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AlphaFold is a deep learning model developed by the DeepMind team at Google for predicting the three-dimensional structure of proteins. In case, you are new to AlphaFold,  here  is a quick introduction to the program. AlphaFold uses a neural network to predict the three-dimensional structure of a protein from its amino acid sequence. The neural network is trained on a large dataset of known protein structures and amino acid sequences, using a technique called supervised learning. The model uses a combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to learn the complex relationships between protein sequences and structures. The output of the AlphaFold model is a prediction of the three-dimensional structure of the protein, represented as a set of coordinates for each atom in the protein. These predictions are evaluated using a range of metrics, including the predicted local distance difference test (PLDDT), predicted torsion angle metric (PTM), and i

PyMOL is a powerful molecular visualization software that also allows users to edit molecular structures. Here are some ways to edit structures with PyMOL: If you are new to Pymol, I would recommend practicing  here . I can also recommend you slightly expert-level tutorial  here . 1. Add Atoms and Residues: To add atoms or residues to a structure, use the "Builder" function. First, select the residue or atom you want to add to. Then, go to "Wizard" > "Builder" and choose the atom or residue to add. 2. Delete Atoms and Residues: To delete atoms or residues from a structure, use the "Delete" function. First, select the atoms or residues you want to delete. Then, go to "Actions" > "Delete" and confirm the deletion. 3. Mutate Residues: To mutate a residue to another amino acid, use the "Mutagenesis" function. First, select the residue you want to mutate. Then, go to "Wizard" > "Mutagenesis"

Pymol is a powerful and versatile molecular visualization software that allows users to create high-quality 3D representations of molecular structures. Here are some specialized tricky and magical codes of Pymol. If you are new to Pymol, it is highly advised to practice  Basic Pymol Tutorial . In case you are looking to learn structure editing in Pymol, please follow  here . 1. Tricks Displaying secondary structure The following code displays the secondary structure of a protein using a color code where alpha helices are red and beta sheets are yellow. show cartoon set cartoon_ring_mode, 3 set cartoon_ladder_mode, 1 set cartoon_fancy_helices, 1 color red, ss h color yellow, ss s 2. Aligning structures The following code aligns two structures based on their alpha carbons and displays the aligned structures in different colors. align 1abc and chain A, 2xyz and chain B color blue, 1abc color green, 2xyz 3. Creating a surface representation The following code creates a surface representati

PyMol is an important package for analyzing and generating publication-quality or high-quality images. After reading this tutorial, you will be able to generate simple ribbon diagrams with labels if required. Generating specific surface representation to your system and the last section is all about how to generate high-quality figures of protein and ligands. The current document is a basic tutorial and those who are already familiar with Pymol are advised to follow  here.   Before going to the following sections, you are required to load your PDB file, and change your display color to the one intended. Display --- Background How to read the below part, if you are new? A sentence that starts with "#" is just an explanation of the code present in the very next line. Pymol offers you two parts in the GUI interface, and you can use the below codes in any interface. 1. Tutorial on generating a ribbon diagram and adding labels: # Load a protein structure load protein_name.pdb # Sh

There are several bioinformatics tools that can be used to prepare a dataset of homologous protein sequences. Here are some commonly used tools:  Basic steps while using the below tools. BLAST: Basic Local Alignment Search Tool (BLAST) is a widely used sequence similarity search tool that can be used to find homologous sequences in a database. It can be run locally or online through the NCBI BLAST website. HMMER: HMMER is a software suite for protein sequence analysis that can be used to search sequence databases for homologous proteins using hidden Markov models (HMMs). It can be used to detect remote homologues that may not be detected by BLAST. CD-HIT: CD-HIT is a clustering tool that can be used to reduce redundancy in a set of homologous sequences. It clusters similar sequences together and removes redundant sequences based on a user-defined sequence identity threshold. MUSCLE: MUSCLE is a multiple-sequence alignment tool that can be used to align a set of homologous sequences.

Preparing a list of homologues involves identifying and collecting protein sequences that are evolutionarily related and share a common ancestor. Here are some general steps that can be followed to prepare a list of homologues: Determine the protein of interest: Identify the protein of interest for which you want to find homologues. This protein should be well-characterized and have a known function.  Tools to Generate Homologues Dataset Perform a sequence search: Use a sequence search tool, such as BLAST or PSI-BLAST, to search for sequences that are similar to the protein of interest. These tools compare the protein sequence against a database of known protein sequences and return a list of hits that have significant sequence similarity. Filter the hits: The sequence search may return a large number of hits, many of which may be irrelevant or redundant. To filter the hits, you can set a threshold for sequence similarities, such as a minimum percentage identity or a minimum e-value.

When you have to perform molecular docking, the selection of an appropriate scoring function becomes necessary and that depends on the specific research question and the availability of computational resources. Both empirical and physics-based scoring functions have their own strengths and weaknesses, and the choice of the scoring function will depend on the accuracy and speed required for the particular research question.  What are scoring functions? Empirical scoring functions are faster and less computationally expensive than physics-based scoring functions. They are based on a statistical analysis of known protein-ligand complexes and use a set of parameters to describe the interactions between the protein and the ligand. Empirical scoring functions have been shown to accurately predict the binding affinities of a wide range of ligands to diverse proteins. Physics-based scoring functions, on the other hand, are based on physical principles such as force fields and quantum mechanics

Molecular docking is a computational method used to predict the binding mode and affinity of a ligand to a protein or other molecular target. One of the key components of molecular docking is the scoring function, which is used to evaluate the binding affinity between the protein and the ligand. In this blog post, we will discuss the different types of scoring functions used in molecular docking programs.  Scoring functions are mathematical algorithms that evaluate the interactions between the protein and the ligand and calculate a score that reflects the binding affinity. The score is used to rank different ligands and predict their binding affinity to the protein. The most commonly used scoring functions can be divided into two categories: empirical and physics-based scoring functions. Empirical scoring functions are based on a statistical analysis of known protein-ligand complexes. These functions use a set of parameters that describe the interactions between the protein and the li

In the field of bioinformatics, sequence alignment is a fundamental task for comparing and analyzing nucleotide or protein sequences. One of the key measures used in sequence alignment is the Hamming distance and Levenshtein distance, which are used to quantify the difference between two sequences. In this blog, we will discuss how Hamming and Levenshtein distances are used in the sequence alignment and their applications. Hamming Distance in Sequence Alignment: Hamming distance is used in the sequence alignment to measure the difference between two sequences of equal length. It is defined as the number of positions at which the corresponding symbols in the two sequences are different. For example, the Hamming distance between the DNA sequences "ATGCTAG" and "AGGCTAG" is 1, because the second symbol is different in the two sequences. In sequence alignment, Hamming distance is used to identify conserved regions in multiple sequence alignments. Conserved regions are r

 BioEdit is a popular freeware biological sequence alignment editor and analysis program that is widely used in the field of molecular biology. It allows users to create, edit, and analyze nucleotide and protein sequences. This tutorial will provide a step-by-step guide on how to use BioEdit for sequence alignment and analysis. BioEdit software is a useful utility in  Sequence Alignment and Research Installation and Launching: The first step is to download the BioEdit program from the official website and install it on your computer. Install BioEdit  After installation, launch the program by clicking on the BioEdit icon on your desktop. Sequence Import: Once the program is launched, the next step is to import your sequence data into BioEdit. You can do this by selecting “File” from the menu bar and then choosing “Open” from the dropdown list. This will bring up a file dialog box, where you can navigate to the folder where your sequence files are stored and select the file you want to w