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Protocol for the prediction, interpretation, and mutation evaluation of post-translational modification using MIND-S.

Yu Yan1, Dean Wang2, Ruiqi Xin3

  • 1NIH BRIDGE2AI Center at UCLA & NHLBI Integrated Cardiovascular Data Science Training Program at UCLA, Suite 1-609, MRL Building, 675 Charles E. Young Dr. South, Los Angeles, CA 90095-1760, USA; Medical Informatics Program, University of California at Los Angeles (UCLA), Los Angeles, CA 90095, USA; Department of Physiology, UCLA School of Medicine, Suite 1-609, MRL Building, 675 Charles E. Young Dr., Los Angeles, CA 90095-1760, USA.

STAR Protocols
|November 18, 2023
PubMed
Summary

This study introduces MIND-S, a deep-learning tool for predicting post-translational modifications (PTMs). MIND-S aids in identifying key amino acids and understanding how mutations impact PTMs, crucial for disease research.

Keywords:
BioinformaticsComputer SciencesProtein BiochemistrySequence Analysis

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Genomics

Background:

  • Post-translational modifications (PTMs) are critical regulators of cellular functions.
  • Dysregulation of PTMs is implicated in the pathogenesis of various human diseases.
  • Accurate prediction and analysis of PTMs are essential for understanding cellular mechanisms and disease.

Purpose of the Study:

  • To present a detailed protocol for utilizing MIND-S (multi-label interpretable deep-learning approach for PTM prediction-structure version).
  • To demonstrate the application of MIND-S for PTM prediction from protein sequences.
  • To showcase MIND-S capabilities in identifying crucial amino acids and analyzing mutation-induced PTM alterations.

Main Methods:

  • Development and application of the MIND-S computational tool.
  • Utilizing deep learning for multi-label PTM prediction.
  • Sequence-based analysis for PTM site identification.
  • Structure-based insights into PTM regulation.

Main Results:

  • MIND-S provides a robust protocol for PTM analysis.
  • The tool enables accurate prediction of PTMs directly from protein sequences.
  • MIND-S facilitates the identification of key amino acid residues involved in PTMs.
  • The approach elucidates the impact of molecular mutations on the PTM landscape.

Conclusions:

  • The MIND-S protocol offers a valuable resource for studying PTMs.
  • This deep-learning approach enhances our understanding of PTM regulation and its role in disease.
  • MIND-S serves as a powerful tool for both basic research and clinical applications in PTM analysis.