Computation in BioInformatics. Группа авторов

provide rational basis for rapidly identifying novel synthetic drugs [4]. Information available regarding the 3D structure of proteins makes it easier to design molecule in such a way that they are capable of binding to the receptor site of a target protein with great affinity and specificity. Consequently, it significantly reduces time and cost necessary to develop drugs with higher potency, fewer side effects, and less toxicity than using the traditional trial-and-error approach.

      This field of computational study has also reduced the sacrifice of animals in research. Nowadays, the number of potential drug candidate molecules is increasing with the use of computational simulation and informatics methods. These methods help in reducing the number of animals sacrificed in drug discovery process [5]. By efficient use of existing knowledge, computational studies have also helped in reducing the number of animal experiments which is required in basic biological sciences [6].

      Bioinformatics tools are now appreciably used for developing novel drugs, leading to a new variety of research. Discovery and development of a new drug is generally very complex process consuming a whole lot of time and resources. So, bioinformatics techniques in designing tools are now broadly used so as to growth the efficiency of designing and developing a novel synthetic drug [4]. Drug discovery is the method of identifying, validating a disease target, followed by designing a chemical compound which can interact with that target resulting in inhibition of biological response which increases the rate of the disease. All these processes can be supported by various computational tools and methodology. Some of the factors which need to be observed during identification of the drug target are sequences of protein and nucleotide, mapping information, functional prediction, and data of protein and gene expression. Bioinformatics tools have helped in collecting the information of all these factors leading to the development of primary and secondary databases of nucleic acid sequences, protein sequences, and structures. Some of the commonly used databases include GenBank, SWISS-PROT, PDB, PIR, SCOP, and CATH. These databases have become indispensable tools to accumulate information regarding disease target. Databases like PubChem and ChemFaces provide structural and biological information of known drug like compounds which helps to identify the drug target for designing drug in research field [7]. These databases help in saving time, money, and efforts of the researchers.

      Designing of drugs using bioinformatics tools can be broadly classified into two main categories, viz.,

      1 a) Structure-based drug design (SBDD)

      2 b) Ligand-based drug design (LBDD)

      1 a) Structure-Based Drug Design (SBDD): Designing of drugs using SBDD method utilizes the 3D structure of the biological target which can be acquired via X-ray crystallography or NMR spectroscopy techniques [8]. Candidate drugs can be predicted on the basis of its binding affinity to the target using the structural information of the biological target. If the structure of the biological target/receptor is unavailable, then in that case, the structure can be predicted using homology modeling. It usually requires the amino acid sequence of the target protein, which when submitted constructs models that can be compared with the 3D structure of similar homologous protein (template). In order to know the interactions or bio-affinity for all tested compounds, molecular docking of each compound is performed into the binding site of the target, predicting the electrostatic fit between them.

      2 b) Ligand-Based Drug Design (LBDD): In this method of designing drug, the structural information of the small molecule/compound is known which binds to the target. The compounds/ligands which help in developing a Pharmacophore model possess all the important structural features necessary for binding to a target active site. Most common techniques used in this approach are Pharmacophore modeling and quantitative structure activity relationships (3D QSAR). These techniques are used in developing models with predictive ability that are suitable for lead identification and optimization [9]. Compound which are similar in structure also possess the same biological interaction with their target protein.

1.2 Steps Involved in Drug Designing

The flowchart in Figure 1.1 has been constructed to outline the phases that are involved in drug designing using in-silico approaches.

Figure 1.1 Flowchart of in silico approaches in drug designing.

      1.2.1 Identification of the Target Protein/Enzyme

      Before designing a novel synthetic drug, one needs to know all about the signaling pathways which lead to the disease. A novel drug needs to be designed in such a way that can interact with the target protein without interfering with normal metabolism. The most conventional method is to block the activity of the protein with a small molecule which can be the prospective drug. Virtual screenings of the target for compounds that can bind and inhibit the protein/enzyme are now performed using various bioinformatics softwares. Another strategy is to find other proteins which can regulate the activity of the target by binding and forming a complex, thereby controlling the disease.

       PDB: The Protein Data Bank (PDB) is the repository of information about the 3D structure structures of biological molecules which include nucleic acids and proteins (https://www.rcsb.org). The main function of this database is to provide 3D structural data of all the organisms which includes yeast, bacteria, plants, and other animals including humans. Techniques such as X-ray crystallography, electron microscopy, and nuclear magnetic resonance (NMR) spectroscopy help in extracting the information of the 3Dstructure of the macromolecules [10].

       Swiss Target Prediction: It is a web server which can accurately predict the targets of bioactive molecules based on similarity measures with known ligands [11]. In this web server, the predictions can be carried out in five different organisms, and mapping predictions by homology. The SwissTargetPrediction server is easily is accessible and is free of charge without any registration (www.swisstargetprediction.ch)

       SPPIDER: The SPPIDER protein interface recognition is a server that can be used to predict residues that needs to be in the putative protein interfaces by considering single protein chain with resolved 3D structure [12]. It can analyze protein-protein complex with given 3D structural information and can identify residues that are being in contact (http://sppider.cchmc.org/).

1.2.2 Detection of Molecular Site (Active Site) in the Target Protein

      If a drug that needs to bind to a particular on a particular protein or nucleotide is known, then it can be tailor made to bind at that site. This is often performed computationally using several different techniques. Traditionally, the primary way is to identify compounds which can interact with the specific molecular site responsible for the disease. A second method is to test the specific compound against various molecular sites known for the occurrence of the disease. However, if the 3D structure of the protein target is not available, then the method of molecular modeling needs to be performed in order to construct the structure for further analysis.

       CASTp: Computed Atlas of Surface Topography of proteins (CASTp) is an online resource which is used for locating, delineating and measuring of concave surface regions on the 3D structures of protein [13]. It includes pockets which are located on protein surfaces. This server can be used to study surface features and functional regions of proteins. The server is updated daily and can be accessed at http://cast.engr.uic.edu

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