ProteinInferencer: Confident protein identification and multiple experiment comparison for large scale proteomics projects

Abstract

Shotgun proteomics generates valuable information from large-scale and target protein characterizations, including protein expression, protein quantification, protein post-translational modifications (PTMs), protein localization, and protein-protein interactions. Typically, peptides derived from proteolytic digestion, rather than intact proteins, are analyzed by mass spectrometers because peptides are more readily separated, ionized and fragmented. The amino acid sequences of peptides can be interpreted by matching the observed tandem mass spectra to theoretical spectra derived from a protein sequence database. Identified peptides serve as surrogates for their proteins and are often used to establish what proteins were present in the original mixture and to quantify protein abundance. Two major issues exist for assigning peptides to their originating protein. The first issue is maintaining a desired false discovery rate (FDR) when comparing or combining multiple large datasets generated by shotgun analysis and the second issue is properly assigning peptides to proteins when homologous proteins are present in the database. Herein we demonstrate a new computational tool, ProteinInferencer, which can be used for protein inference with both small- or large-scale data sets to produce a well-controlled protein FDR. In addition, ProteinInferencer introduces confidence scoring for individual proteins, which makes protein identifications evaluable.

Proteomics informed by transcriptomics for characterising active transposable elements and genome annotation in Aedes aegypti

“…1Bii). Although routinely only proteins identified by at least two peptides are reported in many studies, proteins identified using only one peptide using a stringent peptide-spectrum match (PSM) false discovery rate (FDR) can be informative [45, 46], especially when combined with specific transcript data [2, 3]. There was a good correlation between the protein groups identified using the Ae.…”

Angiogenic and Immunologic Proteins Identified by Deep Proteomic Profiling of Human Retinal and Choroidal Vascular Endothelial Cells: Potential Targets for New Biologic Drugs

“…An experiment-wide protein ranking value was computed as the sum of all discriminant function scores for all peptide-spectral matches made for each protein. 78 Target and decoy proteins were ranked from highest to lowest scores, and target and decoy scores were used to compute protein q- scores. 79 Final protein identifications were reported at protein FDR of 0.01.…”

Preparing Proteoforms of Therapeutic Proteins for Top-Down Mass Spectrometry

“…The resulting peptide mixture is subjected to liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) analysis. Proteins are identified from the LC-MS/MS data by comparing the peptide fragment spectra against in-silico fragment spectra generated from a protein database [4]. As a rule of thumb, a protein is claimed to be identified, if at least two unique peptides are identified representing parts of the sequence.…”