Methods for Protein Structure Prediction and Its Application in Drug Design Using Hidden Markov Model

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.