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Using collections of structural models to predict changes of binding affinity caused by mutations in protein–protein interactions
Author(s) -
Meseguer Alberto,
Dominguez Lluis,
Bota Patricia M.,
AguirrePlans Joaquim,
Bonet Jaume,
FernandezFuentes Narcis,
Oliva Baldo
Publication year - 2020
Publication title -
protein science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.353
H-Index - 175
eISSN - 1469-896X
pISSN - 0961-8368
DOI - 10.1002/pro.3930
Subject(s) - colicin , endonuclease , homology modeling , computational biology , rna splicing , biology , genetics , chemistry , escherichia coli , dna , biochemistry , gene , enzyme , rna
Protein–protein interactions (PPIs) in all the molecular aspects that take place both inside and outside cells. However, determining experimentally the structure and affinity of PPIs is expensive and time consuming. Therefore, the development of computational tools, as a complement to experimental methods, is fundamental. Here, we present a computational suite: MODPIN, to model and predict the changes of binding affinity of PPIs. In this approach we use homology modeling to derive the structures of PPIs and score them using state‐of‐the‐art scoring functions. We explore the conformational space of PPIs by generating not a single structural model but a collection of structural models with different conformations based on several templates. We apply the approach to predict the changes in free energy upon mutations and splicing variants of large datasets of PPIs to statistically quantify the quality and accuracy of the predictions. As an example, we use MODPIN to study the effect of mutations in the interaction between colicin endonuclease 9 and colicin endonuclease 2 immune protein from Escherichia coli . Finally, we have compared our results with other state‐of‐art methods.