GroupFilter: A software tool for efficient filtering of Morpheus search engine results

Morpheus is a search algorithm developed recently for high-resolution tandem mass spectra. According to the developers, its intrinsic property is discriminating short sequence length peptides. Therefore, elimination of direct comparisons between peptide spectrum matches (PSMs) for short and long peptides may potentially increase the search sensitivity for a given FDR level. In the proposed approach, all PSMs are grouped according to the number of matched fragment ions, followed by separate filtering of identifications in each group using target-decoy approach. The approach is applied to Morpheus output results and does not cause a significant increase in the overall data analysis time. The proposed approach was implemented as a Python command-line tool, called GroupFilter. Several data sets from different types of mass spectrometers were used for testing of the software, including the data from the original Morpheus search engine paper. Separate FDR filtering for grouped identifications increased the number of identified peptides by up to 18% compared with the default Morpheus post-processing procedure. The proposed approach can be considered as an addition to the Morpheus search engine.     

Quantitative analysis of low‐abundance peptides in HeLa cell cytoplasm by targeted liquid chromatography/mass spectrometry and stable isotope dilution: emphasising the distinction between peptide detection and peptide identification

We present the application of a targeted liquid chromatography/mass spectrometry (LC/MS) approach developed on a linear ion trap for the evaluation of the abundance of cytoplasmic proteins from a HeLa cell extract. Using a standard data‐dependent approach, we identified some specific peptides from this extract which were also commercially available in their AQUA form (use for absolute quantitation). For some of the peptides, we observed a non‐linear response between the intensity and the added quantity which was then fitted using a quadratic fit. All AQUA peptides spiked into a mix of 3 µg of the HeLa cell digest extract were detected down to 16 fmol. We placed an emphasis on peptide detection which, in this study, is performed using a combination of properties such as three specific Q3‐like ion signatures (for a given Q1‐like selection) and co‐elution with the AQUA peptide counterparts. Detecting a peptide without necessarily identifying it using a search engine imposes less constraint in terms of tandem mass (MS/MS) spectra purity. An example is shown where a peptide is detected using those criteria but could not be identified by Mascot due to its lower abundance. To complement this observation, we used a cross‐correlation analysis approach in order to separate two populations of MS/MS fragments based on differences in their elution patterns. Such an approach opens the door to new strategies to analyse lower intensity peptide fragments. An in silico analysis of the human trypsinosome allows the evaluation of how unique are the sets of features that we are using for peptide detection. Copyright © 2010 John Wiley & Sons, Ltd.     

A Bayesian statistical approach of improving knowledge‐based scoring functions for protein–ligand interactions

Knowledge‐based scoring functions are widely used for assessing putative complexes in protein–ligand and protein–protein docking and for structure prediction. Even with large training sets, knowledge‐based scoring functions face the inevitable problem of sparse data. Here, we have developed a novel approach for handling the sparse data problem that is based on estimating the inaccuracies in knowledge‐based scoring functions. This inaccuracy estimation is used to automatically weight the knowledge‐based scoring function with an alternative, force‐field‐based potential (FFP) that does not rely on training data and can, therefore, provide an improved approximation of the interactions between rare chemical groups. The current version of STScore, a protein–ligand scoring function using our method, achieves a binding mode prediction success rate of 91% on the set of 100 complexes by Wang et al., and a binding affinity correlation of 0.514 with the experimentally determined affinities in PDBbind. The method presented here may be used with other FFPs and other knowledge‐based scoring functions and can also be applied to protein–protein docking and protein structure prediction. © 2014 Wiley Periodicals, Inc.     

Consistent Gaussian basis sets of triple‐zeta valence with polarization quality for solid‐state calculations

Consistent basis sets of triple‐zeta valence with polarization quality for main group elements and transition metals from row one to three have been derived for periodic quantum‐chemical solid‐state calculations with the crystalline‐orbital program CRYSTAL. They are based on the def2‐TZVP basis sets developed for molecules by the Ahlrichs group. Orbital exponents and contraction coefficients have been modified and reoptimized, to provide robust and stable self‐consistant field (SCF) convergence for a wide range of different compounds. We compare results on crystal structures, cohesive energies, and solid‐state reaction enthalpies with the modified basis sets, denoted as pob‐TZVP, with selected standard basis sets available from the CRYSTAL basis set database. The average deviation of calculated lattice parameters obtained with a selected density functional, the hybrid method PW1PW, from experimental reference is smaller with pob‐TZVP than with standard basis sets, in particular for metallic systems. The effects of basis set expansion by diffuse and polarization functions were investigated for selected systems. © 2012 Wiley Periodicals, Inc.     

Exploring the parameter space of the coarse‐grained UNRES force field by random search: Selecting a transferable medium‐resolution force field

We explored the energy‐parameter space of our coarse‐grained UNRES force field for large‐scale ab initio simulations of protein folding, to obtain good initial approximations for hierarchical optimization of the force field with new virtual‐bond‐angle bending and side‐chain‐rotamer potentials which we recently introduced to replace the statistical potentials. 100 sets of energy‐term weights were generated randomly, and good sets were selected by carrying out replica‐exchange molecular dynamics simulations of two peptides with a minimal α‐helical and a minimal β‐hairpin fold, respectively: the tryptophan cage (PDB code: 1L2Y) and tryptophan zipper (PDB code: 1LE1). Eight sets of parameters produced native‐like structures of these two peptides. These eight sets were tested on two larger proteins: the engrailed homeodomain (PDB code: 1ENH) and FBP WW domain (PDB code: 1E0L); two sets were found to produce native‐like conformations of these proteins. These two sets were tested further on a larger set of nine proteins with α or α + β structure and found to locate native‐like structures of most of them. These results demonstrate that, in addition to finding reasonable initial starting points for optimization, an extensive search of parameter space is a powerful method to produce a transferable force field. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2009     

An automated, sheathless capillary electrophoresis-mass spectrometry platform for discovery of biomarkers in human serum

A capillary electrophoresis-mass spectrometry (CE-MS) method has been developed to perform routine, automated analysis of low-molecular-weight peptides in human serum. The method incorporates transient isotachophoresis for in-line preconcentration and a sheathless electrospray interface. To evaluate the performance of the method and demonstrate the utility of the approach, an experiment was designed in which peptides were added to sera from individuals at each of two different concentrations, artificially creating two groups of samples. The CE-MS data from the serum samples were divided into separate training and test sets. A pattern-recognition/feature-selection algorithm based on support vector machines was used to select the mass-to-charge (m/z) values from the training set data that distinguished the two groups of samples from each other. The added peptides were identified correctly as the distinguishing features, and pattern recognition based on these peptides was used to assign each sample in the independent test set to its respective group. A twofold difference in peptide concentration could be detected with statistical significance (p-value < 0.0001). The accuracy of the assignment was 95%, demonstrating the utility of this technique for the discovery of patterns of biomarkers in serum.     

Tucker1 model algorithms for fast solutions to large PARAFAC problems

We describe a method of performing trilinear analysis on large data sets using a modification of the PARAFAC‐ALS algorithm. Our method iteratively decomposes the data matrix into a core matrix and three loading matrices based on the Tucker1 model. The algorithm is particularly useful for data sets that are too large to upload into a computer's main memory. While the performance advantage in utilizing our algorithm is dependent on the number of data elements and dimensions of the data array, we have seen a significant performance improvement over operating PARAFAC‐ALS on the full data set. In one case of data comprising hyperspectral images from a confocal microscope, our method of analysis was approximately 60 times faster than operating on the full data set, while obtaining essentially equivalent results. Copyright © 2008 by John Wiley & Sons, Ltd.     

Synthesis of Rhenium‐Centric Reverse Turn Mimics

Molecular scaffolds have been shown to facilitate and stabilise secondary structural turn elements, with a central core‐arranging functionality in a defined three‐dimensional orientation. In a peptide‐based molecular imaging probe, this approach is of particular value as it would essentially “hide” a metal radioisotope within the ligand framework, making the labelling element a critical component of the receptor‐bound structure. Starting from a 1,2‐diaminoethane loaded 2‐chlorotrityl resin, a versatile set of triamine ligand systems were synthesised by using solid‐phase Fmoc‐based peptide chemistry. The resultant resin‐bound peptides then underwent amide reduction by treatment with borane‐THF at 65 °C. This provided complete conversion to the corresponding polyamine entities in high purity for the majority of the amino acids utilised. The triamines were then coordinated on solid support by using [NEt₄]₂[Re(CO)₃(Br)₃] followed by resin cleavage and HPLC purification, to give the desired rhenium coordinated species. We have shown that amino acid sequences can be assembled, reduced and coordinated on‐resin, resulting in a versatile set of metal–ligand constructs. These studies could be expanded to generate libraries of turn‐based peptidomimetics containing Re/Tc^I organometallic scaffolds, with the intention of developing an improved approach for finding new diagnostic and therapeutic radiopharmaceutical entities.     

Peptides as New Smart Bionanomaterials: Molecular‐Recognition and Self‐Assembly Capabilities

Biomolecules express exquisite properties that are required for molecular recognition and self‐assembly on the nanoscale. These smart capabilities have developed through evolution and such biomolecules operate based on smart functions in natural systems. Recently, these remarkable smart capabilities have been utilized in not only biologically related fields, but also in materials science and engineering. A peptide‐screening technology that uses phage‐display systems has been developed based on this natural smart evolution for the generation of new functional peptide bionanomaterials. We focused on peptides that specifically bound to synthetic polymers. These polymer‐binding peptides were screened by using a phage‐display peptide library to recognize nanostructures that were derived from polymeric structural features and were utilized for possible applications as new bionanomaterials. We also focused on self‐assembling peptides with β‐sheet structures that formed nanoscale, fibrous structures for applications in new bottom‐up nanomaterials. Moreover, nanofiber‐binding peptides were also screened to introduce the desired functionalities into nanofibers without the need for additional molecular design. Our approach to construct new bionanomaterials that employ peptides will open up excellent opportunities for the next generation of materials science and technology.     

Fast and Accurate Protein False Discovery Rates on Large-Scale Proteomics Data Sets with Percolator 3.0

Percolator is a widely used software tool that increases yield in shotgun proteomics experiments and assigns reliable statistical confidence measures, such as q values and posterior error probabilities, to peptides and peptide-spectrum matches (PSMs) from such experiments. Percolator’s processing speed has been sufficient for typical data sets consisting of hundreds of thousands of PSMs. With our new scalable approach, we can now also analyze millions of PSMs in a matter of minutes on a commodity computer. Furthermore, with the increasing awareness for the need for reliable statistics on the protein level, we compared several easy-to-understand protein inference methods and implemented the best-performing method—grouping proteins by their corresponding sets of theoretical peptides and then considering only the best-scoring peptide for each protein—in the Percolator package. We used Percolator 3.0 to analyze the data from a recent study of the draft human proteome containing 25 million spectra (PM:24870542). The source code and Ubuntu, Windows, MacOS, and Fedora binary packages are available from http://percolator.ms/ under an Apache 2.0 license.

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A fast method for large‐scale De Novo peptide and miniprotein structure prediction

Although peptides have many biological and biomedical implications, an accurate method predicting their equilibrium structural ensembles from amino acid sequences and suitable for large‐scale experiments is still missing. We introduce a new approach—PEP‐FOLD—to the de novo prediction of peptides and miniproteins. It first predicts, in the terms of a Hidden Markov Model‐derived structural alphabet, a limited number of local conformations at each position of the structure. It then performs their assembly using a greedy procedure driven by a coarse‐grained energy score. On a benchmark of 52 peptides with 9–23 amino acids, PEP‐FOLD generates lowest‐energy conformations within 2.8 and 2.3 Å Cα root‐mean‐square deviation from the full nuclear magnetic resonance structures (NMR) and the NMR rigid cores, respectively, outperforming previous approaches. For 13 miniproteins with 27–49 amino acids, PEP‐FOLD reaches an accuracy of 3.6 and 4.6 Å Cα root‐mean‐square deviation for the most‐native and lowest‐energy conformations, using the nonflexible regions identified by NMR. PEP‐FOLD simulations are fast—a few minutes only—opening therefore, the door to in silico large‐scale rational design of new bioactive peptides and miniproteins. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010     

Precursor ion scans for the targeted detection of stable‐isotope‐labeled peptides

Stable isotope labels are routinely introduced into proteomes for quantification purposes. Full labeling of cells in varying biological states, followed by sample mixing, fractionation and intensive data acquisition, is used to obtain accurate large‐scale quantification of total protein levels. However, biological processes often affect only a small group of proteins for a short time, resulting in changes that are difficult to detect against the total proteome background. An alternative approach could be the targeted analysis of the proteins synthesized in response to a given biological stimulus. Such proteins can be pulse‐labeled with a stable isotope by metabolic incorporation of ‘heavy’ amino acids. In this study we investigated the specific detection and identification of labeled proteins using acquisition methods based on Precursor Ion Scans (PIS) on a triple‐quadrupole ion trap mass spectrometer. PIS‐based methods were set to detect unique immonium ions originating from labeled peptides. Different labels and methods were tested in standard mixtures to optimize performance. We showed that, in comparison with an untargeted analysis on the same instrument, the approach allowed a several‐fold increase in the specificity of detection of labeled proteins over unlabeled ones. The technique was applied to the identification of proteins secreted by human cells into growth media containing bovine serum proteins, allowing the preferential detection of labeled cellular proteins over unlabeled bovine ones. However, compared with untargeted acquisitions on two different instruments, the PIS‐based strategy showed some limitations in sensitivity. We discuss possible perspectives of the technique. Copyright © 2009 John Wiley & Sons, Ltd.     

Chemical dephosphorylation for identification of multiply phosphorylated peptides and phosphorylation site determination

We have developed a novel strategy to improve the efficiency of identification of multiply phosphorylated peptides isolated by hydroxy acid modified metal oxide chromatography (HAMMOC). This strategy consists of alkali‐induced chemical dephosphorylation (beta‐elimination reaction) of phosphopeptides isolated by HAMMOC prior to analysis by liquid chromatography/mass spectrometry (LC/MS). This approach identified 1.9‐fold more multiply phosphorylated peptides than the conventional approach without beta‐elimination from a digested mixture of three standard phosphoproteins. In addition, the accuracy of phosphorylation site determination in synthetic phosphopeptides was significantly improved. Finally, we applied this approach to a cell lysate. By combining this dephosphorylation approach with the conventional approach, we successfully identified 1649 unique phosphopeptides, including 325 multiply phosphorylated phosphopeptides, from 200 µg of cultured Arabidopsis cells. These results indicate that chemical dephosphorylation prior to LC/MS analysis increases the efficiency of identification of multiply phosphorylated peptides, as well as the accuracy of phosphorylation site determination. Copyright © 2010 John Wiley & Sons, Ltd.     

Targeted data acquisition for improved reproducibility and robustness of proteomic mass spectrometry assays

Quantitative mass spectrometry-based proteomic assays often suffer from a lack of robustness and reproducibility. We here describe a targeted mass spectrometric data acquisition strategy for affinity enriched subproteomes—in our case the kinome—that enables a substantially improved reproducibility of detection, and improved quantification via isobaric tags. Inclusion mass lists containing m/z, charge state, and retention time were created based on a set of 80 shotgun-type experiments performed under identical experimental conditions. For each target protein, peptides were selected according to their frequency of observation and isobaric tag for relative and absolute quantitation (iTRAQ) reporter ion quality. Retention times of selected peptides were aligned using similarity driven pairwise alignment strategy yielding <1 min standard deviation for 4 h gradients. Multiple fragmentation of the same peptides resulted in better statistics and more precise reporter ion based quantification without any loss in coverage. Overall, 24% more target proteins were quantified using the targeted data acquisition approach, and precision of quantification improved by >1.5-fold. We also show that a combination of higher energy collisional dissociation (HCD) with collisional induced dissociation (CID) outperformed pulsed-Q-dissociation (PQD) on the OrbitrapXL. With the CID/ HCD based targeted data acquisition approach 10% more quantifiable target proteins were identified and a 2-fold increase in quantification precision was achieved. We have observed excellent reproducibility between different instruments, underlining the robustness of the approach.     

Analysis and optimization of saturation transfer difference NMR experiments designed to map early self-association events in amyloidogenic peptides

Saturation transfer difference (STD) methods recently have been proposed to be a promising tool for self-recognition mapping at residue and atomic resolution in amyloidogenic peptides. Despite the significant potential of the STD approach for systems undergoing oligomer/monomer (O/M) equilibria, a systematic analysis of the possible artifacts arising in this novel application of STD experiments is still lacking. Here, we have analyzed the STD method as applied to O/M peptides, and we have identified three major sources of possible biases: offset effects, intramonomer cross-relaxation, and partial spin-diffusion within the oligomers. For the purpose of quantitatively assessing these artifacts, we employed a comparative approach that relies on 1-D and 2-D STD data acquired at different saturation frequencies on samples with different peptide concentrations and filtration states. This artifact evaluation protocol was applied to the Abeta(12-28) model system, and all three types of artifacts appear to affect the measured STD spectra. In addition, we propose a method to minimize the biases introduced by these artifacts in the Halpha STD distributions used to obtain peptide self-recognition maps at residue resolution. This method relies on the averaging of STD data sets acquired at different saturation frequencies and provides results comparable to those independently obtained through other NMR pulse sequences that probe oligomerization, such as nonselective off-resonance relaxation experiments. The artifact evaluation protocol and the multiple frequencies averaging strategy proposed here are of general utility for the growing family of amyloidogenic peptides, as they provide a reliable analysis of STD spectra in terms of polypeptide self-recognition epitopes.     

基于等质量肽段末端标记策略的质谱鉴定新算法

等质量肽段末端标记(Isobaric peptide termini labeling,IPTL)是一种使用轻、重同位素分别对肽段的C端和N端进行等重标记的技术.在对使用这种标记技术得到的数据进行一级谱分析时,由于肽段的质量相同,不会增加样本的复杂性,而在处理二级谱的数据时,可利用成对的b、y离子进行分析.本研究利用IPTL方法得到的实验数据设计了一种新的打分算法:全部离子打分算法(All ions scoring algorithm,AISA).AISA在对数据进行处理时,可以同时得到定性和定量信息.在Q-Exactive HeLa和Human-HCC-HL数据集上的蛋白定量覆盖率分别达到99%和100%.在Q-Exactive HeLa 2D RPLC数据集上,AISA算法鉴定到的PSM、唯一肽段和蛋白质分别比Morpheus高15%、26%和22%.在Human-HCC-HL数据集上,AISA算法鉴定到的PSM、唯一肽段和蛋白质分别比Morpheus高24%、39%和27%.在Q-Exactive HeLa和Human-HCC-HL数据集上蛋白质定量比值的平均值非常接近1,分别为1.18和0.90;在0.5~2.0区间内的定量比值分别为91%和94%.     

Introduction of a new critical p value correction method for statistical significance analysis of metabonomics data

Nuclear magnetic resonance (NMR) spectroscopy-based metabonomics is of growing importance for discovery of human disease biomarkers. Identification and validation of disease biomarkers using statistical significance analysis (SSA) is critical for translation to clinical practice. SSA is performed by assessing a null hypothesis test using a derivative of the Student's t test, e.g., a Welch's t test. Choosing how to correct the significance level for rejecting null hypotheses in the case of multiple testing to maintain a constant family-wise type I error rate is a common problem in such tests. The multiple testing problem arises because the likelihood of falsely rejecting the null hypothesis, i.e., a false positive, grows as the number of tests applied to the same data set increases. Several methods have been introduced to address this problem. Bonferroni correction (BC) assumes all variables are independent and therefore sacrifices sensitivity for detecting true positives in partially dependent data sets. False discovery rate (FDR) methods are more sensitive than BC but uniformly ascribe highest stringency to lowest p value variables. Here, we introduce standard deviation step down (SDSD), which is more sensitive and appropriate than BC for partially dependent data sets. Sensitivity and type I error rate of SDSD can be adjusted based on the degree of variable dependency. SDSD generates fundamentally different profiles of critical p values compared with FDR methods potentially leading to reduced type II error rates. SDSD is increasingly sensitive for more concentrated metabolites. SDSD is demonstrated using NMR-based metabonomics data collected on three different breast cancer cell line extracts.     

Global proteomic analysis of lysine acetylation in zebrafish (Danio rerio) embryos

Lysine acetylation is an important post‐translational modification (PTM). Since the development of MS‐based proteomics technology, important roles of lysine acetylation beyond histones have focused on chromatin remodeling during the cell cycle and regulation of nuclear transport, metabolism, and translation. Zebrafish (Danio rerio) is a widely used vertebrate model in genetics and biologic studies. Although studies in several mammalian species have been performed, the mechanism of lysine acetylation in D. rerio embryos is incompletely understood. Here, we investigated the global acetylome in D. rerio embryos by using an MS‐based proteomics approach. We identified 351 acetylated peptides and 377 nonredundant acetylation sites on 189 lysine‐acetylated proteins in 5‐day postfertilization (hpf) embryos of D. rerio. Among lysine‐acetylated peptides, 40.2% indicated three motifs: (ac)KxxxK, (ac)KxxxxK, and Lx(ac)K. Of 190 acetylated proteins, 81 (42.6%) were mainly distributed in the cytoplasm. Gene ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses showed that lysine acetylation in D. rerio was enriched in metabolic pathways. Additionally, 17 of 30 acetylated ribosomal proteins were evolutionarily conserved between zebrafish and humans. Our results indicate that acetyllysine might have regulatory effects on ribosomal proteins involved in protein biosynthesis.