Computational protocol: Effect of position-specific single-point mutations and biophysical characterization of amyloidogenic peptide fragments identified from lattice corneal dystrophy patients

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Protocol publication

[…] Raw data were processed using two different approaches to achieve high confidence of label-free quantitation. First, all raw data were converted into the mascot generic format (MGF) using Proteome Discoverer 1.4.1.14 (Thermo Fisher, MA, U.S.A.), and searches were performed using an in-house search engine (Mascot, version 2.4.1; Matrix Science, London, U.K.). The UniProt Knowledgebase (UniProtKB) of human proteins (downloaded on July 25, 2016, including 70 849 sequences and 23 964 784 residues) was used as the search database. The following settings were applied: static modification = carbamidomethyl at cysteine; dynamic modifications = methionine oxidation, asparagine/glutamine deamidation; digestion parameters = full trypsin with maximum two missed cleavages, semi-trypsin with one non-specific cleavage; search parameters = #13C is 2; precursor mass is 10 ppm; fragment mass tolerance is 0.02 Da. Selection of proteins for final analysis was performed using a target-decoy search strategy with a false discovery rate (FDR) of ≤1% considering only those proteins identified with multiple peptides. An additional search was performed using semi-tryptic digestion as the enzyme parameter in order to identify non-tryptic peptide cleavage sites. The emPAI value reported by Mascot and spectral counting was used to perform quantification of proteins and peptides, respectively.The raw data files were also analyzed using the Proteome Discoverer 1.4 software package. MS/MS spectra were searched against the same UniProt human database using the Mascot search engine with parameters identical with those described above. The peptide-to-spectrum matches were filtered by application of a <1% FDR threshold using Percolator. Only peptides identified with high confidence as assigned by the software were selected for further analysis. The precursor ion area detection node was enabled in order to obtain the area under the curve (AUC, or extracted ion chromatogram/XIC) of the LC elution profiles of the detected peptides. The AUCs of the same peptides obtained from both patient and control samples were used for quantitative analysis to determine the fold changes and extent of enrichment in patient tissues. Peptide/protein lists were exported to Excel and processed using in-house analytical scripts. All statistical calculations were performed using three technical replicates for each sample. […]

Pipeline specifications

Software tools Proteome Discoverer, Mascot Server
Application MS-based untargeted proteomics
Organisms Homo sapiens
Diseases Blood Platelet Disorders, Corneal Dystrophies, Hereditary, Genetic Diseases, Inborn, Drug-Related Side Effects and Adverse Reactions, Corneal Injuries
Chemicals Amino Acids