Journal:Laboratory information management software for engineered mini-protein therapeutic workflow
| Full article title | Laboratory information management software for engineered mini-protein therapeutic workflow |
|---|---|
| Journal | BMC Bioinformatics |
| Author(s) | Brusniak, Mi-Youn; Ramos, Hector; Lee, Bernard; Olson, James M. |
| Author affiliation(s) | Fred Hutchinson Cancer Research Center |
| Primary contact | Email: mbrusnia at fredhutch dot org |
| Year published | 2019 |
| Volume and issue | 20 |
| Page(s) | 343 |
| DOI | 10.1186/s12859-019-2935-x |
| ISSN | 1471-2105 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2935-x |
| Download | https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-019-2935-x (PDF) |
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Abstract
Background: Protein-based therapeutics are one of the fastest growing classes of novel medical interventions in areas such as cancer, infectious disease, and inflammation. Protein engineering plays an important role in the optimization of desired therapeutic properties such as reducing immunogenicity, increasing stability for storage, increasing target specificity, etc. One category of protein therapeutics is nature-inspired bioengineered cystine-dense peptides (CDPs) for various biological targets. These engineered proteins are often further modified by synthetic chemistry. For example, candidate mini-proteins can be conjugated into active small molecule drugs. We refer to modified mini-proteins as "optides" (optimized peptides). To efficiently serve the multidisciplinary lab scientists with varied therapeutic portfolio research goals in a non-commercial setting, a cost-effective, extendable laboratory information management system (LIMS) is/was needed.
Results: We have developed a LIMS named Optide-Hunter for a generalized engineered protein compounds workflow that tracks entities and assays from creation to preclinical experiments. The implementation and custom modules are built using LabKey Server, which is an open-source platform for scientific data integration and analysis. Optide-Hunter contains a compound registry, in-silico assays, high-throughput production, large-scale production, in vivo assays, and data extraction from a specimen-tracking database. It is used to store, extract, and view data for various therapeutics projects. Optide-Hunter also includes stand-alone external processing software (HPLCPeakClassifierApp) for automated chromatogram classification. The HPLCPeakClassifierApp is used for pre-processing of high-performance liquid chromatography (HPLC) data prior to loading to Optide-Hunter. The custom implementation is done using data transformation modules in R, SQL, JavaScript, and Java, while being open-source to assist new users in customizing it for their unique workflows. (Instructions for exploring a deployed version of Optide-Hunter can be found on the LabKey website.)
Conclusion: The Optide-Hunter LIMS system is designed and built to track the processes of engineering, producing, and prioritizing protein therapeutic candidates. It can be easily adapted and extended for use in small or large research laboratories where multidisciplinary scientists are collaborating to engineer compounds for potential therapeutic or protein science applications. Exploration of open-source Optide-Hunter can help any bioinformatics scientist adapt, extend, and deploy an equivalent system tailored to each laboratory’s workflow.
Keywords: laboratory information management system, therapeutic protein, HPLC/UPLC peak classification, protein engineering, LabKey software
Background
Following significant advancements in biologics and biopharmaceuticals, protein-based therapeutics has surpassed 10 percent of the entire pharmaceutical market and is expected to be an even larger proportion of the market in the future.[1] Peptide and protein drugs target a wide variety of therapeutic areas such as cancer, inflammation, endocrine disorders, infectious diseases, and more.[2] In the development of peptide and protein therapeutics, protein engineering is an essential part of achieving the desired therapeutic properties in terms of target specificity, stability, pharmacokinetics, pharmacodynamics, etc. Protein engineering is not limited to amino acid sequence alteration. Conjugation with small molecule (dye or drug) can be used to produce antibody drug conjugates (ADCs) or peptide-drug conjugates (PDCs).[3]
Tracking conjugations and other modification steps while manufacturing and producing therapeutic proteins is challenging because it involves many more processing steps than the small-molecule, high-throughput design equivalent. These steps can be complex, and it is crucial that the process steps are captured for repeatability, whether the engineered protein production is being performed in a good manufacturing practice (GMP) or good laboratory practice (GLP) research lab, pre-clinical lab, or in an academic lab. It is also important to keep track of protein generation lineage for retrieval of data with related sequences, especially in high-throughput engineering processes. Frequently, it is beneficial to search previously engineered proteins that possess sequence similarity. As an example, our laboratory investigates nature-inspired cystine-dense peptides (CDPs) that originated from spider, snake, grasshopper, and other species. We have made more than a thousand CDPs and characterized them based on their expressability in our mammalian cell expression system.[4] We usually start from natural amino acid sequences (homologues) that are then modified to improve binding, serum half-life, and many other pharmacodynamic or pharmacokinetic properties (e.g., 14C-labelled peptides for autoradiography-based biodistribution or alanine scanning for structure activity relationship). During the sequence engineering, it is desirable to maintain the evolutionary lineage of the candidate CDPs from the natural homologue sequences so as to better inform further mutation or modification strategies.
There are several commercially available laboratory information management systems (LIMS) that can be used to address lineage and process tracking. Many can be configured as needed and some can be customized through software development. Our lab simultaneously faced the building of a LIMS system while creating an experiment pipeline, as is common among academic research groups. Therefore, it was difficult to prepare reasonable software requirement specifications to establish ready-made/turn-key solutions up front. We found that the open-source LabKey platform provided a budget-friendly and easily extendable and adaptable LIMS solution. LabKey is a well-documented open-source platform for scientific data integration and analysis in a broad array of experimental settings.[5] This manuscript describes the customization of LabKey Server into Optide-Hunter for application to our engineered peptide therapeutic candidates’ workflow. The customization includes our custom code for multiple open-source modules under an Apache 2.0 license. The hope is that Optide-Hunter can assist other academic labs or small biotechnology organizations to jump-start their protein engineering-based therapeutics workflows and easily adapt the provided code and example server for their unique needs. With the exception of the FreezerPro integration connector, the modules introduced in this publication are free of charge to set up. LabKey provides purchasable add-on special instrument connection packages and annual support if a user desires guidance from LabKey personnel rather than its user community.
References
- ↑ Usmani, S.S.; Bedi, G.; Samuel, J.S. et al. (2017). "THPdb: Database of FDA-approved peptide and protein therapeutics". PLoS One 12 (7): e0181748. doi:10.1371/journal.pone.0181748. PMC PMC5536290. PMID 28759605. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5536290.
- ↑ Lagassé, H.A.; Alexaki, A.; Simhadri, V.L. et al. (2017). "Recent advances in (therapeutic protein) drug development". F1000Research 6: 113. doi:10.12688/f1000research.9970.1. PMC PMC5302153. PMID 28232867. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5302153.
- ↑ Ma, L.; Wang, C.; He, Z. et al. (2017). "Peptide-Drug Conjugate: A Novel Drug Design Approach". Current Medicinal Chemistry 24 (31): 3373-3396. doi:10.2174/0929867324666170404142840. PMID 28393694.
- ↑ Correnti, C.E.; Gewe, M.M.; Mehlin, C. et al. (2018). Screening, large-scale production and structure-based classification of cystine-dense peptides. 25. pp. 170–78. doi:10.1038/s41594-018-0033-9. PMC PMC5840021. PMID 29483648. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5840021.
- ↑ Nelson, E.K.; Britt P.; Josh E. et al. (2011). "LabKey Server: An open source platform for scientific data integration, analysis and collaboration". BMC Bioinformatics 12 (71). doi:10.1186/1471-2105-12-71. PMC PMC3062597. PMID 21385461. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3062597.
Notes
This presentation is faithful to the original, with only a few minor changes to presentation, spelling, and grammar. We also added PMCID and DOI when they were missing from the original reference.
