DL COMP BIOTECH

You'll need the MMTSB toolkit for this tutorial. This is not included in AMBER but is freely available via GitHub. We have discussed how to install it on Linux (Centos 7) in our earlier blog post:  mmtsb-toolset-installation . The following instructions are also provided in AMBER tutorials ( https://ambermd.org/tutorials/basic/tutorial3/section6.htm , which includes scripts and commands for clustering. However, we've provided the quick tour of the steps we took to execute it in our study. In this case, the K-means clustering algorithm is applied, which traverses the pathway in search of clusters of identical structures. It then generates centroids for each cluster and calculates the root mean square deviation (RMSD) of each structure in the trajectory with respect to each cluster. 1. Prepare the directories mkdir clustering cd clustering mkdir PDBfit 2. Use cpptraj to extract multiple PDB's from the trajectory in the clustering folder #first save this script: "extrac...

MMTSB stands for  Multiscale Modeling Tools for Structural Biology.  It is a novel collection of utilities and programming libraries that enable the simulation of proteins and nucleic acids to use advanced sampling and multiscale modelling techniques. For classical all-atom simulations, the tool set interacts with the current molecular modelling packages CHARMM and Amber, and with MONSSTER for lattice-based low-resolution conformational sampling. The tool set is intended for structural biology applications that need the prediction, refining, and/or extended conformational sampling of protein or nucleic acid structures. It also incorporates an ensemble computing capability that enables the control and execution of modelling tasks on a broad range of conformations. For Further details the links are given below. Installation instructions for centos 7: 1. The package is available on GITHUB: https://github.com/mmtsb/toolset #From here download the zip file from the "code" option. ...

PCA analysis through CPPTRAJ module of AmberTools: > source $AMBERHOME/amber.sh > $AMBERHOME/bin/cpptraj > parm comp_solv.prmtop > trajin comp_150ns.nc > center > autoimage > rms first @CA mass > average crdset avg_set > createcrd traj_set > run > crdaction traj_set rms ref avg_set @CA > crdaction traj_set matrix covar :116-229@CA name pro_covar out covmat.dat #writes covariance matrix based on the selection of residues > runanalysis diagmatrix pro_covar out evecs.dat vecs 100 name myevecs nmwiz nmwizvecs 100 nmwizfile porcupine.nmd nmwizmask :116-229@CA #writes the information about 100 principal components (first 100 PCs) for that particular selection,  produces .nmd file that can be used to generate porcupine plots in normal mode wizard plugin of VMD. > runanalysis modes eigenval name myevecs out evalfrac.dat  gives output about the eigen values, fraction of contribution, cumulative contributions of  eigen vectors(or PCs) (in ...

 The basic instructions remain same as  FEL 3D plots .  These plots are published in  Interplay among Structural Stability, Plasticity, and Energetics Determined by Conformational Attuning of Flexible Loops in PD-1 Fel2d.py ## FEL using plot_surface import numpy as np from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt import random from matplotlib import cm from scipy.interpolate import griddata from scipy.interpolate import Rbf from numpy import linspace #from matplotlib.ticker import LinearLocator, FormatStrFormatter import matplotlib from scipy import interpolate import matplotlib.mlab as ml fig = plt.figure(figsize=None, dpi=300, facecolor=None, edgecolor=None, linewidth=0.0, frameon=None, subplotpars=None, tight_layout=None) ax = fig.add_subplot(111) ### input file with x,y,z coordinates x,y,z = np.loadtxt('5wt9gibbs.txt').T  X,Y = np.unique(x),np.unique(y) xi = linspace(min(X),max(X),len(X)) yi = linspace(min(Y),max(Y),len(Y)) xi,yi = ...

  # To plot FEL in 3D use this python script and run as python2.7 fel3d.py. Here the file uses gibbs.txt. therefore before running the script make sure u have the gibbs.txt in the same folder of this script or else provide the location of the script.  I have attached some images from my research article using the same script for 3D FEL plot These plots are published in  Interplay among Structural Stability, Plasticity, and Energetics Determined by Conformational Attuning of Flexible Loops in PD-1 The 2D FEL scripts are also mentioned in the next blog:  Python script to plot 2D FEL Another point to consider is: when u r plotting FEL plots for multiple systems, make sure your color bar/gradient should be same in all the plots. So u can  change the values in vmax,  ax.set_zlim(0, 18),  ticks=range(0, 21, 2)) in the script below.  Like in my case the highest value was 18 in gibbs free energy (the z axis) in all the axis. therefore i mentioned 18 there...

ParmEd supports reading and writing to a broad variety of file types, including Amber topology and coordinate files, CHARMM PSF, parameter, topology, and coordinate files, and Tinker parameter, topology, and coordinate files. (Ref: https://github.com/ParmEd/ParmEd) installing ParmED 1. download from https://github.com/ParmEd/ParmEd.git  or type this command in the terminal: wget https://github.com/ParmEd/ParmEd/archive/refs/heads/master.zip 2. cd parmedED-master 3. python2.7 setup.py install 4. python setup.py install --prefix=$AMBERHOME 5. source $AMBERHOME/amber.sh python2.7 >>>import parmed as pmd >>> amber=pmd.load_file('pro_solv.prmtop', 'pro_solv.inpcrd') >>> amber.save('prosolv.top') >>> amber.save('prosolv.gro') vmd >>> save .trr trajectory file  OR  trajout .xtc file in cpptraj It can also convert various files to other formats such as: 1. convert GROMACS topology to AMBER format 2. convert AMBER to...

Before beginning any homology modelling, verify that the templates in Blastp (protein blast) are suitable for constructing the model for your query sequence using the pdb as the search set.  If you obtain greater than 40% sequence similarity, you can proceed with homology modelling.  If you have Schrodinger, you can use the PRIME homology modelling module in this suite. Otherwise, I-TASSER, SWISS-MODEL, or Phyre2 servers may be used.  Additionally, Modeller can be utilized. It is a self-contained programme. If the sequence similarity of your protein is really low, then try to search with  DELTA-BLAST  (Domain Enhanced Lookup Time Accelerated BLAST) option in BLASTp . It will yield better homology detection. Or you can proceed with threading-based or ab-initio modelling.  Additionally, secondary structure prediction can be performed prior to modelling. This can be accomplished with PSIPRED or JPRED, for example. There are two forms of modelling: single templ...

#Web servers for calculation of RMSD values between a ligand poses.  Please note: One pose should be a reference pose and another should be a query pose. PLDbench:  The RMSD value between two molecules can be calculated using PLDbench. There are four modules in this webserver. Single: This module's functionality is limited to calculating RMSD for the 57 complexes utilized in the benchmarking study. Users must submit the docked pose of a ligand that corresponds to one of the 57 PDB-IDs offered in the drop down option in this module. It accepts three file formats: '.pdb', '.mol2', and '.sdf'. Users can choose the type of RMSD they want to compute. Standard heavy-atom RMSD, Hungarian (symmetry-corrected) heavy-atom RMSD, and Minimum-distance heavy-atom RMSD can all be calculated using this module. As a result, the Hungarian (symmetry-corrected) heavy-atom RMSD value is returned by default.  Link for the server:https://webs.iiitd.edu.in/raghava/pldbench/single.p...

Blog#5 ChemProp can be used to estimate the properties of molecules by utilising a Message Passing Neural Network (MPNN). To make predictions, an MPNN must first be trained on a dataset of molecules with known property values. Once trained, the MPNN can be used to predict similar properties in any new molecule. Here is the link:http://chemprop.csail.mit.edu Till now it has provided models for predicting that molecules can inhibit the growth of bacteria ( antibiotic property), 3Cl pro/mpro of sars-cov. The SARS balanced model is for more exhaustive prediction. AMU SARS-CoV-2 in vitro is for the  estimation of probability that molecule with inhibit the virus replication in vitro)