Protein features as determinants of wild-type glycoside hydrolase thermostability.

Thermostable enzymes for conversion of lignocellulosic biomass into biofuels have significant advantages over enzymes with more moderate themostability due to the challenging application conditions. Experimental discovery of thermostable enzymes is highly cost intensive, and the development of in-silico methods guiding the discovery process would be of high value. To develop such an in-silico method and provide the data foundation of it, we determined the melting temperatures of 602 fungal glycoside hydrolases from the families GH5, 6, 7, 10, 11, 43, and AA9 (formerly GH61). We, then used sequence and homology modeled structure information of these enzymes to develop the ThermoP melting temperature prediction method. Futhermore, in the context of thermostability, we determined the relative importance of 160 molecular features, such as amino acid frequencies and spatial interactions, and exemplified their biological significance. The presented prediction method is made publicly available at http://www.cbs.dtu.dk/services/ThermoP.

Module walking using an SH3-like cell-wall-binding domain leads to a new GH184 family of muramidases.

Muramidases (also known as lysozymes) hydrolyse the peptidoglycan component of the bacterial cell wall and are found in many glycoside hydrolase (GH) families. Similar to other glycoside hydrolases, muramidases sometimes have noncatalytic domains that facilitate their interaction with the substrate. Here, the identification, characterization and X-ray structure of a novel fungal GH24 muramidase from Trichophaea saccata is first described, in which an SH3-like cell-wall-binding domain (CWBD) was identified by structure comparison in addition to its catalytic domain. Further, a complex between a triglycine peptide and the CWBD from T. saccata is presented that shows a possible anchor point of the peptidoglycan on the CWBD. A `domain-walking' approach, searching for other sequences with a domain of unknown function appended to the CWBD, was then used to identify a group of fungal muramidases that also contain homologous SH3-like cell-wall-binding modules, the catalytic domains of which define a new GH family. The properties of some representative members of this family are described as well as X-ray structures of the independent catalytic and SH3-like domains of the Kionochaeta sp., Thermothielavioides terrestris and Penicillium virgatum enzymes. This work confirms the power of the module-walking approach, extends the library of known GH families and adds a new noncatalytic module to the muramidase arsenal.

Combining peptide recognition specificity and context information for the prediction of the 14-3-3-mediated interactome in S. cerevisiae and H. sapiens.

Large-scale interaction studies contribute the largest fraction of protein interactions information in databases. However, co-purification of non-specific or indirect ligands, often results in data sets that are affected by a considerable number of false positives. For the fraction of interactions mediated by short linear peptides, we present here a combined experimental and computational strategy for ranking the reliability of the inferred partners. We apply this strategy to the family of 14-3-3 domains. We have first characterized the recognition specificity of this domain family, largely confirming the results of previous analyses, while revealing new features of the preferred sequence context of 14-3-3 phospho-peptide partners. Notably, a proline next to the carboxy side of the phospho-amino acid functions as a potent inhibitor of 14-3-3 binding. The position-specific information about residue preference was encoded in a scoring matrix and two regular expressions. The integration of these three features in a single predictive model outperforms publicly available prediction tools. Next we have combined, by a naïve Bayesian approach, these "peptide features" with "protein features", such as protein co-expression and co-localization. Our approach provides an orthogonal reliability assessment and maps with high confidence the 14-3-3 peptide target on the partner proteins.

Comparative interactomics: comparing apples and pears?

The study of the complex web of interactions that link biological molecules in a cell is the subject of interactomics--currently one of the fastest moving fields in molecular biology. The recent completion of high-throughput studies to investigate systematically all the possible interactions in a variety of model organisms has provided unique opportunities to compare interaction networks and ask questions about their conservation during evolution. It is expected that this approach will yield a scientific return as rich as that obtained in the past decade from comparing genomes and proteomes from different organisms.

Presence of a functional but dispensable nuclear export signal in the HTLV-2 Tax protein.

BACKGROUND: Human T-cell leukemia virus type 1 and type 2 are related human retroviruses. HTLV-1 is the etiological agent of the Adult T-cell Leukemia/Lymphoma and of the Tropical Spastic Paraparesis/HTLV-1 Associated Myelopathy, whereas, HTLV-2 infection has not been formally associated with any T-cell malignancy. HTLV-1 and 2 genomes encode, respectively, the Tax1 and Tax2 proteins whose role is to transactivate the viral promoter. HTLV-1 and HTLV-2 Tax sequences display 28% divergence at the amino acid level. Tax1 is a shuttling protein that possesses both a non canonical nuclear import (NLS) and a nuclear export (NES) signal. We have recently demonstrated that Tax1 and Tax2 display different subcellular localization and that residues 90-100 are critical for this process. We investigate in the present report, whether Tax2 also possesses a functional NES. RESULTS: We first used a NES prediction method to determine whether the Tax2 protein might contain a NES and the results do suggest the presence of a NES sequence in Tax2. Using Green Fluorescent Protein-NES (GFP-NES) fusion proteins, we demonstrate that the Tax2 sequence encompasses a functional NES (NES2). As shown by microscope imaging, NES2 is able to mediate translocation of GFP from the nucleus, without the context of a full length Tax protein. Furthermore, point mutations or leptomycin B treatment abrogate NES2 function. However, within the context of full length Tax2, similar point mutations in the NES2 leucine rich stretch do not modify Tax2 localization. Finally, we also show that Tax1 NES function is dependent upon the positioning of the nuclear export signal "vis-à-vis" GFP. CONCLUSION: HTLV-2 Tax NES is functional but dispensable for the protein localization in vitro.

Non-classical protein secretion in bacteria.

BACKGROUND: We present an overview of bacterial non-classical secretion and a prediction method for identification of proteins following signal peptide independent secretion pathways. We have compiled a list of proteins found extracellularly despite the absence of a signal peptide. Some of these proteins also have known roles in the cytoplasm, which means they could be so-called "moon-lightning" proteins having more than one function. RESULTS: A thorough literature search was conducted to compile a list of currently known bacterial non-classically secreted proteins. Pattern finding methods were applied to the sequences in order to identify putative signal sequences or motifs responsible for their secretion. We have found no signal or motif characteristic to any majority of the proteins in the compiled list of non-classically secreted proteins, and conclude that these proteins, indeed, seem to be secreted in a novel fashion. However, we also show that the apparently non-classically secreted proteins are still distinguished from cellular proteins by properties such as amino acid composition, secondary structure and disordered regions. Specifically, prediction of disorder reveals that bacterial secretory proteins are more structurally disordered than their cytoplasmic counterparts. Finally, artificial neural networks were used to construct protein feature based methods for identification of non-classically secreted proteins in both Gram-positive and Gram-negative bacteria. CONCLUSION: We present a publicly available prediction method capable of discriminating between this group of proteins and other proteins, thus allowing for the identification of novel non-classically secreted proteins. We suggest candidates for non-classically secreted proteins in Escherichia coli and Bacillus subtilis. The prediction method is available online.

Coronavirus 3CLpro proteinase cleavage sites: possible relevance to SARS virus pathology.

BACKGROUND: Despite the passing of more than a year since the first outbreak of Severe Acute Respiratory Syndrome (SARS), efficient counter-measures are still few and many believe that reappearance of SARS, or a similar disease caused by a coronavirus, is not unlikely. For other virus families like the picornaviruses it is known that pathology is related to proteolytic cleavage of host proteins by viral proteinases. Furthermore, several studies indicate that virus proliferation can be arrested using specific proteinase inhibitors supporting the belief that proteinases are indeed important during infection. Prompted by this, we set out to analyse and predict cleavage by the coronavirus main proteinase using computational methods. RESULTS: We retrieved sequence data on seven fully sequenced coronaviruses and identified the main 3CL proteinase cleavage sites in polyproteins using alignments. A neural network was trained to recognise the cleavage sites in the genomes obtaining a sensitivity of 87.0% and a specificity of 99.0%. Several proteins known to be cleaved by other viruses were submitted to prediction as well as proteins suspected relevant in coronavirus pathology. Cleavage sites were predicted in proteins such as the cystic fibrosis transmembrane conductance regulator (CFTR), transcription factors CREB-RP and OCT-1, and components of the ubiquitin pathway. CONCLUSIONS: Our prediction method NetCorona predicts coronavirus cleavage sites with high specificity and several potential cleavage candidates were identified which might be important to elucidate coronavirus pathology. Furthermore, the method might assist in design of proteinase inhibitors for treatment of SARS and possible future diseases caused by coronaviruses. It is made available for public use at our website: http://www.cbs.dtu.dk/services/NetCorona/.

Analysis and prediction of leucine-rich nuclear export signals.

We present a thorough analysis of nuclear export signals and a prediction server, which we have made publicly available. The machine learning prediction method is a significant improvement over the generally used consensus patterns. Nuclear export signals (NESs) are extremely important regulators of the subcellular location of proteins. This regulation has an impact on transcription and other nuclear processes, which are fundamental to the viability of the cell. NESs are studied in relation to cancer, the cell cycle, cell differentiation and other important aspects of molecular biology. Our conclusion from this analysis is that the most important properties of NESs are accessibility and flexibility allowing relevant proteins to interact with the signal. Furthermore, we show that not only the known hydrophobic residues are important in defining a nuclear export signals. We employ both neural networks and hidden Markov models in the prediction algorithm and verify the method on the most recently discovered NESs. The NES predictor (NetNES) is made available for general use at http://www.cbs.dtu.dk/.

The SH2 domain interaction landscape.

Members of the SH2 domain family modulate signal transduction by binding to short peptides containing phosphorylated tyrosines. Each domain displays a distinct preference for the sequence context of the phosphorylated residue. We have developed a high-density peptide chip technology that allows for probing of the affinity of most SH2 domains for a large fraction of the entire complement of tyrosine phosphopeptides in the human proteome. Using this technique, we have experimentally identified thousands of putative SH2-peptide interactions for more than 70 different SH2 domains. By integrating this rich data set with orthogonal context-specific information, we have assembled an SH2-mediated probabilistic interaction network, which we make available as a community resource in the PepspotDB database. A predicted dynamic interaction between the SH2 domains of the tyrosine phosphatase SHP2 and the phosphorylated tyrosine in the extracellular signal-regulated kinase activation loop was validated by experiments in living cells.

WI-PHI: a weighted yeast interactome enriched for direct physical interactions.

How is the yeast proteome wired? This important question, central in yeast systems biology, remains unanswered in spite of the abundance of protein interaction data from high-throughput experiments. Unfortunately, these large-scale studies show striking discrepancies in their results and coverage such that biologists scrutinizing the "interactome" are often confounded by a mix of established physical interactions, functional associations, and experimental artifacts. This stimulated early attempts to integrate the available information and produce a list of protein interactions ranked according to an estimated functional reliability. The recent publication of the results of two large protein interaction experiments and the completion of a comprehensive literature curation effort has more than doubled the available information on the wiring of the yeast proteome. This motivates a fresh approach to the compilation of a yeast interactome based purely on evidence of physical interaction. We present a procedure exploiting both heuristic and probabilistic strategies to draft the yeast interactome taking advantage of various heterogeneous data sources: application of tandem affinity purification coupled to MS (TAP-MS), large-scale yeast two-hybrid studies, and results of small-scale experiments stored in dedicated databases. The end result is WI-PHI, a weighted network encompassing a large majority of yeast proteins.

Analysis and prediction of mammalian protein glycation.

Glycation is a nonenzymatic process in which proteins react with reducing sugar molecules and thereby impair the function and change the characteristics of the proteins. Glycation is involved in diabetes and aging where the accumulation of glycation products causes side effects. In this study, we statistically investigate the glycation of epsilon amino groups of lysines and also train a sequence-based predictor. The statistical analysis suggests that acidic amino acids, mainly glutamate, and lysine residues catalyze the glycation of nearby lysines. The catalytic acidic amino acids are found mainly C-terminally from the glycation site, whereas the basic lysine residues are found mainly N-terminally. The predictor was made by combining 60 artificial neural networks in a balloting procedure. The cross-validated Matthews correlation coefficient for the predictor is 0.58, which is quite impressive given the relatively small amount of experimental data available. The method is made available at www.cbs.dtu.dk/services/NetGlycate-1.0.

A wiring of the human nucleolus.

Recent proteomic efforts have created an extensive inventory of the human nucleolar proteome. However, approximately 30% of the identified proteins lack functional annotation. We present an approach of assigning function to uncharacterized nucleolar proteins by data integration coupled to a machine-learning method. By assembling protein complexes, we present a first draft of the human ribosome biogenesis pathway encompassing 74 proteins and hereby assign function to 49 previously uncharacterized proteins. Moreover, the functional diversity of the nucleolus is underlined by the identification of a number of protein complexes with functions beyond ribosome biogenesis. Finally, we were able to obtain experimental evidence of nucleolar localization of 11 proteins, which were predicted by our platform to be associates of nucleolar complexes. We believe other biological organelles or systems could be "wired" in a similar fashion, integrating different types of data with high-throughput proteomics, followed by a detailed biological analysis and experimental validation.

NetAcet: prediction of N-terminal acetylation sites.

We present here a neural network based method for prediction of N-terminal acetylation-by far the most abundant post-translational modification in eukaryotes. The method was developed on a yeast dataset for N-acetyltransferase A (NatA) acetylation, which is the type of N-acetylation for which most examples are known and for which orthologs have been found in several eukaryotes. We obtain correlation coefficients close to 0.7 on yeast data and a sensitivity up to 74% on mammalian data, suggesting that the method is valid for eukaryotic NatA orthologs.