GPS-PAIL: prediction of lysine acetyltransferase-specific modification sites from protein sequences.
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ABSTRACT:
SUBMITTER: Deng W
PROVIDER: S-EPMC5177928 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundNeddylation is a reversible post-translational modification that plays a vital role in maintaining cellular machinery. It is shown to affect localization, binding partners and structure of target proteins. Disruption of protein neddylation was observed in various diseases such as Alzheimer's and cancer. Therefore, understanding the neddylation mechanism and determining neddylation targets possibly bears a huge importance in further understanding the cellular processes. This study is the first attempt to predict neddylated sites from protein sequences by using several sequence and sequence-based structural features.ResultsWe have developed a neddylation site prediction method using a support vector machine based on various sequence properties, position-specific scoring matrices, and disorder. Using 21 amino acid long lysine-centred windows, our model was able to predict neddylation sites successfully, with an average 5-fold stratified cross validation performance of 0.91, 0.91, 0.75, 0.44, 0.95 for accuracy, specificity, sensitivity, Matthew's correlation coefficient and area under curve, respectively. Independent test set results validated the robustness of reported new method. Additionally, we observed that neddylation sites are commonly flexible and there is a significant positively charged amino acid presence in neddylation sites.ConclusionsIn this study, a neddylation site prediction method was developed for the first time in literature. Common characteristics of neddylation sites and their discriminative properties were explored for further in silico studies on neddylation. Lastly, up-to-date neddylation dataset was provided for researchers working on post-translational modifications in the accompanying supplementary material of this article.
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Project description:Identifying interaction sites in proteins provides important clues to the function of a protein and is becoming increasingly relevant in topics such as systems biology and drug discovery. Although there are numerous papers on the prediction of interaction sites using information derived from structure, there are only a few case reports on the prediction of interaction residues based solely on protein sequence. Here, a sliding window approach is combined with the Random Forests method to predict protein interaction sites using (i) a combination of sequence- and structure-derived parameters and (ii) sequence information alone. For sequence-based prediction we achieved a precision of 84% with a 26% recall and an F-measure of 40%. When combined with structural information, the prediction performance increases to a precision of 76% and a recall of 38% with an F-measure of 51%. We also present an attempt to rationalize the sliding window size and demonstrate that a nine-residue window is the most suitable for predictor construction. Finally, we demonstrate the applicability of our prediction methods by modeling the Ras-Raf complex using predicted interaction sites as target binding interfaces. Our results suggest that it is possible to predict protein interaction sites with quite a high accuracy using only sequence information.
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Project description:The MYST protein lysine acetyltransferases are evolutionarily conserved throughout eukaryotes and acetylate proteins to regulate diverse biological processes including gene regulation, DNA repair, cell-cycle regulation, stem cell homeostasis and development. Here, we demonstrate that MYST protein acetyltransferase activity requires active site lysine autoacetylation. The X-ray crystal structures of yeast Esa1 (yEsa1/KAT5) bound to a bisubstrate H4K16CoA inhibitor and human MOF (hMOF/KAT8/MYST1) reveal that they are autoacetylated at a strictly conserved lysine residue in MYST proteins (yEsa1-K262 and hMOF-K274) in the enzyme active site. The structure of hMOF also shows partial occupancy of K274 in the unacetylated form, revealing that the side chain reorients to a position that engages the catalytic glutamate residue and would block cognate protein substrate binding. Consistent with the structural findings, we present mass spectrometry data and biochemical experiments to demonstrate that this lysine autoacetylation on yEsa1, hMOF and its yeast orthologue, ySas2 (KAT8) occurs in solution and is required for acetylation and protein substrate binding in vitro. We also show that this autoacetylation occurs in vivo and is required for the cellular functions of these MYST proteins. These findings provide an avenue for the autoposttranslational regulation of MYST proteins that is distinct from other acetyltransferases but draws similarities to the phosphoregulation of protein kinases.
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Project description:As one of the most essential post-translational modifications (PTMs) of proteins, proteolysis, especially calpain-mediated cleavage, plays an important role in many biological processes, including cell death/apoptosis, cytoskeletal remodeling, and the cell cycle. Experimental identification of calpain targets with bona fide cleavage sites is fundamental for dissecting the molecular mechanisms and biological roles of calpain cleavage. In contrast to time-consuming and labor-intensive experimental approaches, computational prediction of calpain cleavage sites might more cheaply and readily provide useful information for further experimental investigation. In this work, we constructed a novel software package of GPS-CCD (Calpain Cleavage Detector) for the prediction of calpain cleavage sites, with an accuracy of 89.98%, sensitivity of 60.87% and specificity of 90.07%. With this software, we annotated potential calpain cleavage sites for hundreds of calpain substrates, for which the exact cleavage sites had not been previously determined. In this regard, GPS-CCD 1.0 is considered to be a useful tool for experimentalists. The online service and local packages of GPS-CCD 1.0 were implemented in JAVA and are freely available at: http://ccd.biocuckoo.org/.
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Project description:BackgroundProtein post-translational modification (PTM) is a key issue to investigate the mechanism of protein's function. With the rapid development of proteomics technology, a large amount of protein sequence data has been generated, which highlights the importance of the in-depth study and analysis of PTMs in proteins.MethodWe proposed a new multi-classification machine learning pipeline MultiLyGAN to identity seven types of lysine modified sites. Using eight different sequential and five structural construction methods, 1497 valid features were remained after the filtering by Pearson correlation coefficient. To solve the data imbalance problem, Conditional Generative Adversarial Network (CGAN) and Conditional Wasserstein Generative Adversarial Network (CWGAN), two influential deep generative methods were leveraged and compared to generate new samples for the types with fewer samples. Finally, random forest algorithm was utilized to predict seven categories.ResultsIn the tenfold cross-validation, accuracy (Acc) and Matthews correlation coefficient (MCC) were 0.8589 and 0.8376, respectively. In the independent test, Acc and MCC were 0.8549 and 0.8330, respectively. The results indicated that CWGAN better solved the existing data imbalance and stabilized the training error. Alternatively, an accumulated feature importance analysis reported that CKSAAP, PWM and structural features were the three most important feature-encoding schemes. MultiLyGAN can be found at https://github.com/Lab-Xu/MultiLyGAN .ConclusionsThe CWGAN greatly improved the predictive performance in all experiments. Features derived from CKSAAP, PWM and structure schemes are the most informative and had the greatest contribution to the prediction of PTM.
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