Bioinformatics Homology Modelling For 3D Structure PredictionModeller925 Bioinformatics Tutorial

>> YOUR LINK HERE: ___ http://youtube.com/watch?v=2RRGxP_2iRc

Our Courses On Udemy: Please Join us • (1) Learn Bioinformatics From Scratch (Theory and Practical) • https://www.udemy.com/course/learn-bi... • (2) Learn Docking and MD Simulation From Scratch (Theory and Practical) • https://www.udemy.com/course/bioinfor... • (3) Learn Bulk-RNA Seq Data Analysis From Scratch (Theory and Practical) • https://www.udemy.com/course/rnaseq_d... • (4) Learn Statistics and Biostatistics From Scratch with R (Theory and Practical) • https://www.udemy.com/course/learn-st... • • Google Drive link to download Modeller scripts and other necessary files • https://drive.google.com/drive/folder... • Welcome to another bioinformatics tutorial. These bioinformatics tutorials are designed for beginners. So if you are new in this field then you do not need to worry. Just stick with these bioinformatics tutorials for beginners. • Modeller is considered to be a good software for the prediction of 3D protein structure currently. This software is based on homology modeling. Homology modeling was considered to be the gold standard when homologous structural templates are available. • Homology modeling, also known as comparative modeling of protein, refers to constructing an atomic-resolution model of the target protein from its amino acid sequence and an experimental three-dimensional structure of a related homologous protein. This video gives a tutorial on how to perform Homology Modeling using SwissModel. • In the past 50 years, there has been tremendous progress in the experimental determination of protein structure (three-dimensional protein structure), but this has not kept pace with the explosive growth of sequence information that results from massively parallel sequencing technology. We, therefore, know many more protein sequences than protein three-dimensional structures, and the gap is widening rather than diminishing. Yet, many proteins contain enough information in their amino acid sequence to determine their three-dimensional structure, thus opening the possibility of predicting three-dimensional structure from sequence. • Computational prediction of protein structures, which has been a long-standing challenge in molecular biology for more than 40 years, may be able to fill this gap. Many useful and accurate three-dimensional models have been computed from amino acid sequences by using the similarity of the protein sequence of interest to another protein whose three-dimensional structure is known, often called template or homology model building. However, correct de novo predictions from the sequence, when not a single structure in a protein family is known, have been hard to achieve.

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