Kinase-substrate prediction using an autoregressive model Article Swipe
Farzaneh Esmaili
,
Yongfang Qin
,
Duolin Wang
,
Dong Xu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.csbj.2025.03.003
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.csbj.2025.03.003
Kinase-specific phosphorylation plays a critical role in cellular signaling and various diseases. However, even in model organisms, the substrates of most kinases remain unidentified. Currently, there is no reliable method to predict kinase-substrate relationships. In this study, we introduce an innovative approach leveraging an autoregressive model to predict kinase-substrate pairs. Unlike traditional methods focused on predicting site-specific phosphorylation, our approach addresses kinase-specific protein substrate prediction at the protein level. We redefine this problem as a special type of protein-protein interaction prediction task. Our model integrates protein large language model ESM-2 as the encoder and employs an autoregressive decoder to classify protein-kinase interactions in a binary fashion. We adopted a hard negative strategy, based on kinase embedding distances generated from ESM-2, to compel the model to effectively distinguish positive from negative data. We conducted a top‑k analysis to assess how well our model can prioritize the most likely kinase candidates. Our method is also capable of zero-shot prediction, meaning it can predict substrates for a kinase in case of no known substrates, which cannot be achieved by site-specific prediction methods. Our model's robust generalization to novel kinase and underrepresented groups showcases its versatility and broad utility. Code and data are available at https://github.com/farz1995/substrate_kinase_prediction.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.csbj.2025.03.003
- OA Status
- gold
- References
- 38
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4408254815Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.csbj.2025.03.003Digital Object Identifier
- Title
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Kinase-substrate prediction using an autoregressive modelWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
-
Farzaneh Esmaili, Yongfang Qin, Duolin Wang, Dong XuList of authors in order
- Landing page
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https://doi.org/10.1016/j.csbj.2025.03.003Publisher landing page
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.csbj.2025.03.003Direct OA link when available
- Concepts
-
Autoregressive model, Substrate (aquarium), Computer science, Biological system, Computational biology, Statistics, Biology, Mathematics, EcologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
- References (count)
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38Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works_count | 38 |
| abstract_inverted_index.a | 3, 74, 103, 108, 133, 163 |
| abstract_inverted_index.In | 34 |
| abstract_inverted_index.We | 69, 106, 131 |
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| abstract_inverted_index.in | 6, 14, 102, 165 |
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| abstract_inverted_index.Our | 82, 149, 179 |
| abstract_inverted_index.and | 9, 93, 186, 192, 196 |
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| abstract_inverted_index.broad | 193 |
| abstract_inverted_index.data. | 130 |
| abstract_inverted_index.known | 169 |
| abstract_inverted_index.large | 86 |
| abstract_inverted_index.model | 15, 45, 83, 88, 123, 141 |
| abstract_inverted_index.novel | 184 |
| abstract_inverted_index.plays | 2 |
| abstract_inverted_index.task. | 81 |
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| abstract_inverted_index.ESM-2, | 119 |
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| abstract_inverted_index.binary | 104 |
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| abstract_inverted_index.compel | 121 |
| abstract_inverted_index.groups | 188 |
| abstract_inverted_index.kinase | 114, 147, 164, 185 |
| abstract_inverted_index.level. | 68 |
| abstract_inverted_index.likely | 146 |
| abstract_inverted_index.method | 29, 150 |
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| abstract_inverted_index.capable | 153 |
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| abstract_inverted_index.predict | 31, 47, 160 |
| abstract_inverted_index.problem | 72 |
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| abstract_inverted_index.However, | 12 |
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| abstract_inverted_index.language | 87 |
| abstract_inverted_index.methods. | 178 |
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| abstract_inverted_index.positive | 127 |
| abstract_inverted_index.redefine | 70 |
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| abstract_inverted_index.addresses | 60 |
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| abstract_inverted_index.phosphorylation, | 57 |
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| abstract_inverted_index.https://github.com/farz1995/substrate_kinase_prediction. | 201 |
| cited_by_percentile_year | |
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| institutions_distinct_count | 4 |
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| citation_normalized_percentile.is_in_top_10_percent | False |