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Prediction of 492 human protein kinase substrate specificities.

Safaei J, Maňuch J, Gupta A, Stacho L, Pelech S - Proteome Sci (2011)

Bottom Line: Complex intracellular signaling networks monitor diverse environmental inputs to evoke appropriate and coordinated effector responses.This represents a marked advancement over existing methods such as those used in NetPhorest (179 kinases in 76 groups) and NetworKIN (123 kinases), which consider only positive determinants for kinase substrate prediction.Furthermore for many of the better known kinases, the predicted optimal phosphosite sequences were more accurate than the consensus phosphosite sequences inferred by simple alignment of the phosphosites of known kinase substrates.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, University of British Columbia, Vancouver, Canada. jsafaei@cs.ubc.ca.

ABSTRACT

Background: Complex intracellular signaling networks monitor diverse environmental inputs to evoke appropriate and coordinated effector responses. Defective signal transduction underlies many pathologies, including cancer, diabetes, autoimmunity and about 400 other human diseases. Therefore, there is high impetus to define the composition and architecture of cellular communications networks in humans. The major components of intracellular signaling networks are protein kinases and protein phosphatases, which catalyze the reversible phosphorylation of proteins. Here, we have focused on identification of kinase-substrate interactions through prediction of the phosphorylation site specificity from knowledge of the primary amino acid sequence of the catalytic domain of each kinase.

Results: The presented method predicts 488 different kinase catalytic domain substrate specificity matrices in 478 typical and 4 atypical human kinases that rely on both positive and negative determinants for scoring individual phosphosites for their suitability as kinase substrates. This represents a marked advancement over existing methods such as those used in NetPhorest (179 kinases in 76 groups) and NetworKIN (123 kinases), which consider only positive determinants for kinase substrate prediction. Comparison of our predicted matrices with experimentally-derived matrices from about 9,000 known kinase-phosphosite substrate pairs revealed a high degree of concordance with the established preferences of about 150 well studied protein kinases. Furthermore for many of the better known kinases, the predicted optimal phosphosite sequences were more accurate than the consensus phosphosite sequences inferred by simple alignment of the phosphosites of known kinase substrates.

Conclusions: Application of this improved kinase substrate prediction algorithm to the primary structures of over 23, 000 proteins encoded by the human genome has permitted the identification of about 650, 000 putative phosphosites, which are posted on the open source PhosphoNET website (http://www.phosphonet.ca).

No MeSH data available.


Related in: MedlinePlus

Comparison with NetPhorest predictions. This table shows how many times the NetPhorest kinases groups fall to the ranking groups 1 to 30 as determined in our kinase substrate predictor algorithm. For instance the first row illustrates that 1058 NetPhorest kinase groups (16.8%) were similarly predicted by our algorithm as the best kinase groups for the specific phospho-peptides. Because every kinase group in NetPhorest contains 3.3 kinases in average, the rank can be adjusted and we can say 1058 NetPhorest kinases (and not kinase groups) were similarly predicted by our algorithm as the best three kinases for the specific phospho-peptides.
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Figure 8: Comparison with NetPhorest predictions. This table shows how many times the NetPhorest kinases groups fall to the ranking groups 1 to 30 as determined in our kinase substrate predictor algorithm. For instance the first row illustrates that 1058 NetPhorest kinase groups (16.8%) were similarly predicted by our algorithm as the best kinase groups for the specific phospho-peptides. Because every kinase group in NetPhorest contains 3.3 kinases in average, the rank can be adjusted and we can say 1058 NetPhorest kinases (and not kinase groups) were similarly predicted by our algorithm as the best three kinases for the specific phospho-peptides.

Mentions: In this part we compare consensus module of the predictor with NetPhorest based on confirmed phospho-peptides existing in NetPhorest database. At this juncture, NetPhorest contains 10, 261 confirmed phosphosites and has 76 specified groups for a total of 179 kinases linked to phosphorylation of 8, 746 of those sites. In this dataset, some phosphosites had more than one kinase phosphorylating them. To compare our predictor with NetPhorest easier we retained only the best kinase for each phosphosite. We also considered only those kinases with our predictor algorithm that were included in the list of 179 protein kinases covered by NetPhorest. As a result, the number of kinase–phosphosite pairs was reduced to 6, 299. To examine how many of these kinase-phosphosite pairs were consistent with our predictor, we subjected these 6, 299 phosphosites to our predictor algorithm to determine which individual kinases were more likely to phosphorylate these sites. We ranked the 179 protein kinases based on their calculated PSSM scores for each NetPhorest confirmed phospho-site region. It was desirable that the experimentally confirmed kinases for each phosphosite region had high PSSM scores in our predictor. However, we cannot expect these confirmed kinases always have maximum PSSM scores, because although these kinases were experimentally demonstrated to phosphorylate those phosphosites, it is unclear that they are always the best possible matches. Figure 8 shows that 1058 NetPhorest kinase groups were similarly predicted by our algorithm as the best kinase groups for the specific phospho-peptides, and 651 kinase groups were predicted as the second best kinase groups, etc. On average each NetPhorest kinase family has 3.3 kinases and because our algorithm works based on individual kinases and not a group, we adjusted the ranks and intervals for the results from our algorithm accordingly to provide direct comparison. It is evident that 35 percent of the NetPhorest predicted kinases groups corresponded to the top 10 candidate kinases proposed by our algorithm. Therefore, our predictor had similar prediction accuracy to NetPhorest, but we achieved coverage with three times as many different protein kinases and with individual assignments rather than groups of kinases. This result is also shown in our previous work in BIBM 2010 [21].


Prediction of 492 human protein kinase substrate specificities.

Safaei J, Maňuch J, Gupta A, Stacho L, Pelech S - Proteome Sci (2011)

Comparison with NetPhorest predictions. This table shows how many times the NetPhorest kinases groups fall to the ranking groups 1 to 30 as determined in our kinase substrate predictor algorithm. For instance the first row illustrates that 1058 NetPhorest kinase groups (16.8%) were similarly predicted by our algorithm as the best kinase groups for the specific phospho-peptides. Because every kinase group in NetPhorest contains 3.3 kinases in average, the rank can be adjusted and we can say 1058 NetPhorest kinases (and not kinase groups) were similarly predicted by our algorithm as the best three kinases for the specific phospho-peptides.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3379035&req=5

Figure 8: Comparison with NetPhorest predictions. This table shows how many times the NetPhorest kinases groups fall to the ranking groups 1 to 30 as determined in our kinase substrate predictor algorithm. For instance the first row illustrates that 1058 NetPhorest kinase groups (16.8%) were similarly predicted by our algorithm as the best kinase groups for the specific phospho-peptides. Because every kinase group in NetPhorest contains 3.3 kinases in average, the rank can be adjusted and we can say 1058 NetPhorest kinases (and not kinase groups) were similarly predicted by our algorithm as the best three kinases for the specific phospho-peptides.
Mentions: In this part we compare consensus module of the predictor with NetPhorest based on confirmed phospho-peptides existing in NetPhorest database. At this juncture, NetPhorest contains 10, 261 confirmed phosphosites and has 76 specified groups for a total of 179 kinases linked to phosphorylation of 8, 746 of those sites. In this dataset, some phosphosites had more than one kinase phosphorylating them. To compare our predictor with NetPhorest easier we retained only the best kinase for each phosphosite. We also considered only those kinases with our predictor algorithm that were included in the list of 179 protein kinases covered by NetPhorest. As a result, the number of kinase–phosphosite pairs was reduced to 6, 299. To examine how many of these kinase-phosphosite pairs were consistent with our predictor, we subjected these 6, 299 phosphosites to our predictor algorithm to determine which individual kinases were more likely to phosphorylate these sites. We ranked the 179 protein kinases based on their calculated PSSM scores for each NetPhorest confirmed phospho-site region. It was desirable that the experimentally confirmed kinases for each phosphosite region had high PSSM scores in our predictor. However, we cannot expect these confirmed kinases always have maximum PSSM scores, because although these kinases were experimentally demonstrated to phosphorylate those phosphosites, it is unclear that they are always the best possible matches. Figure 8 shows that 1058 NetPhorest kinase groups were similarly predicted by our algorithm as the best kinase groups for the specific phospho-peptides, and 651 kinase groups were predicted as the second best kinase groups, etc. On average each NetPhorest kinase family has 3.3 kinases and because our algorithm works based on individual kinases and not a group, we adjusted the ranks and intervals for the results from our algorithm accordingly to provide direct comparison. It is evident that 35 percent of the NetPhorest predicted kinases groups corresponded to the top 10 candidate kinases proposed by our algorithm. Therefore, our predictor had similar prediction accuracy to NetPhorest, but we achieved coverage with three times as many different protein kinases and with individual assignments rather than groups of kinases. This result is also shown in our previous work in BIBM 2010 [21].

Bottom Line: Complex intracellular signaling networks monitor diverse environmental inputs to evoke appropriate and coordinated effector responses.This represents a marked advancement over existing methods such as those used in NetPhorest (179 kinases in 76 groups) and NetworKIN (123 kinases), which consider only positive determinants for kinase substrate prediction.Furthermore for many of the better known kinases, the predicted optimal phosphosite sequences were more accurate than the consensus phosphosite sequences inferred by simple alignment of the phosphosites of known kinase substrates.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, University of British Columbia, Vancouver, Canada. jsafaei@cs.ubc.ca.

ABSTRACT

Background: Complex intracellular signaling networks monitor diverse environmental inputs to evoke appropriate and coordinated effector responses. Defective signal transduction underlies many pathologies, including cancer, diabetes, autoimmunity and about 400 other human diseases. Therefore, there is high impetus to define the composition and architecture of cellular communications networks in humans. The major components of intracellular signaling networks are protein kinases and protein phosphatases, which catalyze the reversible phosphorylation of proteins. Here, we have focused on identification of kinase-substrate interactions through prediction of the phosphorylation site specificity from knowledge of the primary amino acid sequence of the catalytic domain of each kinase.

Results: The presented method predicts 488 different kinase catalytic domain substrate specificity matrices in 478 typical and 4 atypical human kinases that rely on both positive and negative determinants for scoring individual phosphosites for their suitability as kinase substrates. This represents a marked advancement over existing methods such as those used in NetPhorest (179 kinases in 76 groups) and NetworKIN (123 kinases), which consider only positive determinants for kinase substrate prediction. Comparison of our predicted matrices with experimentally-derived matrices from about 9,000 known kinase-phosphosite substrate pairs revealed a high degree of concordance with the established preferences of about 150 well studied protein kinases. Furthermore for many of the better known kinases, the predicted optimal phosphosite sequences were more accurate than the consensus phosphosite sequences inferred by simple alignment of the phosphosites of known kinase substrates.

Conclusions: Application of this improved kinase substrate prediction algorithm to the primary structures of over 23, 000 proteins encoded by the human genome has permitted the identification of about 650, 000 putative phosphosites, which are posted on the open source PhosphoNET website (http://www.phosphonet.ca).

No MeSH data available.


Related in: MedlinePlus