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Conserved Binding Sites01:49

Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Artificial Intelligence in Aptamer-Target Binding Prediction.

Zihao Chen1, Long Hu2, Bao-Ting Zhang1

  • 1School of Chinese Medicine, The Chinese University of Hong Kong, Hong Kong, China.

International Journal of Molecular Sciences
|April 3, 2021
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) can accelerate aptamer discovery by predicting binding affinity, overcoming limitations of traditional methods like SELEX. This review explores AI

Keywords:
SELEXaptamerartificial intelligencebindingdeep learningmachine learningstructure prediction

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Area of Science:

  • Biotechnology and Bioinformatics
  • Computational Chemistry and Molecular Modeling
  • Nucleic Acid Therapeutics and Diagnostics

Background:

  • Aptamers, short nucleic acid or XNA molecules, offer high affinity binding to targets, serving as alternatives to antibodies in diagnostics and therapeutics.
  • Current aptamer selection relies on Systematic Evolution of Ligands by Exponential Enrichment (SELEX), a process that is time-consuming and labor-intensive.
  • The need for faster, more efficient methods for aptamer identification and characterization is critical for advancing their clinical applications.

Purpose of the Study:

  • To review and discuss the advancements in Artificial Intelligence (AI) pipelines and methods for predicting aptamer-target binding affinity.
  • To explore the application of structure-based and machine/deep learning-based AI approaches for in silico aptamer selection and characterization.
  • To provide perspectives on future development and implementation strategies for AI-driven aptamer research.

Main Methods:

  • Review of structure-based methods including secondary/tertiary structure prediction, molecular docking, and molecular dynamics simulations for aptamer-target interactions.
  • Analysis and comparison of the accuracy of different computational methods for predicting aptamer structures.
  • Exploration of machine learning (ML) and deep learning (DL) models for predicting aptamer-target binding, including current limitations and future potential.

Main Results:

  • Structure-based methods are widely used in computational drug design and can predict aptamer-target binding.
  • Machine/deep learning models show promise for accurate prediction of ligand-target binding, offering a potential breakthrough for aptamer research.
  • Current research on ML/DL for aptamer-target binding prediction is limited, highlighting a significant area for future investigation.

Conclusions:

  • AI, encompassing structure-based and ML/DL methods, offers a powerful toolkit to accelerate aptamer discovery and characterization.
  • Integrating AI can significantly reduce the time and labor associated with traditional SELEX, facilitating high-throughput aptamer development.
  • Further research and development in ML/DL algorithms and implementation strategies are crucial for realizing the full potential of in silico aptamer selection.