Methods for Protein Structure Prediction and Its Application in Drug Design Using Hidden Markov Model
Journal Title: International journal of Emerging Trends in Science and Technology - Year 2016, Vol 3, Issue 3
Abstract
Drug design and drug innovation are critical importance in human fitness. To design a drug must successfully to the compound target from the substitute structures present in the organism. Many traditional methods are used to design a drug in the laboratory. Now day computational methods have become a major role in the drug design. A structure-based drug design is so complemented because structure-based drug design uses the 3-dimensional structure of protein. To design the candidate drug that is predicted to bind with high affinity and selectivity to the target. For prediction the new drug structure many methods are used like artificial neural networks (ANN), fuzzy neural networks and hidden Markov Model (HMM). All of these methods require the identification of peptide binding (chain of amino acid) cores for model building. HMM modeling has become more popular in the all area of applications from last several years because the models are very rich in mathematical structure and also theoretical structure. HMM also play an important role in trans-membrane region prediction and trans-membrane topology prediction in drug design. A computational base Hidden Markov Model became recently important among bioinformatics research and many software tools are based on them.
Authors and Affiliations
Nidhi Katiyar
Keywords
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