Splitting the BLOSUM Score into Numbers of Biological Significance

Mathematical tools developed in the context of Shannon information theory were used to analyze the meaning of the BLOSUM score, which was split into three components termed as the BLOSUM spectrum (or BLOSpectrum). These relate respectively to the sequence convergence (the stochastic similarity of the two protein sequences), to the background frequency divergence (typicality of the amino acid probability distribution in each sequence), and to the target frequency divergence (compliance of the amino acid variations between the two sequences to the protein model implicit in the BLOCKS database). This treatment sharpens the protein sequence comparison, providing a rationale for the biological significance of the obtained score, and helps to identify weakly related sequences. Moreover, the BLOSpectrum can guide the choice of the most appropriate scoring matrix, tailoring it to the evolutionary divergence associated with the two sequences, or indicate if a compositionally adjusted matrix could perform better.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

Analysis of Free Energy Signals Arising from Nucleotide Hybridization Between rRNA and mRNA Sequences during Translation in Eubacteria

A decoding algorithm is tested that mechanistically models the progressive alignments that arise as the mRNA moves past the rRNA tail during translation elongation. Each of these alignments provides an opportunity for hybridization between the single-stranded, -terminal nucleotides of the 16S rRNA and the spatially accessible window of mRNA sequence, from which a free energy value can be calculated. Using this algorithm we show that a periodic, energetic pattern of frequency 1/3 is revealed. This periodic signal exists in the majority of coding regions of eubacterial genes, but not in the non-coding regions encoding the 16S and 23S rRNAs. Signal analysis reveals that the population of coding regions of each bacterial species has a mean phase that is correlated in a statistically significant way with species () content. These results suggest that the periodic signal could function as a synchronization signal for the maintenance of reading frame and that codon usage provides a mechanism for manipulation of signal phase.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

A Peptide Mimic of a Protective Epitope of Respiratory Syncytial Virus Selected from a Combinatorial Library Induces Virus-Neutralizing Antibodies and Reduces Viral Load In Vivo

Respiratory syncytial virus (RSV) is the most important cause of bronchiolitis and pneumonia in infants and young children worldwide. As yet, there is no effective vaccine against RSV infection, and previous attempts to develop a formalin-inactivated vaccine resulted in exacerbated disease in recipients subsequently exposed to the virus. In the work described here, a combinatorial solid-phase peptide library was screened with a protective monoclonal antibody (MAb 19) to identify peptide mimics (mimotopes) of a conserved and conformationally-determined epitope of RSV fusion (F) protein. Two sequences identified (S1 [HWYISKPQ] and S2 [HWYDAEVL]) reacted specifically with MAb 19 when they were presented as solid-phase peptides. Furthermore, after amino acid substitution analyses, three sequences derived from S1 (S1S [HWSISKPQ], S1K [KWYISKPQ], and S1P [HPYISKPQ]), presented as multiple antigen peptides (MAPs), also showed strong reactivity with MAb 19. The affinity constants of the binding of MAb 19, determined by surface plasmon resonance analyses, were 1.19 × 10⁹ and 4.93 × 10⁹ M⁻¹ for S1 and S1S, respectively. Immunization of BALB/c mice with these mimotopes, presented as MAPs, resulted in the induction of anti-peptide antibodies that inhibited the binding of MAb 19 to RSV and neutralized viral infection in vitro, with titers equivalent to those in sera from RSV-infected animals. Following RSV challenge of S1S mimotope-immunized mice, a 98.7% reduction in the titer of virus in the lungs was observed. Furthermore, there was a greatly reduced cell infiltration in the lungs of immunized mice compared to that in controls. These results indicate the potential of peptide mimotopes to protect against RSV infection without exacerbating pulmonary pathology.Respiratory syncytial virus (RSV) is the major cause of serious lower respiratory tract illness in infants and immunosuppressed individuals worldwide and is estimated to be responsible for 65 million infections and 1 million deaths annually (12, 19). Although the severity of disease declines with repeated infection, previous infection with RSV does not prevent illness in subsequent infections, and it is apparent that immunity is incomplete. Furthermore, attempts to develop a vaccine against RSV have encountered a series of problems. In the late 1960s, a formalin-inactivated vaccine not only failed to protect infants against RSV but also induced exacerbated disease during a subsequent epidemic (5, 19). Retrospective analysis of the sera demonstrated that the inactivated vaccine induced high anti-F (fusion) protein antibody titers, but with poor neutralizing activity, suggesting that the inactivation treatment had denatured or modified epitopes which were the target for neutralizing antibodies (19, 25). Live attenuated vaccines were also investigated as candidates; however, these were either poorly immunogenic (overattenuated) or genetically unstable (5, 45). Although new attenuated vaccines have given encouraging results in animal models (10, 11), there is an urgent need for alternative approaches to the development of an effective vaccine.Studies with experimental animals have provided evidence that RSV-specific neutralizing antibodies can prevent infection in the lungs when administered prophylactically (29). Intravenous administration of pooled human immunoglobulin G (IgG) containing a high titer of neutralizing antibodies prevented serious RSV lower respiratory tract illness in high-risk children (18). Both the F and G (attachment) glycoproteins of RSV play a major role in eliciting this humoral immunity. The conserved F glycoprotein induces protective immune responses (8, 36), and passive immunization with neutralizing anti-F monoclonal antibodies (MAbs) or recombinant Fab protects small animals from RSV infection (12, 38, 39). Based on the results of these studies, a neutralizing and protective MAb (MAb 19) has been reshaped and humanized (as MAb RSHZ19) and has been demonstrated to have protective properties superior to those of anti-RSV polyclonal antibodies in a rodent model (46). Its safety and efficacy have recently been assessed in human volunteers (13).The immunochemical characterization of the epitopes recognized by these protective antibodies is critical for the development of a vaccine against RSV, but the conformational constraints associated with the protective epitopes in RSV F protein make it unlikely that they will be identified from analysis of the primary amino acid sequence. Indeed, earlier attempts in our laboratory and elsewhere with synthetic peptides representing continuous sequences of the F protein (amino acids [aa] 205 to 225, 221 to 237, 261 to 273, 215 to 275, 417 to 438, and 481 to 491) failed to generate protective humoral responses (4, 23, 32, 42). The use of peptides (3), antigen fragments (24), and antibody escape mutants (39) has confirmed the involvement of discontinuous residues within the F protein in epitopes recognized by protective MAbs (39). Thus, the use of alternative approaches for the identification of these epitopes is essential.Solid-phase and bacteriophage combinatorial peptide libraries (22, 31) have been used for identification of peptide mimics (mimotopes) of ligands, and the identification of peptide sequences that mimic the conformational structure of protective epitopes would have great potential for the development of a synthetic peptide vaccine. Recently, using a solid-phase combinatorial peptide library, we identified 8-mer peptides that mimic an epitope recognized by a monoclonal anti-measles virus F protein antibody. The mimotopes identified by this approach did not bear any primary sequence relationship to sequences in the viral protein but mimicked its conformation. When used as an immunogen, one of the mimotopes induced virus-neutralizing and protective antibody responses (35). Phage-displayed peptide libraries have been used to delineate sequences which mimic a discontinuous epitope of hepatitis B surface antigen (7), the secondary structure of a neutralizing epitope of human immunodeficiency virus type 1 (15), and peptides that reacted with MAbs specific for polysaccharide antigens (43).In this paper, we describe the identification of mimotopes of a protective epitope of RSV F protein, as defined by the protective MAb 19, following the screening of a solid-phase combinatorial peptide library. One of these mimotopes induced a virus-neutralizing antibody response equivalent to that in RSV-immunized animals, which significantly reduced the viral load in RSV-challenged mice. These findings indicate the potential of synthetic peptide mimotopes for the development of a novel vaccine against RSV.     

Enhanced Prediction of Src Homology 2 (SH2) Domain Binding Potentials Using a Fluorescence Polarization-derived c-Met,c-Kit,ErbB, and Androgen Receptor Interactome

Many human diseases are associated with aberrant regulation of phosphoprotein signaling networks. Src homology 2 (SH2) domains represent the major class of protein domains in metazoans that interact with proteins phosphorylated on the amino acid residue tyrosine. Although current SH2 domain prediction algorithms perform well at predicting the sequences of phosphorylated peptides that are likely to result in the highest possible interaction affinity in the context of random peptide library screens, these algorithms do poorly at predicting the interaction potential of SH2 domains with physiologically derived protein sequences. We employed a high throughput interaction assay system to empirically determine the affinity between 93 human SH2 domains and phosphopeptides abstracted from several receptor tyrosine kinases and signaling proteins. The resulting interaction experiments revealed over 1000 novel peptide-protein interactions and provided a glimpse into the common and specific interaction potentials of c-Met, c-Kit, GAB1, and the human androgen receptor. We used these data to build a permutation-based logistic regression classifier that performed considerably better than existing algorithms for predicting the interaction potential of several SH2 domains.Src homology 2 protein domains (SH2)¹ are modular self-folding entities of about 100 amino acids that bind to tyrosine-phosphorylated peptide sequences contained within target proteins. The SH2 domain (1–3) was originally described nearly 20 years ago as an N-terminal region of the FES protein kinase that was not required for kinase activity but was important for its regulation. More recent studies have demonstrated that SH2 domains exist in many signaling molecules, including PLCγ1, Ras GAP, c-Src, and PI3KR. SH2 domains have been shown to enable the interaction of these signaling proteins with growth factor receptors such as FGFR1, EGFR, c-Met, and PDGFR in a phosphospecific manner (4–9). Subsequently, random peptide library screening approaches were used to define sequence motifs that resulted in the highest affinity interactions within particular SH2 domain classes (10, 11). For example, peptide sequences containing the pYEEI, pYXN, and pYMXM motifs were described to result in the highest affinity interactions with the SH2 domains from c-Src, Grb2, and the PI3KR SH2 domains, respectively. Data from such experiments have been used to generate predictions regarding the likelihood that any particular peptide sequence will interact with any particular SH2 domain (12–14).Unfortunately, the predictive performance of these algorithms has not been thoroughly empirically tested or optimized for biologically derived peptide sequences. We and others reported the first comprehensive cloning, expression, and functional analysis of human genome-encoded SH2 domains using a protein microarray-based interaction analysis approach (15–17). Similarly, peptide arrays have been used to query the interaction potential of SH2 domains with biologically derived peptide sequences in a semi-quantitative manner (18). These studies demonstrated that most biologically derived peptide sequences contained within RTKs and signaling proteins do not represent best fit sequence motifs and interact at a much lower affinity than with the optimal sequence motifs identified previously from random peptide libraries. Studies with biologically derived peptides indicated that context nonpermissive amino acids often contribute as much predictive information regarding interaction selectivity as positively contributing amino acids (19). Taken together, these results suggest that the collection of large quantitative protein interaction datasets between SH2 domains and biologically derived peptide sequences might be informative for building better algorithms that predict bona fide SH2 domain interaction sites within human protein sequences.Although protein microarrays enabled the first systems-level glimpse at SH2 domain selectivity (15, 17), they had several limitations that resulted in reduced ability to identify low affinity interactions in comparison with solution phase methods (20). We therefore designed a high throughput fluorescence polarization approach that allowed for lower affinity interactions to be defined between SH2 domains and phosphopeptides of the ErbB family of receptor tyrosine kinases (RTKs) than was possible with protein microarrays (20).RTKs are vital mediators of signal transduction in multicellular organisms. RTKs typically function as transmembrane receptors that contain a tyrosine kinase and other motifs that enable interaction with other intracellular proteins. Human cells often express many different RTK proteins from the set of 57 RTK genes encoded by the human genome (21). These RTKs may be activated in different combinations to transduce common and specific downstream signals (22). For a recent review of the complexity of RTK signaling networks, see Ref. 23. Following activation, RTKs are phosphorylated on several intracellular tyrosine residues that serve as recruitment sites for SH2 domains (15–18, 20). Activation of RTK signaling networks may cause changes in cellular motility, proliferation, survival, and cytoskeletal arrangement. Definition of their signaling capacity represents an important and unsolved problem in cell biology. Although most studies to date have focused on the role of singular RTKs in cancer progression, co-activation of RTKs derived from several unique RTK genes has recently emerged as an important driver of cancer progression (24–27). Co-activation of modules of RTKs may provide robustness against therapies designed to inhibit a single RTK (25).Herein, we profiled the interaction potential of two RTKs and two signaling proteins and compared them with the recruitment potential of the ErbB family that we have previously profiled (28). The ErbB family, c-Met, and c-Kit RTKs have been shown to drive the progression of many cancer types, including breast, head and neck, lung (29), gastrointestinal, and stomach cancers (30). Downstream adaptor proteins often augment the signaling potential of RTKs by acting as scaffolds for recruitment of many additional proteins (31–33). Therefore, we also included peptides in our study derived from the Gab1 adaptor protein, which is critical for mediating signaling networks downstream of c-Met and potentially other RTKs (34).Finally, alternative oncogenic signaling networks may have points of cross-talk with tyrosine kinase signaling networks. Steroid hormone receptors such as the androgen receptor (AR) have been shown to associate with RTKs such as EGFR (35), to be substrates of tyrosine kinases (36, 37), and to drive the progression of prostate cancer (36). We therefore queried the interaction potential of phosphopeptides derived from AR with a set of 93 of the 120 SH2 domains encoded in the human genome. We subsequently used this interaction dataset to develop a permutation-based logistic regression classifier (PEBL) for predicting the interaction potential of SH2 domains and biologically derived phosphotyrosine-containing peptides.     

High-definition De Novo Sequencing of Crustacean Hyperglycemic Hormone (CHH)-family Neuropeptides

A complete understanding of the biological functions of large signaling peptides (>4 kDa) requires comprehensive characterization of their amino acid sequences and post-translational modifications, which presents significant analytical challenges. In the past decade, there has been great success with mass spectrometry-based de novo sequencing of small neuropeptides. However, these approaches are less applicable to larger neuropeptides because of the inefficient fragmentation of peptides larger than 4 kDa and their lower endogenous abundance. The conventional proteomics approach focuses on large-scale determination of protein identities via database searching, lacking the ability for in-depth elucidation of individual amino acid residues. Here, we present a multifaceted MS approach for identification and characterization of large crustacean hyperglycemic hormone (CHH)-family neuropeptides, a class of peptide hormones that play central roles in the regulation of many important physiological processes of crustaceans. Six crustacean CHH-family neuropeptides (8–9.5 kDa), including two novel peptides with extensive disulfide linkages and PTMs, were fully sequenced without reference to genomic databases. High-definition de novo sequencing was achieved by a combination of bottom-up, off-line top-down, and on-line top-down tandem MS methods. Statistical evaluation indicated that these methods provided complementary information for sequence interpretation and increased the local identification confidence of each amino acid. Further investigations by MALDI imaging MS mapped the spatial distribution and colocalization patterns of various CHH-family neuropeptides in the neuroendocrine organs, revealing that two CHH-subfamilies are involved in distinct signaling pathways.Neuropeptides and hormones comprise a diverse class of signaling molecules involved in numerous essential physiological processes, including analgesia, reward, food intake, learning and memory (1). Disorders of the neurosecretory and neuroendocrine systems influence many pathological processes. For example, obesity results from failure of energy homeostasis in association with endocrine alterations (2, 3). Previous work from our lab used crustaceans as model organisms found that multiple neuropeptides were implicated in control of food intake, including RFamides, tachykinin related peptides, RYamides, and pyrokinins (4–6).Crustacean hyperglycemic hormone (CHH)¹ family neuropeptides play a central role in energy homeostasis of crustaceans (7–17). Hyperglycemic response of the CHHs was first reported after injection of crude eyestalk extract in crustaceans. Based on their preprohormone organization, the CHH family can be grouped into two sub-families: subfamily-I containing CHH, and subfamily-II containing molt-inhibiting hormone (MIH) and mandibular organ-inhibiting hormone (MOIH). The preprohormones of the subfamily-I have a CHH precursor related peptide (CPRP) that is cleaved off during processing; and preprohormones of the subfamily-II lack the CPRP (9). Uncovering their physiological functions will provide new insights into neuroendocrine regulation of energy homeostasis.Characterization of CHH-family neuropeptides is challenging. They are comprised of more than 70 amino acids and often contain multiple post-translational modifications (PTMs) and complex disulfide bridge connections (7). In addition, physiological concentrations of these peptide hormones are typically below picomolar level, and most crustacean species do not have available genome and proteome databases to assist MS-based sequencing.MS-based neuropeptidomics provides a powerful tool for rapid discovery and analysis of a large number of endogenous peptides from the brain and the central nervous system. Our group and others have greatly expanded the peptidomes of many model organisms (3, 18–33). For example, we have discovered more than 200 neuropeptides with several neuropeptide families consisting of as many as 20–40 members in a simple crustacean model system (5, 6, 25–31, 34). However, a majority of these neuropeptides are small peptides with 5–15 amino acid residues long, leaving a gap of identifying larger signaling peptides from organisms without sequenced genome. The observed lack of larger size peptide hormones can be attributed to the lack of effective de novo sequencing strategies for neuropeptides larger than 4 kDa, which are inherently more difficult to fragment using conventional techniques (34–37). Although classical proteomics studies examine larger proteins, these tools are limited to identification based on database searching with one or more peptides matching without complete amino acid sequence coverage (36, 38).Large populations of neuropeptides from 4–10 kDa exist in the nervous systems of both vertebrates and invertebrates (9, 39, 40). Understanding their functional roles requires sufficient molecular knowledge and a unique analytical approach. Therefore, developing effective and reliable methods for de novo sequencing of large neuropeptides at the individual amino acid residue level is an urgent gap to fill in neurobiology. In this study, we present a multifaceted MS strategy aimed at high-definition de novo sequencing and comprehensive characterization of the CHH-family neuropeptides in crustacean central nervous system. The high-definition de novo sequencing was achieved by a combination of three methods: (1) enzymatic digestion and LC-tandem mass spectrometry (MS/MS) bottom-up analysis to generate detailed sequences of proteolytic peptides; (2) off-line LC fractionation and subsequent top-down MS/MS to obtain high-quality fragmentation maps of intact peptides; and (3) on-line LC coupled to top-down MS/MS to allow rapid sequence analysis of low abundance peptides. Combining the three methods overcomes the limitations of each, and thus offers complementary and high-confidence determination of amino acid residues. We report the complete sequence analysis of six CHH-family neuropeptides including the discovery of two novel peptides. With the accurate molecular information, MALDI imaging and ion mobility MS were conducted for the first time to explore their anatomical distribution and biochemical properties.     

Multipattern Consensus Regions in Multiple Aligned Protein Sequences and Their Segmentation

Decomposing a biological sequence into its functional regions is an important prerequisite to understand the molecule. Using the multiple alignments of the sequences, we evaluate a segmentation based on the type of statistical variation pattern from each of the aligned sites. To describe such a more general pattern, we introduce multipattern consensus regions as segmented regions based on conserved as well as interdependent patterns. Thus the proposed consensus region considers patterns that are statistically significant and extends a local neighborhood. To show its relevance in protein sequence analysis, a cancer suppressor gene called p53 is examined. The results show significant associations between the detected regions and tendency of mutations, location on the 3D structure, and cancer hereditable factors that can be inferred from human twin studies.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27]     

Algorithms for Finding Small Attractors in Boolean Networks

A Boolean network is a model used to study the interactions between different genes in genetic regulatory networks. In this paper, we present several algorithms using gene ordering and feedback vertex sets to identify singleton attractors and small attractors in Boolean networks. We analyze the average case time complexities of some of the proposed algorithms. For instance, it is shown that the outdegree-based ordering algorithm for finding singleton attractors works in time for , which is much faster than the naive time algorithm, where is the number of genes and is the maximum indegree. We performed extensive computational experiments on these algorithms, which resulted in good agreement with theoretical results. In contrast, we give a simple and complete proof for showing that finding an attractor with the shortest period is NP-hard.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

JUMP: A Tag-based Database Search Tool for Peptide Identification with High Sensitivity and Accuracy

Database search programs are essential tools for identifying peptides via mass spectrometry (MS) in shotgun proteomics. Simultaneously achieving high sensitivity and high specificity during a database search is crucial for improving proteome coverage. Here we present JUMP, a new hybrid database search program that generates amino acid tags and ranks peptide spectrum matches (PSMs) by an integrated score from the tags and pattern matching. In a typical run of liquid chromatography coupled with high-resolution tandem MS, more than 95% of MS/MS spectra can generate at least one tag, whereas the remaining spectra are usually too poor to derive genuine PSMs. To enhance search sensitivity, the JUMP program enables the use of tags as short as one amino acid. Using a target-decoy strategy, we compared JUMP with other programs (e.g. SEQUEST, Mascot, PEAKS DB, and InsPecT) in the analysis of multiple datasets and found that JUMP outperformed these preexisting programs. JUMP also permitted the analysis of multiple co-fragmented peptides from “mixture spectra” to further increase PSMs. In addition, JUMP-derived tags allowed partial de novo sequencing and facilitated the unambiguous assignment of modified residues. In summary, JUMP is an effective database search algorithm complementary to current search programs.Peptide identification by tandem mass spectra is a critical step in mass spectrometry (MS)-based¹ proteomics (1). Numerous computational algorithms and software tools have been developed for this purpose (2–6). These algorithms can be classified into three categories: (i) pattern-based database search, (ii) de novo sequencing, and (iii) hybrid search that combines database search and de novo sequencing. With the continuous development of high-performance liquid chromatography and high-resolution mass spectrometers, it is now possible to analyze almost all protein components in mammalian cells (7). In contrast to rapid data collection, it remains a challenge to extract accurate information from the raw data to identify peptides with low false positive rates (specificity) and minimal false negatives (sensitivity) (8).Database search methods usually assign peptide sequences by comparing MS/MS spectra to theoretical peptide spectra predicted from a protein database, as exemplified in SEQUEST (9), Mascot (10), OMSSA (11), X!Tandem (12), Spectrum Mill (13), ProteinProspector (14), MyriMatch (15), Crux (16), MS-GFDB (17), Andromeda (18), BaMS² (19), and Morpheus (20). Some other programs, such as SpectraST (21) and Pepitome (22), utilize a spectral library composed of experimentally identified and validated MS/MS spectra. These methods use a variety of scoring algorithms to rank potential peptide spectrum matches (PSMs) and select the top hit as a putative PSM. However, not all PSMs are correctly assigned. For example, false peptides may be assigned to MS/MS spectra with numerous noisy peaks and poor fragmentation patterns. If the samples contain unknown protein modifications, mutations, and contaminants, the related MS/MS spectra also result in false positives, as their corresponding peptides are not in the database. Other false positives may be generated simply by random matches. Therefore, it is of importance to remove these false PSMs to improve dataset quality. One common approach is to filter putative PSMs to achieve a final list with a predefined false discovery rate (FDR) via a target-decoy strategy, in which decoy proteins are merged with target proteins in the same database for estimating false PSMs (23–26). However, the true and false PSMs are not always distinguishable based on matching scores. It is a problem to set up an appropriate score threshold to achieve maximal sensitivity and high specificity (13, 27, 28).De novo methods, including Lutefisk (29), PEAKS (30), NovoHMM (31), PepNovo (32), pNovo (33), Vonovo (34), and UniNovo (35), identify peptide sequences directly from MS/MS spectra. These methods can be used to derive novel peptides and post-translational modifications without a database, which is useful, especially when the related genome is not sequenced. High-resolution MS/MS spectra greatly facilitate the generation of peptide sequences in these de novo methods. However, because MS/MS fragmentation cannot always produce all predicted product ions, only a portion of collected MS/MS spectra have sufficient quality to extract partial or full peptide sequences, leading to lower sensitivity than achieved with the database search methods.To improve the sensitivity of the de novo methods, a hybrid approach has been proposed to integrate peptide sequence tags into PSM scoring during database searches (36). Numerous software packages have been developed, such as GutenTag (37), InsPecT (38), Byonic (39), DirecTag (40), and PEAKS DB (41). These methods use peptide tag sequences to filter a protein database, followed by error-tolerant database searching. One restriction in most of these algorithms is the requirement of a minimum tag length of three amino acids for matching protein sequences in the database. This restriction reduces the sensitivity of the database search, because it filters out some high-quality spectra in which consecutive tags cannot be generated.In this paper, we describe JUMP, a novel tag-based hybrid algorithm for peptide identification. The program is optimized to balance sensitivity and specificity during tag derivation and MS/MS pattern matching. JUMP can use all potential sequence tags, including tags consisting of only one amino acid. When we compared its performance to that of two widely used search algorithms, SEQUEST and Mascot, JUMP identified ∼30% more PSMs at the same FDR threshold. In addition, the program provides two additional features: (i) using tag sequences to improve modification site assignment, and (ii) analyzing co-fragmented peptides from mixture MS/MS spectra.     

Proinsulin and the Genetics of Diabetes Mellitus

Insulin plays a central role in the regulation of vertebrate metabolism. The hormone, the post-translational product of a single-chain precursor, is a globular protein containing two chains, A (21 residues) and B (30 residues). Recent advances in human genetics have identified dominant mutations in the insulin gene causing permanent neonatal-onset DM² (1–4). The mutations are predicted to block folding of the precursor in the ER of pancreatic β-cells. Although expression of the wild-type allele would in other circumstances be sufficient to maintain homeostasis, studies of a corresponding mouse model (5–7) suggest that the misfolded variant perturbs wild-type biosynthesis (8, 9). Impaired β-cell secretion is associated with ER stress, distorted organelle architecture, and cell death (10). These findings have renewed interest in insulin biosynthesis (11–13) and the structural basis of disulfide pairing (14–19). Protein evolution is constrained not only by structure and function but also by susceptibility to toxic misfolding.Insulin plays a central role in the regulation of vertebrate metabolism. The hormone, the post-translational product of a single-chain precursor, is a globular protein containing two chains, A (21 residues) and B (30 residues). Recent advances in human genetics have identified dominant mutations in the insulin gene causing permanent neonatal-onset DM² (1–4). The mutations are predicted to block folding of the precursor in the ER of pancreatic β-cells. Although expression of the wild-type allele would in other circumstances be sufficient to maintain homeostasis, studies of a corresponding mouse model (5–7) suggest that the misfolded variant perturbs wild-type biosynthesis (8, 9). Impaired β-cell secretion is associated with ER stress, distorted organelle architecture, and cell death (10). These findings have renewed interest in insulin biosynthesis (11–13) and the structural basis of disulfide pairing (14–19). Protein evolution is constrained not only by structure and function but also by susceptibility to toxic misfolding.     

The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data

A variety of high-throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes. Common methods for analysis of these large data sets can be problematic. One challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time. We propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays. We demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions. This general strategy can be applied in the analysis of data sets of thousands of genes under different conditions.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]