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At least 19 records

STEPS: A Grid Search Methodology for Optimized Peptide Identification Filtering of MS/MS Database Search Results

For bottom-up proteomics there are a wide variety of database searching algorithms in use for matching peptide sequences to tandem MS spectra. Likewise, there are numerous strategies being employed to produce a confident list of peptide identifications from the different search algorithm outputs. Here we introduce a grid search approach for determining optimal database filtering criteria in shotgun proteomics data analyses that is easily adaptable to any search. Systematic Trial and Error Parameter Selection - referred to as STEPS - utilizes user-defined parameter ranges to test a wide array of parameter combinations to arrive at an optimal "parameter set" for data filtering, thus maximizing confident identifications. The benefits of this approach in terms of numbers of true positive identifications are demonstrated using datasets derived from immunoaffinity-depleted blood serum and a bacterial cell lysate, two common proteomics sample types.

Environmental Molecular Sciences Laboratory↗

A Statistical Selection Strategy for Normalization Procedures in LC-MS Proteomics Experiments through Dataset Dependent Ranking of Normalization Scaling Factors

Quantification of LC-MS peak intensities assigned during peptide identification in a typical comparative proteomics experiment will deviate from run-to-run of the instrument due to both technical and biological variation. Thus, normalization of peak intensities across a LC-MS proteomics dataset is a fundamental step in pre-processing. However, the downstream analysis of LC-MS proteomics data can be dramatically affected by the normalization method selected . Current normalization procedures for LC-MS proteomics data are presented in the context of normalization values derived from subsets of the full collection of identified peptides. The distribution of these normalization values is unknown a priori. If they are not independent from the biological factors associated with the experiment the normalization process can introduce bias into the data, which will affect downstream statistical biomarker discovery. We present a novel approach to evaluate normalization strategies, where a normalization strategy includes the peptide selection component associated with the derivation of normalization values. Our approach evaluates the effect of normalization on the between-group variance structure in order to identify candidate normalization strategies that improve the structure of the data without introducing bias into the normalized peak intensities.

60 APPLIED LIFE SCIENCES↗

Three-dimensional feature matching improves coverage for single-cell proteomics based on ion mobility filtering

Single-cell proteomics (scProteomics) promises to advance our understanding of cell functions within complex biological systems. However, a major challenge of current methods is their inability to identify and provide accurate quantitative information for low abundance proteins. Herein, we describe an ion mobility-enhanced mass spectrometry acquisition and peptide identification method, TIFF (Transferring Identification based on FAIMS Filtering), to improve the sensitivity and accuracy of label-free scProteomics. TIFF extends the ion accumulation times for peptide ions by filtering out singly charged ions. The peptide identities are assigned by a three-dimensional MS1 feature matching approach (retention time, accurate mass, and FAIMS compensation voltage). TIFF method enabled unbiased proteome analysis to a depth of >1,700 proteins in single HeLa cells with >1,100 proteins consistently identified. As a demonstration, we applied the TIFF method to obtain temporal proteome profiles of >150 single murine macrophage cells during lipopolysaccharide stimulation and identified time-dependent proteome changes.

59 BASIC BIOLOGICAL SCIENCES↗

Optimized Extraction Method To Remove Humic Acid Interferences from Soil Samples Prior to Microbial Proteome Measurements

The microbial composition and their activities in soil environments play a critical role in organic matter transformation and nutrient cycling, perhaps most specifically with respect to impact on plant growth but also more broadly to global impact on carbon and nitrogen-cycling. Liquid chromatography coupled to high performance mass spectrometry provides a powerful approach to characterize soil microbiomes; however, the limited microbial biomass and the presence of abundant interferences in soil samples present major challenges to soil proteome extraction and subsequent MS measurement. To address some of the major issues, we have designed and optimized an experimental method to enhance microbial proteome extraction concomitant with minimizing the soil-borne humic substances co-extraction from soils. Among the range of interferences, humic substances are often the worst in terms of adversely impacting proteome extraction and mass spectrometry measurement. Our approach employs an in-situ detergent-based microbial lysis / TCA precipitation coupled with an additional acidification precipitation step at the peptide level which efficiently removes humic acids. By combing filtration and pH adjustment of the final peptide solution, the remaining humic acids can be differentially precipitated and removed with a membrane filter, thereby leaving much cleaner proteolytic peptide samples for MS measurement. As a result, this modified method is a reliable and straight-forward protein extraction method that efficiently removes soil-borne humic substances without inducing proteome sample loss or reducing or biasing protein identification in mass spectrometry.

59 BASIC BIOLOGICAL SCIENCES↗

A fast mollified impulse method for biomolecular atomistic simulations

Classical integration methods for molecular dynamics are inherently limited due to resonance phenomena occurring at certain time-step sizes. The mollified impulse method can partially avoid this problem by using appropriate filters based on averaging or projection techniques. However, existing filters are computationally expensive and tedious in implementation since they require either analytical Hessians or they need to solve nonlinear systems from constraints. In this work we follow a different approach based on corotation for the construction of a new filter for (flexible) biomolecular simulations. The main advantages of the proposed filter are its excellent stability properties and ease of implementation in standard softwares without Hessians or solving constraint systems. By simulating multiple realistic examples such as peptide, protein, ice equilibrium and ice–ice friction, the new filter is shown to speed up the computations of long-range interactions by approximately 20%. The proposed filtered integrators allow step sizes as large as 10 fs while keeping the energy drift less than 1% on a 50 ps simulation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Strategies for Metagenomic-Guided Whole-Community Proteomics of Complex Microbial Environments

Accurate protein identification in large-scale proteomics experiments relies upon a detailed, accurate protein catalogue, which is derived from predictions of open reading frames based on genome sequence data. Integration of mass spectrometry-based proteomics data with computational proteome predictions from environmental metagenomic sequences has been challenging because of the variable overlap between proteomic datasets and corresponding short-read nucleotide sequence data. In this study, we have benchmarked several strategies for increasing microbial peptide spectral matching in metaproteomic datasets using protein predictions generated from matched metagenomic sequences from the same human fecal samples. Additionally, we investigated the impact of mass spectrometry-based filters (high mass accuracy, delta correlation), and de novo peptide sequencing on the number and robustness of peptide-spectrum assignments in these complex datasets. In summary, we find that high mass accuracy peptide measurements searched against non-assembled reads from DNA sequencing of the same samples significantly increased identifiable proteins without sacrificing accuracy.

59 BASIC BIOLOGICAL SCIENCES↗

Nuclear export receptor CRM1 recognizes diverse conformations in nuclear export signals

Nuclear export receptor CRM1 binds highly variable nuclear export signals (NESs) in hundreds of different cargoes. Previously we have shown that CRM1 binds NESs in both polypeptide orientations. Here, we show crystal structures of CRM1 bound to eight additional NESs which reveal diverse conformations that range from loop-like to all-helix, which occupy different extents of the invariant NES-binding groove. Analysis of all NES structures show 5-6 distinct backbone conformations where the only conserved secondary structural element is one turn of helix that binds the central portion of the CRM1 groove. All NESs also participate in main chain hydrogen bonding with human CRM1 Lys568 side chain, which acts as a specificity filter that prevents binding of non-NES peptides. The large conformational range of NES backbones explains the lack of a fixed pattern for its 3-5 hydrophobic anchor residues, which in turn explains the large array of peptide sequences that can function as NESs.

59 BASIC BIOLOGICAL SCIENCES↗

[Interview]: Alexandre Shvartsburg, Pacific Northwest National Laboratory, Richland, WA, USA

Q1. What are your main research activities in ion mobility mass spectrometry (past or present)? My early efforts focused on the structural characterization of atomic (carbon and semiconductor) clusters. After the production of bulk fullerenes, many hoped that other nanoclusters discovered in the gas phase could also coalesce into new materials. As these studies required accurate and robust mobility calculations for any ion geometry, I strived to build the needed theory and implement it in the Mobcal software widely employed today. Since 2004, I have been developing methods and novel applications of differential IMS (FAIMS) at PNNL. The principal achievement has been raising the resolving power by over tenfold (up to ~400 for multiply-charged peptides) using elevated fields, helium and hydrogen-rich buffers, and extended filtering times. This performance broadly allows previously unthinkable separations of very similar species, for example sequence inversions and post-translational modification localization isomers of peptides (including “middle-down” peptides such as histone tails), lipid regioisomers, and even isotopomers. Another major direction is investigating the dipole alignment of larger proteins, which creates an exceptionally strong FAIMS effect that is a potential tool for structural biology. Q2: What have been the most significant instrumentation or applications developments in the history of ion mobility - mass spectrometry? In 1995 when I started graduate research at Northwestern, only two groups worldwide worked with IMS/MS and “the literature” meant papers by Bowers (UCSB). Well-wishers counseled me to “learn something useful like HPLC, as IMS would never have real utility”. This booklet showcases the scale of change since. First, the practical IMS/ToF platforms for complex biological analyses demonstrated by Clemmer have turned IMS/MS from an esoteric physical chemistry technique into a powerful analytical tool. By commercializing the IMS/ToF technology in Synapt instruments, Waters has greatly increased its impact via expanded number and diversity of applications. Concurrently, Guevremont at Canadian NRC has perfected FAIMS coupled to MS, deployed it for real-world bio and environmental analyses, and widely distributed it in the Ionalytics Selectra system (subsequently installed on Thermo MS platforms). The latest breakthrough is ultra-FAIMS by Owlstone, where extreme fields allow numerous qualitatively new separations and operational modes that we just begin to explore. Q3: Where do you see ion mobility - mass spectrometry making the most impact in the next 5 years? Any predictions for where the field will go? Sciences dealing with perturbations in media (such as optics or acoustics) at some point shift from the linear to nonlinear paradigm, where propagation depends on the magnitude of perturbation or its driving force. While the linear part remains industrially important (e.g., eyewear and architectural glass for optics), frontline research moves to nonlinear phenomena. IMS is undergoing that transition now with the rise of FAIMS, which should continue as the fundamental understanding improves, new modalities and applications emerge, and more instrumentation is introduced by vendors. Modifying and augmenting FAIMS separations through vapor dopants that render ion mobilities less linear is becoming routine. I expect this area to advance, extending to more specific interactions and to complexation with solution additives. Another route to higher separation power is integrating FAIMS with conventional IMS; proliferation of both technologies would make such 2-D platforms common. Along with mass spectrometry and conventional IMS, FAIMS will address increasingly large macromolecules, including proteins and their complexes.

Shvartsburg, Alexandre A.↗

In-Source Fragmentation and the Sources of Partially Tryptic Peptides in Shotgun Proteomics

Partially tryptic peptides are often identified in shotgun proteomics using trypsin as the proteolytic enzyme; however, it has been controversial regarding the sources of such partially tryptic peptides. Herein we investigate the impact of in-source fragmentation on shotgun proteomics using three biological samples, including a standard protein mixture, a mouse brain tissue homogenate, and a mouse plasma sample. Since the in-source fragments of a peptide retain the same elution time with its parent fully tryptic peptide, the partially tryptic peptides from in-source fragmentation can be distinguished from the other partially tryptic peptides by plotting the observed retention times against the computationally predicted retention times. Most partially tryptic in-source fragmentation artifacts were misaligned from the linear distribution of fully tryptic peptides. The impact of in-source fragmentation on peptide identifications was clearly significant in a less complex sample such as a standard protein digest, where ~60 % of unique peptides were observed as partially tryptic peptides from in-source fragmentation. In mouse brain or mouse plasma samples, in-source fragmentation contributed to 1-3 % of all identified peptides. The other major source of partially tryptic peptides in complex biological samples is presumably proteolytic processing by endogenous proteases in the samples. By filtering out the in-source fragmentation artifacts from the identified partially tryptic or non-tryptic peptides, it is possible to directly survey in-vivo proteolytic processing in biological samples such as blood plasma.

59 BASIC BIOLOGICAL SCIENCES↗

Sipros Ensemble improves database searching and filtering for complex metaproteomics

Complex microbial communities can be characterized by metagenomics and metaproteomics. However, metagenome assemblies often generate enormous, and yet incomplete, protein databases, which undermines the identification of peptides and proteins in metaproteomics. This challenge calls for increased discrimination of true identifications from false identifications by database searching and filtering algorithms in metaproteomics. Sipros Ensemble was developed here for metaproteomics using an ensemble approach. Three diverse scoring functions from MyriMatch, Comet and the original Sipros were incorporated within a single database searching engine. Supervised classification with logistic regression was used to filter database searching results. Benchmarking with soil and marine microbial communities demonstrated a higher number of peptide and protein identifications by Sipros Ensemble than MyriMatch/Percolator, Comet/Percolator, MS-GF+/Percolator, Comet & MyriMatch/iProphet and Comet & MyriMatch & MS-GF+/iProphet. Sipros Ensemble was computationally efficient and scalable on supercomputers. Freely available under the GNU GPL license at http://sipros.omicsbio.org.

59 BASIC BIOLOGICAL SCIENCES↗

Ultrasensitive single-cell proteomics workflow identifies >1000 protein groups per mammalian cell

Here, we report on the combination of nanodroplet sample preparation, ultra-low-flow nanoLC, high-field asymmetric ion mobility spectrometry (FAIMS), and the latest-generation Orbitrap Eclipse Tribrid mass spectrometer for greatly improved single-cell proteome profiling. FAIMS effectively filtered out singly charged ions for more effective MS analysis of multiply charged peptides, resulting in an average of 1056 protein groups identified from single HeLa cells without MS1-level feature matching. This is 2.3 times more identifications than without FAIMS and a far greater level of proteome coverage for single mammalian cells than has been previously reported for a label-free study. Differential analysis of single microdissected motor neurons and interneurons from human spinal tissue indicated a similar level of proteome coverage, and the two subpopulations of cells were readily differentiated based on single-cell label-free quantification.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Cryo-EM structures of the small-conductance Ca 2+ -activated K Ca 2.2 channel

Small-conductance Ca 2+ -activated K + (K Ca 2.1-K Ca 2.3) channels modulate neuronal and cardiac excitability. We report cryo-electron microscopy structures of the K Ca 2.2 channel in complex with calmodulin and Ca 2+ , alone or bound to two small molecule inhibitors, at 3.18, 3.50, 2.99 and 2.97 angstrom resolution, respectively. Extracellular S3-S4 loops in β-hairpin configuration form an outer canopy over the pore with an aromatic box at the canopy’s center. Each S3-S4 β-hairpin is tethered to the selectivity filter in the neighboring subunit by inter-subunit hydrogen bonds. This hydrogen bond network flips the aromatic residue (Tyr362) in the filter’s GYG signature by 180°, causing the outer selectivity filter to widen and water to enter the filter. Disruption of the tether by a mutation narrows the outer selectivity filter, realigns Tyr362 to the position seen in other K + channels, and significantly increases unitary conductance. UCL1684, a mimetic of the bee venom peptide apamin, sits atop the canopy and occludes the opening in the aromatic box. AP14145, an analogue of a therapeutic for atrial fibrillation, binds in the central cavity below the selectivity filter and induces closure of the inner gate. These structures provide a basis for understanding the small unitary conductance and pharmacology of K Ca 2.x channels.

59 BASIC BIOLOGICAL SCIENCES↗

Structural Basis for Hormone Recognition by the Human CRFR2[alpha] G Protein-coupled Receptor

The mammalian corticotropin releasing factor (CRF)/urocortin (Ucn) peptide hormones include four structurally similar peptides, CRF, Ucn1, Ucn2, and Ucn3, that regulate stress responses, metabolism, and cardiovascular function by activating either of two related class B G protein-coupled receptors, CRFR1 and CRFR2. CRF and Ucn1 activate both receptors, whereas Ucn2 and Ucn3 are CRFR2-selective. The molecular basis for selectivity is unclear. Here, we show that the purified N-terminal extracellular domains (ECDs) of human CRFR1 and the CRFR2{alpha} isoform are sufficient to discriminate the peptides, and we present three crystal structures of the CRFR2{alpha} ECD bound to each of the Ucn peptides. The CRFR2{alpha} ECD forms the same fold observed for the CRFR1 and mouse CRFR2{beta} ECDs but contains a unique N-terminal {alpha}-helix formed by its pseudo signal peptide. The CRFR2{alpha} ECD peptide-binding site architecture is similar to that of CRFR1, and binding of the {alpha}-helical Ucn peptides closely resembles CRF binding to CRFR1. Comparing the electrostatic surface potentials of the ECDs suggests a charge compatibility mechanism for ligand discrimination involving a single amino acid difference in the receptors (CRFR1 Glu104/CRFR2{alpha} Pro-100) at a site proximate to peptide residue 35 (Arg in CRF/Ucn1, Ala in Ucn2/3). CRFR1 Glu-104 acts as a selectivity filter preventing Ucn2/3 binding because the nonpolar Ala-35 is incompatible with the negatively charged Glu-104. The structures explain the mechanisms of ligand recognition and discrimination and provide a molecular template for the rational design of therapeutic agents selectively targeting these receptors.

59 BASIC BIOLOGICAL SCIENCES↗

A Retrospective Evaluation of the Use of Mass Spectrometry in FDA Biologics License Applications

The characterization sections of biologics license applications (BLAs) approved by the United States Food and Drug Administration (FDA) between 2000 and 2015 were investigated to examine the extent of the use of mass spectrometry. Mass spectrometry was found to be integral to the characterization of these biotherapeutics. Of the 80 electronically submitted monoclonal antibody and protein biotherapeutic BLAs included in this study, 79 were found to use mass spectrometric workflows for protein or impurity characterization. To further examine how MS is being used in successful BLAs, the applications were filtered based on the type and number of quality attributes characterized, the mass spectrometric workflows used (peptide mapping, intact mass analysis, and cleaved glycan analysis), the methods used to introduce the proteins into the gas phase (ESI, MALDI, or LC-ESI), and the specific types of instrumentation used. Analyses were conducted over a time course based on the FDA BLA approval to determine if any trends in utilization could be observed over time. Additionally, the different classes of protein-based biotherapeutics among the approved BLAs were clustered to determine if any trends could be attributed to the specific type of biotherapeutic. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Quality Assessments of Long-Term Quantitative Proteomic Analysis of Breast Cancer Xenograft Tissues

The identification of protein biomarkers requires large-scale analysis of human specimens to achieve statistical significance. In this study, we evaluated the long-term reproducibility of an iTRAQ (isobaric tags for relative and absolute quantification) based quantitative proteomics strategy using one channel for universal normalization across all samples. A total of 307 liquid chromatography tandem mass spectrometric (LC-MS/MS) analyses were completed, generating 107 one-dimensional (1D) LC-MS/MS datasets and 8 offline two-dimensional (2D) LC-MS/MS datasets (25 fractions for each set) for human-in-mouse breast cancer xenograft tissues representative of basal and luminal subtypes. Such large-scale studies require the implementation of robust metrics to assess the contributions of technical and biological variability in the qualitative and quantitative data. Accordingly, we developed a quantification confidence score based on the quality of each peptide-spectrum match (PSM) to remove quantification outliers from each analysis. After combining confidence score filtering and statistical analysis, reproducible protein identification and quantitative results were achieved from LC-MS/MS datasets collected over a 16 month period.

Environmental Molecular Sciences Laboratory↗

Better understanding and prediction of antiviral peptides through primary and secondary structure feature importance

The emergence of viral epidemics throughout the world is of concern due to the scarcity of available effective antiviral therapeutics. The discovery of new antiviral therapies is imperative to address this challenge, and antiviral peptides (AVPs) represent a valuable resource for the development of novel therapies to combat viral infection. We present a new machine learning model to distinguish AVPs from non-AVPs using the most informative features derived from the physicochemical and structural properties of their amino acid sequences. To focus on those features that are most likely to contribute to antiviral performance, we filter potential features based on their importance for classification. These feature selection analyses suggest that secondary structure is the most important peptide sequence feature for predicting AVPs. Our Feature-Informed Reduced Machine Learning for Antiviral Peptide Prediction (FIRM-AVP) approach achieves a higher accuracy than either the model with all features or current state-of-the-art single classifiers. Understanding the features that are associated with AVP activity is a core need to identify and design new AVPs in novel systems. The FIRM-AVP code and standalone software package are available at https://github.com/pmartR/FIRM-AVP with an accompanying web application at https://msc-viz.emsl.pnnl.gov/AVPR.

59 BASIC BIOLOGICAL SCIENCES↗

Combining Three-Dimensional Modeling with Artificial Intelligence to Increase Specificity and Precision in Peptide–MHC Binding Predictions

The reliable prediction of the affinity of candidate peptides for the MHC is important for predicting their potential antigenicity and thus influences medical applications, such as decisions on their inclusion in T cell–based vaccines. In this study, we present a rapid, predictive computational approach that combines a popular, sequence-based artificial neural network method, NetMHCpan 4.0, with three-dimensional structural modeling. We find that the ensembles of bound peptide conformations generated by the programs MODELLER and Rosetta FlexPepDock are less variable in geometry for strong binders than for low-affinity peptides. In tests on 1271 peptide sequences for which the experimental dissociation constants of binding to the well-characterized murine MHC allele H-2D b are known, by applying thresholds for geometric fluctuations the structure-based approach in a standalone manner drastically improves the statistical specificity, reducing the number of false positives. Furthermore, filtering candidates generated with NetMHCpan 4.0 with the structure-based predictor led to an increase in the positive predictive value (PPV) of the peptides correctly predicted to bind very strongly (i.e., Kd < 100 nM) from 40 to 52% (p = 0.027). The combined method also significantly improved the PPV when tested on five human alleles, including some with limited data for training. Overall, an average increase of 10% in the PPV was found over the standalone sequence-based method. The combined method should be useful in the rapid design of effective T cell–based vaccines.

60 APPLIED LIFE SCIENCES↗

Pushing the Frontier of High-Definition Ion Mobility Spectrometry Using FAIMS

Differential ion mobility spectrometry (FAIMS) separates ions in gases based on the difference between their mobilities in strong and weak electric fields, captured directly employing a periodic waveform with dissimilar profiles in opposite polarities. As that difference is not tightly correlated with the ion size or mass, FAIMS separations are generally quite orthogonal to both conventional IMS (based on the absolute ion mobility that reflects the physical ion size) and mass spectrometry (based on mass). Until a few years ago, that advantage was largely offset by poor FAIMS resolving power (~10–20), an order of magnitude below that achieved with conventional (drift-tube) IMS. This article summarizes the major recent technical developments that have raised FAIMS resolving power up to ~500. These include use of higher and more stable voltages provided by new waveform generators, novel buffer gas compositions comprising high helium or hydrogen fractions, and extended filtering times up to ~1 s. These advances have enabled previously unthinkable analyses such as broad baseline separations of peptide sequence inversions, localization variants (post-translationally modified peptides with differing PTM attachment sites) even for the larger “middle-down” peptides and smallest PTMs, and lipid regioisomers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗