Project description:De novo protein sequencing is one of the key problems in mass spectrometry-based proteomics, especially for novel proteins such as monoclonal antibodies for which genome information is often limited or not available. However, due to limitations in peptides fragmentation and coverage, as well as ambiguities in spectra interpretation, complete de novo assembly of unknown protein sequences still remains challenging. To address this problem, we propose an integrated system, ALPS, which for the first time can automatically assemble full-length monoclonal antibody sequences. Our system integrates de novo sequencing peptides, their quality scores and error-correction information from databases into a weighted de Bruijn graph to assemble protein sequences. We evaluated ALPS performance on two antibody data sets, each including a heavy chain and a light chain. The results show that ALPS was able to assemble three complete monoclonal antibody sequences of length 216-441 AA, at 100% coverage, and 96.64-100% accuracy.
Project description:Monoclonal gammopathy of undetermined significance (MGUS) is a plasma cell disorder, leading to the presence of monoclonal antibody (i.e., M-protein) in serum, without clinical symptoms. Here we present a case study in which we detect MGUS by liquid-chromatography coupled with mass spectrometry (LC-MS) profiling of IgG1 in human serum. We detected a Fab-glycosylated M-protein and determined the full heavy and light chain sequences by bottom-up proteomics techniques using multiple proteases, further validated by top-down LC-MS. Moreover, the composition and location of the Fab-glycan could be determined in CDR1 of the heavy chain.
Project description:A comparative canine-human therapeutics model is being developed in B-cell lymphoma through the generation of a hybridoma cell that produces a murine monoclonal antibody specific for canine CD20. The hybridoma cell produces two light chains, light chain-3 and light chain-7. However, the contribution of either light chain to the authentic full-length hybridoma derived IgG is undefined. Mass spectrometry was used to identify only one of the two light chains, light chain-7, as predominating in the full-length IgG. Gene synthesis created a recombinant murine-canine chimeric monoclonal antibody expressing light chain-7 that reconstituted the IgG binding to CD20. Hydrogen deuterium exchange mass spectrometry was used to define two stages in the mode of binding of the CD20 epitope the antibody. In the early stage of the reaction, the antigen interacted with CDR3 (VH). In the equilibrium stages, stable binding occurred to CDR2 (VH) and CDR2 (VL), without any detectable CDR3 (VH) interactions. These data suggest that CDR3 (VH) functions as a transient antigen docking motif to nucleate the peptide into the antibody active site which resolves into antigen binding with the heavy and light chain CDR2 domains. These approaches define a methodology for fine mapping of CDR contacts using nested enzymatic reactions and hydrogen deuterium exchange mass spectrometry to map the kinetic mode of antigen binding. These data support the further development of an engineered synthetic antibody for use as a canine lymphoma therapeutic that mimics the human anti-CD20 antibody therapeutic.
Project description:A comparative canine-human therapeutics model is being developed in B-cell lymphoma through the generation of a hybridoma cell that produces a murine monoclonal antibody specific for canine CD20. The hybridoma cell produces two light chains, light chain-3 and light chain-7. However, the contribution of either light chain to the authentic full-length hybridoma derived IgG is undefined. Mass spectrometry was used to identify only one of the two light chains, light chain-7, as predominating in the full-length IgG. Gene synthesis created a recombinant murine-canine chimeric monoclonal antibody expressing light chain-7 that reconstituted the IgG binding to CD20. Hydrogen deuterium exchange mass spectrometry was used to define two stages in the mode of binding of the CD20 epitope the antibody. In the early stage of the reaction, the antigen interacted with CDR3 (VH). In the equilibrium stages, stable binding occurred to CDR2 (VH) and CDR2 (VL), without any detectable CDR3 (VH) interactions. These data suggest that CDR3 (VH) functions as a transient antigen docking motif to nucleate the peptide into the antibody active site which resolves into antigen binding with the heavy and light chain CDR2 domains. These approaches define a methodology for fine mapping of CDR contacts using nested enzymatic reactions and hydrogen deuterium exchange mass spectrometry to map the kinetic mode of antigen binding. These data support the further development of an engineered synthetic antibody for use as a canine lymphoma therapeutic that mimics the human anti-CD20 antibody therapeutic.
Project description:De novo sequencing and expression of recombinant 1D3 antibody (R1D3). To achieve maximum coverage for antibody sequencing, 5 µg of the antibody original 1D3 was digested in parallel with five different proteases: trypsin, elastase, chymotrypsin, and Asp-N. For each digestion mixture, peptides were loaded onto a nanoflow C18 HPLC column, and peptides were resolved using an aqueous to organic gradient over the course of 90 minutes. As they eluted from the column, peptides were directly ionized on a Thermo Fisher Orbitrap orbitrap Velos mass spectrometer. In a data-dependent manner, both high-resolution full mass measurements and multiple different tandem mass fragmentation (MS/MS) modalities were collected to give the greatest likelihood of correct sequence interpretation. These include standard collision-induced dissociation (CID), higher-energy dissociation (HCD), and electron transfer dissociation (ETD).
After acquisition, data were transferred to Abterra Bioscience for analysis using their proprietary Valens platform. Briefly, an analysis of bottom-up mass spectra generated by the Vanderbilt University Proteomics facility using multiple enzymes was conducted. The framework sequence was identified by performing a database search of the spectra against the germline immunoglobulin gene sequences.
Project description:We analyzed the transcriptome (RNA-seq) of glomeruli in a mouse model of light chain deposition disease (LCDD) as compared to control mice (WT and DH-LMP2A, the latter producing no complete immunoglobulins, only free Ig light chains). Kidney lesions in LCDD are due to the deposition of an abnormal monoclonal free Ig light chain and comprise progressive thickening of basement membranes (tubular and glomerular) and nodular glomerulosclerosis resembling the lesions observed in diabetic nephropathy. The aim of the present study is to analyse the transcriptomic changes at early steps of glomerulosclerosis.
Project description:We analyzed the transcriptome (RNA-seq) of mouse plasma cells (PC) expressing a human Ig light chain from a patient suffering light chain deposition disease (LCDD) as compared to control plasma cells (WT and DH-LMP2A, the latter producing no complete immunoglobulins, only free Ig light chains). Ig light chains causing LCDD present structural peculiarities leading to their aggregation and deposition into tissues and organs, mainly the kidney. We sought to determine if plasma cells producing these abnormal Ig could present specific phenotypes. The aim of the present study is to analyse the transcriptomic signature of plasma cells producing abnormal Ig free light chains.
Project description:Previous work has shown that binding of target proteins to a sparse, unbiased sample of all possible peptide sequences is sufficient to train a machine learning model that can then predict, with statistically high accuracy, target binding to any possible peptide sequence of similar length. Here, highly sequence-specific molecular recognition is explored by measuring binding of 8 monoclonal antibodies (mAbs) with specific linear cognate epitopes to an array containing 121,715 near-random sequences about 10 residues in length. Network models trained on resulting sequence-binding values are used to predict the binding of each mAb to its cognate sequence and to an in silico generated one million random sequences. The model always ranks the binding of the cognate sequence in the top 100 sequences, and for 6 of the 8 mAbs, the cognate sequence ranks in the top ten. Practically, this approach has potential utility in selecting highly specific mAbs for therapeutics or diagnostics. More fundamentally, this demonstrates that very sparse random sampling of a large amino acid sequence spaces is sufficient to generate comprehensive models predictive of highly specific molecular recognition.