title: Seeing the trees through the forest: sequence-based homo- and heteromeric protein-protein interaction sites prediction using random forest.
abstract: BACKGROUND
  Genome sequencing is producing an ever-increasing amount of associated protein sequences. Few of these sequences have experimentally validated annotations, however, and computational predictions are becoming increasingly successful in producing such annotations. One key challenge remains the prediction of the amino acids in a given protein sequence that are involved in protein-protein interactions. Such predictions are typically based on machine learning methods that take advantage of the properties and sequence positions of amino acids that are known to be involved in interaction. In this paper, we evaluate the importance of various features using Random Forest (RF), and include as a novel feature backbone flexibility predicted from sequences to further optimise protein interface prediction.
  
  
  RESULTS
  We observe that there is no single sequence feature that enables pinpointing interacting sites in our Random Forest models. However, combining different properties does increase the performance of interface prediction. Our homomeric-trained RF interface predictor is able to distinguish interface from non-interface residues with an area under the ROC curve of 0.72 in a homomeric test-set. The heteromeric-trained RF interface predictor performs better than existing predictors on a independent heteromeric test-set. We trained a more general predictor on the combined homomeric and heteromeric dataset, and show that in addition to predicting homomeric interfaces, it is also able to pinpoint interface residues in heterodimers. This suggests that our random forest model and the features included capture common properties of both homodimer and heterodimer interfaces.
  
  
  METHODS
  The predictors and test datasets used in our analyses are freely available ( http://www.ibi.vu.nl/downloads/RF_PPI/ ).
  
  
  BACKGROUND
  k.a.feenstra@vu.nl.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Center for Integrative Bioinformatics VU (IBIVU), Amsterdam, HV, The Netherlands.|Amsterdam Institute for Molecules Medicines and Systems (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, HV, The Netherlands.
authors.forename: Qingzhen
authors.initials: Q
authors.lastname: Hou
authors.affiliation: Center for Integrative Bioinformatics VU (IBIVU), Amsterdam, HV, The Netherlands.|Amsterdam Institute for Molecules Medicines and Systems (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, HV, The Netherlands.
authors.forename: Paul F G
authors.initials: PFG
authors.lastname: De Geest
authors.affiliation: Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium.|Structural Biology Brussels, Vrije Universiteit Brussel,Brussels, Belgium.|Structural Biology Research Centre, VIB, Brussels, Belgium.
authors.forename: Wim F
authors.initials: WF
authors.lastname: Vranken
authors.affiliation: Center for Integrative Bioinformatics VU (IBIVU), Amsterdam, HV, The Netherlands.|Amsterdam Institute for Molecules Medicines and Systems (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, HV, The Netherlands.
authors.forename: Jaap
authors.initials: J
authors.lastname: Heringa
authors.affiliation: Center for Integrative Bioinformatics VU (IBIVU), Amsterdam, HV, The Netherlands.|Amsterdam Institute for Molecules Medicines and Systems (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, HV, The Netherlands.
authors.forename: K Anton
authors.initials: KA
authors.lastname: Feenstra
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D015233:Models, Statistical; D054730:Protein Interaction Domains and Motifs; D025941:Protein Interaction Mapping / Q000379:methods*; D055503:Protein Multimerization; D012372:ROC Curve; D020539:Sequence Analysis, Protein / Q000379:methods