Bioinformatics has brought revolution in the field of genomics. Massive DNA sequencing projects have evolved and added in the growth of the science of bioinformatics. The ultimate goal of bioinformatics is to uncover the wealth of biological information hidden in the mass of sequence, structure, literature and other biological data and obtains a clearer insight into the fundamental biology of organisms and to use this information to enhance the standard of life for mankind. The significant role of bioinformatics is to collect information on the links between the disease, genetics and DNA or proteins and to search protein or DNA sequence database.

Through the integration of biological data, scientists hope to infer information about the structure and functions of uncharacterised gene products. It helps us in understanding the metabolism of the cell and hence the mechanism of the disease. It accelerates the development of diagnostics and therapeutics. Bioinformatics is the central hub that unites several disciplines and methodologies. On the scientific side of the hub, bioinformatics methods are used extensively in molecular biology, genomics, proteomics, metabolomics, transcriptomics, CADD research, etc.

Computational drug discovery is a well established scientific discipline in pharmaceutical research and critical in the development of new treatments. It has created many opportunities to gear up and rationalise the multidisciplinary drug discovery process and provide new approaches to the design of drugs. This supports in the development of integrative bioinformatics and chemo-informatics methods that can be translated to support modern drug discovery by designing new drugs and to improve human health.

The structural stereochemistry was first considered in 1860. Van't Hoff discovered tetrahedral carbon in 1874. Barton introduced conformational analysis in 1953. The year 1958 saw the 3D structure of myoglobin with the help of X-ray crystallography. The computer models began in 1970. Today, molecular modelling is done via computers. Computer modelling allows access to molecular information useful for predicting properties and utilities. Modelling usually involve computation and graphics. It includes prediction and visualisation of shape and properties, comparison of these shapes and properties, prediction of molecular interactions and reactions, investigation of unstable molecules and modelling of dynamics systems.
Proteins are the polymeric macromolecules where the sequence of amino acids giving rise to the specific structure and function. The relationship between sequence, structure and functions can be broken down into three important principles, i.e., macromolecular structure, non-covalent interactions and specific sequence of monomeric subunits. Peptide and proteins are polymers constructed from sequences of 20 amino acids. Proteins consist of a polypeptide backbone with attached side chains. Each type of protein differs in its sequence and number of amino acids; therefore, it is the sequence of the chemically different side chains that makes each protein distinct. The two ends of a polypeptide chain are chemically different. The amino acid sequence of a protein is always presented in the nitrogen to carbon direction. Proteins fold in three dimensions. Protein structure is organized hierarchically from primary to quaternary structure. The structure of the protein is directly related to the protein's functionality, probably even determining it. The 3D structure of the protein helps in the field of medicine, agriculture and industry. The details of the protein structure are revealed through X-ray crystallography, NMR spectroscopy and Cryo-EM resulting in the modelling of theoretical prediction of 3D structure of proteins.

Protein data bank consists of more than 40,000 protein structure files. Files contain text annotation and numerical 3D atomic coordinates. The 3D structures can be visualised and manipulated with imaging programs like, Chime and Rasmol. The three dimensional molecular structure is one of the foundations of structure based rational drug design. It offers some of the best hopes for putting an end to several diseases. It relies on the knowledge of the structure of the target protein or knowledge about the available potential compounds.
The functions of many proteins are to bind some target molecule or set of target molecules and perform some action. The understanding of protein-ligand interactions is the fundamental basis of medicinal chemistry. With only few exceptions, drugs interact with macromolecular targets, most often with specific binding sites of membrane bound or nuclear receptors, enzymes, transporters or ion channels.

Molecular docking involves computational filtering of a large body of molecules. It identify those binders that have a high probability of activity in the biological test system. Docking also explores the interaction that occurs when a ligand attaches itself to the docking site of another molecule known as a receptor. By understanding the interplay between a ligand and its receptor, researchers may be able to design drugs that would block the docking site of the receptor and thus disable the functional properties of the protein. Finally, this innovation would lead to the cure of some most dangerous diseases. Most docking programs basically use the same principles to calculate the bound structure looking for the sites of interaction between ligand and a target.

It is classified into the following types-
a) Fast shape matching (DOCK, EuDOCK).
b) Construction algorithm (FlexX, Hammerhead).
c) Tabu search (PRO_LEAD, SFDOCK).
d) Genetic algorithm (GOLD, AUTODOCK).
e) Monte carlo simulations (AFFINITY, MCDOCK).
f) Distance geometry (DOCKIT).

Biochemical databases have progressed over the past 15 years from being a mere repository of the compounds synthesised within an organization, to being a powerful research tool for discovering new lead compounds. The growth of these databases in drug discovery research fuelled a growth in commercial software for biochemical database management and publicly available biochemical databases.

Pharmacophoric or 3D substructure search involves a set of atoms or groups that is combined with specific 3D constraints such as distances and angles. This process is generally much slower than a 2D substructure search as it requires the examination of all the three coordinates for atoms of the structure to compute 3D constraints. Pharmacophoric searching can provide an indication of whether a set of structure can bind to a receptor or enzyme. 3D database pharmacophoric queries are generalised types rather than specific chemical elements. Commonly used types are hydrogen bond acceptors, hydrogen bond receptors, acids, bases, aromatic rings and hydrophobic groups.
This is the bitter truth of the pharmaceutical companies that over 90% of drugs entering into the clinical trials fail to make it to the market. The average cost of bringing a new drug into the market is estimated at $770 million approximately. The application of bioinformatics in drug discovery and development is expected to reduce the annual cost of developing a new drug by 33% and the time taken for drug discovery by 30%. This will accelerate the key steps of drug designing processes.

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Geetanjali Murari
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