviews how this server was used as part of the solution to the research challenges.
04-Visualizing PSNs within the 3D Protein structure
04-Visualizing PSNs within the 3D Protein structure
04-Visualizing PSNs within the 3D Protein structure
Py3DMol: This section is one part fun and one part utility. The network interactions generated by RING are mapped to the alpha-carbons and visualized within the 3D protein structure using Py3DMol.
05-PCA for Grouping LRH-1 by Ligand Binding
04-Visualizing PSNs within the 3D Protein structure
04-Visualizing PSNs within the 3D Protein structure
Clustering by PCA: Based on the distribution of eigenvector centrality values along the primary structure (amino acid sequence) a PCA was performed and structures were clustered into two groups based on ligand binding.
Comparing Four Measures of Network Centrality
Visualizing Relationships Between Measures of Centrality
Visualizing Relationships Between Measures of Centrality
Plotly and IPyWidgets: This section provides an interactive plot for comparing how centrality metrics are distributed along the 1YOK primary protein structure.
Visualizing Relationships Between Measures of Centrality
Visualizing Relationships Between Measures of Centrality
Visualizing Relationships Between Measures of Centrality
Plotly and IPyWidgets: This project focuses on the utility of eigenvector centrality to discern conformational changes in protein structure. This section creates an interactive tool for investigating the relationship between eigenvector centrality and three other network metrics.
3D PSN for Fun!
Visualizing Relationships Between Measures of Centrality
Multiplot Webapp
Plotly and IPyWidgets: This is an interactive3D visualization of the 1YOK PSN. The coordinates are the xyz coordinates of the alpha carbons from the PDB structure file.
Multiplot Webapp
Multiplot Webapp
Multiplot Webapp
Plotly and IPyWidgets: This is an interactive web application that allows the user to select which network data to visualize. This uses network metrics from 1YOK PSN.