The objectives of the computer based researches is to put together the information on biological problems and make predictions utilizing the data generated from genomics and proteomics via computational methods. Several computational methods were in place for the development and application of drug designing studies.
• Protein-ligand interactions
• Virtual screening
• QSAR
• Prediction of bio-molecular function from structure
An exemplified case study – Cyclooxygenase-2:
Is there a need to study on better inhibitors than those that are already present for a particular protein?
COX-2 is expressed after inflammatory stimuli and releases metabolites that are used to induce pain and inflammation. During normal physiology, COX-2 levels are undetectable in most tissues whereas during periods of acute and chronic inflammation, the level of COX-2 is significantly higher. NSAIDs (Non-steroidal anti-inflammatory drugs) exhibit their effect through inhibition of cyclooxygenase (COX) enzymes by blocking the synthesis of prostaglandins from arachidonic acid [1, 2]. Conventional non-steroidal anti-inflammatory drugs (NSAIDs) are profoundly used in the treatment of wide variety of inflammatory conditions including osteoarthritis and rheumatoid arthritis.
Several classes of compounds having selective COX-2 inhibitory activity have been reported in the literature for example, diaryl heterocylics as oxazoles [3], thiophens [4], pyrazoles [5], imidazoles [6], and those common classical agents modified to have selective COX-2 inhibitory activity as esters and amides of indomethacin [7], meclofenamic acid [8]. The classical NSAIDs produce their adverse effects via inhibition of the COX-1 isoform, hence many investigations have been directed to find compounds able to act as selective COX-2 inhibitors such as 6COX bound SC-558, celecoxib, rofecoxib, valdecoxib etc. [9] and more recently, nitroxy substituted 1,5-diarylimidazoles [10], phenylazobenzenesulfonamides [11], respectively. However, evidence suggests that adverse reactions such as gastro-intestinal irritation or ulceration and renal liabilities are associated with prolonged use of COX-2 selective inhibitors [12].
These inhibitors are also known to suppress synthesis of prostacyclin, a potent vasodilator, gastroprotectant, and platelet inhibitor, via inhibition of endothelial COX-2. Also, COX-2 selective inhibitors intrinsically lack anti-thrombotic activity, and some cardiovascular liabilities have been associated preclinically with them [13]. Thus, there is still a need for novel, selective, and potent COX-2 inhibitors with an improved profile compared to current COX-2 inhibitors.
Traditional synthesis of a series of new compounds utilizing combinatorial chemistry and high-throughput screening can be carried out at high expense and also are time consuming; whereas on the other hand, screening small molecule databases for novel compounds represents an alternative process.
Docking various ligands to the protein of interest followed by scoring to determine the affinity of binding and to reveal the strength of interaction has become increasingly important in the context of drug discovery. Screening large databases of compounds can provide a feasible, alternative technique against conventional high-throughput screening; however, they strongly depend on the speed and accuracy of the docking algorithm.
Moreover the output of structure activity relationships can be considered as an input on the other hand for studying further, such as–
• In search of similar non-tested compounds in literature and predicting activities by applying QSAR equation on these compounds.
• New ligand design by incorporating the influential descriptor properties.
• Property based search - can be an input to virtual screening strategies.
Further, lead optimization methodologies can also be employed to propose influential properties on the ligand which in turn will guide the wet lab experiments by minimizing the number of trails and cost. While working with QSARs it is necessary to follow certain stringent conditions that were laid over time by many researchers, For example: Golbraikh et al, [14] so that the proposed models would be robust [15]. Further structural and functional genomics are important aspects in drug designing when structure of the target protein is not known.
Hence, the basic work-flow of the proposed research would use many elements related to drug design, a typical flow-chart is given below:
Constraints
Although there are many software applications for docking and protein-ligand interactions, they are not always on par with the experimental results. These studies also require predefined structures of the receptor protein and ligand however that can be overcome by considering the previous experimental output and also by the use of ligand databases. In particular, synthesis of the ligand is a constraint whereas that can be overcome by ligand designing.
Technical challenges and uncertainties
Developments of software, incorporating far more accurate methodologies along with the skills required to use the software for achieving our objectives remain as technical challenges. For example, scoring functions of different software use different strategies and at times orientation of the ligand may be reversed to give a better score; however, this cannot be accepted as best score as the probability of robustness is very less in such case. These problems can be minimized by careful visual Inspection of the complexes after the docking runs and comparing with the experimental results, which might greatly help in determining the best orientations of ligands. Besides considering the consensus scoring method can bring about the accuracy to the research. Moreover, with the addition to computational analysis, wet lab experiments can be conducted in order to provide a proof-of-concept and to validate the computer-based predictions experimentally.
References
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