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Computational approaches for identifying disease-causing mutations in proteins.

Medha Pandey1, Suraj Kumar Shah1, M Michael Gromiha2

  • 1Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India.

Advances in Protein Chemistry and Structural Biology
|March 6, 2024
PubMed
Summary
This summary is machine-generated.

Identifying disease-causing protein mutations is crucial for developing targeted therapies. This work reviews databases and computational methods for pinpointing these critical genetic alterations.

Keywords:
Cancer hotspotsDatabasesDeep learningDisease-causing mutationsDriverMachine-learning

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Area of Science:

  • Genomics and Bioinformatics
  • Molecular Biology
  • Computational Biology

Background:

  • Genome sequencing advancements enable broader investigation of protein mutations in various diseases.
  • Amino acid mutations can alter protein structure, stability, and function, potentially leading to diseases.
  • Identifying disease-causing mutations is a complex challenge but vital for therapeutic strategy development.

Purpose of the Study:

  • To review databases containing information on disease-causing and neutral mutations.
  • To discuss sequence and structure-based properties of proteins relevant to mutations.
  • To explore computational methods for identifying deleterious mutations and cancer hotspots.

Main Methods:

  • Database curation and utilization for mutation data.
  • Analysis of sequence-based protein properties.
  • Application of structure-based protein properties.
  • Development and discussion of computational identification methods.

Main Results:

  • Databases provide valuable resources for studying mutation impacts.
  • Sequence and structure-based features are key indicators of mutation effects.
  • Computational approaches can effectively identify disease-causing mutations and cancer hotspots.

Conclusions:

  • Leveraging curated mutation databases and computational methods aids in identifying critical protein alterations.
  • Understanding mutation characteristics is essential for advancing disease research and therapeutic design.