Bioinformatics; Learn Docking & Mol Dynamics Simulation

Explore basics of proteins, their amino acids and peptide bonds, and how primary structures fold into secondary and tertiary forms stabilized by hydrogen bonds, van der Waals, and hydrophobic interactions.

Learn three experimental techniques to determine the three dimensional structure of a protein: x ray crystallography, NMR spectroscopy, and cryo electron microscopy, and download structures from the protein data bank.

learn how to use the protein data bank (PDB) to find and download protein structures, understand the PDB ID, deposition and release dates, and download options for offline visualization.

Visualize a protein’s three-dimensional structure on your local machine using pymol; install the educational version, open a pdb, and switch among wire, sticks, cartoon, ribbon, sphere, and surface views.

Predict a protein's 3d structure when no pdb entry exists using homology-based, threading-based, and Alphafold methods, with tutorials on modular and Alphafold CoLab for docking and dynamics.

Apply homology modeling with Modeller to predict a protein’s three-dimensional structure by installing Modeller, selecting templates from the protein data bank, and generating and optimizing multiple models.

Explore AlphaFold 3 and its 5000-token daily limit for predicting protein structures, including DNA, RNA, ions, ligands, and glycan modifications, and download CIF files for multimeric proteins.

Learn molecular dynamics simulations from scratch to understand biomolecule behavior, protein thermal stability, and how functional groups improve drug binding through the steps involved.

Explore the basic theory of molecular dynamics simulation, using initial positions and velocities to observe atomic motion over time and quantify movement in molecular systems.

Learn how atomic interactions depend on distance and energy, identify the minimum energy distance, and derive force from interaction energy to compute acceleration via Newton's third law.

Apply Newton's second law to compute an atom's acceleration from the force and mass, using the relation F = ma with the force derived from interaction energy.

Compute an atom’s position, velocity, and interaction energy, then derive acceleration from force and mass to predict the next position in the molecular dynamics cycle toward a nanosecond.

Prepare protein structures for molecular dynamics by selecting accurate 3D structures from the protein data bank or Alphafold predictions, and fix missing residues, side chains, and non-protein atoms.

Prepare the topology file for molecular dynamics by choosing a suitable force field, which defines atom interactions, energy, and forces for proteins, DNA, and lipids; correct selection prevents wrong simulations.

Solvation and ionization involve placing a protein in a cubical box, solvating with water to reflect cytoplasmic conditions, and adding ions to neutralize the system for realistic simulations.

Perform energy minimization to obtain the native, low-energy state of a protein. Use steepest descent, with Newton-Raphson as an alternative, to minimize potential energy in docking and mol dynamics simulations.

Equilibration relaxes macromolecules and surrounding solvent to a uniform temperature and pressure before the production phase. Production runs last from nanoseconds to microseconds to reflect protein half-life and computational resources.

Explore the Linux environment for molecular dynamics simulations and bioinformatics, compare Windows, iOS, and Linux, and learn why Gromacs relies on Linux.

Explore Linux as an open source, free operating system with strong security, rarely needing reboots, privacy, and broad hardware compatibility, and see why Ubuntu offers both command-line and graphical interfaces.

Explore the Linux terminal as the input interface for commands, distinguishing it from kernel, system libraries, shell, and utilities, with Ubuntu terminal cues like the green prompt and dollar sign.

Learn essential Linux commands to support molecular dynamics work, including using pwd and cd, understanding absolute paths, listing subfolders, and installing Gromacs with sudo apt-get.

Install linux in windows using windows subsystem for linux and ubuntu, enabling gromacs for molecular dynamics in bioinformatics.

Learn core bioinformatics gromacs modules for molecular dynamics, including pdb2 for topology with a force field and solvate for solvation. Discover group_map, j9, and mdr1 roles and essential input/output flags.

Create an Ubuntu folder for molecular dynamics simulations using the Dolphin file manager and terminal.

Learn to run molecular dynamics simulations in Gromacs by copying ready-to-use Linux commands, with one naming change: set the protein name to underscore structure for analysis of ligand protein complexes.

Learn how to create a protein topology file in Gromacs, selecting a force field and water model, and inspect the topology's atom, residue, and charge information for molecular dynamics simulations.

Learn to solvate a protein by first creating the box around it and centering the protein, then adding water with gmx solvate and updating the topology.

Ionize the system to achieve neutrality for molecular dynamics simulations using Gromacs, creating an atomic level input file from coordinate and topology files and applying ions.

Perform energy minimization in Gromacs to relax the protein, using the minimum.mdp with coordinate and topology files to generate a .tpr, and verify negative potential energy and acceptable max force.

Equilibrate the system by calibrating solvent and ions to a uniform temperature, then apply pressure to reach the proper density, using a two-phase NVT and NPT process.

Observe temperature equilibration results. Confirm the system runs molecular dynamics at about 32 nanoseconds per day, and verify a 300 kelvin calibration using GROMACS energy from np.edr.

Calibrate pressure to achieve a homogeneous density by using a np.mdp file with gromacs, generating ztpr output, then run with verbose mode to define output names.

Calibrate a protein molecular dynamics run in GROMACS, estimating a speed of about 31.109 nanoseconds per day and confirming equilibration by density plots showing 1021.84 near 1000.

Open the md.mdp file, adjust n steps to set the molecular dynamics duration in nanoseconds, and change ref_t to set the simulation temperature in kelvin.

Perform molecular dynamics simulations with Gromacs using the mdp, coordinate (dt.gro), topology (tuple.top), and cpt files to produce the tpr output, then run md with verbose output.

Analyze molecular dynamics data using rmsd, rmsf, radius of gyration, solvent accessible surface area, and hydrogen bonds to assess protein stability and flexibility, then center the protein in the box.

Learn to analyze molecular dynamics data using commands to center the protein in the box, compute radius of gyration, hydrogen bonds, and solvent accessible surface area, copy into ubuntu terminal.

Center the protein in the system with Gromacs trajectory con, using Wtpr and dot inputs, to prepare for RMSD, RMSF, solvent accessible surface area, and hydrogen bond analysis.

Learn to calculate rmsd values for a protein during a molecular dynamics run using gromacs with input files and specific flags, select backbone, and generate svg outputs in nanoseconds.

Calculate root mean square fluctuation (RMSF) values with Gromacs using the RMSF module, feeding md_0_1.tpr and md_0_1_center-exc as inputs, outputting msf.xvg to reveal flexible protein regions.

Use the gyrate module in Gromacs to calculate the radius of gyration for the whole protein during a molecular dynamics simulation, producing an xvg output file.

Learn to calculate the solvent accessible surface area of a protein during molecular dynamics using gromacs, by supplying the tpr and md_0_1_center.cc files, and saving results to area.xvg.

Visualize a protein's molecular dynamics trajectory in PyMOL by loading the solvation_ions.gro trajectory, playing the animation to see conformational changes, and optionally removing water to focus on the protein.

Explore Romancer, a Windows tool to plot md simulation data from Gromacs, handling xvg files and visualizing rmsd, rmsf, radius of gyration, hydrogen bonds, and energies.

Explore molecular docking in bioinformatics, predicting how a ligand or peptide binds to a protein using sampling and scoring algorithms to identify poses with the lowest binding energy.

Explore how docking evaluates drug conformations against a target protein, using a sampling algorithm to identify the pose with the lowest binding energy as the most accurate.

Examine six sampling algorithms used in docking programs, including shape matching, incremental construction, multiple copies, simultaneous search, ludi Monte Carlo, and the genetic algorithm.

Explore the shape complementarity algorithm for docking, modeling geometrical overlap between ligand and receptor to generate multiple poses at surface pockets, and the incremental fragmentation approach that builds ligands stepwise.

Explore fragment-based de novo ligand design and modification, selecting top binding groups to improve protein binding. Compare simultaneous search with ludi, focusing on de novo design via force sampling.

Explore Monte Carlo and genetic algorithms for docking, generating conformations via bond rotation, scoring with energy values, and selecting top poses with shape complementary methods like patch dock and Autodock.

Learn how scoring functions calculate binding energies of ligand poses from non-bonded interactions to select the best conformation using force field, empirical, knowledge-based, and consensus algorithms.

Explore three docking protocol types: rigid docking, constrained docking, and flexible docking, examining their rigid and flexible components, costs, and drawbacks. The lecture suggests constrained docking as a balanced choice.

Master steps of molecular docking by obtaining or predicting a protein’s 3D structure, preparing it by removing non protein components and adding missing atoms and hydrogens, and starting ligand preparation.

Prepare the ligand’s 3d structure from databases or by drawing; choose blind or specific docking, predict the active site, and select the best conformation by minimum binding energy.

Discover the installation of MGL Tools, Vina, AutoDock4, and Autogrid, and see practical docking demonstrations using these tools. Follow step-by-step download and installation guidance for Windows, Mac, and Linux.

Download and prepare the protein 3D structure for docking by removing water and non-protein components, repairing missing atoms, adding polar hydrogens and charges, and saving as a pdb receptor.

Learn to download three-dimensional ligand structures from the zinc 15 database, using dopamine as an example, with similarity filtering and commercial availability for docking and virtual screening.

Learn to convert a ligand from SDF to PDB using Open Babel GUI, then prepare ligand and receptor for docking with AutoDock and Vina, noting the 32 rotatable-bond limit.

Learn how to predict ligand binding site residues using blind and specific docking, and use the couch server to predict active sites from 3D structures or sequences.

Set the grid box around the selected ligand binding site residues to guide docking, then adjust its dimensions and center before choosing between blind or specific docking.

Prepare receptor and ligand PDB files, create a vina configuration with center, grid, and exhaustiveness, and run vina to obtain docking energies such as -7.7 kcal/mol.

Visualize the nine Vina docking poses, load receptor and ligand, view interactions with residues glycine 95, aspartate 58, and arginine 67, and save a transparent background image.

Learn to perform ligand protein docking with Autodock4 by configuring grid parameters, performing rigid docking with a genetic algorithm, and reading the docking log for best conformations.

Visualize Autodock four docking results by loading docking log file and macromolecule, then adjust representations to show the cartoon receptor and pink ball-and-stick ligand, and analyze hydrogen bonds across conformations.

Learn to perform automated AutoDock Vina docking on SwissDock by uploading ligand SMILES or PDB, preparing ligand and protein, setting the grid box, and reviewing docking results and affinities.

Learn how treating the protein as rigid in conventional docking can mislead results, and explore coupling molecular dynamics simulations with docking to study ligand–protein complexes.

Explore how ligand-protein molecular dynamics largely follows standard md steps, while requiring two separate topologies and force fields merged into a single protein–ligand complex for analysis.

Explore practical steps to prepare a ligand for molecular dynamics simulations of a protein–ligand complex, using cytochrome c and warfarin, selecting the best docked pose for molecular dynamics with Abogado.

Prepare ligand and protein topology in linux using mdp files and Roadmap, and generate ligand parameter file with perl sorter and python script using charms force field from macro lab.

Navigate to the working directory, unzip the Charm 36 force field, and generate the protein topology with Gromacs pdb2gmx from receptor.pdb to receptor.gro, using Charm 36 and the water model.

Merge protein and ligand topology files to create the complex topology, generate the ligand gro with gromacs editconf, then update topper.top with the ligand parameter file and name.

Restrict the ligand at the binding site during equilibration by creating a non-hydrogen ligand index and generating a position restraint topology with Gromacs. Integrate the restraint into the topology file.

Set the thermal state and enable temperature coupling to prevent excessive kinetic energy, generate an index file from the grow file, and select protein and ligand before proceeding to equilibration.

Proceed to production phase by using the roadmap module to create the MD_0_10.ptprj file from MDP and index, then run the MD run module for a 10 ns simulation.

Center the protein in the molecular dynamics trajectory and analyze the ligand-protein complex. Compute rmsd, rmsf, hydrogen bonds, radius of gyration, and binding energy, then plot results.

Highlight the role of virtual screening in rapidly accelerating drug discovery, offering a robust alternative to conventional methods, and recognizing its global use.

Bioinformatics virtual screening uses computational methods to test chemical libraries against a target protein, identifying binders with higher hit rates while considering admet and exploring drug repurposing with fda-approved compounds.

Master virtual screening by preparing a protein 3D structure, removing non-protein atoms, repairing missing atoms, adding hydrogens and charges, then docking ligands with vina and analyzing energy values.

Install openbabel and autodockvina on ubuntu, update and upgrade the linux system, verify installations, prepare receptor in windows, and prepare ligands in linux for virtual screening with vina.

Prepare the protein receptor for docking by removing water, checking for missing atoms, repairing the structure, adding hydrogens and charges, then save as receptor.pdb in the docking two directory.

The protein databank now includes about 1 million predicted structures from Alpha four and Rosetta four alongside experimental data, enabling search and download of predicted models.

Visualize docking data with LigPlot+ to identify the ligand binding site and the interactions with the receptor, including hydrophobic contacts and hydrogen bonds, and explore the 3D visualization in PyMOL.