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2.5 Role of Computation in Protein Structure Prediction
ОглавлениеThere are various critical and important processes and materials like personalized medicine, gene pathway, determination organs functioning, gene therapy, vaccine and drug development etc. Nowadays bioinformatics has been extensively used for the development of artificial intelligence. It also comprises softwares & programming for prediction of structure of protein however, it is still difficult to find the structure of a protein.
The two most powerful approaches are being used for determining protein structure .These are Nuclear Magnetic Resonance and X-ray crystallography but these are too expensive & time consuming which are disadvantages associated with these techniques.
Recent advancement for getting precise & fine protein structure a powerful technique has been introduced named cryo-electron microscope (Cryo-EM). This revolutionary technique predicts high resolution large scale molecular structures. The principle of this approach is mainly used in machine learning & artificial intelligence. For interpretation of cryo-EM maps, machine learning & artificial intelligence are extensively used [26–29].
Many liquid proteins cannot be crystallized. Getting Cryo-EM map crystallization of protein is mandatory. The solution of this problem can be done by AI which gives remedy for sequencing of protein without its crystallization.
Artificial intelligence has numerous programmes which are trained enough to give enormous information on atomic features of protein like: bond angles, bond length, type of bonds, physical-chemical properties, bond energy, amino acids interaction, potential energy etc. Artificial intelligence is used for image recognition [30, 31]. It helps in giving precise, broad and accurate thousands of protein structure [32, 33].
In this way these programmes suggest prediction model outputs which can be compared to the known crystal structures. There are several events organized for prediction model for protein.
Critical Assessment of Structure Prediction (CASP) is an annual gathering for comparison of protein structures by various models to assess the quality of the model and find the most accurate model making it the important milestone for protein structure prediction for multiple applications.
MULTICOM: in every two years all over the world researchers submit predicted protein structure while deep learning (Machine Learning) has been applied to make protein structure prediction with help of protein contact distance prediction. Professionals analyze the performance of these methods [34] and decide on the best models.