Fast Parallel Tandem Mass Spectral Library Searching Using GPU Hardware Acceleration

Mass spectrometry-based proteomics is a maturing discipline of biologic research that is experiencing substantial growth. Instrumentation has steadily improved over time with the advent of faster and more sensitive instruments collecting ever larger data files. Consequently, the computational process of matching a peptide fragmentation pattern to its sequence, traditionally accomplished by sequence database searching and more recently also by spectral library searching, has become a bottleneck in many mass spectrometry experiments. In both of these methods, the main rate-limiting step is the comparison of an acquired spectrum with all potential matches from a spectral library or sequence database. This is a highly parallelizable process because the core computational element can be represented as a simple but arithmetically intense multiplication of two vectors. In this paper, we present a proof of concept project taking advantage of the massively parallel computing available on graphics processing units (GPUs) to distribute and accelerate the process of spectral assignment using spectral library searching. This program, which we have named FastPaSS (for Fast Parallelized Spectral Searching), is implemented in CUDA (Compute Unified Device Architecture) from NVIDIA, which allows direct access to the processors in an NVIDIA GPU. Our efforts demonstrate the feasibility of GPU computing for spectral assignment, through implementation of the validated spectral searching algorithm SpectraST in the CUDA environment

Tools for 3D Interactome Visualization

In cells, intra- and intermolecular interactions of proteins confer function, and the dynamic modulation of this interactome is critical to meet the changing needs required to support life. Cross-linking and mass spectrometry (XL–MS) enable the detection of both intra- and intermolecular protein interactions in organelles, cells, tissues, and organs. Quantitative XL–MS enables the detection of interactome changes in cells due to environmental, phenotypic, pharmacological, or genetic perturbations. We have developed new informatics capabilities, the first to enable 3D visualization of multiple quantitative interactome data sets, acquired over time or with varied perturbation levels, to reveal relevant dynamic interactome changes. These new tools are integrated within release 3.0 of our online cross-linked peptide database and analysis tool suite XLinkDB. With the recent rapid expansion in XL–MS for protein structural studies and the extension to quantitative XL–MS measurements, 3D interactome visualization tools are of critical need

Investigation of Neutral Loss during Collision-Induced Dissociation of Peptide Ions

MS/MS fragmentation of peptides is dominated by overlapping b and y ion series. However, alternative fragmentation possibilities exist, including neutral loss. A database was generated containing 8400 MS/MS spectra of tryptic peptides assigned with high probability to an amino acid sequence (true positives) and a set of certified false (true negative) assignments for analysis of the amino terminus. A similar database was created for analysis of neutral loss at the carboxy termini using a data set of chymotryptic peptides. The analysis demonstrated that the presence of an internal basic residue, limiting proton mobility, has a profound effect on neutral loss. Peptides with fully mobile protons demonstrated minimal neutral loss, with the exception of amide bonds with proline on the carboxy terminal side, which created an intense neutral loss peak. In contrast, peptides with partial proton mobility contained many amino acids on either side of the amide bond associated with a strong neutral loss peak. Most notable among these was proline on the carboxy terminal side of an amide bond and aspartic acid on the amino terminal side of a bond. All results were found to be consistent for doubly and triply charged peptides and after adjustment for pairings across the amide bonds with particularly labile residues. The carboxy terminal of chymotryptic peptides also demonstrated significant neutral loss events associated with numerous amino acid residues. Clarification of the rules that govern neutral loss, when incorporated into analysis software, will improve our ability to correctly assign spectra to peptide sequences

Accurate Peptide Fragment Mass Analysis: Multiplexed Peptide Identification and Quantification

Fourier transform-all reaction monitoring (FT-ARM) is a novel approach for the identification and quantification of peptides that relies upon the selectivity of high mass accuracy data and the specificity of peptide fragmentation patterns. An FT-ARM experiment involves continuous, data-independent, high mass accuracy MS/MS acquisition spanning a defined <i>m</i>/<i>z</i> range. Custom software was developed to search peptides against the multiplexed fragmentation spectra by comparing theoretical or empirical fragment ions against every fragmentation spectrum across the entire acquisition. A dot product score is calculated against each spectrum to generate a score chromatogram used for both identification and quantification. Chromatographic elution profile characteristics are not used to cluster precursor peptide signals to their respective fragment ions. FT-ARM identifications are demonstrated to be complementary to conventional data-dependent shotgun analysis, especially in cases where the data-dependent method fails because of fragmenting multiple overlapping precursors. The sensitivity, robustness, and specificity of FT-ARM quantification are shown to be analogous to selected reaction monitoring-based peptide quantification with the added benefit of minimal assay development. Thus, FT-ARM is demonstrated to be a novel and complementary data acquisition, identification, and quantification method for the large scale analysis of peptides

Fast Parallel Tandem Mass Spectral Library Searching Using GPU Hardware Acceleration

Mass spectrometry-based proteomics is a maturing discipline of biologic research that is experiencing substantial growth. Instrumentation has steadily improved over time with the advent of faster and more sensitive instruments collecting ever larger data files. Consequently, the computational process of matching a peptide fragmentation pattern to its sequence, traditionally accomplished by sequence database searching and more recently also by spectral library searching, has become a bottleneck in many mass spectrometry experiments. In both of these methods, the main rate-limiting step is the comparison of an acquired spectrum with all potential matches from a spectral library or sequence database. This is a highly parallelizable process because the core computational element can be represented as a simple but arithmetically intense multiplication of two vectors. In this paper, we present a proof of concept project taking advantage of the massively parallel computing available on graphics processing units (GPUs) to distribute and accelerate the process of spectral assignment using spectral library searching. This program, which we have named FastPaSS (for Fast Parallelized Spectral Searching), is implemented in CUDA (Compute Unified Device Architecture) from NVIDIA, which allows direct access to the processors in an NVIDIA GPU. Our efforts demonstrate the feasibility of GPU computing for spectral assignment, through implementation of the validated spectral searching algorithm SpectraST in the CUDA environment

Tools for 3D Interactome Visualization

In cells, intra- and intermolecular interactions of proteins confer function, and the dynamic modulation of this interactome is critical to meet the changing needs required to support life. Cross-linking and mass spectrometry (XL–MS) enable the detection of both intra- and intermolecular protein interactions in organelles, cells, tissues, and organs. Quantitative XL–MS enables the detection of interactome changes in cells due to environmental, phenotypic, pharmacological, or genetic perturbations. We have developed new informatics capabilities, the first to enable 3D visualization of multiple quantitative interactome data sets, acquired over time or with varied perturbation levels, to reveal relevant dynamic interactome changes. These new tools are integrated within release 3.0 of our online cross-linked peptide database and analysis tool suite XLinkDB. With the recent rapid expansion in XL–MS for protein structural studies and the extension to quantitative XL–MS measurements, 3D interactome visualization tools are of critical need

Fast Parallel Tandem Mass Spectral Library Searching Using GPU Hardware Acceleration

Mass spectrometry-based proteomics is a maturing discipline of biologic research that is experiencing substantial growth. Instrumentation has steadily improved over time with the advent of faster and more sensitive instruments collecting ever larger data files. Consequently, the computational process of matching a peptide fragmentation pattern to its sequence, traditionally accomplished by sequence database searching and more recently also by spectral library searching, has become a bottleneck in many mass spectrometry experiments. In both of these methods, the main rate-limiting step is the comparison of an acquired spectrum with all potential matches from a spectral library or sequence database. This is a highly parallelizable process because the core computational element can be represented as a simple but arithmetically intense multiplication of two vectors. In this paper, we present a proof of concept project taking advantage of the massively parallel computing available on graphics processing units (GPUs) to distribute and accelerate the process of spectral assignment using spectral library searching. This program, which we have named FastPaSS (for Fast Parallelized Spectral Searching), is implemented in CUDA (Compute Unified Device Architecture) from NVIDIA, which allows direct access to the processors in an NVIDIA GPU. Our efforts demonstrate the feasibility of GPU computing for spectral assignment, through implementation of the validated spectral searching algorithm SpectraST in the CUDA environment

Mango: A General Tool for Collision Induced Dissociation-Cleavable Cross-Linked Peptide Identification

Chemical cross-linking combined with mass spectrometry provides a method to study protein structures and interactions. The introduction of cleavable bonds in a cross-linker provides an avenue to decouple released peptide masses from their precursor species, greatly simplifying the downstream search, allowing for whole proteome investigations to be performed. Typically, these experiments have been challenging to carry out, often utilizing nonstandard methods to fully identify cross-linked peptides. Mango is an open source software tool that extracts precursor masses from chimeric spectra generated using cleavable cross-linkers, greatly simplifying the downstream search. As it is designed to work with chimeric spectra, Mango can be used on traditional high-resolution tandem mass spectrometry (MS/MS) capable mass spectrometers without the need for additional modifications. When paired with a traditional proteomics search engine, Mango can be used to identify several thousand cross-linked peptide pairs searching against the entire <i>Escherichia coli</i> proteome. Mango provides an avenue to perform whole proteome cross-linking experiments without specialized instrumentation or access to nonstandard methods

Fast Parallel Tandem Mass Spectral Library Searching Using GPU Hardware Acceleration

Mass spectrometry-based proteomics is a maturing discipline of biologic research that is experiencing substantial growth. Instrumentation has steadily improved over time with the advent of faster and more sensitive instruments collecting ever larger data files. Consequently, the computational process of matching a peptide fragmentation pattern to its sequence, traditionally accomplished by sequence database searching and more recently also by spectral library searching, has become a bottleneck in many mass spectrometry experiments. In both of these methods, the main rate-limiting step is the comparison of an acquired spectrum with all potential matches from a spectral library or sequence database. This is a highly parallelizable process because the core computational element can be represented as a simple but arithmetically intense multiplication of two vectors. In this paper, we present a proof of concept project taking advantage of the massively parallel computing available on graphics processing units (GPUs) to distribute and accelerate the process of spectral assignment using spectral library searching. This program, which we have named FastPaSS (for Fast Parallelized Spectral Searching), is implemented in CUDA (Compute Unified Device Architecture) from NVIDIA, which allows direct access to the processors in an NVIDIA GPU. Our efforts demonstrate the feasibility of GPU computing for spectral assignment, through implementation of the validated spectral searching algorithm SpectraST in the CUDA environment

Mango: A General Tool for Collision Induced Dissociation-Cleavable Cross-Linked Peptide Identification

Chemical cross-linking combined with mass spectrometry provides a method to study protein structures and interactions. The introduction of cleavable bonds in a cross-linker provides an avenue to decouple released peptide masses from their precursor species, greatly simplifying the downstream search, allowing for whole proteome investigations to be performed. Typically, these experiments have been challenging to carry out, often utilizing nonstandard methods to fully identify cross-linked peptides. Mango is an open source software tool that extracts precursor masses from chimeric spectra generated using cleavable cross-linkers, greatly simplifying the downstream search. As it is designed to work with chimeric spectra, Mango can be used on traditional high-resolution tandem mass spectrometry (MS/MS) capable mass spectrometers without the need for additional modifications. When paired with a traditional proteomics search engine, Mango can be used to identify several thousand cross-linked peptide pairs searching against the entire Escherichia coli proteome. Mango provides an avenue to perform whole proteome cross-linking experiments without specialized instrumentation or access to nonstandard methods