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Re-examination of chimp protein kinases suggests "novel architectures" are gene prediction artifacts.

ABSTRACT: BACKGROUND:Anamika et al recently published in this journal a sequence alignment analysis of protein kinases encoded by the chimpanzee genome in comparison to those in the human genome. From this analysis they concluded that several chimpanzee kinases have unusual domain arrangements. RESULTS:Re-examination of these kinases reveals claimed novel arrangements cannot withstand scrutiny; each is either not novel or represents over-analysis of weakly confident computer generated gene models. Additional sequence evidence available at the time of the paper's submission either directly contradict the gene models or suggest alternate gene models. These alternate models would minimize or eliminate the observed differences between human and chimp kinases. CONCLUSION:None of the proposed novel chimpanzee kinase architectures are supported by experiment evidence. Guidelines to prevent such erroneous conclusions in similar papers are proposed.

SUBMITTER: Robison K

PROVIDER: S-EPMC2823696 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature

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Re-examination of chimp protein kinases suggests "novel architectures" are gene prediction artifacts.

Robison Keith K

BMC genomics 20100127

<h4>Background</h4>Anamika et al recently published in this journal a sequence alignment analysis of protein kinases encoded by the chimpanzee genome in comparison to those in the human genome. From this analysis they concluded that several chimpanzee kinases have unusual domain arrangements.<h4>Results</h4>Re-examination of these kinases reveals claimed novel arrangements cannot withstand scrutiny; each is either not novel or represents over-analysis of weakly confident computer generated gene ...[more]

PMID: 20105302

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Project description:BackgroundAberrant protein kinase regulation leading to abnormal substrate phosphorylation is associated with several human diseases. Despite the promise of therapies targeting kinases, many human kinases remain understudied. Most existing computational tools predicting phosphorylation cover less than 50% of known human kinases. They utilize local feature selection based on protein sequences, motifs, domains, structures, and/or functions, and do not consider the heterogeneous relationships of the proteins. In this work, we present KSFinder, a tool that predicts kinase-substrate links by capturing the inherent association of proteins in a network comprising 85% of the known human kinases. We also postulate the potential role of two understudied kinases based on their substrate predictions from KSFinder.MethodsKSFinder learns the semantic relationships in a phosphoproteome knowledge graph using a knowledge graph embedding algorithm and represents the nodes in low-dimensional vectors. A multilayer perceptron (MLP) classifier is trained to discern kinase-substrate links using the embedded vectors. KSFinder uses a strategic negative generation approach that eliminates biases in entity representation and combines data from experimentally validated non-interacting protein pairs, proteins from different subcellular locations, and random sampling. We assess KSFinder's generalization capability on four different datasets and compare its performance with other state-of-the-art prediction models. We employ KSFinder to predict substrates of 68 "dark" kinases considered understudied by the Illuminating the Druggable Genome program and use our text-mining tool, RLIMS-P along with manual curation, to search for literature evidence for the predictions. In a case study, we performed functional enrichment analysis for two dark kinases - HIPK3 and CAMKK1 using their predicted substrates.ResultsKSFinder shows improved performance over other kinase-substrate prediction models and generalized prediction ability on different datasets. We identified literature evidence for 17 novel predictions involving an understudied kinase. All of these 17 predictions had a probability score ≥0.7 (nine at >0.9, six at 0.8-0.9, and two at 0.7-0.8). The evaluation of 93,593 negative predictions (probability ≤0.3) identified four false negatives. The top enriched biological processes of HIPK3 substrates relate to the regulation of extracellular matrix and epigenetic gene expression, while CAMKK1 substrates include lipid storage regulation and glucose homeostasis.ConclusionsKSFinder outperforms the current kinase-substrate prediction tools with higher kinase coverage. The strategically developed negatives provide a superior generalization ability for KSFinder. We predicted substrates of 432 kinases, 68 of which are understudied, and hypothesized the potential functions of two dark kinases using their predicted substrates.

Dataset Information

Re-examination of chimp protein kinases suggests "novel architectures" are gene prediction artifacts.

Publications

Re-examination of chimp protein kinases suggests "novel architectures" are gene prediction artifacts.

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