TY - JOUR
T1 - In Silico screening for functional candidates amongst hypothetical proteins
AU - Desler, Claus
AU - Suravajhala, Prashanth
AU - Sanderhoff, May
AU - Rasmussen, Merete
AU - Rasmussen, Lene Juel
N1 - Keywords: Computational Biology; Databases, Protein; Humans; Open Reading Frames; Proteins; Proteome; Proteomics
PY - 2009
Y1 - 2009
N2 - BACKGROUND: The definition of a hypothetical protein is a protein that is predicted to be expressed from an open reading frame, but for which there is no experimental evidence of translation. Hypothetical proteins constitute a substantial fraction of proteomes of human as well as of other eukaryotes. With the general belief that the majority of hypothetical proteins are the product of pseudogenes, it is essential to have a tool with the ability of pinpointing the minority of hypothetical proteins with a high probability of being expressed. RESULTS: Here, we present an in silico selection strategy where eukaryotic hypothetical proteins are sorted according to two criteria that can be reliably identified in silico: the presence of subcellular targeting signals and presence of characterized protein domains. To validate the selection strategy we applied it on a database of human hypothetical proteins dating to 2006 and compared the proteins predicted to be expressed by our selecting strategy, with their status in 2008. For the comparison we focused on mitochondrial proteins, since considerable amounts of research have focused on this field in between 2006 and 2008. Therefore, many proteins, defined as hypothetical in 2006, have later been characterized as mitochondrial. CONCLUSION: Among the total amount of human proteins hypothetical in 2006, 21% have later been experimentally characterized and 6% of those have been shown to have a role in a mitochondrial context. In contrast, among the selected hypothetical proteins from the 2006 dataset, predicted by our strategy to have a mitochondrial role, 53-62% have later been experimentally characterized, and 85% of these have actually been assigned a role in mitochondria by 2008.Therefore our in silico selection strategy can be used to select the most promising candidates for subsequent in vitro and in vivo analyses.
AB - BACKGROUND: The definition of a hypothetical protein is a protein that is predicted to be expressed from an open reading frame, but for which there is no experimental evidence of translation. Hypothetical proteins constitute a substantial fraction of proteomes of human as well as of other eukaryotes. With the general belief that the majority of hypothetical proteins are the product of pseudogenes, it is essential to have a tool with the ability of pinpointing the minority of hypothetical proteins with a high probability of being expressed. RESULTS: Here, we present an in silico selection strategy where eukaryotic hypothetical proteins are sorted according to two criteria that can be reliably identified in silico: the presence of subcellular targeting signals and presence of characterized protein domains. To validate the selection strategy we applied it on a database of human hypothetical proteins dating to 2006 and compared the proteins predicted to be expressed by our selecting strategy, with their status in 2008. For the comparison we focused on mitochondrial proteins, since considerable amounts of research have focused on this field in between 2006 and 2008. Therefore, many proteins, defined as hypothetical in 2006, have later been characterized as mitochondrial. CONCLUSION: Among the total amount of human proteins hypothetical in 2006, 21% have later been experimentally characterized and 6% of those have been shown to have a role in a mitochondrial context. In contrast, among the selected hypothetical proteins from the 2006 dataset, predicted by our strategy to have a mitochondrial role, 53-62% have later been experimentally characterized, and 85% of these have actually been assigned a role in mitochondria by 2008.Therefore our in silico selection strategy can be used to select the most promising candidates for subsequent in vitro and in vivo analyses.
U2 - 10.1186/1471-2105-10-289
DO - 10.1186/1471-2105-10-289
M3 - Journal article
C2 - 19754976
SN - 1471-2105
VL - 10
SP - 289
JO - BMC Bioinformatics
JF - BMC Bioinformatics
ER -