Author: Sansa Dutta

Student Contributors: Moni Kumari and Soumita Konar, B.Sc 2^nd Year, Biochemistry

The interplay between humans and the surrounding microbes is inevitable and this is undeniably so for our foreseeable future. To combat the infection, numerous antimicrobial agents e.g. antibiotics for bacterial infection are available and have been routinely used over the decades. This, in turn, contributes to the steady rise of antibiotics drug resistance leading to the requirement of novel antibiotics. There is always a huge demand for the development of potential drugs and the identification of new targets in a short time frame.

            The world’s leading pharmaceutical industries are widely using computational tools for the design and discovery of therapeutic products for various life-threatening diseases. For designing new antibiotics, computer-aided drug design (CADD) along with experimental techniques can be used to elucidate the mechanism of drug resistance, to search for new antibiotic targets, and to design novel antibiotics for both known and new targets. Notably, CADD methods can produce an atomic level structure-activity relationship (SAR) thereby facilitating the drug design process by minimizing time and costs. For instance, researchers use bioinformatics approaches to screen various databases computationally for identifying potential targets.

            The hunt for new lead molecules against existing targets remains ceaseless. The computational approaches have been successfully used in various studies. Screening in silico database Chang et al. identified a new set of non-β-lactam antibiotics, the oxadiazoles, which are found to be potent inhibitors of penicillin-binding protein from methicillin-resistant Staphylococcus aureus, the cause of most infections in hospitals. The ligand-based drug design (LBDD) has been employed to explore third-generation ketolide antibiotic telithromycin, thereby successfully addressing the bacterial resistance problem associated with that particular class of antibiotics.

            The basic CADD workflow combines wet-lab methods to explore novel lead compounds and direct iterative ligand optimization as well. The process starts with recognizing a putative target e.g ligand binding site that leads to antimicrobial activity. In the structure-based drug design (SBDD), the three-dimensional structure of the target can be identified by X-ray crystallography or NMR or using homology modeling. The LBDD is a helpful approach when the crystal structure of the desired target is unavailable.

            The information regarding the modification of the lead compound to improve its efficacy is extracted from the structure-activity relationship. The results from CADD methods are then used to design compounds that are subjected to chemical synthesis and biological assays. The information gathered from the experiments is used further to develop the structure-activity relationship thereby increasing the potency of the compounds in terms of activity, absorption, disposition, metabolism, and excretion (ADME) considerations. Notably, CADD methods are updated regularly as researchers are continually implementing new CADD techniques with higher levels of accuracy and speed.

            CADD methods are mathematical tools for determining the efficacy of potential drug candidates as implemented in numerous programs. The examples of such fundamental tools for CADD commonly used in the laboratory are given below.

• Commonly used MD simulation codes include CHARMM, AMBER, NAMD, GROMACS, and OpenMM.
• For SBDD, the crystal structure of the protein, RNA, or other macromolecules can be obtained from the Protein Data Bank (PDB). Alternatively, a three-dimensional structure may be built using homology modeling methods with a program such as MODELLER or an on-line web server such as SWISS-MODEL.
• The program that identifies potential binding sites includes FINDSITE and ConCavity.
• Virtual database screening techniques are generally used to screen huge in silico compound databases to identify potential binders for a query target. Examples of docking software commonly used are DOCK and AutoDock as well as AutoDock Vina. The program Pharmer, uses 3D pharmacophores for database screening.
• The in silico database of drug-like compounds is an essential component of CADD ligand identification based on virtual screening. An easily accessible database of lead molecules for virtual screening is ZINC.
• Commercially available CADD software packages include Discovery Studio, OpenEye, Schrodinger, and MOE.

            In modern drug discovery, the CADD has strong implications since it utilizes experimentally and foretold information in designing new potent lead molecules. So far the success stories of drug molecules generated through molecular modeling are concerned, the structure-based drug design strategies have already contributed to the introduction of some drug compounds into clinical trials and for drug approval. Numerous online courses on CADD are available. Particularly for the students of Biochemistry looking for professional development, expanded skills for CV, the courses give basic knowledge on drug design approaches and methods. No wonder, there is the immense opportunity of jobs in this sector of Molecular Modeling & Drug Designing related to pharmaceutical companies.

References:

1. Schneider G, Fechner U. Computer-based de novo design of drug-like molecules. Nat Rev Drug Discov. 2005; 4:649–663.
2. O’Daniel PI, Peng Z, Pi H, Testero SA, Ding D, Spink E, Leemans E, Boudreau MA, Yamaguchi T, Schroeder VA, Wolter WR, Llarrull LI, Song W, Lastochkin E, Kumarasiri M, Antunes NT, Espahbodi M, Lichtenwalter K, Suckow MA, Vakulenko S, Mobashery S, Chang M. Discovery of a New Class of Non-β-lactam Inhibitors of Penicillin-Binding Proteins with Gram-PositiveAntibacterial Activity. J Am Chem Soc. 2014; 136:3664–3672.
3. Velvadapu V, Paul T, Wagh B, Klepacki D, Guvench O, MacKerell A, Andrade RB. Desmethyl Macrolides: Synthesis and Evaluation of 4, 8, 10-Tridesmethyl Telithromycin. ACS Med Chem Lett. 2011; 2:68–72.