Open Access3DFI: a pipeline to infer protein function using structural homology
Author(s) -
Alexander Thomas Julian,
Anne Caroline Mascarenhas dos Santos,
JeanFrançois Pombert
Publication year - 2021
Publication title -
bioinformatics advances
Language(s) - English
Resource type - Journals
ISSN - 2635-0041
DOI - 10.1093/bioadv/vbab030
Subject(s) - perl , python (programming language) , computer science , computational biology , inference , genome , annotation , structural genomics , visualization , homology (biology) , mit license , homology modeling , protein structure database , in silico , uniprot , genome project , protein structure , biology , programming language , software , data mining , artificial intelligence , sequence database , genetics , gene , biochemistry , enzyme
Inferring protein function is an integral part of genome annotation and analysis. This process is usually performed in silico , and most in silico inferences are based on sequence homology approaches, which can fail when in presence of divergent sequences. However, because protein structures and their biological roles are intertwined, protein function can also be inferred by searching for structural homology. Many excellent tools have been released in recent years with regards to protein structure prediction, structural homology searches and protein visualization. Unfortunately, these tools are disconnected from each other and often use a web server-based approach that is ill-suited to high-throughput genome-wide analyses. To help assist genome annotation, we built a structural homology-based pipeline called 3DFI (for tridimensional functional inference) leveraging some of the best structural homology tools. This pipeline was built with simplicity of use in mind and enables genome-wide structural homology inferences.