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CaRinDB: an integrated database of common cancer mutations and residue interaction network parameters
Guedes Pereira, Daniela Coelho Batista Ferreira Cavalcante, João Vitor Cavalcanti, Laise Florentino Falcão, Raul Maia Santana de Souza, Jorge Estefano Dalmolin, Rodrigo Juliani Siqueira do Rêgo, Thaís Gaudencio Mangul, Serghei de Souza, Gustavo Antônio Terrematte, Patrick
Guedes Pereira, Daniela Coelho Batista
Ferreira Cavalcante, João Vitor
Cavalcanti, Laise Florentino
Falcão, Raul Maia
Santana de Souza, Jorge Estefano
Dalmolin, Rodrigo Juliani Siqueira
do Rêgo, Thaís Gaudencio
Mangul, Serghei
de Souza, Gustavo Antônio
Terrematte, Patrick
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vbaf313.pdf
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Author
Guedes Pereira, Daniela Coelho Batista
Ferreira Cavalcante, João Vitor
Cavalcanti, Laise Florentino
Falcão, Raul Maia
Santana de Souza, Jorge Estefano
Dalmolin, Rodrigo Juliani Siqueira
do Rêgo, Thaís Gaudencio
Mangul, Serghei
de Souza, Gustavo Antônio
Terrematte, Patrick
Lima, João Paulo Matos Santos
Ferreira Cavalcante, João Vitor
Cavalcanti, Laise Florentino
Falcão, Raul Maia
Santana de Souza, Jorge Estefano
Dalmolin, Rodrigo Juliani Siqueira
do Rêgo, Thaís Gaudencio
Mangul, Serghei
de Souza, Gustavo Antônio
Terrematte, Patrick
Lima, João Paulo Matos Santos
Supervisor
Department
Computational Biology
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Type
Journal article
Date
License
http://creativecommons.org/licenses/by/4.0/
Language
English
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Research Projects
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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.
Citation
D.C.B. Guedes Pereira, J.V. Ferreira Cavalcante, L.F. Cavalcanti, R.M. Falcão, J.E. Santana de Souza, R.J.S. Dalmolin , et al., "CaRinDB: an integrated database of common cancer mutations and residue interaction network parameters," Bioinformatics Advances, vol. 6, no. 1, pp. vbaf313-vbaf313, 2026, https://doi.org/10.1093/bioadv/vbaf313.
Source
Bioinformatics Advances
Conference
Keywords
31 Biological Sciences, 3101 Biochemistry and Cell Biology, 3102 Bioinformatics and Computational Biology
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Source
Publisher
Oxford University Press