Sheikh Arslan Sehgal - Quick Guideline for Computational Drug Design
Here you can read online Sheikh Arslan Sehgal - Quick Guideline for Computational Drug Design full text of the book (entire story) in english for free. Download pdf and epub, get meaning, cover and reviews about this ebook. year: 2021, publisher: Bentham Science Publishers, genre: Home and family. Description of the work, (preface) as well as reviews are available. Best literature library LitArk.com created for fans of good reading and offers a wide selection of genres:
Romance novel
Science fiction
Adventure
Detective
Science
History
Home and family
Prose
Art
Politics
Computer
Non-fiction
Religion
Business
Children
Humor
Choose a favorite category and find really read worthwhile books. Enjoy immersion in the world of imagination, feel the emotions of the characters or learn something new for yourself, make an fascinating discovery.
- Book:Quick Guideline for Computational Drug Design
- Author:
- Publisher:Bentham Science Publishers
- Genre:
- Year:2021
- Rating:3 / 5
- Favourites:Add to favourites
- Your mark:
Quick Guideline for Computational Drug Design: summary, description and annotation
We offer to read an annotation, description, summary or preface (depends on what the author of the book "Quick Guideline for Computational Drug Design" wrote himself). If you haven't found the necessary information about the book — write in the comments, we will try to find it.
Sheikh Arslan Sehgal: author's other books
Who wrote Quick Guideline for Computational Drug Design? Find out the surname, the name of the author of the book and a list of all author's works by series.
Quick Guideline for Computational Drug Design — read online for free the complete book (whole text) full work
Below is the text of the book, divided by pages. System saving the place of the last page read, allows you to conveniently read the book "Quick Guideline for Computational Drug Design" online for free, without having to search again every time where you left off. Put a bookmark, and you can go to the page where you finished reading at any time.
Font size:
Interval:
Bookmark:
SA Sehgal,RA Tahir,M Waqas
Bioinformatics is an emerging field of modern biology that solves biological problems with the help of computational approaches by utilizing mathematical and statistical techniques. There are many available databases of protein and gene sequences. The protein sequence can be retrieved from UniProt and 3D structure prediction can be performed by utilizing homology modeling, threading, and ab initio. The predicted structures can be evaluated by utilizing numerous model evaluation tools.
This chapter briefly describes the basic concepts about Bioinformatics and Computer-Aided Drug Designing (CADD). The crucial step of computational drug design includes the selection of protein of interest. In the Fortune magazine, an article titled Next Industrial Revolution: Designing drugs by Computer at Merck was published on the 5th of October, 1981 (, Sliwoski et al., 2014).
Bioinformatics is an interdisciplinary field that develops different tools and methods to understand biological data and solve biological problems (Sehgal et al., 2018).
In Bioinformatics, numerous in silico approaches and computational tools are used to solve biological problems, 3D structural insights of protein, and DNA analyses. For modeling biological systems, static and dynamic approaches are considered the most fundamental approaches (Mitra et al., 2013).
It comprises the sequences of proteins, nucleic acids, and peptides. Static entities include interactional data networks of proteins, microarray data, and metabolites ().
Dynamic approaches comprise many structures of nucleic acids, ligands, and peptides. Various reaction fluxes and concentration of metabolites of system biology also comes in this category (Hendricks et al., 2012). Capturing cellular events by using multi-agent-based modeling techniques, consists of signaling reaction dynamics and transcription observed as dynamic techniques in bioinformatics ().
Protein structure prediction is considered the most significant approach of Bioinformatics. The protein structure is predicted through amino acid sequences. The structure prediction helps to analyze the fold recognition, secondary, tertiary, and quaternary structures from primary structures of the target proteins. The primary structure of proteins determines the coding sequence of the gene. The primary structure determines the native structure of a protein. The structural information of proteins are classified into primary, secondary, tertiary, and quaternary structures ().
Various Bioinformatics companies and many public institutions provide software, tools, servers, and databases for bioinformatics scale from simple command-line tools to more complex graphical programs and standalone services ().
Since the 1980s, many public and private institutes provide free and open-source tools and software. For analysis of rising biological problems, there is a need to develop new algorithms ().
EBI classified bioinformatics services into 3 categories, such as SSS (Sequence Search Services), MSA (Multiple Sequence Alignment), BSA (Biological Sequence Analysis). The service-oriented resources illustrate the applicability of web-based bioinformatics solutions from standalone tools and under a single common data format, integrative, standalone, or web-based interface, and an extensible effort in management system in bioinformatics ().
Virtual screening is an advanced tool of bioinformatics for the screening of a large database to select potential drug targets. Virtual screening has boosted the process of computer-aided drug designing by lowering the time complexity of the ligand selection step (Boarder et al., 2004).
Homology Modeling is an approach that constructs an atomic-resolution model of target protein from its sequence of amino acids by utilizing homologous proteins template structure to an experimental 3D structure. Homology modeling provides help in identifying the resemblance between the query structure and one of the known protein structures. Similar residues of the query sequence with template sequence can be determined by using the sequence alignment tool. The protein structures in homologous sequences are more conserved than sequences having less than 20% sequence identity. The 3D structures of a target protein are produced by template structure and sequence alignment. ().
The homology modeling procedure divide into consecutive four steps: 1) template selection, 2) target-template alignment, 3) model construction, 4) model assessment ().
The most critical step in homology modeling is to identify a suitable template. By using database search techniques the template identification of pair-wise sequence alignment was obtained from FASTA and BLAST ().
In this homology modeling step, alignment of target protein-based against selected template protein. The resulting template protein obtained by X-ray crystallography or NMR approaches were experimentally determined 3D structures of proteins. For protein sequence alignment, the most highly cited tools are BLAST and ClustalW ().
The information of target sequence alignment against template protein sequence alignment utilized in generating 3D structure of the target protein, thus each atom in protein PDB format, represented as Cartesian coordinates ().
In homology modeling, the evidence of predicted protein structure is the most important step. The assessment of predicted protein structure provides evidence of the accuracy of a structure. To validate the experimentally determined design structure various consideration tools are reported such as PDBsum generate, ERRAT, RAMPAGE, and Verify 3D. Finally, the selection of a predicted structure is mainly based on physicochemical properties, protein expression, and available data of proteins ().
The structure accuracy depends on the structural similarity of the target and template sequences. The primary source of error at high sequence similarity in homology modeling from which model is based obtained from the choice of template and templates. While in sequence alignment, errors in >40% sequence similarity exhibit which further inhibits the production of models having high-quality. In sequence alignment, serious errors in <30% sequence identity can cause misprediction of folds. For irrefutable results between target and template similarity should be greater than 65% (). However, the similarity should be greater than 80% for reliable structures.
The predicted structure is used in various bioinformatics analyses including protein-protein interaction, molecular docking, protein-protein docking, and functional annotation of identified genes from the genome of an organism (). With low accuracies of protein, a homology model can be predicted as closely related proteins have many loops in the surface of a protein.
Font size:
Interval:
Bookmark:
Similar books «Quick Guideline for Computational Drug Design»
Look at similar books to Quick Guideline for Computational Drug Design. We have selected literature similar in name and meaning in the hope of providing readers with more options to find new, interesting, not yet read works.
Discussion, reviews of the book Quick Guideline for Computational Drug Design and just readers' own opinions. Leave your comments, write what you think about the work, its meaning or the main characters. Specify what exactly you liked and what you didn't like, and why you think so.