Fragment-based virtual screening with docking and pose scoring

Dock a compound library against a target protein with rDock and validate the poses generated against a reference fragment using SuCOS to compare the feature overlap. Poses are filtered by a user-specified SuCOS threshold.

A list of fragments should be specified which will be used to define the cavity for docking, using the 'Frankenstein ligand' technique. For more details, please see https://www.informaticsmatters.com/blog/2018/11/23/cavities-and-frankenstein-molecules.html ...

GROMACS MMGBSA free energy calculation

Perform an ensemble of MD simulations of a user-specified size using GROMACS, and calculate MMGBSA free energies using AmberTools. An ensemble average is calculated and returned to the user as the final input.

The input protein (PDB) and ligand (SDF) files provided are parameterized by the 'Protein-ligand complex parameterization' subworkflow.

GROMACS dcTMD free energy calculation

Perform an ensemble of targeted MD simulations of a user-specified size using the GROMACS PULL code and calculate dcTMD free energy and friction profiles for the resulting dissocation pathway. Note that pathway separation is not performed by the workflow; the user is responsible for checking the ensemble themselves.

The input protein (PDB) and ligand (SDF) files provided are parameterized by the 'Protein-ligand complex parameterization' subworkflow.

Note ...

Protein-ligand complex parameterization

Parameterizes an input protein (PDB) and ligand (SDF) file prior to molecular dynamics simulation with GROMACS.

This is a simple workflow intended for use as a subworkflow in more complex MD workflows. It is used as a subworkflow by the GROMACS MMGBSA and dcTMD workflows.

Description

The workflow takes an input file with Cancer Driver Genes predictions (i.e. the results provided by a participant), computes a set of metrics, and compares them against the data currently stored in OpenEBench within the TCGA community. Two assessment metrics are provided for that predictions. Also, some plots (which are optional) that allow to visualize the performance of the tool are generated. The workflow consists in three standard steps, defined by OpenEBench. The tools needed ...

Summary

This notebook shows how to integrate genomic and image data resources. This notebook looks at the question Which diabetes related genes are expressed in the pancreas?

Steps:

• Query humanmine.org, an integrated database of Homo sapiens genomic data using the intermine API to find the genes.
• Using the list of found genes, search in the Image Data Resource (IDR) for images linked to the genes, tissue and disease.

We use the intermine API and the IDR API

The notebook can be launched ...