Date: March 28, 2018
Time: 10am PST/1pm EST/6pm GMT
Format: 45 minutes with 10 minutes live Q&A

What challenges do you face in characterizing monoclonal antibodies (mAbs) at the molecular level? Whether it's heterogeneity caused by truncation or glycation, or confounding isobaric amino acids, you've wished there was a better way. Join our webinar to learn about a novel, three-in-one solution for obtaining 100% accurate amino acid sequences for antibody proteins, powered by the PEAKS AB software.

Register Here (10am PST/1pm EST/6pm GMT)

Hear from proteomics analysis expert Wen Zhang, Ph.D., Senior Application Scientist at Bioinformatics Solutions Inc.

The de novo antibody sequencing service offered by BSI is a 3-in-1 solution for obtaining 100% accurate amino acid sequences for your antibody proteins with unknown sequences.

BSI combines the state-of-the-art top-down and bottom-up MS technologies to achieve high accuracy of the constructed sequence with amino-acid level confidence. The latest EThcD fragmentation scheme is incorporated into the bottom-up MS workflow. The rich MS/MS spectra derived from EThcD fragmentation not only improves the accuracy and sensitivity of de novo sequencing, but also generates signature w-ion for direct discrimination of the isobaric amino acids, isoleucine and leucine.

In turn, from their bottom-up MS analysis, BSI derives the antibody protein sequences with isoleucine and leucine differentiation by EThcD method. In addition, the top-down MS workflow focuses on the intact mass of light and heavy chains. This addresses the antibody heterogeneity issues such as C-terminal lysine truncation and glycan forms, which cannot be well answered by bottom-up methods. Furthermore, the top-down MS result can further validate the bottom-up sequencing result. By integrating bottom-up MS analysis with EThcD and top-down MS approaches, the de novo antibody sequencing with Ile / Leu differentiation service offered by BSI can guarantee 100% sequence accuracy.

You will learn about:

• Intact mass analysis of antibody proteins; why is it important?
• de novo antibody protein sequencing workflow used in PEAKS AB service: algorithm and experimental procedure
• Isoleucine and leucine differentiation by using EThcD fragmentation methods

 
Who should attend:

• Antibody drug discovery researchers
• Scientists interested in antibody protein characterization
• Researchers interested in de novo protein sequencing

 
About Our Speaker

Wen Zhang, Ph.D. - Dr. Zhang is the Senior Application Scientist at Bioinformatics Solutions Inc. (BSI). Dr. Zhang received her Bachelor of Science in Biological Sciences at Peking University. She then moved to Canada where she was directly enrolled into a PhD program in Molecular Genetics at the University of Toronto. In her five-year PhD study, she focused primarily on studying human lung cancer proteomics by utilizing state-of-the-art liquid chromatography coupled tandem mass spectrometry technologies and a variety of molecular and cellular biology and biochemistry methods. With her rich experience in proteomics and MS data generation, processing and analysis, she joined BSI as an Application Scientist after graduation. Since then, she has advanced herself to leading the antibody protein de novo sequencing projects, which include intact mass, de novo sequencing, peptide mapping, post-translational modification, disulfide bond and N-linked glycan analyses.

Keep updated with all webinars by emailing training@bioinfor.com and let us know you are interested.

Figure 2(a) and 2(b) show the peptide psm score distribution of PSMs. When FDR estimation is enabled in PEAKS Search parameters, target and decoy matches are respectively shown in two different colors.

Figure 2(a) is a stacked histogram showing the number of target and decoy matches in each score interval. You should observe a similar number of the target and decoy matches with low scores, and observe very few decoy matches with high scores. The vertical line indicates the current score threshold.

Figure 2(b) is a scatterplot showing the peptide score versus precursor mass error in ppm for all PSMs. This figure is most useful for high resolution instruments. You should observe PSMs with high scores centered near the mass error 0, and observe PSMs with low scores scatter to larger mass error. The vertical line indicates the current score threshold.

Figures 4(a) and 4(b) shows precursor mass error of PSMs in filtered result. They help examine whether the mass spectrometer is properly calibrated.

Precursor mass error is calculated in ppm as 106 × (precursor mass - peptide mass) / peptide mass. For a well-calibrated instrument, precursor mass errors should center at 0 ppm across the range of m/z.

Figure 4(a) shows the distribution of precursor mass error in histogram

Figure 4(b) shows precursor m/z versus precursor mass error in scatterplot.

Figure 3(a) and 3(b) provide a guideline for determining a proper ALC score threshold for filtering "de novo only" sequences. The two figures show the local confidence score distribution of residues in de novo sequences that are filtered by the current ALC score threshold.

After database search, de novo sequences can be categorized as:

1. Verifiable de novo sequences

A de novo sequence is verifiable if the associated MS/MS spectrum is confidently matched to a database peptide. Residues in a verifiable de novo sequence can be validated using the database peptide as a reference.

2. "De novo only" sequences

A de novo sequence is "de novo only" if the associated MS/MS spectrum is not confidently matched to any database peptide. "De novo only" sequences may suggest novel peptides, peptides with unknown modifications, or other interesting research subjects.
"De novo only" sequences are crucial for a complete proteomic analysis. However, it is often necessary to remove the low quality sequences. "De novo only" peptides can be filtered by de novo ALC score, which is the average local confidence score of residues in the de novo sequence.

Figure 3(a) shows the score distribution of residues in verifiable de novo sequences. These residues are validated by aligning the de novo sequence with the database peptide. A residue is considered correct if it is consistent in the database peptide. Otherwise, the residue is considered incorrect. The figure shows the score distributions of correct residues and incorrect residues in two different colors.

Figure 3(b) shows the score distribution of residues in "de novo only" sequences. As these residues cannot be directly validated using database peptide, their ratio of correctness is statistically estimated using the distributions in Figure 3(a). The figure shows the estimated score distributions of correct residues and incorrect residues in two different colors.

As a guideline for setting the ALC score threshold, you can gradually increase the threshold until the score distributions of correct and incorrect residues are similar in figure 3(a) and 3(b). In the following example, the ALC threshold is gradually increased to 80. This ensures the filtered "de novo only" sequences are generated from MS/MS spectra that have the same spectral quality as the MS/MS spectra confidently matched in the database search. Figure 3(b) also allows you to estimate the proportion of incorrect residues in the filtered "de novo only" sequences.

If Mascot is part of the inChorus, and the decoy validation is chosen in Mascot search, then Mascot's FDR curve is displayed together with PEAKS DB in the summary view.

When the inChorus result contains no more than three engines, a Venn diagram will be displayed.

A Venn diagram shows the number of peptides identified by each possible combination of the engines.

Note: These numbers do not distinguish the high or low confidence for each engine's score. For example, if a peptide got a high inChorus score but low scores in every individual engine, the peptide is still counted in the intersection of all engines in the Venn diagram. For this reason, this Venn diagram is NOT the best way to compare different engines' performances.

Figure 1(a) is a scatter plot showing de novo sequences' ALC score versus precursor mass error in ppm.

In a typical de novo sequencing analysis, peptides with higher ALC scores are more likely to have a small mass error, indicating a higher chance for the peptide to be correctly sequenced. This figure provides a rule-of-thumb for setting the ALC score threshold. The threshold can be set at the score where precursor mass error starts to converge.

Residue Local Confidence Distribution

Figure 1(b) shows the local confidence score distribution of residues in filtered de novo sequences.

It is essentially a mixed score distribution of both correct and incorrect residues. This figure allows you to examine the proportion of correct and incorrect residues when adjusting the ALC threshold.

By increasing the ALC threshold, the number of incorrect residues will decrease because poor sequences are filtered out first. It is recommend to gradually raise the ALC threshold until the number of correct residues starts to significantly decrease.

Figures 3(a) and 3(b) plots the distribution of feature vector ratios by quality and by average area respectively. These plots help to determine the threshold values for quality and intensity filters to obtain reliable section of the analysis results. The rule of thumb is to set the quality threshold around 8 fold change as shown below in 3(a). Usually quality filter is a better filter to use. But under some circumstance, you may want to apply the filter to the MS signal directly. The rule of thumb is still to choose the average area around 8 fold change. In both figures, the ratio is the group ratio to the base group. If more than two groups present then each ratio value depicts a data point in the figures.