PROSS aims to provide a solution to experimentalists who work with challenging proteins. In a nutshell, the algorithm gets a protein sequence (and structure) as input and provides as output several mutated sequences that are expected to be more stable.
For a detailed explanation about the method and exemplary results, check out our recently published paper in Mol Cell, July 2016
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In citing the PROSS server please refer to the above publication.

For a review about PROSS (very friendly to scientists with little background in structural biology) see Ann Rev., July 2018 Specifically Section 4 and Figure 5.

More studies using PROSS are cited at the end of this page.

This page discusses the following topics/questions:

  • What do we mean by stability?
  • Which proteins are suited for PROSS?
  • What are PROSS main advantages over other stability design methods?
  • All about PROSS results: files, viewing, design selection, experimental validation, troubleshooting.

What do we mean by stability?

PROSS aims to alleviate problems of low expression levels, difficulties in expression in E. coli or other heterologous systems, low solubility, misfolding (the protein is soluble and folded but in an inactive conformation), aggregation, short half-life in-vitro or in-vivo, low Tm, dependency in MBP-tag and others.

To submit a PROSS query you need to provide a PDB-formatted structure of the protein target or a model if a structure is not available and a good model can be obtained. For more details, read also the next section.

Which proteins are suited for stabilization by PROSS?

PROSS must receive an PDB-formatted structure file as input, preferably an X-ray solved structure.

PROSS works best for monomeric or homo-dimeric soluble proteins in which the dimeric interface is not too large (for instance, PROSS worked well for the homo-dimer PTE (pdb entry: 1HZY) but larger interfaces will probably not suit). Higher order oligomers do not suit PROSS.

If your protein does not have an X-ray structure you may use:

  1. a Cryo-EM structure with atomic-level details equivalent to <2.8 angstrom X-ray resolution
  2. an NMR structure that contains only a single conformation. Select the most suited conformation and delete all others from the file.
  3. A structure of a very similar protein (with up to 4-5 mutations relative to your protein of interest). Do not forget to mutate these amino acids to the identities in your protein of interest before ordering PROSS based genes
  4. A model generated by servers like SWISS-PROT or by Rosetta. We recommend using models only if the protein is relatively rich in secondary structure and a homologue that shares at least 40% sequence identity with your protein of interest is available.

What are PROSS main advantages over other stability methods currently available?

  1. PROSS solution requires low throughput validation. The final output is a few designs.
    Each design has a certain number of substitutions, typically between 2%-12% of the total protein.
    The experimental validation is straightforward: select 2-7 designs, order the full genes, clone them, express and test the selected designs to see whether the original problem is alleviated.
  2. PROSS is easy to use. You can't make fundamental errors. You just need to know your protein.

All about PROSS results: files, viewing, design selection, experimental validation, troubleshooting

See PROSS results

More studies using PROSS

  1. Campeotto, I.; Goldenzweig, A.; Davey, J.; Barfod, L.; Marshall, J. M.; Silk, S. E.; Wright, K. E.; Draper, S. J.; Higgins, M. K.; Fleishman, S. J. One-Step Design of a Stable Variant of the Malaria Invasion Protein RH5 for Use as a Vaccine Immunogen. Proc. Natl. Acad. Sci. U. S. A. 2017, 114 (5), 998–1002.
    PMID: 28096331
  2. Brazzolotto, X.; Igert, A.; Guillon, V.; Santoni, G.; Nachon, F. Bacterial Expression of Human Butyrylcholinesterase as a Tool for Nerve Agent Bioscavengers Development. Molecules 2017, 22 (11).
    PMID: 29077024
  3. Tullman J.; Christensen M.; Kelman Z.; Marino J. P. A ClpS-based N-terminal amino acid binding reagent with improved thermostability and selectivity. Biochem Engineering J. 2020, (154).
    Before PMID
  4. Zahradník, J.; Kolářová, L.; Peleg, Y.; Kolenko, P.; Svidenská, S.; Charnavets, T.; Unger, T.; Sussman, J.L.; Schneider, B. Flexible regions govern promiscuous binding of IL-24 to receptors IL-20R1 and IL-22R1. FEBS J. 2019, 286 (19), 3858-3873. 
    PMID: 31152679
  5. Despotović, D.; Brandis, A.; Savidor, A.; Levin, Y.; Fumagalli, L.; Tawfik, D. S. Diadenosine Tetraphosphate (Ap4A) - an E. Coli Alarmone or a Damage Metabolite? FEBS J. 2017, 284 (14), 2194–2215.
    PMID: 28516732
  6. Bandyopadhyay, B.; Goldenzweig, A.; Unger, T.; Adato, O.; Fleishman, S. J.; Unger, R.; Horovitz, A. Local Energetic Frustration Affects the Dependence of Green Fluorescent Protein Folding on the Chaperonin GroEL. J. Biol. Chem. 2017, 292(50):20583-20591.
    PMID: 29066625
  7. Goldsmith, M.; Aggarwal, N.; Ashani, Y.; Jubran, H.; Greisen, P. J.; Ovchinnikov, S.; Leader, H.; Baker, D.; Sussman, J. L.; Goldenzweig, A.; et al. Overcoming an Optimization Plateau in the Directed Evolution of Highly Efficient Nerve Agent Bioscavengers. Protein Eng. Des. Sel. 2017, 30 (4), 333–345
    PMID: 28159998
  8. Buldun, C. M.; Jean, J. X.; Bedford, M. R.; Howarth, M. SnoopLigase Catalyzes Peptide-Peptide Locking and Enables Solid-Phase Conjugate Isolation. J. Am. Chem. Soc. 2018, 140 (8), 3008–3018.
    PMID: 29402082
  9. Lapidoth, G; Khersonsky, O.; Lipsh, R.; Dym O.; Albeck, S.; Rogotner, S.; Fleishman S.J.. Highly Active Enzymes by Automated Combinatorial Backbone Assembly and Sequence Design. Nature Communications 2018, 9 (1): 2780. PMID: 30018322

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