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CaRinDB: an integrated database of common cancer mutations and residue interaction network parameters

Daniela Coelho Batista Guedes Pereira1,2, João Vitor Ferreira Cavalcante1, Laise Florentino Cavalcanti1

  • 1Bioinformatics Multidisciplinary Environment (BioME), Digital Metropolis Institute (IMD), Universidade Federal do Rio Grande do Norte (UFRN), Natal, RN 59078-900, Brazil.

Bioinformatics Advances
|February 9, 2026
PubMed
Abstract

Insights

CaRinDB is a new database integrating cancer mutation data, functional predictions, and structural impacts. It aids researchers in understanding how missense mutations affect protein function and structure, supporting AI tool development.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Predicting missense mutation impact on protein structure and function is crucial for cancer research.
  • Current computational and AI methods struggle to fully assess functional consequences of residue substitutions due to protein complexity.

Purpose of the Study:

  • To introduce CaRinDB, a novel database consolidating cancer-associated missense mutation data with functional and structural information.
  • To provide a comprehensive resource for exploring and analyzing cancer mutation data and their structural impacts.

Main Methods:

  • Integrated cancer-associated missense mutation data, functional predictions, molecular features, and allelic frequencies.
  • Derived residue interaction network (RIN) parameters from Protein Data Bank and AlphaFold structures.
  • Developed an intuitive web portal with custom plots and tables for data exploration.

Main Results:

  • CaRinDB is the first database to combine diverse annotation features of cancer mutations with their structural impacts.
  • Utilizes RIN graph parameters and compiled data for the development of AI tools.
  • Offers a user-friendly interface for visualizing and analyzing cancer-associated mutation data.

Conclusions:

  • CaRinDB provides a valuable, integrated resource for cancer mutation research.
  • Facilitates deeper understanding of mutation-induced structural changes and functional consequences.
  • Supports the advancement of AI-driven cancer research tools.

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