Software
How to perform protein structure modeling using I-Tasser stand-alone tool?
I-Tasser stands for the iterative threading assembly refinement is a well-known tool for ab-initio structure modeling of proteins [1]. It uses secondary-structure enhanced profile-profile threading alignment (PPA) [2] and iterative structure assembly simulations using a threading assembly refinement program [3]. I-Tasser is used for ab-initio prediction when the similarity of a protein is quite low (<=30%). Mostly, the I-Tasser server [4] is used for this purpose, which can be easily accessed by registering with a valid institutional mail ID. In this article, we will learn how to predict a protein structure using the I-Tasser standalone version.
This article is being written by the demand of our esteemed readers and we are not going to get into more details of the algorithm applied by the I-Tasser (if you wish to know about the algorithm, drop me an email). The following sections will explain the downloading, installation, preparation, and submission of the query protein on a Linux platform. So, let’s get started!
Getting started
It is good to update and upgrade your Ubuntu system first. Open the terminal by pressing Ctrl+Alt+T altogether and type the following commands:
$ sudo apt-get update
$ sudo apt-get upgrade
Downloading the Suite package
For downloading the suite package you must have to be registered on I-Tasser website and have to request a password for non-commercial use of the software. After getting the password, you will be able to log in and download the latest version of the package available.
Installation
Open the terminal and enter the directory (let’s say Downloads) where you downloaded the package and unpack it by typing the following commands in the terminal.
$ cd Downloads
$ tar -xvjf I-TASSER5.1.tar.bz2
It will create a new folder named I-Tasser5.1 in the Downloads directory, enter the folder and you will find a Perl script named ‘download_lib.pl’. Run this script in the terminal from the same directory to download important libraries, it will take a while to finish.
$ cd I-Tasser5.1
$ ./download_lib.pl -P true -B true -N true
After downloading all the important libraries, a new folder will be generated named ‘libdir’ inside the I-Tasser5.1 directory. Now you need to prepare your input file as explained in the following section.
Preparing the input
- Create a directory, say example, in the I-Tasser5.1 folder which is required to save the query protein sequence and the output files.
- Let’s save this query protein sequence as ‘seq.fasta’ (the sequence must be in fasta format only and the residues should not be more than 1500).
- Save the same sequence file in the I-Tasser5.1 folder also.
Submitting the job
Now you can submit your query sequence for structure prediction by using the run I-Tasser.pl script present in the I-Tassermod folder. So, enter this folder and write the following commands:
$ cd I-Tasser5.1/I-Tassermod
$ sudo ./runI-Tasser.pl -libdir /home/username/Downloads/I-Tasser5.1/libdir -seqname protein -datadir /home/username/Downloads/I-Tasser5.1/example
-seqname is the name of your query protein file you saved in the I-Tassermod folder (i.e., protein).
-libdir is the folder for libraries which were downloaded earlier, write the full path to this folder.
-datadir is the folder where you have saved your query sequence (i.e., seq.fasta), write the full path to this folder.
There are many other options which you can specify for your job, e.g., to predict the gene ontology, EC number, ligand binding site, and so on. You can find these arguments in a file present in the I-Tasser5.1 folder.
-GO true -EC true -LBS true
After pressing enter, your job will be submitted. I-Tasser runs many simulations on the protein so it could take days to finish one job, in my case, it was finished in 7 days. After the job will be finished, you will be able to see the PDB file for the query protein which you can analyze with a molecular viewer such as PyMol [5].
For any query, you can comment below, or write me at [email protected].
References
- Roy, A., Kucukural, A., & Zhang, Y. (2010). I-TASSER: a unified platform for automated protein structure and function prediction. Nature protocols, 5(4), 725-738.
- Wu, S., & Zhang, Y. (2007). LOMETS: a local meta-threading-server for protein structure prediction. Nucleic acids research, 35(10), 3375-3382.
- Zhang, Y., & Skolnick, J. (2004). Automated structure prediction of weakly homologous proteins on a genomic scale. Proceedings of the National Academy of Sciences of the United States of America, 101(20), 7594-7599.
- Zhang, Y. (2008). I-TASSER server for protein 3D structure prediction. BMC bioinformatics, 9(1), 40.
- DeLano, W. L. (2002). The PyMOL molecular graphics system. http://pymol. org.
CMake is a widely used cross-platform build system that automates the process of compiling and linking software projects. In bioinformatics, CMake can be utilized to manage the build process of software tools and pipelines used for data analysis, algorithm implementation, and other computational tasks. However, managing the versions of CMake or upgrading it on Ubuntu (Linux) can be a trivial task for beginners. In this article, we provide methods for installing and upgrading CMake on Ubuntu.
Bioinformatics Programming
Free_Energy_Landscape-MD: Python package to create Free Energy Landscape using PCA from GROMACS.
In molecular dynamics (MD) simulations, a free energy landscape (FEL) serves as a crucial tool for understanding the behavior of molecules and biomolecules over time. It is difficult to understand and plot a meaningful FEL and then extract the time frames at which the plot shows minima. In this article, we introduce a new Python package (Free_Energy_Landscape-MD) to generate an FEL based on principal component analysis (PCA) from MD simulation done by GROMACS [1].
Virtual screening (VS) is a crucial aspect of bioinformatics. As you may already know, there are various tools available for this purpose, including both paid and freely accessible options such as Autodock Vina. Conducting virtual screening with Autodock Vina requires less effort than analyzing its results. However, the analysis process can be challenging due to the large number of output files generated. To address this, we offer a comprehensive Python package designed to automate the analysis of virtual screening results.
Bioinformatics Programming
vs_interaction_analysis.py: Python script to perform post-virtual screening analysis
Analyzing the results of virtual screening (VS) performed with Autodock Vina [1] can be challenging when done manually. In earlier instances, we supplied two scripts, namely vs_analysis.py [2,3] and vs_analysis_compounds.py [4]. This time, we have developed a new Python script to simplify the analysis of VS results.
Interactive Genome Viewer (IGV) is an interactive tool to visualize genomic data [1]. In this article, we are installing IGV and tools on Ubuntu desktop.
![[Tutorial] Installing VIAMD on Ubuntu (Linux).](https://img.bioinformaticsreview.com/uploads/2023/12/01184130/viamd.jpg)
Visual Interactive Analysis of Molecular Dynamics (VIAMD) is a tool that allows the interactive analysis of molecular dynamics simulations [1]. In this article, we are installing it on Ubuntu (Linux).
![[Tutorial] Performing docking using DockingPie plugin in PyMOL.](https://img.bioinformaticsreview.com/uploads/2023/10/25151930/dockingpie1.jpg)
DockingPie [1] is a PyMOL plugin to perform computational docking within PyMOL [2]. In this article, we will perform simple docking using DockingPie1.2.
DockingPie [1] is a plugin of PyMOL [2] made to fulfill the purpose of docking within the PyMOL interface. This plugin will allow you to dock using four different algorithms, namely, Vina, RxDock, SMINA, and ADFR. It will also allow you to perform flexible docking. Though the installation procedure is the same for all OSs, in this article, we are installing this plugin on Ubuntu (Linux).
This is a video tutorial for calculating binding pocket volume using the PyVol plugin [1] in Pymol [2].
If you need to generate the topology of molecules using their SMILES, a simple Python script is available.
![[Tutorial] Installing jdock on Ubuntu (Linux).](https://img.bioinformaticsreview.com/uploads/2023/05/09092952/jdock.jpg)
jdock is an extended version of idock [1]. It has the same features as the idock along with some bug fixes. However, the binary name and the GitHub repository names are changed. We are installing jdock on Ubuntu (Linux).
In bioinformatics, cmake is used to install multiple software including GROMACS, jdock, and so on. Here is a short tutorial on how to upgrade cmake on Ubuntu and get rid of the previous version. (more…)
GMXPBSA is a tool to calculate binding free energy [1]. It is compatible with Gromacs version 4.5 and later. In this article, we will install GMXPBSA version 2.1.2 on Ubuntu (Linux).
![[Tutorial] Installing Pyrx on Windows.](https://img.bioinformaticsreview.com/uploads/2023/04/13181032/pyrx-3.jpg)
Pyrx [1] is another virtual screening software that also offers to perform docking using Autodock Vina. In this article, we will install Pyrx on Windows. (more…)
MD Simulation
How to solve ‘Could NOT find CUDA: Found unsuitable version “10.1”‘ error during GROMACS installation?
Compiling GROMACS [1] with GPU can be trivial. Previously, we have provided a few articles on the same. In this article, we will solve an error frequently occurring during GROMACS installation.
Previously, we installed the Autodock suite [1] on Ubuntu. Visit this article for details. Now, let’s install it on MacOS.
Autodock suite is used for docking small molecules [1]. Recently, Autodock-GPU [2] is developed to accelerate the docking process. Its installation is described in this article. In this tutorial, we will install Autodock 4.2.6 on Ubuntu.
Discovery Studio (DS) Visualizer (from BIOVIA) is a visualization tool for viewing, sharing, and analyzing proteins [1]. Here are some uses and applications of DS Visualizer.
Remote homologs are similar protein structures that share similar functions, but there is no easily detectable sequence similarity in them. A new study has revealed that the protein folding information can be exploited from remote homologous structures. A new tool is developed to recognize such proteins and predict their structure and folding pathway. (more…)
Detecting viruses and bacteria in RNA-seq data with less false positive rate is a difficult task. A new tool is introduced to detect pathogens in RNA-seq data with high precision and recall known as Pathonoia [1].
SUMIT
August 10, 2023 at 9:41 am
sir could you please confirm where is the newly predicted is saved.
I open the outdir folder and there are 2 folders named model1 and ssite. the model1 folder has 3 sub folder named coach, cofactor and tmsite. inside coach folder there are several pdb files named CH_complex1.pdb, CH_complex2.pdb and so on and there is a file named CH_protein.pdb. I think CH_protein.pdb is the predicted protein stucture but i am not sure. could you please guide me.
Dr. Muniba Faiza
August 11, 2023 at 4:13 pm
Yes, CH_protein.pdb is the one you are looking for. All these structures are predicted but they are ranked.