Jekyll 2024-09-12T00:00:00+00:00 https://training.galaxyproject.org/training-material/topics/computational-chemistry/feed.xml Computational chemistry Recently added tutorials, slides, FAQs, and events in the computational-chemistry topic https://training.galaxyproject.org/training-material/assets/images/GTN-60px.png 🎥 Recording of Data management in Medicinal Chemistry https://training.galaxyproject.org/training-material/topics/computational-chemistry/tutorials/med-chem-data/recordings/#tutorial-recording-12-september-2024 2024-09-12T00:00:00+00:00 A 48M1S long recording is now available. Julia Jakiela https://training.galaxyproject.org/training-material/hall-of-fame/wee-snufkin/ 🛠️ Data management in Medicinal Chemistry workflow https://training.galaxyproject.org/training-material/topics/computational-chemistry/tutorials/med-chem-data/workflows/Data-management-in-Medicinal-Chemistry-workflow.html 2024-01-08T09:55:32+00:00 Workflow includes data import, format conversion and some basic medicinal chemistry analyses. Julia Jakiela https://training.galaxyproject.org/training-material/hall-of-fame/wee-snufkin/ 📚 Data management in Medicinal Chemistry https://training.galaxyproject.org/training-material/topics/computational-chemistry/tutorials/med-chem-data/tutorial.html 2024-01-08T09:55:32+00:00 The development of medicinal chemistry is advancing very rapidly. Big pharmaceutical companies, research institutes and universities are working on ground-breaking solutions to help patients combat all kinds of diseases. During that development process, tons of data are generated – not only from the lab environment but also from clinical trials. Given that the discovery of more potent, safer and cheaper drugs is the ultimate goal of all research bodies, we should all focus on making the data we gather FAIR: Findable, Accessible, Interoperable, and Reusable to push the boundaries of drug development even further. Julia Jakiela https://training.galaxyproject.org/training-material/hall-of-fame/wee-snufkin/ Katarzyna Kamieniecka https://training.galaxyproject.org/training-material/hall-of-fame/kkamieniecka/ Krzysztof Poterlowicz https://training.galaxyproject.org/training-material/hall-of-fame/poterlowicz-lab/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Simon Bray https://training.galaxyproject.org/training-material/hall-of-fame/simonbray/ Khaled Jum'ah https://training.galaxyproject.org/training-material/hall-of-fame/khaled196/ 📰 User story: Where Galaxy meets medicinal chemistry https://training.galaxyproject.org/training-material/news/2023/12/18/medchem-user.html 2023-12-18T00:00:00+00:00 As a medicinal chemistry student, I undertook a semester project on natural products isolation and derivatisation. One of the compounds my group was working on was piperine, extracted from black pepper. Inspired by literature findings, we found out that the derivatives of piperine can inhibit monoamine oxidase-B and thus can be possibly used in Parkinson’s disease. As a novelty element in our project, we came up with new structures of derivatives that could act as inhibitors. Julia Jakiela https://training.galaxyproject.org/training-material/hall-of-fame/wee-snufkin/ 🛠️ Analysis using MDAnalysis https://training.galaxyproject.org/training-material/topics/computational-chemistry/tutorials/analysis-md-simulations/workflows/advanced_workflow.html 2022-03-07T09:36:39+00:00 Analysis of MD trajectories using MDAnalysis, generating a Ramachandran plot and various timeseries. Chris Barnett 🛠️ Zauberkugel https://training.galaxyproject.org/training-material/topics/computational-chemistry/tutorials/zauberkugel/workflows/main_workflow.html 2022-02-15T13:56:40+00:00 Protein target prediction of a bioactive ligand with Align-it and ePharmaLib Aurélien F. A. Moumbock https://training.galaxyproject.org/training-material/hall-of-fame/aurelienmoumbock/ Albert-Ludwigs-Universität Freiburg 📚 Protein target prediction of a bioactive ligand with Align-it and ePharmaLib https://training.galaxyproject.org/training-material/topics/computational-chemistry/tutorials/zauberkugel/tutorial.html 2022-02-15T13:56:40+00:00 Historically, the pharmacophore concept was formulated in 1909 by the German physician and Nobel prize laureate Paul Ehrlich (Ehrlich 1909). According to the International Union of Pure and Applied Chemistry (IUPAC), a pharmacophore is defined as “an ensemble of steric and electronic features that is necessary to ensure the optimal supramolecular interactions with a specific biological target and to trigger (or block) its biological response” (Wermuth et al. 1998). Starting from the cocrystal structure of a non-covalent protein–ligand complex (e.g. Figure 1), pharmacophore perception involves the extraction of the key molecular features of the bioactive ligand at the protein–ligand contact interface into a single model (Moumbock et al. 2019). These pharmacophoric features mainly include: H-bond acceptor (HACC or A), H-bond donor (HDON or D), lipophilic group (LIPO or H), negative center (NEGC or N), positive center (POSC or P), and aromatic ring (AROM or R) moieties. Moreover, receptor-based excluded spheres (EXCL) can be added in order to mimic spatial constraints of the binding pocket (Figure 2). Once a pharmacophore model has been generated, a query can be performed either in a forward manner, using several ligands to search for novel putative hits of a given target, or in a reverse manner, by screening a single ligand against multiple pharmacophore models in search of putative protein targets (Steindl et al. 2006). Aurélien F. A. Moumbock https://training.galaxyproject.org/training-material/hall-of-fame/aurelienmoumbock/ Simon Bray https://training.galaxyproject.org/training-material/hall-of-fame/simonbray/ Simon Bray https://training.galaxyproject.org/training-material/hall-of-fame/simonbray/ Aurélien F. A. Moumbock https://training.galaxyproject.org/training-material/hall-of-fame/aurelienmoumbock/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ 🎥 Recording of High Throughput Molecular Dynamics and Analysis https://training.galaxyproject.org/training-material/topics/computational-chemistry/tutorials/htmd-analysis/recordings/#tutorial-recording-15-february-2021 2021-02-15T00:00:00+00:00 A 1H40M long recording is now available. Simon Bray https://training.galaxyproject.org/training-material/hall-of-fame/simonbray/ Christopher Barnett https://training.galaxyproject.org/training-material/hall-of-fame/chrisbarnettster/ 🛠️ MD protein-ligand workflow (from PDB structure) https://training.galaxyproject.org/training-material/topics/computational-chemistry/tutorials/htmd-analysis/workflows/main_workflow.html 2020-05-20T17:27:02+00:00 High Throughput Molecular Dynamics and Analysis 🛠️ Workflow constructed from history 'Hsp90-MDAnalysis' https://training.galaxyproject.org/training-material/topics/computational-chemistry/tutorials/htmd-analysis/workflows/analysis_workflow.html 2020-05-20T17:27:02+00:00 High Throughput Molecular Dynamics and Analysis 📚 High Throughput Molecular Dynamics and Analysis https://training.galaxyproject.org/training-material/topics/computational-chemistry/tutorials/htmd-analysis/tutorial.html 2020-05-20T17:27:02+00:00 This tutorial provides an introduction to using high-throughput molecular dynamics to study protein-ligand interaction, as applied to the N-terminal domain of Hsp90 (heat shock protein 90). Simon Bray https://training.galaxyproject.org/training-material/hall-of-fame/simonbray/ Tharindu Senapathi https://training.galaxyproject.org/training-material/hall-of-fame/tsenapathi/ Christopher Barnett https://training.galaxyproject.org/training-material/hall-of-fame/chrisbarnettster/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Nadia Goué https://training.galaxyproject.org/training-material/hall-of-fame/nagoue/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Simon Bray https://training.galaxyproject.org/training-material/hall-of-fame/simonbray/ Simon Gladman https://training.galaxyproject.org/training-material/hall-of-fame/slugger70/ Christopher Barnett https://training.galaxyproject.org/training-material/hall-of-fame/chrisbarnettster/ Tharindu Senapathi https://training.galaxyproject.org/training-material/hall-of-fame/tsenapathi/ Stéphanie Robin https://training.galaxyproject.org/training-material/hall-of-fame/stephanierobin/ Anthony Bretaudeau https://training.galaxyproject.org/training-material/hall-of-fame/abretaud/ Martin Čech https://training.galaxyproject.org/training-material/hall-of-fame/martenson/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Armin Dadras https://training.galaxyproject.org/training-material/hall-of-fame/dadrasarmin/ 🛠️ Virtual screening of the SARS-CoV-2 main protease with rDock and pose scoring https://training.galaxyproject.org/training-material/topics/computational-chemistry/tutorials/covid19-docking/workflows/workflow.html 2020-03-27T13:26:13+00:00 Virtual screening of the SARS-CoV-2 main protease with rDock and pose scoring 📚 Virtual screening of the SARS-CoV-2 main protease with rxDock and pose scoring https://training.galaxyproject.org/training-material/topics/computational-chemistry/tutorials/covid19-docking/tutorial.html 2020-03-27T13:26:13+00:00 This tutorial provides a companion to the work performed in March 2020 by InformaticsMatters, the Diamond Light Source, and the European Galaxy Team to perform virtual screening on candidate ligands for the SARS-CoV-2 main protease (MPro). This work is described in our dedicated site. Simon Bray https://training.galaxyproject.org/training-material/hall-of-fame/simonbray/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Simon Bray https://training.galaxyproject.org/training-material/hall-of-fame/simonbray/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ 🛠️ CTB Workflow https://training.galaxyproject.org/training-material/topics/computational-chemistry/tutorials/cheminformatics/workflows/main_workflow.html 2019-10-19T00:16:01+00:00 CTB Unittest workflow 📚 Protein-ligand docking https://training.galaxyproject.org/training-material/topics/computational-chemistry/tutorials/cheminformatics/tutorial.html 2019-10-19T00:16:01+00:00 Cheminformatics is the use of computational techniques and information about molecules to solve problems in chemistry. This involves a number of steps: retrieving data on chemical compounds, sorting data for properties which are of interest, and extracting new information. This tutorial will provide a brief overview of all of these, centered around protein-ligand docking, a molecular modelling technique. The purpose of protein-ligand docking is to find the optimal binding between a small molecule (ligand) and a protein. It is generally applied to the drug discovery and development process with the aim of finding a potential drug candidate. First, a target protein is identified. This protein is usually linked to a disease and is known to bind small molecules. Second, a ‘library’ of possible ligands is assembled. Ligands are small molecules that bind to a protein and may interfere with protein function. Each of the compounds in the library is then ‘docked’ into the protein to find the optimal binding position and energy. Simon Bray https://training.galaxyproject.org/training-material/hall-of-fame/simonbray/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Simon Bray https://training.galaxyproject.org/training-material/hall-of-fame/simonbray/ Christopher Barnett https://training.galaxyproject.org/training-material/hall-of-fame/chrisbarnettster/ Swathi Nataraj https://training.galaxyproject.org/training-material/hall-of-fame/Swathi266/ 🛠️ NAMD MD From CHARMM GUI https://training.galaxyproject.org/training-material/topics/computational-chemistry/tutorials/md-simulation-namd/workflows/main_workflow_charmmgui.html 2019-06-03T07:41:49+00:00 Running molecular dynamics simulations using NAMD 🛠️ MD NAMD https://training.galaxyproject.org/training-material/topics/computational-chemistry/tutorials/md-simulation-namd/workflows/main_workflow.html 2019-06-03T07:41:49+00:00 Running molecular dynamics simulations using NAMD 🛠️ GROMACS Training Workflow https://training.galaxyproject.org/training-material/topics/computational-chemistry/tutorials/md-simulation-gromacs/workflows/main_workflow.html 2019-06-03T07:41:49+00:00 Running molecular dynamics simulations using GROMACS 🛠️ Simple Analysis https://training.galaxyproject.org/training-material/topics/computational-chemistry/tutorials/analysis-md-simulations/workflows/main_workflow.html 2019-06-03T07:41:49+00:00 Analysis of molecular dynamics simulations 📚 Setting up molecular systems https://training.galaxyproject.org/training-material/topics/computational-chemistry/tutorials/setting-up-molecular-systems/tutorial.html 2019-06-03T07:41:49+00:00 In this tutorial, we’ll cover the basics of molecular modelling by setting up a protein in complex with a ligand and uploading the structure to Galaxy. This tutorial will make use of CHARMM-GUI. Please note that the follow-up to this tutorial (located in Running molecular dynamics simulations using NAMD) requires access to NAMD Galaxy tools, which can be accessed using the Docker container but are currently not available on any public Galaxy server. Christopher Barnett https://training.galaxyproject.org/training-material/hall-of-fame/chrisbarnettster/ Simon Bray https://training.galaxyproject.org/training-material/hall-of-fame/simonbray/ Nadia Goué https://training.galaxyproject.org/training-material/hall-of-fame/nagoue/ Simon Bray https://training.galaxyproject.org/training-material/hall-of-fame/simonbray/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Bérénice Batut https://training.galaxyproject.org/training-material/hall-of-fame/bebatut/ Christopher Barnett https://training.galaxyproject.org/training-material/hall-of-fame/chrisbarnettster/ Gildas Le Corguillé https://training.galaxyproject.org/training-material/hall-of-fame/lecorguille/ 📚 Running molecular dynamics simulations using GROMACS https://training.galaxyproject.org/training-material/topics/computational-chemistry/tutorials/md-simulation-gromacs/tutorial.html 2019-06-03T07:41:49+00:00 Molecular dynamics (MD) is a method to simulate molecular motion by iterative application of Newton’s laws of motion. It is often applied to large biomolecules such as proteins or nucleic acids. Simon Bray https://training.galaxyproject.org/training-material/hall-of-fame/simonbray/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Simon Bray https://training.galaxyproject.org/training-material/hall-of-fame/simonbray/ Bérénice Batut https://training.galaxyproject.org/training-material/hall-of-fame/bebatut/ Christopher Barnett https://training.galaxyproject.org/training-material/hall-of-fame/chrisbarnettster/ Gildas Le Corguillé https://training.galaxyproject.org/training-material/hall-of-fame/lecorguille/ Catherine Bromhead https://training.galaxyproject.org/training-material/hall-of-fame/cat-bro/ 📚 Analysis of molecular dynamics simulations https://training.galaxyproject.org/training-material/topics/computational-chemistry/tutorials/analysis-md-simulations/tutorial.html 2019-06-03T07:41:49+00:00 Molecular dynamics simulations return highly complex data. The Cartesian positions of each atom of the system (thousands or even millions) are recorded at every time step of the trajectory; this may again be thousands to millions of steps in length. Therefore, some kind of further analysis is needed to extract useful information from the data. Christopher Barnett https://training.galaxyproject.org/training-material/hall-of-fame/chrisbarnettster/ Tharindu Senapathi https://training.galaxyproject.org/training-material/hall-of-fame/tsenapathi/ Simon Bray https://training.galaxyproject.org/training-material/hall-of-fame/simonbray/ Nadia Goué https://training.galaxyproject.org/training-material/hall-of-fame/nagoue/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Simon Bray https://training.galaxyproject.org/training-material/hall-of-fame/simonbray/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Bérénice Batut https://training.galaxyproject.org/training-material/hall-of-fame/bebatut/ Christopher Barnett https://training.galaxyproject.org/training-material/hall-of-fame/chrisbarnettster/ Gildas Le Corguillé https://training.galaxyproject.org/training-material/hall-of-fame/lecorguille/