Peptide Vs. Protein Arrays: Crucial Pros and Cons to Consider
Biomolecule arrays have been transforming biomedical research for decades. At engine, we’re the proud successors of RZPD, a pioneer institution in the field of protein arrays. Today, specialized arrays have evolved from the crude early versions to empower groundbreaking discoveries. This article will discuss the difference between peptide and protein arrays – and how engine arrays can support your own innovative research.
Microarrays are a fantastic tool for biomarker discovery, and they come in multiple shapes and sizes. Peptide arrays contain shorter amino acid sequences, helping you evaluate protein-protein interactions with amazing accuracy. Protein arrays, on the other hand, offer a more holistic view, free of hypothesis bias. At engine, we make high-throughput arrays with a range of biomolecules, from full-length protein to neoantigens and frameshift protein. This versatility is already supporting life-changing research into conditions like Huntington’s disease or Systemic Lupus Erythematosus.
Peptide arrays are selections of distinct peptide sequences attached to solid support like a glass slide or a membrane. They are an invaluable, high-throughput tool to study protein-protein interactions (Breitling et al., 2009). Key applications include enzyme profiling and immune monitoring.
Using immobilized peptides over protein microarrays comes with multiple benefits for specific research areas:
- Peptide arrays allow you to pinpoint specific antibody binding sites, which is why epitope mapping has been one of the chief use cases for these chips.
- Since folding doesn’t play a significant role in peptide arrays, you are less likely to miss “hidden” epitopes.
- Known peptides are not your only option – peptide microarrays can also include bioinformatically designed “neoantigens.” This allows you to study truly novel antigens without limiting your research to interactions between already investigated proteins and peptides.
Peptide arrays also come with technical advantages.
Using chemical synthesis (rather than protein from native or recombinant sources) eliminates host contamination and gives you freedom for structural modifications.
With peptide arrays, the sequence of each biomolecule is unambiguous. This allows for a highly specific and accurate study of protein interaction domains, which mediate essential signaling pathways and regulatory systems (Katz et al., 2011; Pawson et al., 2002). Finally, it’s much easier to synthesize the peptide you identified, e.g., for ELISA production.
While peptide arrays can be a fantastic tool for epitope mapping or enzyme profiling, they’re not without downsides.
Take folding, for example. While peptide arrays won’t miss hidden epitopes, what if folding is relevant to the study? Solid-phase coupling, linkers, and spacers can change the structure displayed during the assay. Non-native epitopes can also arise – e.g., through binding to these same spacers, linkers, or inserted groups.
In developing clinically relevant research, it is essential to assess interactions closely related to physiological and pathophysiological functions. You want to get as close to how things work in vivo as possible. Unfortunately, peptides may deviate from their native form due to technically necessary changes – for instance, modifying residues to improve stability.
Peptides don’t exist separate from their biological systems. Inside the body, they mainly form during degradation processes, many of which we don’t understand completely. To accurately map the peptides coming out, you need to mimic degradation in its entirety. However, we’re far from comprehensive knowledge about all degradation processes. Thus, using peptides, as accurate as they may be, you might still miss out on crucial interactions.
Lastly, consider what it takes to map a native protein. Peptides are tiny compared to the entire biomolecule. You would need a large number of peptide variants for precise testing, which means a higher time and financial investment.
A protein array is a collection of proteins immobilized on a solid surface (Cutler, 2003). Protein chips have wide application in biomarker discovery, antibody profiling, treatment development, and a range of protein function studies, establishing themselves as one of the essential proteomics techniques of the 21st century.
Choosing protein over peptides means you’re running the experiment with the native, non-fragmented form of the biomolecule. This increases binding opportunities, allowing you to discover more real-life antigens than possible with peptides. It also makes it possible to map out a cross-section of the proteome efficiently and realistically.
Now, let’s consider folding.
Even protein arrays, which are much closer to in vivo molecules, are rarely natively folded. To produce microarrays, the protein is purified and processed, which will affect its structure. However, array incubation still happens in native conditions, allowing the protein to fold back at least partially.
Currently, you can’t buy a truly natively structured protein. This means that even if you discovered an antibody that reacts with the 100% native protein (impossible due to the lack of arrays), developing an efficient and affordable in vitro diagnostic product is not realistic.
In short: while complete native folding is not available, protein arrays give you the closest chip to in vivo possible.
Furthermore, protein arrays can still contain peptides, fragments, and isoforms of the biomolecule. Neoantigens can also be introduced into the microarrays, and it is possible to partially modify protein after producing the array (via enzymes). This significantly expands the use cases. You can study multiple molecules at once, remove preselection bias, and cover the heterogeneity of protein expression.
Even better, you can use protein arrays to understand diseases where neoantigens and frameshift peptides play a significant role in pathophysiology. Learn more about studying out-of-frame peptides and how it opens new frontiers in Huntington’s disease research here (Davies & Rubinsztein, 2006).
And, although most protein arrays are hypothesis-bias-free, you can also use disease and tissue-specific selections. These can help you narrow down your research, although you do have to be careful about preselection partiality.
Protein arrays are a unique opportunity in proteomics, but definitely not a method without fault:
- You can’t have a protein array map of all protein. Because hosts can’t produce all molecules with the correct length, folding, and modification, microarrays are not a comprehensive map of in vivo protein. However, if you’re using them for in vitro applications, this isn’t a concern.
- There can be reproducibility challenges. Some manufacturers only give you the sequence and the hostname, making it difficult to conduct follow-up experiments. At engine, we give you the complete information, as well as the direct clone, to avoid that issue.
- Sequencing errors jeopardize accuracy. In the worst-case scenario, the protein has been synthesized based only on genes that have been cloned. Without a control sequencing of the clones, this can lead to inaccurate protein production. We avoid this by always sequencing our clones and running regular database updates, so you can trust engine to provide the correct biomolecules promised.
Finally, host contamination can be an issue with protein arrays, although its prevalence varies depending on the host.
Protein arrays are an excellent top-down approach to biomarker discovery. You are testing for thousands of interactions in a single run (with very little sample material.) Since you’re not limited by hypothesis, protein microarrays open the door to groundbreaking discoveries in directions that you would otherwise overlook
How engine Supports Cutting-Edge Science
At engine, we’re proud to support research that changes lives. Our protein arrays have already helped over 100 publications, and here is why:
E. coli Host
By using E. coli clones, we avoid unwanted post-translational modifications — compare that to insect cells, which are notorious for glycosylating products. E. coli is also a cost-effective and efficient host, perfect for ensuring accurate follow-up experiments. Since we have empty vector spots, your investigation is not compromised even if the host is detected. Although we mitigate unexpected changes to the protein, we can still modify your array using enzymes to provide the exact molecule set you need.
Experiment replication is a cornerstone of quality research. Since we give you complete information and access to the clones themselves, follow-up experiments are more straightforward and much more accurate.
Largest Neoantigen Library
We know that innovation often comes from the most unexpected sources. Thus, we don’t limit ourselves to known proteins. In fact, 41.8% of the spots in our hEXselect array are neoantigens and frameshift peptides, making it invaluable when researching diseases like Huntington’s.
No Selection Bias
In conditions where the cause-to-disease pathway is complex and poorly understood, hypothesis bias can seriously hinder discovery. Our arrays include many human antigens, from full-length protein to peptides, neoantigens, and frameshifts. You can test for 10,000 different interactions in a single run, significantly increasing your chance of discovery.
Microarrays have revolutionized the world of medical research by introducing a reliable, high-throughput methodology. Different arrays come with unique benefits and downsides for specific experiments. At engine, we emphasize well-established, robust technology for a holistic view of your research problem. Our comprehensive microarrays return precise results, help you avoid hypothesis bias, and support you throughout biomarker study, validation, and follow-up experiments. How can we support your next discovery? Reach out today to learn more!
- Breitling, F., Nesterov, A., Stadler, V., Felgenhauer, T., & Bischoff, F. R. (2009). High-density peptide arrays. Molecular BioSystems, 5(3), 224. https://doi.org/10.1039/b819850k
- Cutler, P. (2003). Protein arrays: The current state-of-the-art. PROTEOMICS, 3(1), 3–18. https://doi.org/10.1002/pmic.200390007
- Davies, J. E., & Rubinsztein, D. C. (2006). Polyalanine and polyserine frameshift products in Huntington’s disease. Journal of Medical Genetics, 43(11), 893–896. https://doi.org/10.1136/jmg.2006.044222
- Katz, C., Levy-Beladev, L., Rotem-Bamberger, S., Rito, T., Rüdiger, S. G. D., & Friedler, A. (2011). Studying protein–protein interactions using peptide arrays. Chemical Society Reviews, 40(5), 2131. https://doi.org/10.1039/c0cs00029a
- Pawson, T., Raina, M., & Nash, P. (2002). Interaction domains: from simple binding events to complex cellular behavior. FEBS Letters, 513(1), 2–10. https://doi.org/10.1016/S0014-5793(01)03292–6