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Computational Methods Used in Prediction of Protein Structure

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Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

This work represents the prediction of protein structures through computational approaches. The advanced tools for computational method are majorly classified into comparative modelling, fold recognition and ab initio techniques. These approaches help to predict the protein side chain, protein structural sequential alignment, protein folding as well as three-dimensional protein structure. These tools determine the molecular structure of proteins from electron microscopy, spectroscopy, nuclear magnetic resonance (NMR), X-ray diffraction measurements, etc. These mentioned computational approaches are also utilized for designing 3D structure-based drug which is used in health care. In this chapter, we are presenting the different computational methods for prediction of the protein structure and their use in drug discovery. Optimization of each method is discussed here including their major significances along with the challenges.

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Author information

Authors and Affiliations

  1. Department of Biotechnology, Maulana Abul Kalam Azad University of Technology, Kolkata, West Bengal, 700064, India

    Poulami Majumder

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  1. Poulami Majumder
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Editors and Affiliations

  1. Department of Informatics, Computer Science & Engineering, National Institute of Technical Teachers Training and Research, Chandigarh, Chandigarh, India

    K. G. Srinivasa

  2. Department of Information Science and Engineering, Ramaiah Institute of Technology, Bengaluru, Karnataka, India

    G. M. Siddesh

  3. Department of Information Science and Engineering, Ramaiah Institute of Technology, Bengaluru, Karnataka, India

    S. R. Manisekhar

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Majumder, P. (2020). Computational Methods Used in Prediction of Protein Structure. In: Srinivasa, K., Siddesh, G., Manisekhar, S. (eds) Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-2445-5_8

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