Proteins are molecules that attain a three dimensional structure capable of carrying out diverse functions. Characterization of proteins means to classify or to understand certain key features such as shape,function,reactivity etc. Protein are complex biomolecules and characterization of the newly discovered protein is a challenging task given the nature of the protein,sheer bulk of protein data and limitations of the methodologies available.Analysis of proteins sequences and prediction of tertiary structure opens up avenues for advanced research in medicine and biology. Protein secondary structure prediction is a key step in predicting the tertiary structure of proteins.It is common in molecular biology to try to discover the function of a DNA or protein sequence by relating it to other sequence. The gap between the numbers of discovered proteins and structurally identified proteins is continuously diverging.It is estimated that by the end of 2015,there will be over 40 million protein sequences deposited in the protein databases. Experimental techniques alone cannot bridge the diverging gap between the number of proteins and the number of structurally and functionally identified proteins.The enormous data available makes it possible to apply computational intelligence and machine learning methods to analyze and interpret this data.
PROTEIN CHARACTERIZATION USING MACHINE LEARNING
- Principal Investigator: Dr. Maulika S Patel
- Co InvestigatorProf. Kinjal Joshi
- Duration2 Years
- Amount Rs .190000
- Funding AgencyGujarat Council on Science & Technology