pepgrep: A Tool for Peptide MS/MS Pattern Matching

Typically, detection of protein sequences in collision-induced dissociation (CID) tandem MS (MS2) dataset is performed by mapping identified peptide ions back to protein sequence by using the protein database search (PDS) engine. Finding a particular peptide sequence of interest in CID MS2 records very often requires manual evaluation of the spectrum, regardless of whether the peptide-associated MS2 scan is identified by PDS algorithm or not. We have developed a compact cross-platform database-free command-line utility, pepgrep, which helps to find an MS2 fingerprint for a selected peptide sequence by pattern-matching of modelled MS2 data using Peptide-to-MS2 scoring algorithm. pepgrep can incorporate dozens of mass offsets corresponding to a variety of post-translational modifications (PTMs) into the algorithm. Decoy peptide sequences are used with the tested peptide sequence to reduce false-positive results. The engine is capable of screening an MS2 data file at a high rate when using a cluster computing environment. The matched MS2 spectrum can be displayed by using built-in graphical application programming interface (API) or optionally recorded to file. Using this algorithm, we were able to find extra peptide sequences in studied CID spectra that were missed by PDS identification. Also we found pepgrep especially useful for examining a CID of small fractions of peptides resulting from, for example, affinity purification techniques. The peptide sequences in such samples are less likely to be positively identified by using routine protein-centric algorithm implemented in PDS. The software is freely available at http://bsproteomics.essex.ac.uk:8080/data/download/pepgrep-1.4.tgz.     

串联质谱数据的从头解析与蛋白质的数据库搜索鉴定

蛋白质的鉴定是蛋白质组学研究中必不可少的一步。用串联质谱 (tandemmassspectrometry ,MS/MS)可以进行多肽的从头测序 (denovosequencing) ,并搜索数据库以鉴定蛋白质。用图论以及真实谱 理论谱联配 (alignment)的方法对串联质谱得到的多肽图谱进行从头解析 ,得到了可靠的多肽序列 ,并应用到数据库搜索中鉴定了相应的蛋白质。同时 ,还用统计的方法对SwissProt以及TrEMBL蛋白质数据库进行了详细的分析。结果表明 ,3个四肽或者 2个五肽或者 1个八肽一般可以唯一地确定一个蛋白质     

A Screening Algorithm for Gastric Cancer-Binding Peptides

Gastric cancer-binding peptides (GCBP) are promising diagnostic and therapeutic agents for gastric cancer management. Their utility lies in their ability t     

Screening Peptide Bead Libraries for Enzyme Inhibitors

Recent developments in the technology of solid-phase peptide synthesis have made it possible to create libraries of peptides mounted on solid supports, which may then be screened for biological activity. By inserting such a randomized library region into a synthetic peptide mounted on resin beads, a full range of potentially useful mutants may be screened simultaneously. This article describes the use of this approach to generate variants of CMTI-I, a conformationally constrained proteinaceous inhibitor of trypsin and chymotrypsin. The result is a library containing mutants for the P′₁-P′₄ reactive-loop sequence of CMTI-I. This is equilibrated with a fluorescentiy labeled target enzyme (in this case human leukocyte elastase), which binds to those beads containing the most potent inhibitors. These are identified and isolated. The method utilizes the conformational rigidity of CMTI-I to improve inhibitory activity. By this means the most potent inhibitors of HLE may be distinguished and then synthesized on a preparative scale for kinetic analysis. This approach may be applied directly to other serine proteinases.     

Critical Evaluation of Product Ion Selection and Spectral Correlation Analysis for Biomarker Screening Using Targeted Peptide Multiple Reaction Monitoring

Introduction  Tandem mass spectrometry (MS/MS) has emerged as a cornerstone of proteomic screens aimed at discovering putative protein biomarkers of disease with potential clinical applications. Systematic validation of lead candidates in large numbers of samples from patient cohorts remains an important challenge. One particularly promising high throughout technique is multiple reaction monitoring (MRM), a targeted form of MS/MS by which precise peptide precursor–product ion combinations, or transitions, are selectively tracked as informative probes. Despite recent progress, however, many important computational and statistical issues remain unresolved. These include the selection of an optimal set of transitions so as to achieve sufficiently high specificity and sensitivity when profiling complex biological specimens, and the corresponding generation of a suitable scoring function to reliably confirm tentative molecular identities based on noisy spectra. Methods  In this study, we investigate various empirical criteria that are helpful to consider when developing and interpreting MRM-style assays based on the similarity between experimental and annotated reference spectra. We also rigorously evaluate and compare the performance of conventional spectral similarity measures, based on only a few pre-selected representative transitions, with a generic scoring metric, termed T _corr, wherein a selected product ion profile is used to score spectral comparisons. Conclusions  Our analyses demonstrate that T _corr is potentially more suitable and effective for detecting biomarkers in complex biological mixtures than more traditional spectral library searches. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users. Jian Liu and Johannes A Hewel contributed equally to this study.     

Screening for basic drugs in equine urine using direct-injection differential-gradient LC-LC coupled to hybrid tandem MS/MS

A rapid, selective and robust direct-injection LC/hybrid tandem MS method has been developed for simultaneous screening of more than 250 basic drugs in the supernatant of enzyme hydrolysed equine urine. Analytes, trapped using a short HLB extraction column, are refocused and separated on a Sunfire C(18) analytical column using a controlled differential gradient generated by proportional dilution of the first column's eluent with water. Independent data acquisition (IDA) was configured to trigger a sensitive enhanced product ion (EPI) scan when a multiple reaction monitoring (MRM) survey scan signal exceeded the defined criteria. The decision on whether or not to report a sample as a positive result was based upon both the presence of a MRM response within the correct retention time range and a qualitative match between the EPI spectrum obtained and the corresponding reference standard. Ninety seven percent of the drugs targeted by this method met our detection criteria when spiked into urine at 100 ng/ml; 199 were found at 10 ng/ml, 83 at 1 ng/ml and 4 at 0.1 ng/ml.     

Low Level,Detection of Modified DNA Bases and Nucleosides by FAB MS/MS

Abstract

The identification and quantitation of DNA adducts formed by reaction of genotoxic chemicals with DNA may provide direct evidence of exposure t o mutagens and carcinogens and may make possible a beginning of risk estimation based on Molecular Dosimetry approach.     

CLPM: A Cross-Linked Peptide Mapping Algorithm for Mass Spectrometric Analysis

Background

Protein-protein, protein-DNA and protein-RNA interactions are of central importance in biological systems. Quadrapole Time-of-flight (Q-TOF) mass spectrometry is a sensitive, promising tool for studying these interactions. Combining this technique with chemical crosslinking, it is possible to identify the sites of interactions within these complexes. Due to the complexities of the mass spectrometric data of crosslinked proteins, new software is required to analyze the resulting products of these studies.

Result

We designed a Cross-Linked Peptide Mapping (CLPM) algorithm which takes advantage of all of the information available in the experiment including the amino acid sequence from each protein, the identity of the crosslinker, the identity of the digesting enzyme, the level of missed cleavage, and possible chemical modifications. The algorithm does in silico digestion and crosslinking, calculates all possible mass values and matches the theoretical data to the actual experimental data provided by the mass spectrometry analysis to identify the crosslinked peptides.

Conclusion

Identifying peptides by their masses can be an efficient starting point for direct sequence confirmation. The CLPM algorithm provides a powerful tool in identifying these potential interaction sites in combination with chemical crosslinking and mass spectrometry. Through this cost-effective approach, subsequent efforts can quickly focus attention on investigating these specific interaction sites.

     

一种滤除医学影像噪声的混合滤波算法

目的：医学影像在获取、存储、传输过程中会不同程度地受到噪声污染,这极大影像了其在临床诊疗中的应用。为了有效地滤除医学影像噪声,提出了一种混合滤波算法。方法：该算法首先将含有高斯和椒盐噪声的图像进行形态学开运算,然后对开运算后的图像进行二维小波分解,得到高频和低频小波分解系数。保留低频系数不变,将高频系数经过维纳滤波器进行滤波,最后进行小波系数重构。结果：采用该混合滤波算法、小波阚值去噪、中值滤波、维纳滤波分别对含有混合噪声的医学影像分别进行滤除噪声处理,该滤波算法去噪后影像的PSNR值明显高于其他三种方法。结论：该混合滤波算法是一种较为有效的医学影像噪声滤除方法。     

A Noise Level Prediction Method Based on Electro-Mechanical Frequency Response Function for Capacitors

The capacitors in high-voltage direct-current (HVDC) converter stations radiate a lot of audible noise which can reach higher than 100 dB. The existing noise level prediction methods are not satisfying enough. In this paper, a new noise level prediction method is proposed based on a frequency response function considering both electrical and mechanical characteristics of capacitors. The electro-mechanical frequency response function (EMFRF) is defined as the frequency domain quotient of the vibration response and the squared capacitor voltage, and it is obtained from impulse current experiment. Under given excitations, the vibration response of the capacitor tank is the product of EMFRF and the square of the given capacitor voltage in frequency domain, and the radiated audible noise is calculated by structure acoustic coupling formulas. The noise level under the same excitations is also measured in laboratory, and the results are compared with the prediction. The comparison proves that the noise prediction method is effective.     

From Design to Screening: A New Antimicrobial Peptide Discovery Pipeline

Antimicrobial peptides (AMPs) belong to a class of natural microbicidal molecules that have been receiving great attention for their lower propensity for inducing drug resistance, hence, their potential as alternative drugs to conventional antibiotics. By generating AMP libraries, one can study a large number of candidates for their activities simultaneously in a timely manner. Here, we describe a novel methodology where in silico designed AMP-encoding oligonucleotide libraries are cloned and expressed in a cellular host for rapid screening of active molecules. The combination of parallel oligonucleotide synthesis with microbial expression systems not only offers complete flexibility for sequence design but also allows for economical construction of very large peptide libraries. An application of this approach to discovery of novel AMPs has been demonstrated by constructing and screening a custom library of twelve thousand plantaricin-423 mutants in Escherichia coli. Analysis of selected clones by both Sanger-sequencing and 454 high-throughput sequencing produced a significant amount of data for positionally important residues of plantaricin-423 responsible for antimicrobial activity and, moreover, resulted in identification of many novel variants with enhanced specific activities against Listeria innocua. This approach allows for generation of fully tailored peptide collections in a very cost effective way and will have countless applications from discovery of novel AMPs to gaining fundamental understanding of their biological function and characteristics.     

A rapid and accurate UPLC/MS/MS method for the determination of benzodiazepines in human urine

A rapid, sensitive, and specific ultra-performance liquid chromatography-tandem mass spectrometry (UPLC/MS/MS) assay method for simultaneous determination of 13 benzodiazepine compounds in human urine was developed and validated. Aliquots of 0.5 mL of urine specimens were used for the analysis and the benzodiazepines were extracted by single step methanol (containing 0.2% formic acid) precipitation and then separated on a BEH C18 (50 mm × 2.1 mm, 1.7 μm) analytical column with the temperature maintained at 45°C. The mobile phases consisted of methanol and water (both containing 0.2% formic acid) and the flow rate was 0.4 mL/min. The TQ detector, equipped with an electrospray ionization ion source, was set up with a positive mode. The acquisitions were performed in multiple-reaction monitoring (MRM) and the limit of quantification was 20 ng/mL for all of the 13 compounds. The low limits of detections (LODs) of the benzodiazepines in this method were between 0.5 and 2 ng/mL. The chromatographic separation time was 4 min and calibration curves in human urine were generated over the range of 20-2000 ng/mL. The method validation parameters such as accuracy, precision, carryover, recovery, stability, and specificity for all of the 13 compounds were within the acceptable range. This method is suitable for the high throughput screening of benzodiazepines in clinical laboratories.     

A Screening Level Mass Balance Model of Sediment Remediation Options

It is suggested that dynamic mass balance models can provide valuable support information when sediment remediation activities, such as dredging, are contemplated. A model with sufficient credibility and accuracy can be used to compare and contrast the results of various remediation actions and the effect of natural remediation processes. A food web model can also be included. The information provided by the model can be summarized in periodic (e.g. annual) “Report Cards” documenting the status of the contaminated system during and after the remediation process. Time plots of relevant properties of the system, such as contaminant masses, concentrations and levels in water, sediment and biota, can convey the essential information required for decision-making. This approach is illustrated by applying it with screening level accuracy to the remediation activities currently being undertaken in the St. Clair River, which flows from Lake Huron to Lake St. Clair near Sarnia, Ontario, Canada. The effects of dredging to reduce concentrations of hexachlorobenzene in sediment, water and biota are explored.     

VitAL: Viterbi Algorithm for de novo Peptide Design

Background

Drug design against proteins to cure various diseases has been studied for several years. Numerous design techniques were discovered for small organic molecules for specific protein targets. The specificity, toxicity and selectivity of small molecules are hard problems to solve. The use of peptide drugs enables a partial solution to the toxicity problem. There has been a wide interest in peptide design, but the design techniques of a specific and selective peptide inhibitor against a protein target have not yet been established.

Methodology/Principal Findings

A novel de novo peptide design approach is developed to block activities of disease related protein targets. No prior training, based on known peptides, is necessary. The method sequentially generates the peptide by docking its residues pair by pair along a chosen path on a protein. The binding site on the protein is determined via the coarse grained Gaussian Network Model. A binding path is determined. The best fitting peptide is constructed by generating all possible peptide pairs at each point along the path and determining the binding energies between these pairs and the specific location on the protein using AutoDock. The Markov based partition function for all possible choices of the peptides along the path is generated by a matrix multiplication scheme. The best fitting peptide for the given surface is obtained by a Hidden Markov model using Viterbi decoding. The suitability of the conformations of the peptides that result upon binding on the surface are included in the algorithm by considering the intrinsic Ramachandran potentials.

Conclusions/Significance

The model is tested on known protein-peptide inhibitor complexes. The present algorithm predicts peptides that have better binding energies than those of the existing ones. Finally, a heptapeptide is designed for a protein that has excellent binding affinity according to AutoDock results.     

Biological monitoring of bisphenol A with HLPC/FLD and LC/MS/MS assays

Biological monitoring is a necessary process for risk assessment of endocrine disrupting chemicals (EDCs), particularly, bisphenol A (BPA), in breast milk, because its human risks are not clear yet, and infants, who feed on breast milk, are highly susceptible for EDCs. Concerning biological monitoring of BPA, the HPLC/FLD has been widely used before the LC/MS/MS. However, there was no report, which simultaneously evaluated the two methods in real analyses. Therefore, we analyzed BPA with LC/MS/MS and HPLC/FLD in human breast milk and conducted comparison of two methods in analyzed BPA levels. After establishing optimal condition, e.g. linearity, recovery, reproducibility and free BPA system, we analyzed BPA levels in human breast milk samples (N = 100). The LOQs were similar in the two methods, i.e. 1.8 and 1.3 ng/mL for the HPLC/FLD and LC/MS/MS assays, respectively. There were strong associations between total BPA levels with the two methods (R² = 0.40, p < 0.01), however, only 11% of them were analyzed as similar levels with 15% CVs. In addition, the detection range of BPA was broader in the HPLC method than the LC/MS/MS method. However, the BPA levels in the HPLC/FLD analysis were lower than those in the LC/MS/MS analysis (p < 0.01). Thus, the differences in BPA levels between the two methods may come from mainly over-estimation with the LC/MS/MS method in low BPA samples and some of poor resolution with the HPLC/FLD in high BPA samples.     

Pooled Screening for Synergistic Interactions Subject to Blocking and Noise

The complex molecular networks in the cell can give rise to surprising interactions: gene deletions that are synthetically lethal, gene overexpressions that promote stemness or differentiation, synergistic drug interactions that heighten potency. Yet, the number of actual interactions is dwarfed by the number of potential interactions, and discovering them remains a major problem. Pooled screening, in which multiple factors are simultaneously tested for possible interactions, has the potential to increase the efficiency of searching for interactions among a large set of factors. However, pooling also carries with it the risk of masking genuine interactions due to antagonistic influence from other factors in the pool. Here, we explore several theoretical models of pooled screening, allowing for synergy and antagonism between factors, noisy measurements, and other forms of uncertainty. We investigate randomized sequential designs, deriving formulae for the expected number of tests that need to be performed to discover a synergistic interaction, and the optimal size of pools to test. We find that even in the presence of significant antagonistic interactions and testing noise, randomized pooled designs can significantly outperform exhaustive testing of all possible combinations. We also find that testing noise does not affect optimal pool size, and that mitigating noise by a selective approach to retesting outperforms naive replication of all tests. Finally, we show that a Bayesian approach can be used to handle uncertainty in problem parameters, such as the extent of synergistic and antagonistic interactions, resulting in schedules for adapting pool size during the course of testing.     

A data-mining scheme for identifying peptide structural motifs responsible for different MS/MS fragmentation intensity patterns

Although tandem mass spectrometry (MS/MS) has become an integral part of proteomics, intensity patterns in MS/MS spectra are rarely weighted heavily in most widely used algorithms because they are not yet fully understood. Here a knowledge mining approach is demonstrated to discover fragmentation intensity patterns and elucidate the chemical factors behind such patterns. Fragmentation intensity information from 28 330 ion trap peptide MS/MS spectra of different charge states and sequences went through unsupervised clustering using a penalized K-means algorithm. Without any prior chemistry assumptions, four clusters with distinctive fragmentation patterns were obtained. A decision tree was generated to investigate peptide sequence motif and charge state status that caused these fragmentation patterns. This data-mining scheme is generally applicable for any large data sets. It bypasses the common prior knowledge constraints and reports on the overall peptide fragmentation behavior. It improves the understanding of gas-phase peptide dissociation and provides a foundation for new or improved protein identification algorithms.     

A Positive Detecting Code and Its Decoding Algorithm for DNA Library Screening

The study of gene functions requires high-quality DNA libraries. However, a large number of tests and screenings are necessary for compiling such libraries. We describe an algorithm for extracting as much information as possible from pooling experiments for library screening. Collections of clones are called pools, and a pooling experiment is a group test for detecting all positive clones. The probability of positiveness for each clone is estimated according to the outcomes of the pooling experiments. Clones with high chance of positiveness are subjected to confirmatory testing. In this paper, we introduce a new positive clone detecting algorithm, called the Bayesian network pool result decoder (BNPD). The performance of BNPD is compared, by simulation, with that of the Markov chain pool result decoder (MCPD) proposed by Knill et al. in 1996. Moreover, the combinatorial properties of pooling designs suitable for the proposed algorithm are discussed in conjunction with combinatorial designs and dhbox{-}{rm disjunct} matrices. We also show the advantage of utilizing packing designs or BIB designs for the BNPD algorithm.     

An Unsupervised, Model-Free, Machine-Learning Combiner for Peptide Identifications from Tandem Mass Spectra

As the speed of mass spectrometers, sophistication of sample fractionation, and complexity of experimental designs increase, the volume of tandem mass spectra requiring reliable automated analysis continues to grow. Software tools that quickly, effectively, and robustly determine the peptide associated with each spectrum with high confidence are sorely needed. Currently available tools that postprocess the output of sequence-database search engines use three techniques to distinguish the correct peptide identifications from the incorrect: statistical significance re-estimation, supervised machine learning scoring and prediction, and combining or merging of search engine results. We present a unifying framework that encompasses each of these techniques in a single model-free machine-learning framework that can be trained in an unsupervised manner. The predictor is trained on the fly for each new set of search results without user intervention, making it robust for different instruments, search engines, and search engine parameters. We demonstrate the performance of the technique using mixtures of known proteins and by using shuffled databases to estimate false discovery rates, from data acquired on three different instruments with two different ionization technologies. We show that this approach outperforms machine-learning techniques applied to a single search engine’s output, and demonstrate that combining search engine results provides additional benefit. We show that the performance of the commercial Mascot tool can be bested by the machine-learning combination of two open-source tools X!Tandem and OMSSA, but that the use of all three search engines boosts performance further still. The Peptide identification Arbiter by Machine Learning (PepArML) unsupervised, model-free, combining framework can be easily extended to support an arbitrary number of additional searches, search engines, or specialized peptide–spectrum match metrics for each spectrum data set. PepArML is open-source and is available from . Electronic supplementary material The online version of this article (doi: ) contains supplementary material, which is available to authorized users.