SmartTMT (Real-time search and Tomahto)
AID for TPP
↓ See below for more information.
The Real-Time Search** (RTS-MS3) provides real-time (<5 ms / spectrum) spectral identification and triggers SPS-MS3 scans that utilize assigned and pure fragment ions for accurate quantitation.Time consuming SPS-MS3 spectra are only acquired after confident peptide identification, greatly increasing the number of peptides interrogated and reducing the effects of isobaric interference.
Erickson, B.K., Mintseris, J., Schweppe, D.K., Navarrete-Perea, J., Erickson, A.R., Nusinow, D.P., Paulo, J.A., and Gygi, S.P. (2019). Active Instrument Engagement Combined with a Real-Time Database Search for Improved Performance of Sample Multiplexing Workflows. J Proteome Res 18, 1299-1306.
Schweppe, D.K., Eng, J.K., Bailey, D., Rad, R., Yu, Q., Navarrete-Perea, J., Huttlin, E.L., Erickson, B.K., Paulo, J.A., and Gygi, S.P. (2019). Full-featured, real-time database searching platform enables fast and accurate multiplexed quantitative proteomics. J Proteome Res (in Press)
**The lab continues to work with the instrument manufacturer to make our software available.
The Tomahto software provides real-time instrument control and decision making. Tomahto enables simplied implementation of TOMAHAQ targeted assay. It provides an array of functionalities including MS1 peak detection, MS2 real-time peak matching (RTPM), MS2 fragmentation pattern match, SPS ion purity filter, MS3 automatic gain control (AGC), MS3 quant scan insertion, and target peptide close-out. In addition to controlling data acquisition, it also allows real-time data visualization and post-acquisition analysis.
If you use Tomahto, please cite:
The Tomahto Program and instructions will be available on GitHub as soon as possible. In the meantime please email firstname.lastname@example.org.
BioPlex Interactome Explorer
Since 2012, we have been profiling protein interactions in human cells via affinity-purification mass spectrometry and systematically analyzing interactions for all accessible human proteins at proteome scale. Leveraging the clones available in the human ORFeome (v. 8.1) developed by Marc Vidal and David Hill at the Dana Farber Cancer Center, we have been expressing C-terminally HA-FLAG-tagged versions of each human protein for immunopurification and LC-MS identification of binding partners. We have then been combining these interaction profiles by the thousands to create a series of models of the human interactome with steadily increasing scale. While the first, BioPlex 1.0 – HEK293T v1.0, included ~24,000 interactions among 8,000 proteins, BioPlex 2.0 – HEK293T v2.0 expanded coverage to ~57,000 interactions among 11,000 proteins. The most recent, BioPlex 3.0 – HEK293T v3.0 & HCT116 v1.0, includes nearly 120,000 interactions among nearly 15,000 proteins and is the most comprehensive experimentally derived model of the human interactome to date. Because each protein’s network position reflects its subcellular localization, biological function, and disease association, these networks have been powerful tools for study of thousands of uncharacterized proteins. They have also provided myriad insights into interactome modularity and organization.
The BioPlex Explorer can be found here:
For more details and information on the BioPlex project see these references:
BioPlex Explorer: Schweppe et. al. JPR (2018)
Search algorithms like Sequest or Mascot often successfully identify the proper peptide sequence, but fail to provide information about the presence or absence of site-determining ions. As a result, users must manually inspect each spectrum to confirm proper site localization. Here, we present a probability-based score, named the Ascore, which measures the probability of correct phosphorylation site localization based on the presence and intensity of site-determining ions in MS/MS spectra
A novel probability-based approach for high-throughput protein phosphorylation analysis and site localization Sean A. Beausoleil, Judit Villen, Scott A. Gerber, John Rush, Steven P Gygi
Motif-x (short for motif extractor) is a software tool designed to extract over-represented patterns from any sequence data set. The algorithm is an iterative strategy which builds successive motifs through comparison to a dynamic statistical background.
Schwartz, D. & Gygi, S.P. (2005). An iterative statistical approach to the identification of protein phosphorylation motifs from large-scale data sets. Nature Biotechnology 23(11), 1391-1398.
AID for TPP
Analysis of Independent Differences (AID) for Thermal Proteome Profiling. AID examines the differences between the fractions of non-denatured protein in order to predict the most likely shifted proteins from thermal proteome profiling experiments.