2003/01/01 by Gabriele Orlando, Daniele Raimondi, Wim Vranken +2 · 1 voice
Arts and Humanities · Biochemistry, Genetics and Molecular Biology · #Bioinformatics and Genomic Networks #Cultural and Mythological Studies #Genomics and Phylogenetic Studies #Historical and Literary Analyses #Protein Structure and Dynamics
paper · pdf · doi:10.1038/srep36679
openalex publication_date 2003/01/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/06/17
Next Generation Sequencing is dramatically increasing the number of known protein sequences, with related experimentally determined protein structures lagging behind. Structural bioinformatics is attempting to close this gap by developing approaches that predict structure-level characteristics for uncharacterized protein sequences, with most of the developed methods relying heavily on evolutionary information collected from homologous sequences. Here we show that there is a substantial observational selection bias in this approach: the predictions are validated on proteins with known structures from the PDB, but exactly for those proteins significantly more homologs are available compared to less studied sequences randomly extracted from Uniprot. Structural bioinformatics methods that were developed this way are thus likely to have over-estimated performances; we demonstrate this for two contact prediction methods, where performances drop up to 60% when taking into account a more realistic amount of evolutionary information. We provide a bias-free dataset for the validation for contact prediction methods called NOUMENON.