Host cell protein (HCP) analysis by LC-MS requires reliable quantitation of many proteins across a sample set. The original HCP workflow in Byos was adapted from the peptide workflow (Byonic for MS2 identification and Byologic for XIC quantitation). It was built to characterize a single protein (or a simple protein mixure) in depth, so applied to HCPs it quantifies hundreds of peptides which can create a bottleneck in processing and review.
The Byos™ Multi-Protein Quantitation (MPQ) workflow is designed specifically for HCP and more complex protein mixtures. Rather than aiming for high protein coverage, smart filters select the best peptides from each protein, so only the data that matters is quantified. The same top N peptides are used for MS1-based XIC area quantitation across every sample, which keeps results consistent and comparable. The result is a faster, more focused workflow that is easier to review and fit for purpose. This article demonstrates how to use the MPQ workflows.
MPQ Module Workflows
This module consists of three workflows:
Multi-Protein Preview - Rapid first pass analysis on MS quality check and sample preparation evaluation.
This workflow suggests search parameters for the database search, such as error tolerances for MS1 and MS2. After defining the samples and the protein FASTA file, the processing parameters must be specified. For a Top-N experiment with trypsin, most default settings are suitable, although the fragmentation type may need adjustment. For each sample, a report is generated providing useful insights into instrument precision and the degree of cysteine alkylation. The results are produced quickly, significantly reducing the time required to optimize search parameters. For more information, refer to the FAQ about Preview.
Multi-Protein Identification - High level protein and peptide identification for focused verification.
This workflow is designed to create a focused protein FASTA database. More details about the creation of Focused Protein Databases can be found here. Previously, only one focused database could be generated per file. However, by enabling the "Merge results into a single project" option, it is now possible to create a focused database containing all protein sequences from the search results across all samples.
Multi-Protein Quantitation - Comprehensive label-free, relative quantification of complex samples.
The primary workflow, Multi-Protein Quantitation, is used to generate results. This workflow will be described in detail in the subsequent sections.
Note: This article describes the workflows for data-dependent acquisition (DDA) data. A separate workflow, Multi-Protein Quan-DIA, is available for data-independent acquisition (DIA) data and is covered in its own article.
Project Creation
To begin, add the samples into the samples table by either dragging and dropping them into the table or by clicking the "Add sample(s)..." button. Within the samples table, the utilized enzyme can be selected for each sample via a drop-down menu. This setting is important for calculating the number of missed cleavages and can also be modified after project creation. Additionally, a condition and replicate number can be assigned to each sample. These settings are critical for grouping the samples for reporting and filtering, which will be discussed during the processing step (see Figure 1).
Figure 1. Adding samples during project creation. Assigning a condition and replicate number along the enzyme the sample was prepared with.
Next, define the protein FASTA file location. Utilizing a focused database can significantly decrease search times. A focused database can be created using the Multi-Protein Identification workflow (see Figure 2).
Figure 2. Adding the protein FASTA file.
Finally, configure the processing nodes (see Figure 3). MPQ Quantitation workflows consist of two steps:
- Database Search: The first step involves a Byonic database search from MS/MS spectra to identify peptides and reassemble the presence of proteins.
- Quantification: The second step is the quantification process. Experienced Byos users will notice similarities to the Byologic node. The primary difference, highlighted in Figure 3 and discussed thoroughly below, lies in the objectives: while protein characterization workflows aim to extract maximum data to characterize a single protein, proteomics workflows focus on reducing and aggregating data to identify and quantify all proteins in the sample effectively. Dynamic filtering is introduced in the MPQ workflow to achieve this goal.
Figure 3. Defining each processing step. MPQ workflow specific parameters are the “PSM filtering”, “Match between runs” and “Reported peptide filtering”
PSM Filtering
The PSM (Peptide-to-Spectra Matches) filtering applies criteria to reduce the number of peptides based on specific attributes. These filters are applied in the order they are listed in the configuration window. These options must be defined prior to project creation and cannot be changed afterward.
| Attribute | Description |
| Minimum peptide length | Default value 5 |
| Maximum peptide length | Default value 32 |
| Maximum missed cleavage count | Maximum number of missed cleavage sites a peptide can contain (Default value: 2) |
| Minimum peptide score | Default value 80 |
| Minimum peptide replicate count | Minimum number of replicates within a condition that identified the same peptide. For example, a value of 2 means each peptide must be identified in at least two replicates. Replicates are defined in the sample table in the samples section |
| Keep homologous sequences | Disabling this feature will filter out all peptide sequences that are not unique to a protein |
| Minimum peptide matching a protein count | Minimum number of peptides that can be assigned to a protein |
| Include modifications | Options: None, all fixed modifications, all variable modifications, or all modifications. |
Table 1. PSM Filtering
Match Between Runs
Match Between Runs creates XICs for peptides that the search engine did not detect in every sample, but did identify in at least one. This keeps peptide coverage consistent across the sample set, because MS2 identification does not need to happen in every sample for MS1-based quantitation to take place.
The feature is similar to “Add missing via existing”, with one key difference: the XICs are created during project creation, so no additional manual step is needed.
Match Between Runs can be applied across all samples or within each condition. Applying it within each condition may be more suitable for a typical HCP study.
Reported peptide filtering
The filters defined in this section can also be changed after the project is created. Table 2 gives a detailed description about each option. The function of both filters is visualized in Figure 4.
| Attribute | Description |
| Retain only primary M2 peptide ID | In cases where a peptide has multiple MS2 spectra, only the one with the highest score will be retained and displayed. Enabling this option reduces loaded spectra in the inspection view for faster response. |
| Use only top charge state for quantitation | Only the charge state with the highest intensity will remain. Disabling this option shows all detected charge states. |
| Use the same top N peptides for quantification | Enables the filter to select only the N most intense peptides. |
| Top N count | Defines N. For example, setting a value of 3 with the above option enabled will select the top 3 peptides based on intensity.. |
| Apply RSD limit for replicates | Filters out all peptides with intensity values across replicates having a higher relative standard deviation (RSD) than the threshold. |
| Minimum acceptable %RSD for replicates | Defines the minimum acceptable %RSD for replicates. |
Table 2. Reported peptide filtering
Figure 4. Overview of the dynamic filtering. The PSM Filtering filters the search engine results. The Reported Peptide Filtering on the other hand filters peptides based on certain attributes after the project was created and can be reversed. Applied Top N count is here equal to 3.
Data inspection
After creating a project, the results can be reviewed in the Inspection View, which is detailed in Figure 5. This view, while largely similar to the one used for PTM projects, features two key differences tailored to the MPQ workflow.
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Search Filter Customization:
The search filter—accessible via a dedicated button—brings up a window containing the "Reported Filtering Options" described earlier. For instance, adjusting the "Top N count" setting dynamically changes the number of peptides displayed per protein, offering flexibility in how data is visualized.
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Protein Table Integration:
A protein table unique to MPQ projects provides enhanced filtering functionality. By selecting the checkbox next to a specific protein in the table, the view narrows to display only the peptides associated with that protein. Resetting this filter via the table's reset button reverts the view to show all peptides.
In addition to these differences, the Inspection View retains powerful features like peptide validation, simultaneous XIC boundary adjustments, and theoretical isotope envelope visualizations. These tools enable efficient and detailed data interrogation, facilitating in-depth proteomic analysis.
Figure 5. Overview of the MPQ inspection view.
Report Creation
All the results can be summarized in a custom report. The report function of Byos, of which the MPQ module is a part of, allows the generation of data tables and plots. Custom solutions can be created like a linear fitting curve to calculate quantities based on a spiked standard. The report template of HCP analysis contains the following pivot table:
Figure 6. Pivot table in the reporting section of the MPQ result file.
Conclusion
The Byos™ Multi-Protein Quantitation (MPQ) module streamlines host cell protein (HCP) analysis by moving away from generating hundreds of peptides XICs per protein and instead focusing on the best ones. Its three workflows cover the full path from method setup to results: Multi-Protein Preview for rapid MS quality checks and search parameter suggestions, Multi-Protein Identification for building focused FASTA databases, and Multi-Protein Quantitation for label-free, MS1-based XIC area quantitation across the sample set.
Two layers of dynamic filtering make this possible. PSM filtering, set at project creation, reduces the search results by peptide length, score, missed cleavages and replicate count. Reported peptide filtering, which can be changed after the project is created, applies the same top N peptides to every sample, with options such as top charge state only and a %RSD limit across replicates. Because this second layer is reversible, the number of peptides per protein can be adjusted in the Inspection View without reprocessing. Match Between Runs creates XICs for peptides identified in at least one sample, removing a manual step and improving consistency across the set.
Together with the protein table in the Inspection View and customizable report templates, the MPQ workflow delivers consistent, comparable HCP quantitation that is faster to process and easier to review.