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Structural proteins are a category of proteins responsible for functions ranging from cell shape and movement to providing support to major structures such as bones, cartilage, hair, and muscles. This group includes proteins such as collagen, actin, myosin, and keratin.
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Deep learning in structural bioinformatics: current applications and future perspectives.

Niranjan Kumar1, Rakesh Srivastava2

  • 1School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India.

Briefings in Bioinformatics
|May 3, 2024
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Summary
This summary is machine-generated.

Deep learning (DL) is revolutionizing structural bioinformatics by enabling analysis of big data in biology and healthcare. This review details DL applications, from basic to advanced neural networks, for understanding biomolecular structures.

Keywords:
big datacomputational drug discoverydeep learningneural networkstructural bioinformatics

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Artificial Intelligence

Background:

  • The exponential growth of biological data necessitates advanced computational methods.
  • Deep learning (DL) offers powerful tools for analyzing complex biological information.
  • Structural bioinformatics is a key area benefiting from these advancements.

Purpose of the Study:

  • To review the impact of deep learning on structural bioinformatics.
  • To provide insights into DL applications in analyzing biomolecular structures.
  • To discuss challenges and future directions of DL in this field.

Main Methods:

  • Review of existing literature on deep learning in structural bioinformatics.
  • Explanation of various deep learning models (e.g., neural networks, convolutional networks, transformer networks).
  • Demonstration of DL applications in understanding protein structures and other biomolecular data.

Main Results:

  • Deep learning is transforming structural bioinformatics, driven by big data and computational resources.
  • DL models, including advanced neural networks, show significant success in analyzing biomolecular structures.
  • DL is becoming integral to healthcare and biological data analysis.

Conclusions:

  • Deep learning is a pivotal technology in the ongoing revolution in structural bioinformatics.
  • Continued development in DL models will further enhance our understanding of biomolecular structures.
  • The integration of DL promises to revolutionize analytical processes in biology and medicine.