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Background: Automatic extraction of motifs from biological sequences is an important research problem in study of molecular biology. For proteins, it is desired to discover sequence motifs containing a large number of wildcard symbols, as the residues associated with functional sites are usually largely separated in sequences. Discovering such patterns is time-consuming because abundant combinations exist when long gaps (a gap consists of one or more successive wildcards) are considered. Mining algorithms often employ constraints to narrow down the search space in order to increase efficiency. However, improper constraint models might degrade the sensitivity and specificity of the motifs discovered by computational methods. We previously proposed a new constraint model to handle large wildcard regions for discovering functional motifs of proteins. The patterns that satisfy the proposed constraint model are called W-patterns. A W-pattern is a structured motif that groups motif symbols into pattern blocks interleaved with large irregular gaps. Considering large gaps reflects the fact that functional residues are not always from a single region of protein sequences, and restricting motif symbols into clusters corresponds to the observation that short motifs are frequently present within protein families. To efficiently discover W-patterns for large-scale sequence annotation and function prediction, this paper first formally introduces the problem to solve and proposes an algorithm named WildSpan (sequential pattern mining across large wildcard regions) that incorporates several pruning strategies to largely reduce the mining cost. Results: WildSpan is shown to efficiently find W-patterns containing conserved residues that are far separated in sequences. We conducted experiments with two mining strategies, protein-based and family-based mining, to evaluate the usefulness of W-patterns and performance of WildSpan. The protein-based mining mode of WildSpan is developed for discovering functional regions of a single protein by referring to a set of related sequences (e.g. its homologues). The discovered W-patterns are used to characterize the protein sequence and the results are compared with the conserved positions identified by multiple sequence alignment (MSA). The family-based mining mode of WildSpan is developed for extracting sequence signatures for a group of related proteins (e.g. a protein family) for protein function classification. In this situation, the discovered W-patterns are compared with PROSITE patterns as well as the patterns generated by three existing methods performing the similar task. Finally, analysis on execution time of running WildSpan reveals that the proposed pruning strategy is effective in improving the scalability of the proposed algorithm. Conclusions: The mining results conducted in this study reveal that WildSpan is efficient and effective in discovering functional signatures of proteins directly from sequences. The proposed pruning strategy is effective in improving the scalability of WildSpan. It is demonstrated in this study that the W-patterns discovered by WildSpan provides useful information in characterizing protein sequences. The WildSpan executable and open source codes are available on the web (http://biominer.csie.cyu.edu.tw/wildspan). [ABSTRACT FROM AUTHOR]

Background: Structural excursions of a protein at equilibrium are key to biomolecular recognition and function modulation. Protein modeling research is driven by the need to aid wet laboratories in characterizing equilibrium protein dynamics. In principle, structural excursions of a protein can be directly observed via simulation of its dynamics, but the disparate temporal scales involved in such excursions make this approach computationally impractical. On the other hand, an informative representation of the structure space available to a protein at equilibrium can be obtained efficiently via stochastic optimization, but this approach does not directly yield information on equilibrium dynamics. Methods: We present here a novel methodology that first builds a multi-dimensional map of the energy landscape that underlies the structure space of a given protein and then queries the computed map for energetically-feasible excursions between structures of interest. An evolutionary algorithm builds such maps with a practical computational budget. Graphical techniques analyze a computed multi-dimensional map and expose interesting features of an energy landscape, such as basins and barriers. A path searching algorithm then queries a nearest-neighbor graph representation of a computed map for energetically-feasible basin-to-basin excursions. Results: Evaluation is conducted on intrinsically-dynamic proteins of importance in human biology and disease. Visual statistical analysis of the maps of energy landscapes computed by the proposed methodology reveals features already captured in the wet laboratory, as well as new features indicative of interesting, unknown thermodynamically-stable and semi-stable regions of the equilibrium structure space. Comparison of maps and structural excursions computed by the proposed methodology on sequence variants of a protein sheds light on the role of equilibrium structure and dynamics in the sequence-function relationship. Conclusions: Applications show that the proposed methodology is effective at locating basins in complex energy landscapes and computing basin-basin excursions of a protein with a practical computational budget. While the actual temporal scales spanned by a structural excursion cannot be directly obtained due to the foregoing of simulation of dynamics, hypotheses can be formulated regarding the impact of sequence mutations on protein function. These hypotheses are valuable in instigating further research in wet laboratories. [ABSTRACT FROM AUTHOR]

Background: Long-term space travel simulation experiments enabled to discover different aspects of human metabolism such as the complexity of NaCl salt balance. Detailed proteomics data were collected during the Mars105 isolation experiment enabling a deeper insight into the molecular processes involved. Results: We studied the abundance of about two thousand proteins extracted from urine samples of six volunteers collected weekly during a 105-day isolation experiment under controlled dietary conditions including progressive reduction of salt consumption. Machine learning using Self Organizing maps (SOM) in combination with different analysis tools was applied to describe the time trajectories of protein abundance in urine. The method enables a personalized and intuitive view on the physiological state of the volunteers. The abundance of more than one half of the proteins measured clearly changes in the course of the experiment. The trajectory splits roughly into three time ranges, an early (week 1-6), an intermediate (week 7-11) and a late one (week 12-15). Regulatory modes associated with distinct biological processes were identified using previous knowledge by applying enrichment and pathway flow analysis. Early protein activation modes can be related to immune response and inflammatory processes, activation at intermediate times to developmental and proliferative processes and late activations to stress and responses to chemicals. Conclusions: The protein abundance profiles support previous results about alternative mechanisms of salt storage in an osmotically inactive form. We hypothesize that reduced NaCl consumption of about 6 g/day presumably will reduce or even prevent the activation of inflammatory processes observed in the early time range of isolation. SOM machine learning in combination with analysis methods of class discovery and functional annotation enable the straightforward analysis of complex proteomics data sets generated by means of mass spectrometry. [ABSTRACT FROM AUTHOR]

Background: Gold nanoparticles are useful tools for biological applications due to their attractive physical and chemical properties. Their applications can be further expanded when they are functionalized with biological molecules. The biological molecules not only provide the interfaces for interactions between nanoparticles and biological environment, but also contribute their biological functions to the nanoparticles. Therefore, we used self-assembling protein nanoparticles (SAPNs) to encapsulate gold nanoparticles. The protein nanoparticles are formed upon self-assembly of a protein chain that is composed of a pentameric coiled-coil domain at the N-terminus and trimeric coiled-coil domain at the C-terminus. The self-assembling protein nanoparticles form a central cavity of about 10 nm in size, which is ideal for the encapsulation of gold nanoparticles with similar sizes. Results: We have used SAPNs to encapsulate several commercially available gold nanoparticles. The hydrodynamic size and the surface coating of gold nanoparticles are two important factors influencing successful encapsulation by the SAPNs. Gold nanoparticles with a hydrodynamic size of less than 15 nm can successfully be encapsulated. Gold nanoparticles with citrate coating appear to have stronger interactions with the proteins, which can interfere with the formation of regular protein nanoparticles. Upon encapsulation gold nanoparticles with polymer coating interfere less strongly with the ability of the SAPNs to assemble into nanoparticles. Although the central cavity of the SAPNs carries an overall charge, the electrostatic interaction appears to be less critical for the efficient encapsulation of gold nanoparticles into the protein nanoparticles. Conclusions: The SAPNs can be used to encapsulate gold nanoparticles. The SAPNs can be further functionalized by engineering functional peptides or proteins to either their N- or C-termini. Therefore encapsulation of gold nanoparticles into SAPNs can provide a useful platform to generate a multifunctional biodevices. [ABSTRACT FROM AUTHOR]

Background: Experimental determination of protein 3D structures is expensive, time consuming and sometimes impossible. A gap between number of protein structures deposited in the World Wide Protein Data Bank and the number of sequenced proteins constantly broadens. Computational modeling is deemed to be one of the ways to deal with the problem. Although protein 3D structure prediction is a difficult task, many tools are available. These tools can model it from a sequence or partial structural information, e.g. contact maps. Consequently, biologists have the ability to generate automatically a putative 3D structure model of any protein. However, the main issue becomes evaluation of the model quality, which is one of the most important challenges of structural biology. Results: GOBA - Gene Ontology-Based Assessment is a novel Protein Model Quality Assessment Program. It estimates the compatibility between a model-structure and its expected function. GOBA is based on the assumption that a high quality model is expected to be structurally similar to proteins functionally similar to the prediction target. Whereas DALI is used to measure structure similarity, protein functional similarity is quantified using standardized and hierarchical description of proteins provided by Gene Ontology combined with Wang's algorithm for calculating semantic similarity. Two approaches are proposed to express the quality of protein model-structures. One is a single model quality assessment method, the other is its modification, which provides a relative measure of model quality. Exhaustive evaluation is performed on data sets of model-structures submitted to the CASP8 and CASP9 contests. Conclusions: The validation shows that the method is able to discriminate between good and bad model-structures. The best of tested GOBA scores achieved 0.74 and 0.8 as a mean Pearson correlation to the observed quality of models in our CASP8 and CASP9-based validation sets. GOBA also obtained the best result for two targets of CASP8, and one of CASP9, compared to the contest participants. Consequently, GOBA offers a novel single model quality assessment program that addresses the practical needs of biologists. In conjunction with other Model Quality Assessment Programs (MQAPs), it would prove useful for the evaluation of single protein models. [ABSTRACT FROM AUTHOR]

Background: Current methods of isolation of muscle satellite cells from different animal species are highly variable making inter-species comparisons problematic. This variation mainly stems from the use of different proteolytic enzymes to release the satellite cells from the muscle tissue (sometimes a single enzyme is used but often a combination of enzymes is preferred) and the different extracellular matrix proteins used to coat culture ware. In addition, isolation of satellite cells is frequently laborious and sometimes may require pre-plating of the cell preparation on uncoated flasks or Percoll centrifugation to remove contaminating fibroblasts. The methodology employed to isolate and culture satellite cells in vitro can critically determine the fusion of myoblasts into multinucleated myotubes. These terminally differentiated myotubes resemble mature myofibres in the muscle tissue in vivo, therefore optimal fusion is a keystone of in vitro muscle culture. Hence, a simple method of muscle satellite cell isolation and culture of different vertebrate species that can result in a high fusion rate is highly desirable. Results: We demonstrate here a relatively simple and rapid method of isolating highly enriched muscle satellite cells from different avian and mammalian species without the need of Percoll centrifugation or pre-plating. In brief, muscle tissue was mechanically dissociated, digested with a single enzyme (pronase), triturated with a 10-ml pipette, filtered and directly plated onto collagen coated flasks. Following this method and after optimization of the cell culture conditions, excellent fusion rates were achieved in the duck, chicken, horse and cow (with more than 50% cell fusion), and to a lesser extent pig, pointing to pronase as a highly suitable enzyme to release satellite cells from muscle tissue. Conclusions: Our simplified method presents a quick and simple alternative to isolating highly enriched muscle satellite cell cultures which can subsequently rapidly differentiate into well developed primary myotubes. The use of the same isolation protocol allows better inter-species comparisons of muscle satellite cells. Of all the farm animal species investigated, harvested chicken muscle cells showed the highest percentage of muscle satellite cells, and equine muscle cells presented the highest fusion index, an impressive ~ 77%. Porcine cells displayed the lowest amount of satellite cells but still achieved a modest fusion rate of ~41%. [ABSTRACT FROM AUTHOR]

Background: Human plasma, representing the most complete record of the individual phenotype, is an appealing sample for proteomics analysis in clinical applications. Up to today, the major obstacle in a proteomics study of plasma is the large dynamic range of protein concentration and the efforts of many researchers focused on the resolution of this important drawback. Findings: In this study, proteins from pooled plasma samples were fractionated according to their chemical characteristics on a home-designed SPE automated platform. The resulting fractions were digested and further resolved by reversed-phase liquid chromatography coupled with MALDI TOF/TOF mass spectrometry. A total of 712 proteins were successfully identified until a concentration level of ng/mL. Pearson correlation coefficient was used to test reproducibility. Conclusions: Our multidimensional fractionation approach reduced the analysis time (2 days are enough to process 16 plasma samples filling a 96-well plate) over the conventional gel-electrophoresis or multi-LC column based methods. The robotic processing, avoiding contaminants or lack of sample handling skill, promises highly reproducible specimen analyses (more than 85% Pearson correlation). The automated platform here presented is flexible and easily modulated changing fractioning elements or detectors. [ABSTRACT FROM AUTHOR]

The article presents information on a study on over 9,500 human proteins and developed and optimized a system for proteome-wide identification of novel methylation events that was catalyzed by the protein lysine methyltransferase (PKMT) SETD6. The study identified novel candidate substrates for SETD6 by using two independent detection approaches The study also verified that all targets tested in vitro as well as in cells were genuine substrates. The study described a novel proteome-wide methodology for identifying the new PKMT substrates.

The increasing availability of large-scale protein-protein interaction data has made it possible to understand the basic components and organization of cell machinery from the network level. The arising challenge is how to analyze such complex interacting data to reveal the principles of cellular organization, processes and functions. Many studies have shown that clustering protein interaction network is an effective approach for identifying protein complexes or functional modules, which has become a major research topic in systems biology. In this review, recent advances in clustering methods for protein interaction networks will be presented in detail. The predictions of protein functions and interactions based on modules will be covered. Finally, the performance of different clustering methods will be compared and the directions for future research will be discussed. [ABSTRACT FROM AUTHOR]

Background: Many studies have provided algorithms or methods to assess a statistical significance in quantitative proteomics when multiple replicates for a protein sample and a LC/MS analysis are available. But, confidence is still lacking in using datasets for a biological interpretation without protein sample replicates. Although a fold-change is a conventional threshold that can be used when there are no sample replicates, it does not provide an assessment of statistical significance such as a false discovery rate (FDR) which is an important indicator of the reliability to identify differentially expressed proteins. In this work, we investigate whether differentially expressed proteins can be detected with a statistical significance from a pair of unlabeled protein samples without replicates and with only duplicate LC/MS injections per sample. A FDR is used to gauge the statistical significance of the differentially expressed proteins. Results: We have experimented to operate on several parameters to control a FDR, including a fold-change, a statistical test, and a minimum number of permuted significant pairings. Although none of these parameters alone gives a satisfactory control of a FDR, we find that a combination of these parameters provides a very effective means to control a FDR without compromising the sensitivity. The results suggest that it is possible to perform a significance analysis without protein sample replicates. Only duplicate LC/MS injections per sample are needed. We illustrate that differentially expressed proteins can be detected with a FDR between 0 and 15% at a positive rate of 4-16%. The method is evaluated for its sensitivity and specificity by a ROC analysis, and is further validated with a [15N]-labeled internal-standard protein sample and additional unlabeled protein sample replicates. Conclusion: We demonstrate that a statistical significance can be inferred without protein sample replicates in label-free quantitative proteomics. The approach described in this study would be useful in many exploratory experiments where a sample amount or instrument time is limited. Naturally, this method is also suitable for proteomics experiments where multiple sample replicates are available. It is simple, and is complementary to other more sophisticated algorithms that are not designed for dealing with a small number of sample replicates. [ABSTRACT FROM AUTHOR]

Background: Conservation of the spatial binding organizations at the level of physico-chemical interactions is important for the formation and stability of protein-protein complexes as well as protein and drug design. Due to the lack of computational tools for recognition of spatial patterns of interactions shared by a set of protein-protein complexes, the conservation of such interactions has not been addressed previously. Results: We performed extensive spatial comparisons of physico-chemical interactions common to different types of protein-protein complexes. We observed that 80% of these interactions correspond to known hot spots. Moreover, we show that spatially conserved interactions allow prediction of hot spots with a success rate higher than obtained by methods based on sequence or backbone similarity. Detection of spatially conserved interaction patterns was performed by our novel MAPPIS algorithm. MAPPIS performs multiple alignments of the physico-chemical interactions and the binding properties in three dimensional space. It is independent of the overall similarity in the protein sequences, folds or amino acid identities. We present examples of interactions shared between complexes of colicins with immunity proteins, serine proteases with inhibitors and T-cell receptors with superantigens. We unravel previously overlooked similarities, such as the interactions shared by the structurally different RNase-inhibitor families. Conclusion: The key contribution of MAPPIS is in discovering the 3D patterns of physicochemical interactions. The detected patterns describe the conserved binding organizations that involve energetically important hot spot residues and are crucial for the protein-protein associations. [ABSTRACT FROM AUTHOR]

More than a hundred naturally occurring mutations of human glucose-6-phosphate dehydrogenase (G6PD) have been identified at the amino acid level. The abundance of distinct mutation sites and their clinical manifestations make this enzyme ideal for structure-function analysis studies. We present here a sequence and structure combined analysis by which the severity of clinical symptoms resulting from point mutations of this enzyme is correlated with quantified degrees of amino acid conservation within 23 G6PD sequences from different organisms. Our analysis verifies, on a quantitative basis, a widely held notion that clinically severer mutations of G6PD usually occur at conserved amino acids. However, marked exceptions to this general trend exist which are most notably revealed by a number of mutations associated with chronic nonspherocytic hemolytic anemia (class I variants). When mapped onto a homology-de- rived structural model of human G6PD, these class I mutational sites of low amino acid conservation appear to localize in two spatially distinct clusters, both of which are populated with mutations consisting mainly of clinically severer variants (i.e. class I and class II). These results of computer-assisted analyses contribute to a further understanding of the structure-function relationships of human G6PD deficiency. [ABSTRACT FROM AUTHOR]