Abstract
MOTIVATION: Predicting the impact of missense mutations on protein structure and function is a fundamental challenge for cancer research and clinical applications. Despite all the computational advances and, more recently, the use of artificial intelligence (AI), assessing the functional consequences of residue substitutions remains a challenging task. Proteins have complex three-dimensional structures, where the maintenance of their functionality depends on chemical interactions between amino acid residues. Single substitutions can affect these interactions, leading to more profound structural changes that are difficult to visualize. RESULTS: Here, we present CaRinDB, a database that integrates cancer-associated missense mutation data, functional predictions, molecular features, allelic frequencies, and residue interaction network (RIN) parameters derived from Protein Data Bank structures and AlphaFold models. Users can access and explore variant information through an intuitive web portal, with custom plots and tables to visualize and analyze cancer-associated mutation data. CaRinDB is the first database that unites distinct annotation features of cancer-associated mutations and their structural impacts, utilizing RINs graph parameters and a source of compiled and processed data for the development of AI tools. AVAILABILITY AND IMPLEMENTATION: CaRinDB is freely available at https://bioinfo.imd.ufrn.br/CaRinDB/. The integrated development environment used was Jupyter notebooks, available on GitHub (https://github.com/evomol-lab/CaRinDB). CaRinDB web interface was implemented in R and Shiny.