SILACtor: Software To Enable Dynamic SILAC Studies

Stable isotope labeling by amino acids in cell culture (SILAC) is a versatile tool in proteomics that has been used to explore protein turnover on a large scale. However, these studies pose a significant undertaking that can be greatly simplified through the use of computational tools that automate the data analysis. While SILAC technology has enjoyed rapid adoption through the availability of several software tools, algorithms do not exist for the automated analysis of protein turnover data generated using SILAC technology. Presented here is a software tool, SILACtor, designed to trace and compare SILAC-labeled peptides across multiple time points. SILACtor is used to profile protein turnover rates for more than 500 HeLa cell proteins using a SILAC label-chase approach. Additionally, SILACtor contains a method for the automated generation of accurate mass and retention time inclusion lists that target peptides of interest showing fast or slow turnover rates relative to the other peptides observed in the samples. SILACtor enables improved protein turnover studies using SILAC technology and also provides a framework for features extensible to comparative SILAC analyses and targeted methods

Improved Strategies for Rapid Identification of Chemically Cross-Linked Peptides Using Protein Interaction Reporter Technology

Protein interaction reporter (PIR) technology can enable identification of in vivo protein interactions with the use of specialized chemical cross-linkers, liquid chromatography, and high-resolution mass spectrometry. PIR-cross-linkers contain labile bonds that are specifically fragmented under low energy collision or photodissociation conditions in the mass spectrometer source, thus releasing cross-linked peptides. Successful analysis of PIR-cross-linked proteins requires the use of expected mathematical relationships between cross-linked complexes and released peptides after fragmentation of the labile PIR bonds. Presented here is a next-generation software tool, BLinks, for use in the analysis and identification of PIR-cross-linked proteins. BLinks is an advancement beyond our previous efforts by incorporation of chromatographic profiles that must match between cross-linked complexes and released peptides to enable estimation of p-values to help filter true relationships from complex data sets. Additionally, BLinks was used to incorporate Mascot database searching results from subsequent MS/MS analysis of the released peptides to facilitate identification of cross-linked proteins. BLinks was used in the analysis of human serum albumin, and 46 interpeptide relationships were found spanning 30 proximal residues with a 2.2% false discovery rate. BLinks was also used to track peptides involved in multiple, coeluting relationships that make accurate identification of protein interactions difficult. An additional 10 interpeptide relationships were identified despite poor correlation using the profiling tools provided with BLinks. Additionally, BLinks can be used to globally map all interpeptide relationships from the data analysis and customize subsequent analysis to target specific peptides of interest, thus making it a useful tool for both discovery of protein interactions and mapping protein topology

SILACtor: Software To Enable Dynamic SILAC Studies

Stable isotope labeling by amino acids in cell culture (SILAC) is a versatile tool in proteomics that has been used to explore protein turnover on a large scale. However, these studies pose a significant undertaking that can be greatly simplified through the use of computational tools that automate the data analysis. While SILAC technology has enjoyed rapid adoption through the availability of several software tools, algorithms do not exist for the automated analysis of protein turnover data generated using SILAC technology. Presented here is a software tool, SILACtor, designed to trace and compare SILAC-labeled peptides across multiple time points. SILACtor is used to profile protein turnover rates for more than 500 HeLa cell proteins using a SILAC label-chase approach. Additionally, SILACtor contains a method for the automated generation of accurate mass and retention time inclusion lists that target peptides of interest showing fast or slow turnover rates relative to the other peptides observed in the samples. SILACtor enables improved protein turnover studies using SILAC technology and also provides a framework for features extensible to comparative SILAC analyses and targeted methods

SILACtor: Software To Enable Dynamic SILAC Studies

Stable isotope labeling by amino acids in cell culture (SILAC) is a versatile tool in proteomics that has been used to explore protein turnover on a large scale. However, these studies pose a significant undertaking that can be greatly simplified through the use of computational tools that automate the data analysis. While SILAC technology has enjoyed rapid adoption through the availability of several software tools, algorithms do not exist for the automated analysis of protein turnover data generated using SILAC technology. Presented here is a software tool, SILACtor, designed to trace and compare SILAC-labeled peptides across multiple time points. SILACtor is used to profile protein turnover rates for more than 500 HeLa cell proteins using a SILAC label-chase approach. Additionally, SILACtor contains a method for the automated generation of accurate mass and retention time inclusion lists that target peptides of interest showing fast or slow turnover rates relative to the other peptides observed in the samples. SILACtor enables improved protein turnover studies using SILAC technology and also provides a framework for features extensible to comparative SILAC analyses and targeted methods

Comparison of Database Search Strategies for High Precursor Mass Accuracy MS/MS Data

In shotgun proteomics, the analysis of tandem mass spectrometry data from peptides can benefit greatly from high mass accuracy measurements. In this study, we have evaluated two database search strategies which use high mass accuracy measurements of the peptide precursor ion. Our results indicate that peptide identifications are improved when spectra are searched with a wide mass tolerance window and precursor mass is used as a filter to discard incorrect matches. Database searches with a peptide data set constrained to peptides within a narrow mass window resulted in fewer peptide identifications but a significantly faster database search time

Isotope Signatures Allow Identification of Chemically Cross-Linked Peptides by Mass Spectrometry: A Novel Method to Determine Interresidue Distances in Protein Structures through Cross-Linking

Knowledge of protein structures and protein−protein interactions is essential for understanding of biological processes. Recent advances in protein cross-linking and mass spectrometry (MS) have shown significant potential to contribute to this area. Here we report a novel method to rapidly and accurately identify cross-linked peptides based on their unique isotope signature when digested in the presence of H218O. This method overcomes the need for specially synthesized cross-linkers and/or multiple MS runs required by other techniques. We validated our method by performing a “blind” analysis of 5 proteins/complexes of known structure. Side chain repacking calculations using Rosetta show that 17 of our 20 positively identified cross-links fit the published atomic structures. The remaining 3 cross-links are likely due to protein aggregation. The accuracy and rapid throughput of our workflow will advance the use of protein cross-linking in structural biology

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

Label-Free Comparative Analysis of Proteomics Mixtures Using Chromatographic Alignment of High-Resolution μLC−MS Data

Label-free relative quantitative proteomics is a powerful tool for the survey of protein level changes between two biological samples. We have developed and applied an algorithm using chromatographic alignment of μLC−MS runs to improve the detection of differences between complex protein mixtures. We demonstrate the performance of our software by finding differences in E. coli protein abundance upon induction of the lac operon genes using isopropyl β-d-thiogalactopyranoside. The use of our alignment gave a 4-fold decrease in mean relative retention time error and a 6-fold increase in the number of statistically significant differences between samples. Using a conservative threshold, we have identified 5290 total μLC−MS regions that have a different abundance between these samples. Of the detected difference regions, only 23% were mapped to MS/MS peptide identifications. We detected 74 proteins that had a greater relative abundance in the induced sample and 21 with a greater abundance in the uninduced sample. We have developed an effective tool for the label-free detection of differences between samples and demonstrate an increased sensitivity following chromatographic alignment

Label-Free Comparative Analysis of Proteomics Mixtures Using Chromatographic Alignment of High-Resolution μLC−MS Data

Label-free relative quantitative proteomics is a powerful tool for the survey of protein level changes between two biological samples. We have developed and applied an algorithm using chromatographic alignment of μLC−MS runs to improve the detection of differences between complex protein mixtures. We demonstrate the performance of our software by finding differences in E. coli protein abundance upon induction of the lac operon genes using isopropyl β-d-thiogalactopyranoside. The use of our alignment gave a 4-fold decrease in mean relative retention time error and a 6-fold increase in the number of statistically significant differences between samples. Using a conservative threshold, we have identified 5290 total μLC−MS regions that have a different abundance between these samples. Of the detected difference regions, only 23% were mapped to MS/MS peptide identifications. We detected 74 proteins that had a greater relative abundance in the induced sample and 21 with a greater abundance in the uninduced sample. We have developed an effective tool for the label-free detection of differences between samples and demonstrate an increased sensitivity following chromatographic alignment

Label-Free Comparative Analysis of Proteomics Mixtures Using Chromatographic Alignment of High-Resolution μLC−MS Data

Label-free relative quantitative proteomics is a powerful tool for the survey of protein level changes between two biological samples. We have developed and applied an algorithm using chromatographic alignment of μLC−MS runs to improve the detection of differences between complex protein mixtures. We demonstrate the performance of our software by finding differences in E. coli protein abundance upon induction of the lac operon genes using isopropyl β-d-thiogalactopyranoside. The use of our alignment gave a 4-fold decrease in mean relative retention time error and a 6-fold increase in the number of statistically significant differences between samples. Using a conservative threshold, we have identified 5290 total μLC−MS regions that have a different abundance between these samples. Of the detected difference regions, only 23% were mapped to MS/MS peptide identifications. We detected 74 proteins that had a greater relative abundance in the induced sample and 21 with a greater abundance in the uninduced sample. We have developed an effective tool for the label-free detection of differences between samples and demonstrate an increased sensitivity following chromatographic alignment