IntPred: a structure-based predictor of protein–protein interaction sites | Zendy

Thomas C Northey | Zendy; Anja Barešić | Zendy; Andrew C.R. Martin | Zendy

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IntPred: a structure-based predictor of protein–protein interaction sites

Author(s) -

Thomas C Northey,

Anja Barešić,

Andrew C.R. Martin

Publication year - 2017

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btx585

Subject(s) - obligate , computer science , in silico , protein data bank (rcsb pdb) , spec# , perl , set (abstract data type) , test set , protein data bank , protein–protein interaction , web server , protein structure , random forest , data mining , exploit , machine learning , computational biology , biology , operating system , programming language , genetics , ecology , biochemistry , the internet , gene , computer security

Protein-protein interactions are vital for protein function with the average protein having between three and ten interacting partners. Knowledge of precise protein-protein interfaces comes from crystal structures deposited in the Protein Data Bank (PDB), but only 50% of structures in the PDB are complexes. There is therefore a need to predict protein-protein interfaces in silico and various methods for this purpose. Here we explore the use of a predictor based on structural features and which exploits random forest machine learning, comparing its performance with a number of popular established methods.

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