title: Coevolutionary analyses require phylogenetically deep alignments and better null models to accurately detect inter-protein contacts within and between species.
abstract: BACKGROUND
  When biomolecules physically interact, natural selection operates on them jointly. Contacting positions in protein and RNA structures exhibit correlated patterns of sequence evolution due to constraints imposed by the interaction, and molecular arms races can develop between interacting proteins in pathogens and their hosts. To evaluate how well methods developed to detect coevolving residues within proteins can be adapted for cross-species, inter-protein analysis, we used statistical criteria to quantify the performance of these methods in detecting inter-protein residues within 8 angstroms of each other in the co-crystal structures of 33 bacterial protein interactions. We also evaluated their performance for detecting known residues at the interface of a host-virus protein complex with a partially solved structure.
  
  
  RESULTS
  Our quantitative benchmarking showed that all coevolutionary methods clearly benefit from alignments with many sequences. Methods that aim to detect direct correlations generally outperform other approaches. However, faster mutual information based methods are occasionally competitive in small alignments and with relaxed false positive rates. Two commonly used null distributions are anti-conservative and have high false positive rates in some scenarios, although the empirical distribution of scores performs reasonably well with deep alignments.
  
  
  CONCLUSIONS
  We conclude that coevolutionary analysis of cross-species protein interactions holds great promise but requires sequencing many more species pairs.
authors: Bioinformatics Graduate Program, University of California, San Francisco, USA. aram.avilaherrera@ucsf.edu.|Gladstone Institute of Cardiovascular Disease, University of California, San Francisco, USA. aram.avilaherrera@ucsf.edu.
authors: Aram
authors: A
authors: Avila-Herrera
authors: Bioinformatics Graduate Program, University of California, San Francisco, USA. kpollard@gladstone.ucsf.edu.|Gladstone Institute of Cardiovascular Disease, University of California, San Francisco, USA. kpollard@gladstone.ucsf.edu.|Department of Epidemiology and Biostatistics, University of California, San Francisco, USA. kpollard@gladstone.ucsf.edu.|Institute for Human Genetics, University of California, San Francisco, 94158, CA, USA. kpollard@gladstone.ucsf.edu.
authors: Katherine S
authors: KS
authors: Pollard
mesh_terms: D000071480:APOBEC-3G Deaminase; D003564:Cytidine Deaminase / Q000378:metabolism*; D019143:Evolution, Molecular; D054884:Host-Pathogen Interactions; D006801:Humans; D010802:Phylogeny; D012641:Selection, Genetic; D054875:Ubiquitination; D054320:vif Gene Products, Human Immunodeficiency Virus / Q000378:metabolism*
chemical_list: D054320:vif Gene Products, Human Immunodeficiency Virus; C514847:vif protein, Human immunodeficiency virus 1; D000071480:APOBEC-3G Deaminase; C464924:APOBEC3G protein, human; D003564:Cytidine Deaminase