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Computational Approaches for Investigating Disease-causing Mutations in Membrane Proteins: Database Development,

Arulsang Kulandaisamy1, Fathima Ridha1, Dmitrij Frishman2

  • 1Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai 600 036, Tamilnadu, India.

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|July 27, 2022
PubMed
Summary

Membrane proteins are crucial drug targets. This review analyzes disease-causing mutations in membrane proteins using sequence and structural features, aiding in developing targeted disease strategies.

Keywords:
DatabasesDisease-causing mutationsFunctionMachine-learningMembrane proteinsNeutral mutationsStructureTopologytools

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Area of Science:

  • Biochemistry and Molecular Biology
  • Genomics and Bioinformatics

Background:

  • Membrane proteins (MPs) are vital for cellular functions and represent ~60% of drug targets.
  • Mutations in MPs can alter structure/function, leading to diseases.
  • Databases like MutHTP and TMSNP store data on disease-causing and neutral MP mutations.

Approach:

  • Reviewing available databases for disease-causing MP mutations.
  • Performing statistical analysis of MP mutations using sequence and structural features.
  • Describing and evaluating machine learning-based prediction tools for mutation identification.

Key Points:

  • MPs exhibit α-helical or β-barrel structures and are implicated in numerous diseases.
  • Sequence and structural features are key to understanding mutation effects.
  • Machine learning tools leverage diverse features for accurate mutation prediction.

Conclusions:

  • Comprehensive analysis of MP mutation databases and prediction tools is essential.
  • Understanding mutation mechanisms aids in developing disease-specific strategies.
  • This review offers insights for targeted therapeutic interventions.