Orthogonal Comparison of Analytical Methods by Theoretical Reconstruction from Bottom-up Assay Data.

In the never-ending endeavor to produce stable and efficacious protein therapeutics, biopharmaceutical companies often employ numerous analytical techniques to characterize and quantify a drug candidate's stability. Mass spectrometry, due to the information-rich data it produces, is commonly used in its numerous configurations to ascertain chemical and structural stability. At issue is the comparison of the various configurations utilized, that is, comparing bottom-up methods such as proteolytic digest followed by reversed phase LC-MS with intact LC-MS methods. Similar issues also arise when using capillary isoelectric focusing to see how charge variants change over time, that is, monitoring the progression of charge altering modifications like deamidation. To this end, site-specific degradations as quantified from bottom-up methods like peptide mapping can be used to build reconstructions of both theoretical intact mass spectra as well as theoretical electropherograms. The result can then be superimposed over the experimental data to qualitatively, and perhaps quantitatively, evaluate differences. In theory, if both experimental bottom-up data and intact data are accurate, the theoretical reconstruction produced from the bottom-up data should perfectly overlay with that of the experimental data. Valuable secondary information can also be ascertained from reconstructions, such as whether modifications are stochastic, as well as a detailed view of all possible combinations of modifications and their quantities used in the reconstruction. This comparison is also useful in determining unknown mass differences in deconvoluted intact protein spectra that may be a result of multiple modifications in combination. The comparison of data from alternate sources provides a holistic and more comprehensive view of the molecule under study.

Classification of quinoa varieties based on protein fingerprinting by capillary electrophoresis with ultraviolet absorption diode array detection and advanced chemometrics.

Quinoa (Chenopodium quinoa Willd.) is an andean grain with exceptional nutritional properties that has been progressively introduced in western countries as a protein-rich super food with a broad amino acid spectrum. Quinoa is consumed as whole grain, but it is also milled to produce high-value flour, which is susceptible to adulteration. Therefore, there is a growing interest in developing novel analytical methods to get further information about quinoa at the chemical level. In this study, we developed a rapid and simple capillary electrophoresis-ultraviolet absorption diode array detection (CE-UV-DAD) method to obtain characteristic multiwavelength electrophoretic profiles of soluble protein extracts from different quinoa grain varieties. Then, advanced chemometric methods (i.e. multivariate curve resolution alternating least squares, MCR-ALS, followed by principal component analysis, PCA, and partial least squares discriminant analysis, PLS-DA) were applied to deconvolute the components present in the electropherograms and classify the quinoa varieties according to their differential protein composition.

MALDI-TOF mass spectrometry on intact bacteria combined with a refined analysis framework allows accurate classification of MSSA and MRSA.

Fast and reliable detection coupled with accurate data-processing and analysis of antibiotic-resistant bacteria is essential in clinical settings. In this study, we use MALDI-TOF on intact cells combined with a refined analysis framework to demonstrate discrimination between methicillin-susceptible (MSSA) and methicillin-resistant (MRSA) Staphylococcus aureus. By combining supervised and unsupervised machine learning methods, we firstly show that the mass spectroscopy data contains strong signal for the clustering of MSSA and MRSA. Then we concentrate on applying supervised learning to extract and verify the important features. A new workflow is proposed that allows for extracting a fixed set of reference peaks so that any new data can be aligned to it and hence consistent feature matrices can be obtained. Also note that by doing so we are able to examine the robustness of the important features that have been found. We also show that appropriate size of the benchmark data, appropriate alignment of the testing data and use of an optimal set of features via feature selection results in prediction accuracy over 90%. In summary, as proof-of-principle, our integrated experimental and bioinformatics study suggests a novel intact cell MALDI-TOF to be of great promise for fast and reliable detection of MRSA strains.

Protein fingerprinting with digital sequences of linear protein subsequence volumes: a computational study.

Proteins in a proteome can be identified from a sequence of K integers equal to the digitized volumes of subsequences with L residues from the primary sequence of a stretched protein. Exhaustive computations on the proteins of Helicobacter pylori (UniProt id UP000000210) with L and K in the range 4-8 show that approx. 90% of the proteins can be identified uniquely in this manner. This computational result can be translated into practice with a nanopore, an emerging technology that does not require analyte immobilization, proteolysis or labeling. Unlike other methods, most of which focus on a specific target protein, nanopore-based methods enable the identification of multiple proteins from a sample in a single run. Recent work by Kennedy, Kolmogorov and associates shows that the blockade current due to a protein molecule translocating through a nanopore is roughly proportional to one or more contiguous residues. The present study points to a modified version in which the volumes of subsequences (rather than of single residues) may be obtained by integrating the blockade current due to L contiguous residues. The advantages arising from this include lower detector bandwidth, elimination of the homopolymer problem and reduced noise. Because an identifier is based on near as well as distant (up to 2KL-L) residues, this approach uses more global information than an approach based on single residues and short-range correlations. The results of the study, which are available in a data supplement, are discussed in detail. Potential implementation issues are addressed.

ProteomeGenerator: A Framework for Comprehensive Proteomics Based on de Novo Transcriptome Assembly and High-Accuracy Peptide Mass Spectral Matching.

Modern mass spectrometry now permits genome-scale and quantitative measurements of biological proteomes. However, analysis of specific specimens is currently hindered by the incomplete representation of biological variability of protein sequences in canonical reference proteomes and the technical demands for their construction. Here, we report ProteomeGenerator, a framework for de novo and reference-assisted proteogenomic database construction and analysis based on sample-specific transcriptome sequencing and high-accuracy mass spectrometry proteomics. This enables the assembly of proteomes encoded by actively transcribed genes, including sample-specific protein isoforms resulting from non-canonical mRNA transcription, splicing, or editing. To improve the accuracy of protein isoform identification in non-canonical proteomes, ProteomeGenerator relies on statistical target-decoy database matching calibrated using sample-specific controls. Its current implementation includes automatic integration with MaxQuant mass spectrometry proteomics algorithms. We applied this method for the proteogenomic analysis of splicing factor SRSF2 mutant leukemia cells, demonstrating high-confidence identification of non-canonical protein isoforms arising from alternative transcriptional start sites, intron retention, and cryptic exon splicing as well as improved accuracy of genome-scale proteome discovery. Additionally, we report proteogenomic performance metrics for current state-of-the-art implementations of SEQUEST HT, MaxQuant, Byonic, and PEAKS mass spectral analysis algorithms. Finally, ProteomeGenerator is implemented as a Snakemake workflow within a Singularity container for one-step installation in diverse computing environments, thereby enabling open, scalable, and facile discovery of sample-specific, non-canonical, and neomorphic biological proteomes.

Combining High-Resolution and Exact Calibration To Boost Statistical Power: A Well-Calibrated Score Function for High-Resolution MS2 Data.

To achieve accurate assignment of peptide sequences to observed fragmentation spectra, a shotgun proteomics database search tool must make good use of the very high-resolution information produced by state-of-the-art mass spectrometers. However, making use of this information while also ensuring that the search engine's scores are well calibrated, that is, that the score assigned to one spectrum can be meaningfully compared to the score assigned to a different spectrum, has proven to be challenging. Here we describe a database search score function, the "residue evidence" (res-ev) score, that achieves both of these goals simultaneously. We also demonstrate how to combine calibrated res-ev scores with calibrated XCorr scores to produce a "combined p value" score function. We provide a benchmark consisting of four mass spectrometry data sets, which we use to compare the combined p value to the score functions used by several existing search engines. Our results suggest that the combined p value achieves state-of-the-art performance, generally outperforming MS Amanda and Morpheus and performing comparably to MS-GF+. The res-ev and combined p-value score functions are freely available as part of the Tide search engine in the Crux mass spectrometry toolkit ( http://crux.ms ).

Development of a Fingerprint-Based Scoring Function for the Prediction of the Binding Mode of Carbonic Anhydrase II Inhibitors.

Carbonic anhydrase II (CAII) is a zinc-containing metalloenzyme whose aberrant activity is associated with various diseases such as glaucoma, osteoporosis, and different types of tumors; therefore, the development of CAII inhibitors, which can represent promising therapeutic agents for the treatment of these pathologies, is a current topic in medicinal chemistry. Molecular docking is a commonly used tool in structure-based drug design of enzyme inhibitors. However, there is still a need for improving docking reliability, especially in terms of scoring functions, since the complex pattern of energetic contributions driving ligand⁻protein binding cannot be properly described by mathematical functions only including approximated energetic terms. Here we report a novel CAII-specific fingerprint-based (IFP) scoring function developed according to the ligand⁻protein interactions detected in the CAII-inhibitor co-crystal structures of the most potent CAII ligands. Our IFP scoring function outperformed the ability of Autodock4 scoring function to identify native-like docking poses of CAII inhibitors and thus allowed a considerable improvement of docking reliability. Moreover, the ligand⁻protein interaction fingerprints showed a useful application in the binding mode analysis of structurally diverse CAII ligands.

Experimental conformational energy maps of proteins and peptides.

We have presented an extensive analysis of the peptide backbone dihedral angles in the PDB structures and computed experimental Ramachandran plots for their distributions seen under a various constraints on X-ray resolution, representativeness at different sequence identity percentages, and hydrogen bonding distances. These experimental distributions have been converted into isoenergy contour plots using the approach employed previously by F. M. Pohl. This has led to the identification of energetically favored minima in the Ramachandran (ϕ, ψ) plots in which global minima are predominantly observed either in the right-handed α-helical or the polyproline II regions. Further, we have identified low energy pathways for transitions between various minima in the (ϕ,ψ) plots. We have compared and presented the experimental plots with published theoretical plots obtained from both molecular mechanics and quantum mechanical approaches. In addition, we have developed and employed a root mean square deviation (RMSD) metric for isoenergy contours in various ranges, as a measure (in kcal.mol-1 ) to compare any two plots and determine the extent of correlation and similarity between their isoenergy contours. In general, we observe a greater degree of compatibility with experimental plots for energy maps obtained from molecular mechanics methods compared to most quantum mechanical methods. The experimental energy plots we have investigated could be helpful in refining protein structures obtained from X-ray, NMR, and electron microscopy and in refining force field parameters to enable simulations of peptide and protein structures that have higher degree of consistency with experiments. Proteins 2017; 85:979-1001. © 2017 Wiley Periodicals, Inc.

Algorithms and design strategies towards automated glycoproteomics analysis.

Glycoproteomics involves the study of glycosylation events on protein sequences ranging from purified proteins to whole proteome scales. Understanding these complex post-translational modification (PTM) events requires elucidation of the glycan moieties (monosaccharide sequences and glycosidic linkages between residues), protein sequences, as well as site-specific attachment of glycan moieties onto protein sequences, in a spatial and temporal manner in a variety of biological contexts. Compared with proteomics, bioinformatics for glycoproteomics is immature and many researchers still rely on tedious manual interpretation of glycoproteomics data. As sample preparation protocols and analysis techniques have matured, the number of publications on glycoproteomics and bioinformatics has increased substantially; however, the lack of consensus on tool development and code reuse limits the dissemination of bioinformatics tools because it requires significant effort to migrate a computational tool tailored for one method design to alternative methods. This review discusses algorithms and methods in glycoproteomics, and refers to the general proteomics field for potential solutions. It also introduces general strategies for tool integration and pipeline construction in order to better serve the glycoproteomics community. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 36:475-498, 2017.

Mapping of the binding landscape for a picomolar protein-protein complex through computation and experiment.

Our understanding of protein evolution would greatly benefit from mapping of binding landscapes, i.e., changes in protein-protein binding affinity due to all single mutations. However, experimental generation of such landscapes is a tedious task due to a large number of possible mutations. Here, we use a simple computational protocol to map the binding landscape for two homologous high-affinity complexes, involving a snake toxin fasciculin and acetylcholinesterase from two different species. To verify our computational predictions, we experimentally measure binding between 25 Fas mutants and the 2 enzymes. Both computational and experimental results demonstrate that the Fas sequence is close to the optimum when interacting with its targets, yet a few mutations could further improve Kd, kon, and koff. Our computational predictions agree well with experimental results and generate distributions similar to those observed in other high-affinity PPIs, demonstrating the potential of simple computational protocols in capturing realistic binding landscapes.

Predicting ligand binding modes from neural networks trained on protein-ligand interaction fingerprints.

We herewith present a novel approach to predict protein-ligand binding modes from the single two-dimensional structure of the ligand. Known protein-ligand X-ray structures were converted into binary bit strings encoding protein-ligand interactions. An artificial neural network was then set up to first learn and then predict protein-ligand interaction fingerprints from simple ligand descriptors. Specific models were constructed for three targets (CDK2, p38-α, HSP90-α) and 146 ligands for which protein-ligand X-ray structures are available. These models were able to predict protein-ligand interaction fingerprints and to discriminate important features from minor interactions. Predicted interaction fingerprints were successfully used as descriptors to discriminate true ligands from decoys by virtual screening. In some but not all cases, the predicted interaction fingerprints furthermore enable to efficiently rerank cross-docking poses and prioritize the best possible docking solutions.

A stringent approach to improve the quality of nitrotyrosine peptide identifications.

Tyrosine nitration is the consequence of a complex machinery of formation and merging of oxygen and nitrogen radicals, and has been associated with both physiological pathways as well as with several human diseases. The latter turned this posttranslational protein modification into an interesting biomarker, being either a consequence of the disease or a factor contributing to the disease onset. However, the interpretation of MS and MS/MS data of peptides containing nitrotyrosine has proven to be very challenging and consequently, the risk of linking MS/MS spectra to incorrect peptide sequences exists and has been reported. Here, we discuss the causes of data misinterpretation and describe a general method to avoid mistakes of MS/MS spectrum misinterpretation. Central in our approach is the reduction of nitrotyrosine into aminotyrosine and the use of the Peptizer algorithm to inspect MS/MS quality-related assumptions.

Relative, label-free protein quantitation: spectral counting error statistics from nine replicate MudPIT samples.

Nine replicate samples of peptides from soybean leaves, each spiked with a different concentration of bovine apotransferrin peptides, were analyzed on a mass spectrometer using multidimensional protein identification technology (MudPIT). Proteins were detected from the peptide tandem mass spectra, and the numbers of spectra were statistically evaluated for variation between samples. The results corroborate prior knowledge that combining spectra from replicate samples increases the number of identifiable proteins and that a summed spectral count for a protein increases linearly with increasing molar amounts of protein. Furthermore, statistical analysis of spectral counts for proteins in two- and three-way comparisons between replicates and combined replicates revealed little significant variation arising from run-to-run differences or data-dependent instrument ion sampling that might falsely suggest differential protein accumulation. In these experiments, spectral counting was enabled by PANORAMICS, probability-based software that predicts proteins detected by sets of observed peptides. Three alternative approaches to counting spectra were also evaluated by comparison. As the counting thresholds were changed from weaker to more stringent, the accuracy of ratio determination also changed. These results suggest that thresholds for counting can be empirically set to improve relative quantitation. All together, the data confirm the accuracy and reliability of label-free spectral counting in the relative, quantitative analysis of proteins between samples.

A designed peptide chip: protein fingerprinting technology with a dry peptide array and statistical data mining.

There has recently been increased interest in the potential for microarray technologies to study protein networks in a whole cell system within a single experiment. Protein-detecting microarrays are composed of numerous agents immobilized within a tiny area on solid surfaces to capture targeted proteins and to detect interactions in a high-throughput fashion. In this chapter, in order to extend the usability of peptide microarrays, we describe a novel dry peptide microarray format to obtain protein fingerprint (PFP) data sets and a statistical PFP data manipulation technique to quantitatively analyze targeted proteins.

Similarity searching using fingerprints of molecular fragments involved in protein-ligand interactions.

To incorporate protein-ligand interaction information into conventional two-dimensional (2D) fingerprint searching, interacting fragments of active compounds were extracted from X-ray structures of protein-ligand complexes and encoded as structural key-type fingerprints. Similarity search calculations with fingerprints derived from interacting fragments were compared to fingerprints of complete ligands and control fragments. In these calculations, fingerprints of interacting fragments produced significantly higher compound recall than other fingerprints. These results indicate that ligand fragments involved in protein-ligand interactions carry much activity-specific chemical information that can be exploited in similarity searching without explicitly accounting for interaction information.

Data Self-Recalibration and Mixture Mass Fingerprint Searching (DASER-MMF) to enhance protein identification within complex mixtures.

A novel algorithm based on Data Self-Recalibration and a subsequent Mixture Mass Fingerprint search (DASER-MMF) has been developed to improve the performance of protein identification from online 1D and 2D-LC-MS/MS experiments conducted on high-resolution mass spectrometers. Recalibration of 40% to 75% of the MS spectra in a human serum dataset is demonstrated with average errors of 0.3 +/- 0.3 ppm, regardless of the original calibration quality. With simple protein mixtures, the MMF search identifies new proteins not found in the MS/MS based search and increases the sequence coverage for identified proteins by six times. The high mass accuracy allows proteins to be identified with as little as three peptide mass hits. When applied to very complex samples, the MMF search shows less dramatic performance improvements. However, refinements such as additional discriminating factors utilized within the search space provide significant gains in protein identification ability and indicate that further enhancements are possible in this realm.

Calibration laws based on multiple linear regression applied to matrix-assisted laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry.

Operation of any mass spectrometer requires implementation of mass calibration laws to translate experimentally measured physical quantities into a m/z range. While internal calibration in Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) offers several attractive features, including exposure of calibrant and analyte ions to identical experimental conditions (e.g. space charge), external calibration affords simpler pulse sequences and higher throughput. The automatic gain control method used in hybrid linear trap quadrupole (LTQ) FT-ICR-MS to consistently obtain the same ion population is not readily amenable to matrix-assisted laser desorption/ionization (MALDI) FT-ICR-MS, due to the heterogeneous nature and poor spot-to-spot reproducibility of MALDI. This can be compensated for by taking external calibration laws into account that consider magnetic and electric fields, as well as relative and total ion abundances. Herein, an evaluation of external mass calibration laws applied to MALDI-FT-ICR-MS is performed to achieve higher mass measurement accuracy (MMA).

Rapid identification of differentiation markers from whole epithelial cells by matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry and statistical analysis.

Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOFMS) was applied to identify markers for cellular differentiation. The differentiation of a human colon epithelial carcinoma T84 cell line was monitored over a period of 28 days by transepithelial electrical resistance (TER) measurements, alkaline phosphatase (AP) assay, and MALDI-TOF mass spectral fingerprints combined with statistical analysis. MALDI-MS generated specific mass spectral fingerprints characteristic of cell differentiation. Twenty-two ions were selected as diagnostic signals of fully differentiated T84 cells. Ten protein ion signals, detected by MALDI-MS and validated by statistical analysis, were proposed as T84 cell differentiation markers. Among these signals, ubiquitin was identified as a T84 cell differentiation marker by nanospray liquid chromatography/tandem mass spectrometry (nanoLC/MS/MS). Moreover, depending on the concentration of the cells seeded on the growth support, it was possible to predict the timing of the exponential phase and of cellular differentiation by MALDI-MS-derived marker ions. MALDI-TOFMS was compared to other methods for the determination of cellular differentiation: TER measurements are rapid but yield limited information as to the cellular differentiation state. AP assays are more specific for the differentiation state but take more time. By contrast, MALDI-MS has been found to be a fast, sensitive and precise method for cell differentiation assessment and provides the opportunity for multiplexing and high throughput. Moreover, the consumable costs per assay are very low.

Post alignment clustering procedure for comparative quantitative proteomics LC-MS data.

Comparative LC-MS is a powerful method for detailed quantitative comparison of complex protein mixtures. Dedicated software is required for detection, matching, and alignment of peaks in multiple LC-MS datasets. However, retention time shifts, saturation effects, limitations of experimental accuracy, and possible occurrence of split peaks make it difficult for software to perfectly match all chromatograms. We describe a procedure to assess the above problems and show that dataset quality can be enhanced with the aid of cluster analysis.

On the analysis of glycomics mass spectrometry data via the regularized area under the ROC curve.

BACKGROUND: Novel molecular and statistical methods are in rising demand for disease diagnosis and prognosis with the help of recent advanced biotechnology. High-resolution mass spectrometry (MS) is one of those biotechnologies that are highly promising to improve health outcome. Previous literatures have identified some proteomics biomarkers that can distinguish healthy patients from cancer patients using MS data. In this paper, an MS study is demonstrated which uses glycomics to identify ovarian cancer. Glycomics is the study of glycans and glycoproteins. The glycans on the proteins may deviate between a cancer cell and a normal cell and may be visible in the blood. High-resolution MS has been applied to measure relative abundances of potential glycan biomarkers in human serum. Multiple potential glycan biomarkers are measured in MS spectra. With the objection of maximizing the empirical area under the ROC curve (AUC), an analysis method was considered which combines potential glycan biomarkers for the diagnosis of cancer. RESULTS: Maximizing the empirical AUC of glycomics MS data is a large-dimensional optimization problem. The technical difficulty is that the empirical AUC function is not continuous. Instead, it is in fact an empirical 0-1 loss function with a large number of linear predictors. An approach was investigated that regularizes the area under the ROC curve while replacing the 0-1 loss function with a smooth surrogate function. The constrained threshold gradient descent regularization algorithm was applied, where the regularization parameters were chosen by the cross-validation method, and the confidence intervals of the regression parameters were estimated by the bootstrap method. The method is called TGDR-AUC algorithm. The properties of the approach were studied through a numerical simulation study, which incorporates the positive values of mass spectrometry data with the correlations between measurements within person. The simulation proved asymptotic properties that estimated AUC approaches the true AUC. Finally, mass spectrometry data of serum glycan for ovarian cancer diagnosis was analyzed. The optimal combination based on TGDR-AUC algorithm yields plausible result and the detected biomarkers are confirmed based on biological evidence. CONCLUSION: The TGDR-AUC algorithm relaxes the normality and independence assumptions from previous literatures. In addition to its flexibility and easy interpretability, the algorithm yields good performance in combining potential biomarkers and is computationally feasible. Thus, the approach of TGDR-AUC is a plausible algorithm to classify disease status on the basis of multiple biomarkers.