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At least 613 records · Page 34Linked to original sources
PubMed2005

CAPRI rounds 3-5 reveal promising successes and future challenges for RosettaDock.

CAPRI Rounds 3, 4, and 5 are the first public test of the published RosettaDock algorithm. The targets cover a wide range of sizes and shapes. For most targets, published biological information indicated the region of the binding site on at least one docking partner. The RosettaDock algorithm produced high accuracy predictions for three targets, medium-accuracy predictions for two targets, and an acceptable prediction for one target. RosettaDock predicted all five targets with less than 450 residues to high or medium accuracy, but it predicted only one of seven targets with above 450 residues to acceptable accuracy. RosettaDock's high-accuracy predictions for small to moderately large targets reveal the predictive power and fidelity of the algorithm, especially the high-resolution refinement and scoring protocol. In addition, RosettaDock can predict complexes from at least one homology-modeled docking partner with comparable accuracy to unbound cases of similar size. Larger targets present a more intensive sampling problem, and some large targets present repulsive barriers to entering the binding site. Ongoing improvements to RosettaDock's low-resolution search may alleviate this problem. This first public test suggests that RosettaDock can be useful in a significant range of applications in biochemistry and cell biology.

Algorithms↗
PubMed2005

Progress in protein-protein docking: atomic resolution predictions in the CAPRI experiment using RosettaDock with an improved treatment of side-chain flexibility.

RosettaDock uses real-space Monte Carlo minimization (MCM) on both rigid-body and side-chain degrees of freedom to identify the lowest free energy docked arrangement of 2 protein structures. An improved version of the method that uses gradient-based minimization for off-rotamer side-chain optimization and includes information from unbound structures was used to create predictions for Rounds 4 and 5 of CAPRI. First, large numbers of independent MCM trajectories were carried out and the lowest free energy docked configurations identified. Second, new trajectories were started from these lowest energy structures to thoroughly sample the surrounding conformation space, and the lowest energy configurations were submitted as predictions. For all cases in which there were no significant backbone conformational changes, a small number of very low-energy configurations were identified in the first, global search and subsequently found to be close to the center of the basin of attraction in the free energy landscape in the second, local search. Following the release of the experimental coordinates, it was found that the centers of these free energy minima were remarkably close to the native structures in not only the rigid-body orientation but also the detailed conformations of the side-chains. Out of 8 targets, the lowest energy models had interface root-mean-square deviations (RMSDs) less than 1.1 A from the correct structures for 6 targets, and interface RMSDs less than 0.4 A for 3 targets. The predictions were top submissions to CAPRI for Targets 11, 12, 14, 15, and 19. The close correspondence of the lowest free energy structures found in our searches to the experimental structures suggests that our free energy function is a reasonable representation of the physical chemistry, and that the real space search with full side-chain flexibility to some extent solves the protein-protein docking problem in the absence of significant backbone conformational changes. On the other hand, the approach fails when there are significant backbone conformational changes as the steric complementarity of the 2 proteins cannot be modeled without incorporating backbone flexibility, and this is the major goal of our current work.

Algorithms↗
PubMed2001

Dynamically assisted gated trapping for Fourier transform ion cyclotron mass spectrometry.

An efficient approach for trapping ions and enhancing signal based on 'adiabatic amplitude reduction' for Fourier transform ion cyclotron resonance (FTICR) mass spectrometry is described and evaluated. This method is a modification to the widely used gated trapping technique in which the trapping potential is raised adiabatically rather than instantaneously (non-adiabatically). Compared with non-adiabatic gated trapping, the final amplitudes of ion axial oscillations and energies are lower in the proposed method. All performance aspects of the FTICR spectrum (e.g., peak intensities, mass resolution, and mass accuracy) improve significantly compared to the conventional gated trapping technique.

Amino Acid Sequence↗
PubMed2025

Identification of Plant Chromatin Interaction Networks Using IP-MS and co-IP.

Proteins often act in concert to perform their function. Thus, the identification of protein complexes is crucial if we want to understand how they work. In this chapter, we present a highly sensitive protocol for the immunoprecipitation of nuclear chromatin-linked proteins in Arabidopsis thaliana that does not rely on time-consuming nuclei extraction. Interaction partners are identified using mass spectrometry and confirmed by co-immunoprecipitation. To help solubilize chromatin-bound proteins and eliminate nonspecific interactions of proteins binding the same DNA stretch, we include an enzymatic digestion step to remove DNA before immunoprecipitation. Our protocol offers a simplified process using optimized buffers, which facilitates quick and effective immunoprecipitation. The outcome is high-quality eluates that are ideal for identifying proteins through MS.

Chromatin↗
PubMed2004

Increased levels of adenine nucleotide translocator 1 protein and response to oxidative stress are early events in facioscapulohumeral muscular dystrophy muscle.

Facioscapulohumeral muscular dystrophy (FSHD), an autosomal dominant neuromuscular disorder, has been causally related to deletion of tandemly arrayed 3.3 kb repeats (D4Z4) on chromosome 4q35. Although increased expression of several 4q35 genes has been reported, two recent studies dispute this, finding no significant changes in the transcriptional level of any of the 4q35 genes, among which is the heart and muscle-specific isoform of the adenine nucleotide translocator (ANT1). We found markedly increased levels of ANT1 protein in both unaffected and affected FSHD muscles in comparison to control healthy muscles. Comparative protein expression analysis between healthy, Duchenne muscular dystrophy, and FSHD muscle shows that proteins involved in mitochondrial function and protection from oxidative stress are also reproducibly and specifically modified in all FSHD muscles, including clinically unaffected muscles. Increased ANT1 expression and mitochondrial dysfunction may thus be initial events in FSHD pathogenesis and represent potential therapeutic targets.

Adenine Nucleotide Translocator 1↗
PubMed2006

Systems for the detection and analysis of protein-protein interactions.

The analysis of protein-protein interactions is important for developing a better understanding of the functional annotations of proteins that are involved in various biochemical reactions in vivo. The discovery that a protein with an unknown function binds to a protein with a known function could provide a significant clue to the cellular pathway concerning the unknown protein. Therefore, information on protein-protein interactions obtained by the comprehensive analysis of all gene products is available for the construction of interactive networks consisting of individual protein-protein interactions, which, in turn, permit elaborate biological phenomena to be understood. Systems for detecting protein-protein interactions in vitro and in vivo have been developed, and have been modified to compensate for limitations. Using these novel approaches, comprehensive and reliable information on protein-protein interactions can be determined. Systems that permit this to be achieved are described in this review.

Chromatography, Affinity↗
PubMed2006

Microbial metabolomics: past, present and future methodologies.

Microbial metabolomics has received much attention in recent years mainly because it supports and complements a wide range of microbial research areas from new drug discovery efforts to metabolic engineering. Broadly, the term metabolomics refers to the comprehensive (qualitative and quantitative) analysis of the complete set of all low molecular weight metabolites present in and around growing cells at a given time during their growth or production cycle. This review focuses on the past, current and future development of various experimental protocols in the rapid developing area of metabolomics in the ongoing quest to reliably quantify microbial metabolites formed under defined physiological conditions. These developments range from rapid sample collection, instant quenching of microbial metabolic activity, extraction of the relevant intracellular metabolites as well as quantification of these metabolites using enzyme based and or modern high tech hyphenated analytical protocols, mainly chromatographic techniques coupled to mass spectrometry (LC-MS(n), GC-MS(n), CE-MS(n)), where n indicates the number of tandem mass spectrometry, and nuclear magnetic resonance spectroscopy (NMR).

Bacteria↗
PubMed2005

Identification of the -1 translational frameshift sites using a liquid chromatography-tandem mass spectrometric approach.

Translational frameshifting, a ubiquitous mechanism used to produce alternative proteins for different biological purposes, appears in a variety of genes in probably all organisms. In the past, the combinational use of sophisticated expression vectors, specific endopeptidases, and Edman degradation has been the main approach for identification of the translational frameshift sites. Although Edman degradation is highly reliable, it is also time-consuming and costly. In this article, we report a new liquid chromatography-tandem mass spectrometric (LC-MS/MS) approach for identifying the -1 translational frameshift sites. The approach consists of three steps: (i) LC-MS/MS analysis of the protein digests, (ii) primary data analysis using the known mRNA sequence, and (iii) advanced data analysis using a new database containing distinct mRNA sequences with single insertion at particular positions. We first validated our approach by analyzing the previously documented slippery sequence, A4G, from IS3. With this approach, we further determined whether the TTTTTTG (T6G) sequence of IS1372 from Streptomyces lividans had the -1 translational frameshifting potential. The identified amino acid sequence of the transframe peptide indicated that the -1 frameshifting occurred at the T6G motif, as predicted previously. The results on IS3 (A4G) and IS1372 (T6G) suggested that this approach is effective for the translational frameshifting studies.

Amino Acid Sequence↗
PubMed2005

Quantitation of adenovirus type 5 empty capsids.

Adenovirus empty capsids are immature intermediates that lack DNA and viral core proteins. Highly purified preparations of empty and full capsids were generated by subjecting purified adenovirus preparations to repeated cesium chloride gradient separations. PAGE results revealed that empty capsids contain at least five bands that correspond to proteins absent from the mature virus proteome. Peptide mapping by matrix-assisted laser desorption/ionization time-of-flight MS revealed that three of these bands correspond to varying forms of L1 52/55kDa, a protein involved in the encapsidation of the viral DNA. One band at around 31kDa was found to include precursors to proteins VI and VIII. These precursors correspond to proteins that have not been cleaved by the adenovirus-encoded protease and are not present in the mature full capsids. The precursor to protein VIII (pVIII), a capsid cement protein, is used in this study as a marker in reverse-phased HPLC (RP-HPLC) analyses of adenovirus for the quantitation of empty capsids. A novel calculation method applied to the integration of RP-HPLC chromatograms allowed for the generation of a percentage empty capsid value in a given adenovirus preparation. The percentage empty capsid values generated to date by this method show a high degree of precision and good agreement with a cesium chloride gradient/SDS-PAGE quantitation method of empty capsids. The advantage of this method lies in the accurate, precise, and rapid generation of the percentage of empty capsids in a given purified virus preparation without relying on tedious and time-consuming cesium chloride gradient separations and extractions.

Adenoviridae↗
PubMed2005

Determination of site-specificity of S-glutathionylated cellular proteins.

Redox modification by S-glutathionylation is an expanding field within cell signalling research. However, the methods available for analysis of S-glutathionylated proteins in complex mixtures are not sufficiently accurate to specifically and in a high-throughput manner on a structural level establish the effects of S-glutathionylation on the individual proteins. A method has been developed for rapid identification of the S-glutathionylation sites of proteins in diamide-treated ECV304 cells, through tagging of deglutathionylated proteins with a cysteine-reactive biotin-affinity tag, trypsinisation, avidin-affinity purification of tagged peptides, and subsequent analysis by liquid chromatography and quadrupole time-of-flight tandem mass spectrometry. The method has led to identification of the glutathionylation sites of gamma-actin (Cys(217)), heat shock protein 60 (Cys(447)), and elongation factor 1-alpha-1 (Cys(411)). Further developments of accuracy within the field of peptide-affinity capture and mass spectrometry are discussed.

Binding Sites↗
PubMed2005

A gene expression fingerprint of mouse stomach ECL cells.

Many of the endocrine cells in the stomach are poorly characterized with respect to physiological significance. In some cases, the anticipated hormone has not yet been identified. Global gene expression analysis of mouse stomach was performed in an attempt to identify the ECL-cell peptide/protein. Specific functional activation (omeprazole-induced hypergastrinaemia) was used as a tool to generate a gene expression fingerprint of the ECL cells. The proposed fingerprint includes 14 genes, among them six are known to be expressed by ECL cells (=positive controls), and some novel ones, which are likely to be ECL-cell-related. The known ECL-cell-related genes are those encoding histidine decarboxylase, chromogranin A and B, vesicular monoamine transporter 2, synaptophysin, and the cholecystokinin-B receptor. In addition, the fingerprint included five genes, which might be involved in the process of secretion and three ESTs with unknown function. Interestingly, parathyroid hormone-like hormone (Pthlh) was identified as a candidate ECL-cell peptide hormone.

Animals↗
PubMed2006

Biochemical interactions among intercellular adhesion molecules expressed by airway epithelial cells.

Intercellular adhesion between adjacent airway epithelial cells plays a critical role in maintaining the barrier function of the respiratory mucosa. In the current study, we examined the expression and interaction of cell surface adhesion molecules (E-cadherin, ICAM-1, and MUC1) and their intracellular binding partners (alpha-catenin, beta-catenin, gamma-catenin, and ezrin) in 16HBE14o-, HBE1, 1HAEo-, BEAS-2B, A549, and NCI-H292 human airway epithelial cells. Expression of E-cadherin and MUC1, both in whole cell lysates and biotinylated surface proteins, was observed in 16HBE14o-, HBE1, A549, and NCI-H292 cells, while ICAM-1 was detected only in NCI-H292. In contrast, alpha-, beta-, and gamma-catenin and ezrin were expressed in all of the cells. E-cadherin formed coimmunoprecipitation complexes with beta- and gamma-catenin, whereas MUC1 only associated with beta-catenin. ICAM-1, but not MUC1, coimmunoprecipitated with ezrin in NCI-H292 cells. We conclude that airway epithelial cell-cell adhesion involves a complex network of protein-protein interactions mediated by a diverse array of membrane-bound and cytosolic protein partners.

Cell Adhesion Molecules↗
PubMed2006

Integrated analysis of multiple data sources reveals modular structure of biological networks.

It has been a challenging task to integrate high-throughput data into investigations of the systematic and dynamic organization of biological networks. Here, we presented a simple hierarchical clustering algorithm that goes a long way to achieve this aim. Our method effectively reveals the modular structure of the yeast protein-protein interaction network and distinguishes protein complexes from functional modules by integrating high-throughput protein-protein interaction data with the added subcellular localization and expression profile data. Furthermore, we take advantage of the detected modules to provide a reliably functional context for the uncharacterized components within modules. On the other hand, the integration of various protein-protein association information makes our method robust to false-positives, especially for derived protein complexes. More importantly, this simple method can be extended naturally to other types of data fusion and provides a framework for the study of more comprehensive properties of the biological network and other forms of complex networks.

Algorithms↗
PubMed2005

Learning Petri net models of non-linear gene interactions.

Understanding how an individual's genetic make-up influences their risk of disease is a problem of paramount importance. Although machine-learning techniques are able to uncover the relationships between genotype and disease, the problem of automatically building the best biochemical model or "explanation" of the relationship has received less attention. In this paper, I describe a method based on random hill climbing that automatically builds Petri net models of non-linear (or multi-factorial) disease-causing gene-gene interactions. Petri nets are a suitable formalism for this problem, because they are used to model concurrent, dynamic processes analogous to biochemical reaction networks. I show that this method is routinely able to identify perfect Petri net models for three disease-causing gene-gene interactions recently reported in the literature.

Algorithms↗
PubMed2005

Probabilistic prediction of protein-protein interactions from the protein sequences.

Prediction of protein-protein interactions is very important for several bioinformatics tasks though it is not a straightforward problem. In this paper, employing only protein sequence information, a framework is presented to predict protein-protein interactions using a probabilistic-based tree augmented nai ve (TAN) Bayesian network. Our framework also provides a confidence level for every predicted interaction, which is useful for further analysis by the biologists. The framework is applied to the yeast interaction datasets for predicting interactions and it is shown that our framework gives better performance than support vector machine (SVM). The framework is implemented as a webserver and is available for prediction.

Artificial Intelligence↗
PubMed2006

Yeast two-hybrid contributions to interactome mapping.

Interactome mapping, the systematic identification of protein interactions within an organism, promises to facilitate systems-level studies of biological processes. Using in vitro technologies that measure specific protein interactions, static maps are being generated that include many of the protein networks that occur in vivo. Most of the binary protein interaction data currently available was generated by large-scale yeast two-hybrid screens. Recent efforts to map interactions in model organisms and in humans illustrate the promise and some of the limitations of the two-hybrid approach. Although these maps are incomplete and include false positives, they are proving useful as a framework around which to elaborate and model the in vivo interactome.

Animals↗