Colander: a probability-based support vector machine algorithm for automatic screening for CID spectra of phosphopeptides prior to database search

We developed a probability-based machine-learning program, Colander, to identify tandem mass spectra that are highly likely to represent phosphopeptides prior to database search. We identified statistically significant diagnostic features of phosphopeptide tandem mass spectra based on ion trap CID MS/MS experiments. Statistics for the features are calculated from 376 validated phosphopeptide spectra and 376 nonphosphopeptide spectra. A probability-based support vector machine (SVM) program, Colander, was then trained on five selected features. Data sets were assembled both from LC/LC-MS/MS analyses of large-scale phosphopeptide enrichments from proteolyzed cells, tissues and synthetic phosphopeptides. These data sets were used to evaluate the capability of Colander to select pS/pT-containing phosphopeptide tandem mass spectra. When applied to unknown tandem mass spectra, Colander can routinely remove 80% of tandem mass spectra while retaining 95% of phosphopeptide tandem mass spectra. The program significantly reduced computational time spent on database search by 60-90%. Furthermore, prefiltering tandem mass spectra representing phosphopeptides can increase the number of phosphopeptide identifications under a predefined false positive rate.     

General methods for identification of carotenoids

General methods for identification of usual carotenoids including C₁₈-HPLC retention time and spectroscopic methods, such as absorption spectra, mass spectra, NMR spectra, are briefly summarized.     

CDSpecTech: A single software suite for multiple chiroptical spectroscopic analyses

The program CDSpecTech was developed to facilitate the analysis of chiroptical spectra, which include the following: vibrational circular dichroism (VCD) and corresponding vibrational absorption (VA) spectra; vibrational Raman optical activity (VROA) and corresponding vibrational Raman spectra; electronic circular dichroism (ECD) and corresponding electronic absorption (EA) spectra. In addition, the program allows for generating optical rotatory dispersion (ORD) as the Kramers–Kronig transform of ECD spectra. The simulation of theoretical spectra from transition strengths can be achieved using different bandshape profiles. The experimental and simulated theoretical spectra can be visually compared by displaying them together. A unique feature of CDSpecTech is performing spectral analysis using the ratio spectra; i.e., the dimensionless dissymmetry factor (DF) spectrum, which is the ratio of CD to absorption spectra, and the dimensionless circular intensity difference (CID) spectrum, which is the ratio of VROA to vibrational Raman spectra. The quantitative agreement between experimental and simulated theoretical spectra can also be assessed from the numerical similarity overlap between them. Two different similarity overlap methods are available. The program uses a graphical user interface which allows for ease of use and facilitates the analysis. All these features make CDSpecTech a valuable tool for the analysis of chiroptical spectra. The program is freely available on the World Wide Web.     

Semiconductor nanocrystals for biological imaging

Conventional organic fluorophores suffer from poor photo stability, narrow absorption spectra and broad emission spectra. Semiconductor nanocrystals, however, are highly photo-stable with broad absorption spectra and narrow size-tunable emission spectra. Recent advances in the synthesis of these materials have resulted in the generation of bright, sensitive, extremely photo-stable and biocompatible semiconductor fluorophores. Commercial availability facilitates their application in a variety of unprecedented biological experiments, including multiplexed cellular imaging, long-term in vitro and in vivo labeling, deep tissue structure mapping and single particle investigation of dynamic cellular processes. Semiconductor nanocrystals are one of the first examples of nanotechnology enabling a new class of biomedical applications.     

Methods for peptide identification by spectral comparison

Background  

Tandem mass spectrometry followed by database search is currently the predominant technology for peptide sequencing in shotgun proteomics experiments. Most methods compare experimentally observed spectra to the theoretical spectra predicted from the sequences in protein databases. There is a growing interest, however, in comparing unknown experimental spectra to a library of previously identified spectra. This approach has the advantage of taking into account instrument-dependent factors and peptide-specific differences in fragmentation probabilities. It is also computationally more efficient for high-throughput proteomics studies.     

An improved method for the construction of decoy peptide MS/MS spectra suitable for the accurate estimation of false discovery rates

The relevance of libraries of annotated MS/MS spectra is growing with the amount of proteomic data generated in high-throughput experiments. These reference libraries provide a fast and accurate way to identify newly acquired MS/MS spectra. In the context of multiple hypotheses testing, the control of the number of false-positive identifications expected in the final result list by means of the calculation of the false discovery rate (FDR). In a classical sequence search where experimental MS/MS spectra are compared with the theoretical peptide spectra calculated from a sequence database, the FDR is estimated by searching randomized or decoy sequence databases. Despite on-going discussion on how exactly the FDR has to be calculated, this method is widely accepted in the proteomic community. Recently, similar approaches to control the FDR of spectrum library searches were discussed. We present in this paper a detailed analysis of the similarity between spectra of distinct peptides to set the basis of our own solution for decoy library creation (DeLiberator). It differs from the previously published results in some key points, mainly in implementing new methods that prevent decoy spectra from being too similar to the original library spectra while keeping important features of real MS/MS spectra. Using different proteomic data sets and library creation methods, we evaluate our approach and compare it with alternative methods.     

AURELIA,a program for computer-aided analysis of multidimensional NMR spectra

Summary AURELIA is an advanced program for the computer-aided evaluation of two-, three- and four-dimensional NMR spectra of any type of molecule. It can be used for the analysis of spectra of small molecules as well as for evaluation of complicated spectra of biological macromolecules such as proteins. AURELIA is highly interactive and offers a large number of tools, such as artefact reduction, cluster and multiplet analysis, spin system searches, resonance assignments, automated calculation of volumes in multidimensional spectra, calculation of distances with different approaches, including the full relaxation matrix approach, Bayesian analysis of peak features, correlation of molecular structures with NMR data, comparison of spectra via spectral algebra and pattern match techniques, automated sequential assignments on the basis of triple resonance spectra, and automatic strip calculation. In contrast to most other programs, many tasks are performed automatically.     

Use of a rank correlation coefficient for comparing mutational spectra]

It is suggest to use the Kendall's rank correlation coefficient tau for the analysis of correlation between mutation spectra. The results of computer simulations and analysis of real spectra showed that the approach can be recommended for the analysis of mutation spectra.     

Fractional factorial design for optimization of the SELDI protocol for human adipose tissue culture media

The early factors inducing insulin resistance are not known. Therefore, we are interested in studying the secretome of the human visceral adipose tissue as a potential source of unknown peptides and proteins inducing insulin resistance. Surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry is a high-throughput proteomics technology to generate peptide and protein profiles (MS spectra). To obtain good quality and reproducible data from SELDI-TOF, many factors in the sample pretreatment and SELDI protocol should be optimized. To identify the optimal combination of factors resulting in the best and the most reproducible spectra, we designed an experiment where factors were varied systematically according to a fractional factorial design. In this study, seven protein chip preparation protocol factors were tested in 32 experiments. The main effects of these factors and their interactions contributing to the best quality spectra were identified by ANOVA. To assess the reproducibility, in a subsequent experiment the eight protocols generating the highest quality spectra were applied to samples in quadruplicates on different chips. This approach resulted in the development of an improved chip protocol, yielding higher quality peaks and more reproducible spectra.     

Using annotated peptide mass spectrum libraries for protein identification

A system for creating a library of tandem mass spectra annotated with corresponding peptide sequences was described. This system was based on the annotated spectra currently available in the Global Proteome Machine Database (GPMDB). The library spectra were created by averaging together spectra that were annotated with the same peptide sequence, sequence modifications, and parent ion charge. The library was constructed so that experimental peptide tandem mass spectra could be compared with those in the library, resulting in a peptide sequence identification based on scoring the similarity of the experimental spectrum with the contents of the library. A software implementation that performs this type of library search was constructed and successfully used to obtain sequence identifications. The annotated tandem mass spectrum libraries for the Homo sapiens, Mus musculus, and Saccharomyces cerevisiae proteomes and search software were made available for download and use by other groups.     

SOMCD: method for evaluating protein secondary structure from UV circular dichroism spectra

This article presents SOMCD, an improved method for the evaluation of protein secondary structure from circular dichroism spectra, based on Kohonen's self-organizing maps (SOM). Protein circular dichroism (CD) spectra are used to train a SOM, which arranges the spectra on a two-dimensional map. Location in the map reflects the secondary structure composition of a protein. With SOMCD, the prediction of beta-turn has been included. The number of spectra in the training set has been increased, and it now includes 39 protein spectra and 6 reference spectra. Finally, SOM parameters have been chosen to minimize distortion and make the network produce clusters with known properties. Estimation results show improvements compared with the previous version, K2D, which, in addition, estimated only three secondary structure components; the accuracy of the method is more uniform over the different secondary structures.     

A method for eliminating Rayleigh scattering from fluorescence spectra

A numerical method is described for the elimination of Rayleigh scattering from protein fluorescence emission spectra. The method is based upon the observation that Rayleigh scattering is symmetrical about a wavelength at or near the wavelength of excitation. It works best when an automated, computer-based approach can be applied to spectra collected on a data-logging device, although manual correction of chart-recorded spectra is also possible.     

Improved machine learning method for analysis of gas phase chemistry of peptides

Background  

Accurate peptide identification is important to high-throughput proteomics analyses that use mass spectrometry. Search programs compare fragmentation spectra (MS/MS) of peptides from complex digests with theoretically derived spectra from a database of protein sequences. Improved discrimination is achieved with theoretical spectra that are based on simulating gas phase chemistry of the peptides, but the limited understanding of those processes affects the accuracy of predictions from theoretical spectra.     

A library of action spectra for erythema and pigmentation

The first action spectra for erythema and delayed pigmentation in human skin were determined 90 years ago by Karl Hausser and Wilhelm Vahle in Germany, and since then a number of studies have been undertaken to redefine these action spectra. In this paper we give an overview of the action spectra for erythema and pigmentation that have been published during this 90-yr period, as well as indicating their uncertainties and shortcomings.     

VirtualSpectrum,a tool for simulating peak list for multi-dimensional NMR spectra

NMR spectroscopy is a widely used technique for characterizing the structure and dynamics of macromolecules. Often large amounts of NMR data are required to characterize the structure of proteins. To save valuable time and resources on data acquisition, simulated data is useful in the developmental phase, for data analysis, and for comparison with experimental data. However, existing tools for this purpose can be difficult to use, are sometimes specialized for certain types of molecules or spectra, or produce too idealized data. Here we present a fast, flexible and robust tool, VirtualSpectrum, for generating peak lists for most multi-dimensional NMR experiments for both liquid and solid state NMR. It is possible to tune the quality of the generated peak lists to include sources of artifacts from peak overlap, noise and missing signals. VirtualSpectrum uses an analytic expression to represent the spectrum and derive the peak positions, seamlessly handling overlap between signals. We demonstrate our tool by comparing simulated and experimental spectra for different multi-dimensional NMR spectra and analyzing systematically three cases where overlap between peaks is particularly relevant; solid state NMR data, liquid state NMR homonuclear ¹H and ¹⁵N-edited spectra, and 2D/3D heteronuclear correlation spectra of unstructured proteins. We analyze the impact of protein size and secondary structure on peak overlap and on the accuracy of structure determination based on data of different qualities simulated by VirtualSpectrum.     

A Dynamic Noise Level Algorithm for Spectral Screening of Peptide MS/MS Spectra

Background  

High-throughput shotgun proteomics data contain a significant number of spectra from non-peptide ions or spectra of too poor quality to obtain highly confident peptide identifications. These spectra cannot be identified with any positive peptide matches in some database search programs or are identified with false positives in others. Removing these spectra can improve the database search results and lower computational expense.     

CD spectral study of Dnp derivatives of amino acids and peptides for their configurational and conformational analysis

Rules relating the stereochemistry of N-Dnp (Dnp: 2,4-dinitrophenyl) derivatives of alpha-amino acids and peptides and the sign of the Cotton effects at the longest wavelength band (ca. 400 nm) are surveyed. Some new data and insights concerning the CD spectra of Dnp-alpha-amino acids are included: i.e., the spectra of Dnp derivatives as the composite of the corresponding o-nitrophenyl and p-nitrophenyl derivatives; the crystal structure of Dnp-I-phenylalanine and its solid-state CD spectra; the CD spectra of Dnp-alpha-amino acids containing sulfur atom on their side chains; and the theoretical approach to the CD spectra using molecular orbital method-based calculation. Conformational analyses of cyclic and linear peptides by the CD spectra of their Dnp derivatives are also discussed.     

Standardization and denoising algorithms for mass spectra to classify whole-organism bacterial specimens

MOTIVATION: Application of mass spectrometry in proteomics is a breakthrough in high-throughput analyses. Early applications have focused on protein expression profiles to differentiate among various types of tissue samples (e.g. normal versus tumor). Here our goal is to use mass spectra to differentiate bacterial species using whole-organism samples. The raw spectra are similar to spectra of tissue samples, raising some of the same statistical issues (e.g. non-uniform baselines and higher noise associated with higher baseline), but are substantially noisier. As a result, new preprocessing procedures are required before these spectra can be used for statistical classification. RESULTS: In this study, we introduce novel preprocessing steps that can be used with any mass spectra. These comprise a standardization step and a denoising step. The noise level for each spectrum is determined using only data from that spectrum. Only spectral features that exceed a threshold defined by the noise level are subsequently used for classification. Using this approach, we trained the Random Forest program to classify 240 mass spectra into four bacterial types. The method resulted in zero prediction errors in the training samples and in two test datasets having 240 and 300 spectra, respectively.     

Regression trees for analysis of mutational spectra in nucleotide sequences.

MOTIVATION: The study and comparison of mutational spectra is an important problem in molecular biology, because these spectra often reveal important features of the action of various mutagens and the functioning of repair/replication enzymes. As is known, mutability varies significantly along nucleotide sequences: mutations often concentrate at certain positions in a sequence, otherwise termed 'hotspots'. RESULTS: Herein, we propose a regression analysis method based on the use of regression trees in order to analyse the influence of nucleotide context on the occurrence of such hotspots. The REGRT program developed has been tested on simulated and real mutational spectra. For the G:C-->T:A mutational spectra induced by Sn1 alkylating agents (nine spectra), the prediction accuracy was 0. 99. AVAILABILITY: The REGRT program is available upon request from V.Berikov.     

Circular dichroism calculations for polyinosinic acid in proposed multi-stranded geometries.

Circular dichroism spectra have been calculated for multi-stranded polyinosinic acid using three different right-handed structures proposed from X-ray diffraction studies. Agreement between calculated spectra and spectra measured at high salt concentration is best for a four strand structure in which the bases are tilted with respect to the helix axis, as proposed by Arnott et al. (1974). For structures in which the bases are perpendicular to the helix axis, the characteristic negative circular dichoroism of polyinosinic acid at long wavelength no longer appears in the calculated spectra. It is clear that a negative circular dichroism at long wavelength does not indicate a left-handed polynucleotide helix.