Sequence Coverage Visualizer: A web application for protein sequence coverage 3D visualization

Protein structure is connected with its function and interaction and plays an extremely important role in protein characterization. As one of the most important analytical methods for protein characterization, Proteomics is widely used to determine protein composition, quantitation, interaction, and even structures. However, due to the gap between identified proteins by proteomics and available 3D structures, it was very challenging, if not impossible, to visualize proteomics results in 3D and further explore the structural aspects of proteomics experiments. Recently, two groups of researchers from DeepMind and Baker lab have independently published protein structure prediction tools that can help us obtain predicted protein structures for the whole human proteome. Although there is still debate on the validity of some of the predicted structures, it is no doubt that these represent the most accurate predictions to date. More importantly, this enabled us to visualize the majority of human proteins for the first time. To help other researchers best utilize these protein structure predictions, we present the Sequence Coverage Visualizer (SCV), http://scv.lab.gy, a web application for protein sequence coverage 3D visualization. Here we showed a few possible usages of the SCV, including the labeling of post-translational modifications and isotope labeling experiments. These results highlight the usefulness of such 3D visualization for proteomics experiments and how SCV can turn a regular result list into structural insights. Furthermore, when used together with limited proteolysis, we demonstrated that SCV can help validate and compare different protein structures, including predicted ones and existing PDB entries. By performing limited proteolysis on native proteins at various time points, SCV can visualize the progress of the digestion. This time-series data further allowed us to compare the predicted structure and existing PDB entries. Although not deterministic, these comparisons could be used to refine current predictions further and represent an important step towards a complete and correct protein structure database. Overall, SCV is a convenient and powerful tool for visualizing proteomics results.

Salt-Mediated Organic Solvent Precipitation for Enhanced Recovery of Peptides Generated by Pepsin Digestion

Conventional solvent-based precipitation makes it challenging to obtain a high recovery of low mass peptides. However, we previously demonstrated that the inclusion of salt ions, specifically ZnSO4, together with high concentrations of acetone, maximizes the recovery of peptides generated from trypsin digestion. We herein generalized this protocol to the rapid (5 min) precipitation of pepsin-digested peptides recovered from acidic matrices. The precipitation protocol extended to other organic solvents (acetonitrile), with high recovery from dilute peptide samples permitting preconcentration and purification. Mass spectrometry profiling of pepsin-generated peptides demonstrated that the protocol captured peptides as small as 800 u, although with a preferential bias towards recovering larger and more hydrophobic peptides. The precipitation protocol was applied to rapidly quench, concentrate, and purify pepsin-digested samples ahead of MS. Complex mixtures of yeast and plasma proteome extracts were successfully precipitated following digestion, with over 95% of MS-identified peptides observed in the pellet fraction. The full precipitation workflow—including the digestion step—can be completed in under 10 min, with direct MS analysis of the recovered peptide pellets showing exceptional protein sequence coverage.

Data-independent acquisition protease-multiplexing enables increased proteome sequence coverage across multiple fragmentation modes

The use of multiple proteases has been shown to increase protein sequence coverage in proteomics experiments, however due to the additional analysis time required, it has not been widely adapted in routine data-dependent acquisition (DDA) proteomic workflows. Alternatively, data-independent acquisition (DIA) has the potential to analyze multiplexed samples from different protease digests, but has been primarily optimized for fragmenting tryptic peptides. Here we evaluate a DIA multiplexing approach that combines three proteolytic digests (Trypsin, AspN, and GluC) into a single sample. We first optimize data acquisition conditions for each protease individually with both the canonical DIA fragmentation mode (beam type CID), as well as resonance excitation CID, to determine optimal consensus conditions across proteases. Next, we demonstrate that application of these conditions to a protease-multiplexed sample of human peptides results in similar protein identifications and quantitative performance as compared to trypsin alone, but enables up to a 63% increase in peptide detections, and a 27% increase non-redundant amino acid detections. Importantly, this resulted in 100% sequence coverage for numerous proteins, suggesting the utility of this approach in applications where sequence coverage is critical, such as proteoform analysis.

DnaJ Heat Shock Protein Family B Member 9 Is a Novel Biomarker for Fibrillary GN

Fibrillary GN (FGN) is a rare primary glomerular disease. Histologic and histochemical features of FGN overlap with those of other glomerular diseases, and no unique histologic biomarkers for diagnosing FGN have been identified. We analyzed the proteomic content of glomeruli in patient biopsy specimens and detected DnaJ heat shock protein family (Hsp40) member B9 (DNAJB9) as the fourth most abundant protein in FGN glomeruli. Compared with amyloidosis glomeruli, FGN glomeruli exhibited a >6-fold overexpression of DNAJB9 protein. Sanger sequencing and protein sequence coverage maps showed that the DNAJB9 protein deposited in FGN glomeruli did not have any major sequence or structural alterations. Notably, we detected DNAJB9 in all patients with FGN but not in healthy glomeruli or in 19 types of non-FGN glomerular diseases. We also observed the codeposition of DNAJB9 and Ig-γ. Overall, these findings indicate that DNAJB9 is an FGN marker with 100% sensitivity and 100% specificity. The magnitude and specificity of DNAJB9 overabundance in FGN also suggests that this protein has a role in FGN pathogenesis. With this evidence, we propose that DNAJB9 is a strong biomarker for rapid diagnosis of FGN in renal biopsy specimens.