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The article focuses on the reasons why the author has spent nine years pursuing a map of the 450-protein nuclear pore complex (NPC). His fascination by how things move in and about the cell's nucleus contribute with his determination to mapping huge complexes. The author, together with his colleagues, has generated proteomics, biochemical and mass spectrometry data for all NPC proteins. Fitting each protein's volume into its hotspot gives a physical map of the pore. It is considered that this serves as a powerful approach to solve large assemblies.

Background: Despite sensitivity of MRI to diagnose multiple sclerosis (MS), prognostic biomarkers are still needed for optimized treatment. Objective: The objective of this paper is to identify cerebrospinal fluid (CSF) diagnostic biomarkers of MS using quantitative proteomics and to analyze their expression at different disease stages. Methods: We conducted differential analysis of the CSF proteome from control and relapsing–remitting MS (RRMS) patients followed by verification by ELISA of candidate biomarkers in CSF and serum in control, clinically isolated syndrome (CIS), RRMS and progressive MS (PMS) patients. Results: Twenty-two of the 527 quantified proteins exhibited different abundances in control and RRMS CSF. These include chitinase 3-like protein 1 (CHI3L1) and 2 (CHI3L2), which showed a strong expression in brain of MS patients, especially in astrocytes and microglial cells from white matter plaques. CSF and serum CHI3L1 levels increased with the disease stage and CIS patients with high CSF (>189 ng/ml) and serum (>33 ng/ml) CHI3L1 converted more rapidly to RRMS (log rank test, p < 0.05 and p < 0.001, respectively). In contrast, CSF CHI3L2 levels were lower in PMS than in RRMS patients. Accordingly, CSF CHI3L1/CHI3L2 ratio accurately discriminated PMS from RRMS. Conclusions: CSF CHI3L1 and CHI3L2 and serum CHI3L1 might help to define MS disease stage and have a prognostic value in CIS. [ABSTRACT FROM AUTHOR]

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]

For a given DNA sequence, it is well known that pair wise alignment schemes are used to determine the similarity with the DNA sequences available in the databanks. The efficiency of the alignment decides the type of amino acids and its corresponding proteins. In order to evaluate the given DNA sequence for its proteomic identity, a pattern matching approach is proposed in this paper. A block based semi-global alignment scheme is introduced to determine the similarity between the DNA sequences (known and given). The two DNA sequences are divided into blocks of equal length and alignment is performed which minimizes the computational complexity. The efficiency of the alignment scheme is evaluated using the parameter, percentage of similarity (POS). Four essential DNA version of the amino acids that emphasize the importance of proteomic functionalities are chosen as patterns and matching is performed with the known and given DNA sequences to determine the similarity between them. The ratio of amino acid counts between the two sequences is estimated and the results are compared with that of the POS value. It is found from the experimental results that higher the POS value and the pattern matching higher are the similarity between the two DNA sequences. The optimal block is also identified based on the POS value and amino acids count. [ABSTRACT FROM AUTHOR]

Abstract: A rapid method to screen and identify unknown bound proteins to specific nucleic acid probes anchored on ProteinChip array surfaces from crude biological samples has been developed in this paper. It was demonstrated with screening specific binding proteins from LPS-stimulated mouse 70Z/3 pre-B cell nuclear extracts by direct coupling of thioaptamer XBY-S2 to the pre-activated ProteinChip array surfaces. With pre-fractionation of crude nuclear extracts by ion exchange method, specific “on-chip” captured proteins have been obtained that were pure enough to do “on-chip” digestion and the subsequent identification of the “on-chip” bound proteins by microsequencing of the trypsin digested peptide fragments through tandem MS. Five mouse heterogeneous nuclear ribonucleoproteins (hnRNPs) A1, A2/B1, A3, A/B, and D0 were identified. To verify those bound hnRNPs, a novel thioaptamer/antibody sandwich assay provides highly sensitive and selective identification of proteins on ProteinChip arrays. [Copyright &y& Elsevier]

Abstract: Thyroid tumorigenesis involves qualitative and quantitative changes in protein expression, which can be comprehensively studied by proteome analysis. However, one of the technical bottlenecks of proteomics remains a reliable, sensitive and inexpensive method for quantification of differentially expressed proteins. This is due to the limited linear range of most available protein stains, i.e. silver and Coomassie blue, and high costs of commercially available fluorescent stains. In this paper we describe our experience with a lab-made ruthenium based fluorescent stain (ruthenium II tris(bathophenanthroline disulfonate) (RuBPs)) to perform proteome analysis of nodular thyroid disease. We first compared the properties of RuBPs with two highly sensitive protein stains: (1) silver staining and (2) the commercially available fluorescent dye Sypro Ruby. We show that in addition to its highly sensitive staining capabilities similar to Sypro Ruby and silver (2ng), RuBPs offers several advantages such as a broad dynamic range (similar to Sypro Ruby and 500 times broader than the dynamic range of silver stain), low costs (€ 0.03 per gel) and excellent compatibility with mass spectrometry. We then applied the inexpensive RuBPs stain to 2D gels (pH 4–7) of four benign thyroid nodules and normal thyroid tissue. We were able to detect ∼1800 protein spots/gel in our thyroid samples. Quantitative changes in protein expression levels of at least 20–42 proteins were noted in the benign nodules compared with the normal thyroid tissue of the same patient. Differentially expressed spots were further characterised by nano-LC-FTICR and MALDI-TOF mass spectrometry. In summary we demonstrate, that the novel fluorescent ruthenium II tris(bathophenanthroline disulfonate) stain is a highly sensitive, reliable and inexpensive tool for quantitative proteome analysis in thyroid nodular disease. [Copyright &y& Elsevier]

Comparing the relative abundance of each protein present in two or more complex samples can be accomplished using isotope-coded tags incorporated at the peplide level. Here we describe a chemical labeling strategy for the incorporation of a single isotope label per peptide, which is completely sequence-independent so that it potentially labels every peptide from a protein including those containing posttranslational modifications. It is based on a gentle chemical labeling strategy that specifically labels the N-terminus of all peptides in a digested sample with either a d5- or d0-propionyl group. Lysine side chains are blocked by guanidination prior to N-terminal labeling to prevent the incorporation of multiple labels. In this paper, we describe the optimization of this N-terminal isotopic tagging strategy and validate its use for peptide-based protein abundance measurements with a 10-protein standard mixture. Using a results-driven strategy, which targets proteins for identification based on MALDI TOF- MS analysis of isotopically labeled peptide pairs, we also show that this labeling strategy can detect a small number of differentially expressed proteins in a mixture as complex as a yeast cell lysate. Only peptides that show a difference in relative abundance are targeted for identification by tandem MS. Despite the fact that many peptides are quantitated, only those few showing a difference in abundance are targeted for protein identification. Proteins ire identified by either targeted LC-ES MS/MS or MAID! TOF/TOF. Identifications can be accomplished equally well by either technique on the basis of multiple peptides. This increases the confidence level for both identification and quantitation. The merits of ES MS/MS or MALDI MS/MS for protein identification in a results- driven strategy are discussed. [ABSTRACT FROM AUTHOR]

In this paper we consider the problem of characterizing and modeling large-scale protein-protein association networks using a class of range-dependent graphs which possess appropriate small world properties. These graphs may be employed in representing given association network using a maximum likelihood approach. This in turn annotates every observed association with its ‘range’, representing the tendency for such an association to be transitive. The application of a very rapidly developing field of graph theory to the emerging field of proetemics is novel and allows for a many-to-many relationship between individual proteins and groupings of proteins, which in turn may correspond to distinct functional behavior. [ABSTRACT FROM AUTHOR]

This paper experimentally verifies the idea presented earlier that the contact of nonpolar clusters located on the surface of protein molecules with water destabilizes proteins. It is demonstrated that protein stabilization can be achieved by artificial hydrophilization of the surface area of protein globules by chemical modification. Two experimental systems are studied for the verification of the hydrophilization approach. 1. The surface tyrosine residues of trypsin are transformed to aminotyrosines using a two-step modification procedure: nitration by tetranitromethane followed by reduction with sodium dithionite. The modified enzyme is much more stable against irreversible thermoinactivation: the stabilizing effect increases with the number of aminotyrosine residues in trypsin and the modified enzyme can become even 100 times more stable than the native one. 2. α-Chymotrypsin is covalently modified by treatment with anhydrides or chloroanhydrides of aromatic carboxytic acids. As a result, different numbers of additional carboxylic groups (up to five depending on the structure of the modifying reagent) are introduced into each Lys residue modified. Acylation of all available amino groups of α-chymotrypsin by cyclic anhydrides of pyromellitic and mellitic acids results in a substantial hydrophilization of the protein as estimated by partitioning in an aqueous Ficoll-400/Dextran-70 biphasic system. These modified enzyme preparations are extremely stable against irreversible thermal inactivation at elevated temperatures (65-98 °C); their thermostability is practically equal to the stability of proteolytic enzymes from extremely thermophilic bacteria, the most stable proteinases known to date. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we describe a combination of the mass spectrometric techniques MALDI–TOF/TOF and MALDI–FTMS to identify proteins in complex samples using prespotted MALDI target plates. By this procedure accurate FTMS mass measurements and TOF/TOF data are obtained from the same spot. We have found that this combination of techniques leads to more reliable identification of peptides. [Copyright &y& Elsevier]