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PubMed2004

LIP index for peptide classification using MS/MS and SEQUEST search via logistic regression.

This study addresses the issue of peptide identification resulting from tandem mass spectrometry proteomics analysis followed by database search. This work shows that the Logistic Identification of Peptides (LIP) Index achieves high sensitivity and specificity for peptide classification relative to a manually verified "gold" standard and also accurately estimates the probability of a correct peptide match. The LIP Index is a weighted average of SEQUEST output variables based on logistic regression models and is a transparent, easy to use, inclusive, extendable, and statistically sound approach to classify correct peptide identifications. Modifications, such as normalizing cross-correlations (Xcorr) for peptide length, adjusting for charge state, and the number of tryptic termini, significantly improve the fit the logistic regression models, as well as increase sensitivity and specificity. The LIP Index also incorporates earlier developed statistical models on spectral quality assessment and peptide identification, which further improves sensitivity and specificity.

Algorithms↗
PubMed2026

On-filter fractionation by empFASP improves identification of membrane peptides in proteomic experiments.

Membrane proteins remain among the most analytically challenging targets in bottom-up proteomics due to their limited solubility and low abundance of protease-accessible sites within transmembrane domains. In addition, hydrophobic peptides are frequently lost during detergent removal and the on-filter processing steps. Here, we present empFASP, a straightforward on-filter-fractionation-based modification of the enhanced filter-aided sample preparation (eFASP) workflow that enhances recovery of membrane-embedded peptides otherwise lost during digestion and cleanup. The method combines controlled on-filter inversion with sequential ethyl acetate extraction at defined pH values, enabling recovery of peptide material retained on the filter and redistributed into detergent micelles. Compared with SP3 and SP4 in HEK293T lysates, empFASP increased unique hydrophobic peptide identifications by up to 48% and increased the proportion of detected transmembrane peptides. Application to mouse mitochondrial membranes and phosphatidylethanolamine-deficient and PE-containing Escherichia coli membranes showed that the additional fractions of empFASP contribute complementary recovery of hydrophobic and membrane-associated peptides, with the strongest gains observed at the peptide level. Because empFASP requires no specialized reagents or instrumentation, it can be readily implemented in standard proteomics workflows to improve coverage of membrane-embedded regions. SIGNIFICANCE: The empFASP (enhanced membrane peptide) workflow offers a practical solution to one of the persistent limitations in membrane proteomics-the underrepresentation of hydrophobic and transmembrane peptides in standard digests. By integrating simple pH-controlled extractions into an on-filter format, empFASP recovers peptides otherwise lost through adsorption or detergent micelle retention, substantially improving coverage of the membrane proteome. This method expands the analytical reach of bottom-up proteomics without requiring specialized instrumentation, making it immediately applicable for studies of membrane topology, protein-lipid interactions, and the structural consequences of altered membrane composition.

Proteomics↗
PubMed2004

Statistical models for protein validation using tandem mass spectral data and protein amino acid sequence databases.

The purpose of this work is to develop and verify statistical models for protein identification using peptide identifications derived from the results of tandem mass spectral database searches. Recently we have presented a probabilistic model for peptide identification that uses hypergeometric distribution to approximate fragment ion matches of database peptide sequences to experimental tandem mass spectra. Here we apply statistical models to the database search results to validate protein identifications. For this we formulate the protein identification problem in terms of two independent models, two-hypothesis binomial and multinomial models, which use the hypergeometric probabilities and cross-correlation scores, respectively. Each database search result is assumed to be a probabilistic event. The Bernoulli event has two outcomes: a protein is either identified or not. The probability of identifying a protein at each Bernoulli event is determined from relative length of the protein in the database (the null hypothesis) or the hypergeometric probability scores of the protein's peptides (the alternative hypothesis). We then calculate the binomial probability that the protein will be observed a certain number of times (number of database matches to its peptides) given the size of the data set (number of spectra) and the probability of protein identification at each Bernoulli event. The ratio of the probabilities from these two hypotheses (maximum likelihood ratio) is used as a test statistic to discriminate between true and false identifications. The significance and confidence levels of protein identifications are calculated from the model distributions. The multinomial model combines the database search results and generates an observed frequency distribution of cross-correlation scores (grouped into bins) between experimental spectra and identified amino acid sequences. The frequency distribution is used to generate p-value probabilities of each score bin. The probabilities are then normalized with respect to score bins to generate normalized probabilities of all score bins. A protein identification probability is the multinomial probability of observing the given set of peptide scores. To reduce the effect of random matches, we employ a marginalized multinomial model for small values of cross-correlation scores. We demonstrate that the combination of the two independent methods provides a useful tool for protein identification from results of database search using tandem mass spectra. A receiver operating characteristic curve demonstrates the sensitivity and accuracy level of the approach. The shortcomings of the models are related to the cases when protein assignment is based on unusual peptide fragmentation patterns that dominate over the model encoded in the peptide identification process. We have implemented the approach in a program called PROT_PROBE.

Amino Acid Sequence↗
PubMed2004

A structure-based approach for prediction of MHC-binding peptides.

Identification of immunodominant peptides is the first step in the rational design of peptide vaccines aimed at T-cell immunity. The advances in sequencing techniques and the accumulation of many protein sequences without the purified protein challenge the development of computer algorithms to identify dominant T-cell epitopes based on sequence data alone. Here, we focus on antigenic peptides recognized by cytotoxic T cells. The selection of T-cell epitopes along a protein sequence is influenced by the specificity of each of the processing stages that precede antigen presentation. The most selective of these processing stages is the binding of the peptides to the major histocompatibility complex molecules, and therefore many of the predictive algorithms focus on this stage. Most of these algorithms are based on known binding peptides whose sequences have been used for the characterization of binding motifs or profiles. Here, we describe a structure-based algorithm that does not rely on previous binding data. It is based on observations from crystal structures that many of the bound peptides adopt similar conformations and placements within the MHC groove. The algorithm uses a structural template of the peptide in the MHC groove upon which peptide candidates are threaded and their fit to the MHC groove is evaluated by statistical pairwise potentials. It can rank all possible peptides along a protein sequence or within a suspected group of peptides, directing the experimental efforts towards the most promising peptides. This approach is especially useful when no previous peptide binding data are available.

Algorithms↗
PubMed1993

Selection of targeted biological modifiers from a bacteriophage library of random peptides. The identification of novel calmodulin regulatory peptides.

The interaction of short amino acid sequences is the basis of molecular recognition and biological regulation in many cellular systems. Libraries of random peptides provide an approach to identify peptides that can be used to modulate, in a targeted fashion, the function of specific gene products. We have used a library of random peptides designed and constructed in the M13 bacteriophage to select calcium-dependent calmodulin binding-peptides. Twenty-eight independent sequences were obtained; all contained a tryptophan within the fifteen-amino acid insert. In 11 sequences, the tryptophan was located in the first possible variable position of the inserted sequence and was followed by a proline. The tryptophan-proline combination was also present in six additional isolates but at various other positions within the peptide insert. Synthetic peptides, representative of the calmodulin binding sequences, bound to calmodulin in a calcium-dependent fashion, competed with known calmodulin inhibitors and, when introduced via a patch pipette, inhibited calcium-activated chloride conductance of the colonic epithelial cell line, T84. This report demonstrates the utility of identifying modifiers of biological function and should prove to be a valuable approach in understanding the cellular role of proteins of unknown function.

Amino Acid Sequence↗
PubMed1987

K88 fimbrial antigens: identification of antigenic determinants by the use of synthetic peptides.

Identification of antigenic determinants of K88 fimbriae was approached by immunization of rabbits with BSA conjugated synthetic peptides mimicking either a predicted common determinant or type specific determinants. Anti-peptide sera were assayed by ELISA and sandwich ELISA. It was shown that sera raised against the peptides corresponding to the K88ab and K88ad variants of the 213-219 segment were able to recognize the corresponding antigenic variant of the native fimbriae whereas the rest of the antisera did not to any significant degree react with intact fimbriae. In Western blots all anti-peptide sera were able to recognize the K88 proteins. Similarly in ELISA assays all raised sera showed affinity to denatured K88 proteins.

Amino Acid Sequence↗
PubMed1995

Multienzymatic non ribosomal peptide biosynthesis: identification of the functional domains catalysing peptide elongation and epimerisation.

Peptide synthetases are multienzymatic complexes that synthesize bioactive peptides molecules by the thiotemplate mechanism. Comparison of the known sequences of peptide synthetases led us to the identification of a 350 amino acids domain catalysing elongation and containing the motif HHxxxDG. This motif is present as many times as acyltransfer or epimerisation reactions occur during biosynthesis of the peptide. The distance between this motif and the phosphopantetheinyl attachment site is nearly invariant. An identical motif is found in other enzymes effecting acyl transfer such as chloramphenicol acetyltransferase from Tn9 and dihydrolipoamide acyltransferase. Altogether, the HHxxxDG motif may constitute the signature of a superfamily sharing a common catalytic mechanism based on the acid-base properties of the second histidine for effecting acyl transfer or peptide epimerisation.

Acyltransferases↗
PubMed2004

Increased identification of peptides by enhanced data processing of high-resolution MALDI TOF/TOF mass spectra prior to database searching.

This paper presents application of sequential enhanced data processing procedures to high-resolution tandem mass spectra for identification of peptides using the Mascot database search algorithm. A strategy for (1) selection of fragment ion peaks from MS/MS spectra, (2) utilization of improved mass accuracy of the precursor ions, and (3) wavelet denoising of the mass spectra prior to fragment ion selection have been developed. The number of peptide identifications obtained using the enhanced processing was then compared with that obtained using software provided by the instrument manufacturer. Approximately 9000 MS/MS spectra acquired by the Applied Biosystems 4700 TOF/TOF MS instrument were used as a model data set. After application of the new processing, an increase of 33% unique peptides and 22% protein identifications with at least two unique peptides were found. The influence of the processing on the percentage of false positives, estimated by searching against a randomized database, was estimated to increase false positive identifications from 2.7 to 3.9%, which was still below the 5% error rate specified in the Mascot search. These data processing approaches increase the amount of information that can be extracted from LC-MS analysis without the necessity of additional experiments.

Algorithms↗
PubMed2004

An alternative to tandem mass spectrometry: isoelectric point and accurate mass for the identification of peptides.

The traditional approach to the identification of peptides in complex biological samples integrally involves the use of tandem mass spectrometry to generate a unique fragmentation pattern in order to accurately assign its identity to a particular protein. In this article we describe the theoretical basis for a new paradigm for the identification of peptides and proteins. This methodology employs the use of accurate mass and peptide isoelectric point (pI) as identification criteria, and represents a change in focus from current tandem mass spectrometry-dominated approaches. A mathematical derivation of the false positive rate associated with accurate mass and pI measurements is presented to demonstrate the utility of the technique. The equations for calculation of the experimental false positive rate allow for the determination of the validity of the data. The false positive rate issue examined in detail here is not restricted to accurate mass-based approaches, but also has application to the tandem mass spectrometry community as well. The theoretical proteomes of Escherichia coli and Rattus norvegicus are used to evaluate the efficacy of this approach. The power of the technique is demonstrated by analyzing a series of peptides with the same monoisotopic masses but with differing isoelectric points. Finally, the speed of algorithm when combined with the experimental peptide analysis has the potential to rapidly accelerate the protein identification process.

Algorithms↗
PubMed2006

Identification of peptides in antimicrobial fractions of cheese extracts by electrospray ionization ion trap mass spectrometry coupled to a two-dimensional liquid chromatographic separation.

Electrospray ionization ion trap mass spectrometry (ESI-ITMS) coupled to a two-dimensional liquid chromatographic separation was applied to the identification of peptides in antimicrobial fractions of the aqueous extracts of nine Italian cheese varieties. In particular, the chromatographic fractions collected during a preliminary fast protein liquid chromatography (FPLC) separation on the cheese extracts were assayed for antimicrobial activity towards Lactobacillus sakei A15. Active fractions were subsequently analyzed by reversed-phase high-performance liquid chromatography electrospray ionization sequential mass spectrometry (HPLC/ESI)-ITMSn, with n up to 3. Peptide identification was then performed starting from a conventional proteomics approach based on tandem mass spectrometric (MS/MS) analysis followed by database searching. In many cases this strategy had to be integrated by a careful correlation between spectral information and predicted peptide fragmentation, in order to reach unambiguous identifications. When even this integrated approach failed, MS3 measurements provided decisive information on the amino acid sequence of some peptides, through fragmentation of pendant groups along the peptide chain. As a result, 45 peptides, all arising from hydrolysis of milk caseins, were identified in nine antimicrobial FPLC fractions of aqueous extracts obtained from five of the nine cheese varieties considered. Many of them corresponded to peptides already known to exhibit biological activity.

Amino Acid Sequence↗
PubMed1995

Water-soluble peptides in Cheddar cheese: isolation and identification of peptides in the diafiltration retentate of the water-soluble fraction.

The water-soluble extract of Cheddar cheese was fractionated by diafiltration using 10 kDa cut-off membranes. Peptides were isolated from the diafiltrate retentate by chromatography on DEAE-cellulose with a linear NaCl gradient in 50 mM-Tris-HCl. pH 8.6, and reversed-phase HPLC or electroblotting from urea-PAGE gels. Peptides were identified by determining N-terminal amino acid sequences and mass spectrometry. Most (45) of the total 51 peptides identified in the diafiltrate retentate originated from beta-casein, especially from a short region in the N-terminal half of the molecule. Only six peptides originated from alpha s1-casein; beta-lactoglobulin was also identified in the retentate. The origin of most of these peptides could be explained on the basis of known specificities of lactococcal cell envelope proteinases.

Amino Acid Sequence↗
PubMed2006

SVMHC: a server for prediction of MHC-binding peptides.

Identification of MHC-binding peptides is a prerequisite in rational design of T-cell based peptide vaccines. During the past decade a number of computational approaches have been introduced for the prediction of MHC-binding peptides, efficiently reducing the number of candidate binders that need to be experimentally verified. Here the SVMHC server for prediction of both MHC class I and class II binding peptides is presented. SVMHC offers fast analysis of a wide range of alleles and prediction results are given in several comprehensive formats. The server can be used to find the most likely binders in a protein sequence and to investigate the effects of single nucleotide polymorphisms in terms of MHC-peptide binding. The SVMHC server is accessible at http://www-bs.informatik.uni-tuebingen.de/SVMHC/.

Alleles↗
PubMed2002

Comparative study between direct mild acid extraction and immunobead purification technique for isolation of HLA class I-associated peptides.

Identification of tumour-specific peptide(s) hidden within the groove of human leucocyte antigens is a crucial prerequisite for peptide vaccine therapy. Conventionally, the peptide(s) are isolated by mild acid extraction (MA) technique followed by sequential ultra-filtrations. Here we describe a new approach for peptide isolation using the immunobead purification (IB-P) technique in conjunction with reverse-phase high-performance liquid chromatography. The obtained data were validated by SDS-PAGE followed by the silver staining technique. The results can be summarised as follows: (1) Comparison of class I-associated peptides isolated from a bladder cell line before and after the correction of class I antigens by gene transfection followed by IB-P technique showed the presence of peptides only from the class I-corrected cells. The data were confirmed using the silver staining technique as a way of detecting individual peptide bands. (2) Whilst peptides could be isolated by both techniques, the MA method led to the isolation of peptides from both class I-negative and class I-positive Fen cell lysates. (3) The IB-P approach could be used for isolation of class I-associated peptides from a normal kidney tissue. The data showed the high efficiency of the IB-P approach for isolation of class I-associated peptides. Unlike the MA technique, where the presence of non- class I-associated peptides was a problem, the IB-P approach isolated only peptides associated with the class I antigens. In addition, the data showed the feasibility of extracting peptides from tissue fragments by the IB-P method. The approach presented here may assist the future development of peptide vaccine therapy in urological cancers.

Antigen Presentation↗
PubMed2006

PRIMA: peptide robust identification from MS/MS spectra.

In proteomics, tandem mass spectrometry is the key technology for peptide sequencing. However, partially due to the deficiency of peptide identification software, a large portion of the tandem mass spectra are discarded in almost all proteomics centers because they are not interpretable. The problem is more acute with the lower quality data from low end but more popular devices such as the ion trap instruments. In order to deal with the noisy and low quality data, this paper develops a systematic machine learning approach to construct a robust linear scoring function, whose coefficients are determined by a linear programming. A prototype, PRIMA, was implemented. When tested with large benchmarks of varying qualities, PRIMA consistently has higher accuracy than commonly used software MASCOT, SEQUEST and X! Tandem.

Amino Acid Sequence↗
PubMed2004

Automated multi-dimensional liquid chromatography: sample preparation and identification of peptides from human blood filtrate.

A comprehensive on-line sample clean-up with an integrated two-dimensional HPLC system was developed for the analysis of natural peptides. Samples comprised of endogenous peptides with molecular weights up to 20 kDa were generated from human hemofiltrate (HF) obtained from patients with chronic renal failure. The (poly-)peptides were separated using novel silica-based restricted access materials with strong cation-exchange functionalities (SCX-RAM). The size-selective sample fractionation step is followed by cation-exchange chromatography as the first dimension. The subsequent second dimension of separation is based on hydrophobic interaction using four parallel short reversed-phase (RP) columns implemented via a fully automated column switching technique. More than 1000 peaks were resolved within the total analysis time of 96 min. Substances of selected peaks were sampled to analyse their molecular weights by off-line MALDI-TOF mass spectrometry and to determine their amino acid sequence by Edman degradation. The potential for comprehensive peptide mapping and identification is demonstrated.

Amino Acid Sequence↗
PubMed2006

Identification of carboxyl-terminal peptide fragments of parathyroid hormone in human plasma at low-picomolar levels by mass spectrometry.

For decades, researchers have tried to identify the primary structures of circulating carboxyl-terminal parathyroid hormone (C-PTH) peptide fragments that may be present at only picomolar levels in human plasma. Although immunoassays and radiosequencing techniques have provided valuable fragment characterizations, no analysis has successfully determined their exact primary structures. In this work, for the first time, four human C-PTH peptide fragments, hPTH(34-84), hPTH(37-84), hPTH(38-84), and hPTH(45-84), have been identified from human plasma using MS-based methods. C-PTH peptide fragments were isolated from plasma samples by immunoaffinity extraction. The eluate was analyzed by capillary LC fractionation followed by MALDI-TOF-MS or by on-line coupling of nano-LC with ESI-TOF-MS. Both the MALDI- and the ESI-based approaches were capable of detecting C-PTH peptide fragments in human plasma at <10 pmol/L. The MALDI-TOF approach was effective in preliminary searches for C-PTH peptide fragments, but the use of high laser power limited the resolution necessary for accurate C-PTH peptide identification. The high mass resolution (10,000) and accuracy (10 ppm) attained by the ESI-TOF approach enabled unambiguous identification of these peptides. The four C-PTH peptide fragments identified in plasma samples from patients with chronic renal insufficiency were also found in the plasma of healthy women receiving recombinant human PTH either by subcutaneous injection or by intravenous infusion. This newly developed analytical capability should greatly enhance the understanding of PTH metabolism and parathyroid gland function.

Humans↗
PubMed2001

Peptide mass mapping constrained with stable isotope-tagged peptides for identification of protein mixtures.

Through proteolysis and peptide mass determination using mass spectrometry, a peptide mass map (PMM) can be generated for protein identification. However, insufficient peptide mass accuracy and protein sequence coverage limit the potential of the PMM approach for high-throughput, large-scale analysis of proteins. In our novel approach, nonlabile protons in particular amino acid residues were replaced with deuteriums to mass-tag proteins of the S. cerevisiae proteome in a sequence-specific manner. The resulting mass-tagged proteolytic peptides with characteristic mass-split patterns can be identified in the data search using constraints of both amino acid composition and mass-to-charge ratio. More importantly, the mass-tagged peptides can further act as internal calibrants with high confidence in a PMM to identify the parent proteins at modest mass accuracy and low sequence coverage. As a result, the specificity and accuracy of a PMM was greatly enhanced without the need for peptide sequencing or instrumental improvements to obtain increased mass accuracy. The power of PMM has been extended to the unambiguous identification of multiple proteins in a 1D SDS gel band including the identification of a membrane protein.

Amino Acid Sequence↗