Functional characterization of a 28-Kilobase Catabolic Island from Pseudomonas sp. Strain M1 involved in biotransformation of β-Myrcene and related plant-derived volatiles

Pseudomonas sp. strain M1 is able to mineralize highly hydrophobic and recalcitrant compounds, such as benzene, phenol, and their methylated/halogenated derivatives, as well as the backbone of several monoterpenes. The ability to use such a spectrum of compounds as the sole carbon source is, most probably, associated with a genetic background evolved under different environmental constraints. The outstanding performance of strain M1 regarding β-myrcene catabolism was elucidated in this work, with a focus on the biocatalytical potential of the β-myrcene-associated core code, comprised in a 28-kb genomic island (GI), predicted to be organized in 8 transcriptional units. Functional characterization of this locus with promoter probes and analytical approaches validated the genetic organization predictedin silicoand associated the β-myrcene-induced promoter activity to the production of β-myrcene derivatives. Notably, by using a whole-genome mutagenesis strategy, different genotypes of the 28-kb GI were generated, resulting in the identification of a novel putative β-myrcene hydroxylase, responsible for the initial oxidation of β-myrcene into myrcen-8-ol, and a sensor-like regulatory protein, whose inactivation abolished themyr + trait of M1 cells. Moreover, it was demonstrated that the range of monoterpene substrates of the M1 enzymatic repertoire, besides β-myrcene, also includes other acyclic (e.g., β-linalool) and cyclic [e.g.,R-(+)-limonene and (-)-β-pinene] molecules. Our findings are the cornerstone for following metabolic engineering approaches and hint at a major role of the 28-kb GI in the biotransformation of a broad monoterpene backbone spectrum for its future biotechnological applications.IMPORTANCEInformation regarding microbial systems able to biotransform monoterpenes, especially β-myrcene, is limited and focused mainly on nonsystematic metabolite identification. Complete and detailed knowledge at the genetic, protein, metabolite, and regulatory levels is essential in order to set a model organism or a catabolic system as a biotechnology tool. Moreover, molecular characterization of reported systems is scarce, almost nonexistent, limiting advances in the development of optimized cell factories with strategies based on the new generation of metabolic engineering platforms. This study provides new insights into the intricate molecular functionalities associated with β-myrcene catabolism inPseudomonas, envisaging the production of a molecular knowledge base about the underlying catalytic and regulatory mechanisms of plant-derived volatile catabolic pathways.Vectors from the Standard European Vector Architecture (SEVA) library and pBAM1 used in this work were kindly provided by Victor de Lorenzo (CNB-CSIC, Madrid, Spain). This work was supported by the strategic program UID/BIA/04050/2013 (POCI-01- 0145-FEDER-007569) funded by national funds through the FCT I.P. and by the ERDF through the COMPETE2020-Programa Operacional Competitividade e Internacionalização (POCI) and through a Ph.D. grant (grant SFRH/BD/76894/2011) to P.S.-C.info:eu-repo/semantics/publishedVersio

PMeS: Prediction of Methylation Sites Based on Enhanced Feature Encoding Scheme

Protein methylation is predominantly found on lysine and arginine residues, and carries many important biological functions, including gene regulation and signal transduction. Given their important involvement in gene expression, protein methylation and their regulatory enzymes are implicated in a variety of human disease states such as cancer, coronary heart disease and neurodegenerative disorders. Thus, identification of methylation sites can be very helpful for the drug designs of various related diseases. In this study, we developed a method called PMeS to improve the prediction of protein methylation sites based on an enhanced feature encoding scheme and support vector machine. The enhanced feature encoding scheme was composed of the sparse property coding, normalized van der Waals volume, position weight amino acid composition and accessible surface area. The PMeS achieved a promising performance with a sensitivity of 92.45%, a specificity of 93.18%, an accuracy of 92.82% and a Matthew’s correlation coefficient of 85.69% for arginine as well as a sensitivity of 84.38%, a specificity of 93.94%, an accuracy of 89.16% and a Matthew’s correlation coefficient of 78.68% for lysine in 10-fold cross validation. Compared with other existing methods, the PMeS provides better predictive performance and greater robustness. It can be anticipated that the PMeS might be useful to guide future experiments needed to identify potential methylation sites in proteins of interest. The online service is available at http://bioinfo.ncu.edu.cn/inquiries_PMeS.aspx

Incorporating Distant Sequence Features and Radial Basis Function Networks to Identify Ubiquitin Conjugation Sites

Ubiquitin (Ub) is a small protein that consists of 76 amino acids about 8.5 kDa. In ubiquitin conjugation, the ubiquitin is majorly conjugated on the lysine residue of protein by Ub-ligating (E3) enzymes. Three major enzymes participate in ubiquitin conjugation. They are – E1, E2 and E3 which are responsible for activating, conjugating and ligating ubiquitin, respectively. Ubiquitin conjugation in eukaryotes is an important mechanism of the proteasome-mediated degradation of a protein and regulating the activity of transcription factors. Motivated by the importance of ubiquitin conjugation in biological processes, this investigation develops a method, UbSite, which uses utilizes an efficient radial basis function (RBF) network to identify protein ubiquitin conjugation (ubiquitylation) sites. This work not only investigates the amino acid composition but also the structural characteristics, physicochemical properties, and evolutionary information of amino acids around ubiquitylation (Ub) sites. With reference to the pathway of ubiquitin conjugation, the substrate sites for E3 recognition, which are distant from ubiquitylation sites, are investigated. The measurement of F-score in a large window size (−20∼+20) revealed a statistically significant amino acid composition and position-specific scoring matrix (evolutionary information), which are mainly located distant from Ub sites. The distant information can be used effectively to differentiate Ub sites from non-Ub sites. As determined by five-fold cross-validation, the model that was trained using the combination of amino acid composition and evolutionary information performs best in identifying ubiquitin conjugation sites. The prediction sensitivity, specificity, and accuracy are 65.5%, 74.8%, and 74.5%, respectively. Although the amino acid sequences around the ubiquitin conjugation sites do not contain conserved motifs, the cross-validation result indicates that the integration of distant sequence features of Ub sites can improve predictive performance. Additionally, the independent test demonstrates that the proposed method can outperform other ubiquitylation prediction tools

Analysis and Prediction of Translation Rate Based on Sequence and Functional Features of the mRNA

Protein concentrations depend not only on the mRNA level, but also on the translation rate and the degradation rate. Prediction of mRNA's translation rate would provide valuable information for in-depth understanding of the translation mechanism and dynamic proteome. In this study, we developed a new computational model to predict the translation rate, featured by (1) integrating various sequence-derived and functional features, (2) applying the maximum relevance & minimum redundancy method and incremental feature selection to select features to optimize the prediction model, and (3) being able to predict the translation rate of RNA into high or low translation rate category. The prediction accuracies under rich and starvation condition were 68.8% and 70.0%, respectively, evaluated by jackknife cross-validation. It was found that the following features were correlated with translation rate: codon usage frequency, some gene ontology enrichment scores, number of RNA binding proteins known to bind its mRNA product, coding sequence length, protein abundance and 5′UTR free energy. These findings might provide useful information for understanding the mechanisms of translation and dynamic proteome. Our translation rate prediction model might become a high throughput tool for annotating the translation rate of mRNAs in large-scale

Rapid Probing of Biological Surfaces with a Sparse-Matrix Peptide Library

Finding unique peptides to target specific biological surfaces is crucial to basic research and technology development, though methods based on biological arrays or large libraries limit the speed and ease with which these necessary compounds can be found. We reasoned that because biological surfaces, such as cell surfaces, mineralized tissues, and various extracellular matrices have unique molecular compositions, they present unique physicochemical signatures to the surrounding medium which could be probed by peptides with appropriately corresponding physicochemical properties. To test this hypothesis, a naïve pilot library of 36 peptides, varying in their hydrophobicity and charge, was arranged in a two-dimensional matrix and screened against various biological surfaces. While the number of peptides in the matrix library was very small, we obtained “hits” against all biological surfaces probed. Sequence refinement of the “hits” led to peptides with markedly higher specificity and binding activity against screened biological surfaces. Genetic studies revealed that peptide binding to bacteria was mediated, at least in some cases, by specific cell-surface molecules, while examination of human tooth sections showed that this method can be used to derive peptides with highly specific binding to human tissue

SNOSite: Exploiting Maximal Dependence Decomposition to Identify Cysteine S-Nitrosylation with Substrate Site Specificity

S-nitrosylation, the covalent attachment of a nitric oxide to (NO) the sulfur atom of cysteine, is a selective and reversible protein post-translational modification (PTM) that regulates protein activity, localization, and stability. Despite its implication in the regulation of protein functions and cell signaling, the substrate specificity of cysteine S-nitrosylation remains unknown. Based on a total of 586 experimentally identified S-nitrosylation sites from SNAP/L-cysteine-stimulated mouse endothelial cells, this work presents an informatics investigation on S-nitrosylation sites including structural factors such as the flanking amino acids composition, the accessible surface area (ASA) and physicochemical properties, i.e. positive charge and side chain interaction parameter. Due to the difficulty to obtain the conserved motifs by conventional motif analysis, maximal dependence decomposition (MDD) has been applied to obtain statistically significant conserved motifs. Support vector machine (SVM) is applied to generate predictive model for each MDD-clustered motif. According to five-fold cross-validation, the MDD-clustered SVMs could achieve an accuracy of 0.902, and provides a promising performance in an independent test set. The effectiveness of the model was demonstrated on the correct identification of previously reported S-nitrosylation sites of Bos taurus dimethylarginine dimethylaminohydrolase 1 (DDAH1) and human hemoglobin subunit beta (HBB). Finally, the MDD-clustered model was adopted to construct an effective web-based tool, named SNOSite (http://csb.cse.yzu.edu.tw/SNOSite/), for identifying S-nitrosylation sites on the uncharacterized protein sequences

Analysis and Prediction of the Metabolic Stability of Proteins Based on Their Sequential Features, Subcellular Locations and Interaction Networks

The metabolic stability is a very important idiosyncracy of proteins that is related to their global flexibility, intramolecular fluctuations, various internal dynamic processes, as well as many marvelous biological functions. Determination of protein's metabolic stability would provide us with useful information for in-depth understanding of the dynamic action mechanisms of proteins. Although several experimental methods have been developed to measure protein's metabolic stability, they are time-consuming and more expensive. Reported in this paper is a computational method, which is featured by (1) integrating various properties of proteins, such as biochemical and physicochemical properties, subcellular locations, network properties and protein complex property, (2) using the mRMR (Maximum Relevance & Minimum Redundancy) principle and the IFS (Incremental Feature Selection) procedure to optimize the prediction engine, and (3) being able to identify proteins among the four types: “short”, “medium”, “long”, and “extra-long” half-life spans. It was revealed through our analysis that the following seven characters played major roles in determining the stability of proteins: (1) KEGG enrichment scores of the protein and its neighbors in network, (2) subcellular locations, (3) polarity, (4) amino acids composition, (5) hydrophobicity, (6) secondary structure propensity, and (7) the number of protein complexes the protein involved. It was observed that there was an intriguing correlation between the predicted metabolic stability of some proteins and the real half-life of the drugs designed to target them. These findings might provide useful insights for designing protein-stability-relevant drugs. The computational method can also be used as a large-scale tool for annotating the metabolic stability for the avalanche of protein sequences generated in the post-genomic age

Prediction of Protein Binding Regions in Disordered Proteins

Many disordered proteins function via binding to a structured partner and undergo a disorder-to-order transition. The coupled folding and binding can confer several functional advantages such as the precise control of binding specificity without increased affinity. Additionally, the inherent flexibility allows the binding site to adopt various conformations and to bind to multiple partners. These features explain the prevalence of such binding elements in signaling and regulatory processes. In this work, we report ANCHOR, a method for the prediction of disordered binding regions. ANCHOR relies on the pairwise energy estimation approach that is the basis of IUPred, a previous general disorder prediction method. In order to predict disordered binding regions, we seek to identify segments that are in disordered regions, cannot form enough favorable intrachain interactions to fold on their own, and are likely to gain stabilizing energy by interacting with a globular protein partner. The performance of ANCHOR was found to be largely independent from the amino acid composition and adopted secondary structure. Longer binding sites generally were predicted to be segmented, in agreement with available experimentally characterized examples. Scanning several hundred proteomes showed that the occurrence of disordered binding sites increased with the complexity of the organisms even compared to disordered regions in general. Furthermore, the length distribution of binding sites was different from disordered protein regions in general and was dominated by shorter segments. These results underline the importance of disordered proteins and protein segments in establishing new binding regions. Due to their specific biophysical properties, disordered binding sites generally carry a robust sequence signal, and this signal is efficiently captured by our method. Through its generality, ANCHOR opens new ways to study the essential functional sites of disordered proteins