title: COUSCOus: improved protein contact prediction using an empirical Bayes covariance estimator.
abstract: BACKGROUND
  The post-genomic era with its wealth of sequences gave rise to a broad range of protein residue-residue contact detecting methods. Although various coevolution methods such as PSICOV, DCA and plmDCA provide correct contact predictions, they do not completely overlap. Hence, new approaches and improvements of existing methods are needed to motivate further development and progress in the field. We present a new contact detecting method, COUSCOus, by combining the best shrinkage approach, the empirical Bayes covariance estimator and GLasso.
  
  
  RESULTS
  Using the original PSICOV benchmark dataset, COUSCOus achieves mean accuracies of 0.74, 0.62 and 0.55 for the top L/10 predicted long, medium and short range contacts, respectively. In addition, COUSCOus attains mean areas under the precision-recall curves of 0.25, 0.29 and 0.30 for long, medium and short contacts and outperforms PSICOV. We also observed that COUSCOus outperforms PSICOV w.r.t. Matthew's correlation coefficient criterion on full list of residue contacts. Furthermore, COUSCOus achieves on average 10% more gain in prediction accuracy compared to PSICOV on an independent test set composed of CASP11 protein targets. Finally, we showed that when using a simple random forest meta-classifier, by combining contact detecting techniques and sequence derived features, PSICOV predictions should be replaced by the more accurate COUSCOus predictions.
  
  
  CONCLUSIONS
  We conclude that the consideration of superior covariance shrinkage approaches will boost several research fields that apply the GLasso procedure, amongst the presented one of residue-residue contact prediction as well as fields such as gene network reconstruction.
authors.affiliation: Computational Science and Engineering, Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar. redarawi411@gmail.com.
authors.forename: Reda
authors.identifier: http://orcid.org/0000-0002-0445-2325
authors.initials: R
authors.lastname: Rawi
authors.affiliation: Computational Science and Engineering, Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.
authors.forename: Raghvendra
authors.initials: R
authors.lastname: Mall
authors.affiliation: Computational Science and Engineering, Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.
authors.forename: Khalid
authors.initials: K
authors.lastname: Kunji
authors.affiliation: Division of Biomedical Informatics, Sidra Medical and Research Center, Doha, Qatar.
authors.forename: Mohammed
authors.initials: M
authors.lastname: El Anbari
authors.affiliation: Computational Science and Engineering, Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.
authors.forename: Michael
authors.initials: M
authors.lastname: Aupetit
authors.affiliation: Computational Science and Engineering, Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.
authors.forename: Ehsan
authors.initials: E
authors.lastname: Ullah
authors.affiliation: Computational Science and Engineering, Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.
authors.forename: Halima
authors.initials: H
authors.lastname: Bensmail
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D008958:Models, Molecular; D011506:Proteins / Q000737:chemistry* / Q000235:genetics; D020539:Sequence Analysis, Protein / Q000379:methods; D012984:Software
chemical_list: D011506:Proteins
keywords: GLasso; Residue-residue contact prediction; Shrinkage