A resource database for protein kinase substrate sequence-preference motifs based on large-scale mass spectrometry data Article Swipe
Brian G. Poll
,
Kirby T. Leo
,
Venky Deshpande
,
Nipun U. Jayatissa
,
Trairak Pisitkun
,
Euijung Park
,
Chin‐Rang Yang
,
Viswanathan Raghuram
,
Mark A. Knepper
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1186/s12964-023-01436-2
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1186/s12964-023-01436-2
Background Protein phosphorylation is one of the most prevalent posttranslational modifications involved in molecular control of cellular processes, and is mediated by over 520 protein kinases in humans and other mammals. Identification of the protein kinases responsible for phosphorylation events is key to understanding signaling pathways. Unbiased phosphoproteomics experiments have generated a wealth of data that can be used to identify protein kinase targets and their preferred substrate sequences. Methods This study utilized prior data from mass spectrometry-based studies identifying sites of protein phosphorylation after in vitro incubation of protein mixtures with recombinant protein kinases. PTM-Logo software was used with these data to generate position-dependent Shannon information matrices and sequence motif ‘logos’. Webpages were constructed for facile access to logos for each kinase and a new stand-alone application was written in Python that uses the position-dependent Shannon information matrices to identify kinases most likely to phosphorylate a particular phosphorylation site. Results A database of kinase substrate target preference logos allows browsing, searching, or downloading target motif data for each protein kinase ( https://esbl.nhlbi.nih.gov/Databases/Kinase_Logos/ ). These logos were combined with phylogenetic analysis of protein kinase catalytic sequences to reveal substrate preference patterns specific to particular groups of kinases ( https://esbl.nhlbi.nih.gov/Databases/Kinase_Logos/KinaseTree.html ). A stand-alone program, KinasePredictor , is provided ( https://esbl.nhlbi.nih.gov/Databases/Kinase_Logos/KinasePredictor.html ). It takes as input, amino-acid sequences surrounding a given phosphorylation site and generates a ranked list of protein kinases most likely to phosphorylate that site. Conclusions This study provides three new resources for protein kinase characterization. It provides a tool for prediction of kinase-substrate interactions, which in combination with other types of data (co-localization, etc.), can predict which kinases are likely responsible for a given phosphorylation event in a given tissue.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s12964-023-01436-2
- https://biosignaling.biomedcentral.com/counter/pdf/10.1186/s12964-023-01436-2
- OA Status
- gold
- Cited By
- 9
- References
- 30
- Related Works
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- OpenAlex ID
- https://openalex.org/W4391928409
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- OpenAlex ID
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https://openalex.org/W4391928409Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1186/s12964-023-01436-2Digital Object Identifier
- Title
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A resource database for protein kinase substrate sequence-preference motifs based on large-scale mass spectrometry dataWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-02-19Full publication date if available
- Authors
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Brian G. Poll, Kirby T. Leo, Venky Deshpande, Nipun U. Jayatissa, Trairak Pisitkun, Euijung Park, Chin‐Rang Yang, Viswanathan Raghuram, Mark A. KnepperList of authors in order
- Landing page
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https://doi.org/10.1186/s12964-023-01436-2Publisher landing page
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https://biosignaling.biomedcentral.com/counter/pdf/10.1186/s12964-023-01436-2Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://biosignaling.biomedcentral.com/counter/pdf/10.1186/s12964-023-01436-2Direct OA link when available
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Phosphorylation, Kinase, Biology, Protein kinase A, Computational biology, Database, Biochemistry, Computer scienceTop concepts (fields/topics) attached by OpenAlex
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9Total citation count in OpenAlex
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2025: 4, 2024: 5Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.logos | 120, 159, 176 |
| abstract_inverted_index.motif | 111, 166 |
| abstract_inverted_index.other | 30, 260 |
| abstract_inverted_index.prior | 74 |
| abstract_inverted_index.site. | 150, 235 |
| abstract_inverted_index.sites | 81 |
| abstract_inverted_index.study | 72, 238 |
| abstract_inverted_index.takes | 212 |
| abstract_inverted_index.their | 66 |
| abstract_inverted_index.these | 101 |
| abstract_inverted_index.three | 240 |
| abstract_inverted_index.types | 261 |
| abstract_inverted_index.vitro | 87 |
| abstract_inverted_index.which | 256, 268 |
| abstract_inverted_index.Python | 132 |
| abstract_inverted_index.access | 118 |
| abstract_inverted_index.allows | 160 |
| abstract_inverted_index.etc.), | 265 |
| abstract_inverted_index.events | 40 |
| abstract_inverted_index.facile | 117 |
| abstract_inverted_index.groups | 195 |
| abstract_inverted_index.humans | 28 |
| abstract_inverted_index.input, | 214 |
| abstract_inverted_index.kinase | 63, 123, 155, 171, 184, 245 |
| abstract_inverted_index.likely | 144, 231, 271 |
| abstract_inverted_index.ranked | 225 |
| abstract_inverted_index.reveal | 188 |
| abstract_inverted_index.target | 157, 165 |
| abstract_inverted_index.wealth | 53 |
| abstract_inverted_index.Methods | 70 |
| abstract_inverted_index.Protein | 2 |
| abstract_inverted_index.Results | 151 |
| abstract_inverted_index.Shannon | 106, 137 |
| abstract_inverted_index.control | 15 |
| abstract_inverted_index.kinases | 26, 36, 142, 197, 229, 269 |
| abstract_inverted_index.predict | 267 |
| abstract_inverted_index.protein | 25, 35, 62, 83, 90, 94, 170, 183, 228, 244 |
| abstract_inverted_index.studies | 79 |
| abstract_inverted_index.targets | 64 |
| abstract_inverted_index.tissue. | 281 |
| abstract_inverted_index.written | 130 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.PTM-Logo | 96 |
| abstract_inverted_index.Unbiased | 47 |
| abstract_inverted_index.Webpages | 113 |
| abstract_inverted_index.analysis | 181 |
| abstract_inverted_index.cellular | 17 |
| abstract_inverted_index.combined | 178 |
| abstract_inverted_index.database | 153 |
| abstract_inverted_index.generate | 104 |
| abstract_inverted_index.identify | 61, 141 |
| abstract_inverted_index.involved | 12 |
| abstract_inverted_index.kinases. | 95 |
| abstract_inverted_index.mammals. | 31 |
| abstract_inverted_index.matrices | 108, 139 |
| abstract_inverted_index.mediated | 21 |
| abstract_inverted_index.mixtures | 91 |
| abstract_inverted_index.patterns | 191 |
| abstract_inverted_index.program, | 203 |
| abstract_inverted_index.provided | 207 |
| abstract_inverted_index.provides | 239, 248 |
| abstract_inverted_index.sequence | 110 |
| abstract_inverted_index.software | 97 |
| abstract_inverted_index.specific | 192 |
| abstract_inverted_index.utilized | 73 |
| abstract_inverted_index.browsing, | 161 |
| abstract_inverted_index.catalytic | 185 |
| abstract_inverted_index.generated | 51 |
| abstract_inverted_index.generates | 223 |
| abstract_inverted_index.molecular | 14 |
| abstract_inverted_index.pathways. | 46 |
| abstract_inverted_index.preferred | 67 |
| abstract_inverted_index.prevalent | 9 |
| abstract_inverted_index.resources | 242 |
| abstract_inverted_index.sequences | 186, 216 |
| abstract_inverted_index.signaling | 45 |
| abstract_inverted_index.substrate | 68, 156, 189 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.amino-acid | 215 |
| abstract_inverted_index.incubation | 88 |
| abstract_inverted_index.particular | 148, 194 |
| abstract_inverted_index.prediction | 252 |
| abstract_inverted_index.preference | 158, 190 |
| abstract_inverted_index.processes, | 18 |
| abstract_inverted_index.searching, | 162 |
| abstract_inverted_index.sequences. | 69 |
| abstract_inverted_index.Conclusions | 236 |
| abstract_inverted_index.application | 128 |
| abstract_inverted_index.combination | 258 |
| abstract_inverted_index.constructed | 115 |
| abstract_inverted_index.downloading | 164 |
| abstract_inverted_index.experiments | 49 |
| abstract_inverted_index.identifying | 80 |
| abstract_inverted_index.information | 107, 138 |
| abstract_inverted_index.recombinant | 93 |
| abstract_inverted_index.responsible | 37, 272 |
| abstract_inverted_index.stand-alone | 127, 202 |
| abstract_inverted_index.surrounding | 217 |
| abstract_inverted_index.phylogenetic | 180 |
| abstract_inverted_index.‘logos’. | 112 |
| abstract_inverted_index.interactions, | 255 |
| abstract_inverted_index.modifications | 11 |
| abstract_inverted_index.phosphorylate | 146, 233 |
| abstract_inverted_index.understanding | 44 |
| abstract_inverted_index.Identification | 32 |
| abstract_inverted_index.KinasePredictor | 204 |
| abstract_inverted_index.phosphorylation | 3, 39, 84, 149, 220, 276 |
| abstract_inverted_index.kinase-substrate | 254 |
| abstract_inverted_index.(co-localization, | 264 |
| abstract_inverted_index.characterization. | 246 |
| abstract_inverted_index.phosphoproteomics | 48 |
| abstract_inverted_index.posttranslational | 10 |
| abstract_inverted_index.position-dependent | 105, 136 |
| abstract_inverted_index.spectrometry-based | 78 |
| abstract_inverted_index.https://esbl.nhlbi.nih.gov/Databases/Kinase_Logos/ | 173 |
| abstract_inverted_index.https://esbl.nhlbi.nih.gov/Databases/Kinase_Logos/KinaseTree.html | 199 |
| abstract_inverted_index.https://esbl.nhlbi.nih.gov/Databases/Kinase_Logos/KinasePredictor.html | 209 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile.value | 0.90680435 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |