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Analysis of the human kinome using methods including fold recognition reveals two novel kinases.

ABSTRACT: Protein sequence similarity is a commonly used criterion for inferring the unknown function of a protein from a protein of known function. However, proteins can diverge significantly over time such that sequence similarity is difficult, if not impossible, to find. In some cases, a structural similarity remains over long evolutionary time scales and once detected can be used to predict function.Here we employed a high-throughput approach to assign structural and functional annotation to the human proteome, focusing on the collection of human protein kinases, the human kinome. We compared human protein sequences to a library of domains from known structures using WU-BLAST, PSI-BLAST, and 123D. This approach utilized both sequence comparison and fold recognition methods. The resulting set of potential protein kinases was cross-checked against previously identified human protein kinases, and analyzed for conserved kinase motifs.We demonstrate that our structure-based method can be used to identify both typical and atypical human protein kinases. We also identify two potentially novel kinases that contain an interesting combination of kinase and acyl-CoA dehydrogenase domains.

SUBMITTER: Briedis KM

PROVIDER: S-EPMC2223070 | biostudies-literature | 2008 Feb

REPOSITORIES: biostudies-literature

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Analysis of the human kinome using methods including fold recognition reveals two novel kinases.

Briedis Kristine M KM Starr Ayelet A Bourne Philip E PE

PloS one 20080213 2

<h4>Background</h4>Protein sequence similarity is a commonly used criterion for inferring the unknown function of a protein from a protein of known function. However, proteins can diverge significantly over time such that sequence similarity is difficult, if not impossible, to find. In some cases, a structural similarity remains over long evolutionary time scales and once detected can be used to predict function.<h4>Methodology/principal findings</h4>Here we employed a high-throughput approach t ...[more]

PMID: 18270584

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Project description:BackgroundThe complexity of phosphoinositide signaling in higher eukaryotes is partly due to expansion of specific families and types of phosphoinositide kinases (PIKs) that can generate all phosphoinositides via multiple routes. This is particularly evident in the PI3Ks and PIPKs, and it is considered an evolutionary trait associated with metazoan diversification. Yet, there are limited comprehensive studies on the PIK repertoire of free living unicellular organisms.Methodology/principal findingsWe undertook a genome-wide analysis of putative PIK genes in two free living ciliated cells, Tetrahymena and Paramecium. The Tetrahymena thermophila and Paramecium tetraurelia genomes were probed with representative kinases from all families and types. Putative homologs were verified by EST, microarray and deep RNA sequencing database searches and further characterized for domain structure, catalytic efficiency, expression patterns and phylogenetic relationships. In total, we identified and characterized 22 genes in the Tetrahymena thermophila genome and 62 highly homologues genes in Paramecium tetraurelia suggesting a tight evolutionary conservation in the ciliate lineage. Comparison to the kinome of fungi reveals a significant expansion of PIK genes in ciliates.Conclusions/significanceOur study highlights four important aspects concerning ciliate and other unicellular PIKs. First, ciliate-specific expansion of PI4KIII-like genes. Second, presence of class I PI3Ks which, at least in Tetrahymena, are associated with a metazoan-type machinery for PIP3 signaling. Third, expansion of divergent PIPK enzymes such as the recently described type IV transmembrane PIPKs. Fourth, presence of possible type II PIPKs and presumably inactive PIKs (hence, pseudo-PIKs) not previously described. Taken together, our results provide a solid framework for future investigation of the roles of PIKs in ciliates and indicate that novel functions and novel regulatory pathways of phosphoinositides may be more widespread than previously thought in unicellular organisms.

Project description:Protein fold recognition is a critical step toward protein structure and function prediction, aiming at providing the most likely fold type of the query protein. In recent years, the development of deep learning (DL) technique has led to massive advances in this important field, and accordingly, the sensitivity of protein fold recognition has been dramatically improved. Most DL-based methods take an intermediate bottleneck layer as the feature representation of proteins with new fold types. However, this strategy is indirect, inefficient and conditional on the hypothesis that the bottleneck layer's representation is assumed as a good representation of proteins with new fold types. To address the above problem, in this work, we develop a new computational framework by combining triplet network and ensemble DL. We first train a DL-based model, termed FoldNet, which employs triplet loss to train the deep convolutional network. FoldNet directly optimizes the protein fold embedding itself, making the proteins with the same fold types be closer to each other than those with different fold types in the new protein embedding space. Subsequently, using the trained FoldNet, we implement a new residue-residue contact-assisted predictor, termed FoldTR, which improves protein fold recognition. Furthermore, we propose a new ensemble DL method, termed FSD_XGBoost, which combines protein fold embedding with the other two discriminative fold-specific features extracted by two DL-based methods SSAfold and DeepFR. The Top 1 sensitivity of FSD_XGBoost increases to 74.8% at the fold level, which is ~9% higher than that of the state-of-the-art method. Together, the results suggest that fold-specific features extracted by different DL methods complement with each other, and their combination can further improve fold recognition at the fold level. The implemented web server of FoldTR and benchmark datasets are publicly available at http://csbio.njust.edu.cn/bioinf/foldtr/.

Dataset Information

Analysis of the human kinome using methods including fold recognition reveals two novel kinases.

Publications

Analysis of the human kinome using methods including fold recognition reveals two novel kinases.

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