Recent years have seen a dramatic improvement in protein-design methodology. Nevertheless, most methods demand expert intervention, limiting their widespread adoption. By contrast, the PROSS algorithm for improving protein stability and heterologous expression levels has been successfully applied to a range of challenging enzymes and binding proteins. Here, we benchmark the application of PROSS as a stand-alone tool for protein scientists with no or limited experience in modeling. Twelve laboratories from the Protein Production and Purification Partnership in Europe (P4EU) challenged the PROSS algorithm with 14 unrelated protein targets without support from the PROSS developers. For each target, up to six designs were evaluated for expression levels and in some cases, for thermal stability and activity. In nine targets, designs exhibited increased heterologous expression levels either in prokaryotic and/or eukaryotic expression systems under experimental conditions that were tailored for each target protein. Furthermore, we observed increased thermal stability in nine of ten tested targets. In two prime examples, the human Stem Cell Factor (hSCF) and human Cadherin-Like Domain (CLD12) from the RET receptor, the wild type proteins were not expressible as soluble proteins in E. coli, yet the PROSS designs exhibited high expression levels in E. coli and HEK293 cells, respectively, and improved thermal stability. We conclude that PROSS may improve stability and expressibility in diverse cases, and that improvement typically requires target-specific expression conditions. This study demonstrates the strengths of community-wide efforts to probe the generality of new methods and recommends areas for future research to advance practically useful algorithms for protein science.

Department of Biochemistry Faculty of Science Charles University Hlavova 2030 8 12840 Prague Czech Republic

Department of Biomolecular Sciences Weizmann Institute of Science Rehovot 7610001 Israel

Department of Life Sciences Core Facilities Weizmann Institute of Science Rehovot 7610001 Israel

Elettra Sincrotrone Trieste SS 14 km 163 5 in Area Science Park 34149 Basovizza Trieste Italy

European Molecular Biology Laboratory Protein Expression and Purification Core Facility Meyerhofstrasse 1 69117 Heidelberg Germany

Institut de Génétique et de Biologie Moléculaire et Cellulaire U1258 Université de Strasbourg France

Laboratory for Environmental and Life Sciences University of Nova Gorica Slovenia

Max Delbrück Center for Molecular Medicine Partner Site Berlin Berlin Germany

Max Delbrück Center for Molecular Medicine Robert Rössle Straße 10 13125 Berlin Buch Germany

Max Planck Institute of Biochemistry Biochemistry Core Facility Am Klopferspitz 18 82152 Martinsried Germany

NORCE Norwegian Research Centre Postboks 22 Nygårdstangen 5038 Bergen Norway

Structural Biology Science Technology Platform The Francis Crick Institute 1 Midland Road London NW1 1AT UK

The University of Queensland Institute for Molecular Bioscience St Lucia Queensland 4072 Australia

Unité Mixte de Recherche Marseille France

Vienna Biocenter Core Facilities GmbH Dr Bohr gasse 3 1030 Vienna Austria

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Structural Genomics Consortium, China Structural Genomics Consortium, Northeast Structural Genomics Consortium. Gräslund S, Nordlund P, Weigelt J, Hallberg BM, Bray J, Gileadi O, Knapp S, Oppermann U, Arrowsmith C, Hui R, Ming J, et al. Gunsalus, Protein production and purification. Nat Methods. 2008;5:135–146. PubMed PMC

Assenberg R, Wan PT, Geisse S, Mayr LM. Advances in recombinant protein expression for use in pharmaceutical research. Curr Opin Struct Biol. 2013;23:393–402. PubMed

de Marco A, Deuerling E, Mogk A, Tomoyasu T, Bukau B. Chaperone-based procedure to increase yields of soluble recombinant proteins produced in E. coli. BMC Biotechnol. 2007;7:32. PubMed PMC

Berrow NS, Büssow K, Coutard B, Diprose J, Ekberg M, Folkers GE, Lévy N, Lieu V, Owens RJ, Peleg Y. Others, Recombinant protein expression and solubility screening in Escherichia coli: a comparative study. Acta Crystallogr D Biol Crystallogr. 2006;62:1218–1226. PubMed

Peleg Y, Unger T. Application of high-throughput methodologies to the expression of recombinant proteins in E. coli. Methods Mol Biol. 2008;426:197–208. PubMed

Peleg Y, Unger T. Resolving bottlenecks for recombinant protein expression in E. coli. Methods Mol. Biol. 2012;800:173–186. PubMed

Busso D, Peleg Y, Heidebrecht T, Romier C, Jacobovitch Y, Dantes A, Salim L, Troesch E, Schuetz A, Heinemann U, Folkers GE, et al. Expression of protein complexes using multiple Escherichia coli protein co-expression systems: a benchmarking study. J Struct Biol. 2011;175:159–170. PubMed

Magliery TJ. Protein stability: computation, sequence statistics, and new experimental methods. Curr Opin Struct Biol. 2015;33:161–168. PubMed PMC

Bednar D, Beerens K, Sebestova E, Bendl J, Khare S, Chaloupkova R, Prokop Z, Brezovsky J, Baker D, Damborsky J. FireProt: Energy- and Evolution-Based Computational Design of Thermostable Multiple-Point Mutants. PLoS Comput Biol. 2015;11:e1004556. PubMed PMC

Goldenzweig A, Fleishman SJ. Principles of Protein Stability and Their Application in Computational Design. Annu Rev Biochem. 2018;87:105–129. PubMed

Wijma HJ, Floor RJ, Jekel PA, Baker D, Marrink SJ, Janssen DB. Computationally designed libraries for rapid enzyme stabilization. Protein Eng Des Sel. 2014;27:49–58. PubMed PMC

Yin S, Ding F, Dokholyan NV. Eris: an automated estimator of protein stability. Nat Methods. 2007;4:466–467. PubMed

Schymkowitz J, Borg J, Stricher F, Nys R, Rousseau F, Serrano L. The FoldX web server: an online force field. Nucleic Acids Res. 2005;33:W382–8. PubMed PMC

Laimer J, Hiebl-Flach J, Lengauer D, Lackner P. MAESTROweb: a web server for structure-based protein stability prediction. Bioinformatics. 2016;32:1414–1416. PubMed

Crank MC, Ruckwardt TJ, Chen M, Morabito KM, Phung E, Costner PJ, Holman LA, Hickman SP, Berkowitz NM, Gordon IJ, Yamshchikov GV, et al. VRC 317 Study Team, A proof of concept for structure-based vaccine design targeting RSV in humans. Science. 2019;365:505–509. PubMed

Tournier V, Topham CM, Gilles A, David B, Folgoas C, Moya-Leclair E, Kamionka E, Desrousseaux M-L, Texier H, Gavalda S, Cot M, et al. An engineered PET depolymerase to break down and recycle plastic bottles. Nature. 2020;580:216–219. PubMed

Zhao H, Arnold FH. Directed evolution converts subtilisin E into a functional equivalent of thermitase. Protein Eng. 1999;12:47–53. PubMed

Baker D. What has de novo protein design taught us about protein folding and biophysics? Protein Sci. 2019;28:678–683. PubMed PMC

Shoichet BK, Baase Wa, Kuroki R, Matthews BW. A relationship between protein stability and protein function. Proceedings of the National Academy of Sciences. 1995;92:452–456. PubMed PMC

Fleishman SJ, Baker D. Role of the biomolecular energy gap in protein design, structure, and evolution. Cell. 2012;149:262–273. PubMed

Goldenzweig A, Goldsmith M, Hill SE, Gertman O, Laurino P, Ashani Y, Dym O, Unger T, Albeck S, Prilusky J, Lieberman RL, et al. Automated Structure- and Sequence-Based Design of Proteins for High Bacterial Expression and Stability. Mol Cell. 2016;63:337–346. PubMed PMC

Goldsmith M, Aggarwal N, Ashani Y, Jubran H, Greisen PJ, Ovchinnikov S, Leader H, Baker D, Sussman JL, Goldenzweig A, Fleishman SJ, et al. Overcoming an optimization plateau in the directed evolution of highly efficient nerve agent bioscavengers. Protein Eng Des Sel. 2017;30:333–345. PubMed

Zahradník J, Kolářová L, Peleg Y, Kolenko P, Svidenská S, Charnavets T, Unger T, Sussman JL, Schneider B. Flexible regions govern promiscuous binding of IL-24 to receptors IL-20R1 and IL-22R1. FEBS J. 2019 doi: 10.1111/febs.14945. PubMed DOI

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 doi: 10.3390/molecules22111828. PubMed DOI PMC

Hettiaratchi MH, O’Meara MJ, O’Meara TR, Pickering AJ. of chondroitinase ABC improves efficacy and stability, (n.d.). https://advances.sciencemag.org/content/advances/6/34/eabc6378.full.pdf. PubMed PMC

Campeotto I, Goldenzweig A, Davey J, Barfod L, Marshall JM, Silk SE, Wright KE, Draper SJ, Higgins MK, Fleishman SJ. 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;114:998–1002. PubMed PMC

Malladi SK, Schreiber D, Pramanick I, Sridevi MA, Goldenzweig A, Dutta S, Fleishman SJ, Varadarajan R. One-step sequence and structure-guided optimization of HIV-1 envelope gp140. Current Research in Structural Biology. 2020 doi: 10.1016/j.crstbi.2020.04.001. PubMed DOI PMC

Lambert AR, Hallinan JP, Werther R, Głów D, Stoddard BL. Optimization of Protein Thermostability and Exploitation of Recognition Behavior to Engineer Altered Protein-DNA Recognition. Structure. 2020 doi: 10.1016/j.str.2020.04.009. PubMed DOI PMC

Georgoulia PS, Bjelic S, Friedman R. Deciphering the molecular mechanism of FLT3 resistance mutations. FEBS J. 2020 doi: 10.1111/febs.15209. PubMed DOI

Buldun CM, Jean JX, Bedford MR, Howarth M. SnoopLigase Catalyzes Peptide–Peptide Locking and Enables Solid-Phase Conjugate Isolation. J Am Chem Soc. 2018;140:3008–3018. PubMed

He W, Gauri M, Li T, Wang R, Lin S-X. Current knowledge of the multifunctional 17β-hydroxysteroid dehydrogenase type 1 (HSD17B1) Gene. 2016;588:54–61. PubMed PMC

Li M-Y, Lai P-L, Chou Y-T, Chi A-P, Mi Y-Z, Khoo K-H, Chang G-D, Wu C-W, Meng T-C, Chen G-C. Protein tyrosine phosphatase PTPN3 inhibits lung cancer cell proliferation and migration by promoting EGFR endocytic degradation. Oncogene. 2015;34:3791–3803. PubMed

Chen K-E, Lin S-Y, Wu M-J, Ho M-R, Santhanam A, Chou C-C, Meng T-C, Wang AHJ. Reciprocal allosteric regulation of p38γ and PTPN3 involves a PDZ domain-modulated complex formation. Sci Signal. 2014;7 ra98. PubMed

Swami T, Weber HC. Updates on the biology of serotonin and tryptophan hydroxylase. Curr Opin Endocrinol Diabetes Obes. 2018;25:12–21. PubMed

Prassas I, Eissa A, Poda G, Diamandis EP. Unleashing the therapeutic potential of human kallikrein-related serine proteases. Nat Rev Drug Discov. 2015;14:183–202. PubMed

Chin-Yee IH, Keeney M, Stewart AK, Belch A, Bence-Buckler I, Couban S, Howson-Jan K, Rubinger M, Stewart D, Sutherland R, Paragamian V, Bhatia M, Foley R. Optimising parameters for peripheral blood leukapheresis after r-metHuG-CSF (filgrastim) and r-metHuSCF (ancestim) in patients with multiple myeloma: a temporal analysis of CD34(+) absolute counts and subsets. Bone Marrow Transplant. 2002;30:851–860. PubMed

Johnsen HE, Geisler C, Juvonen E, Remes K, Juliusson G, Hörnsten P, Kvaloy S, Kvalheim G, Jürgensen GW, Pedersen LM, Bergmann OJ, Schmitz A, Boegsted M. Priming with r-metHuSCF and filgrastim or chemotherapy and filgrastim in patients with malignant lymphomas: a randomized phase II pilot study of mobilization and engraftment. Bone Marrow Transplant. 2011;46:44–51. PubMed

Yu Z, Song H, Jia M, Zhang J, Wang W, Li Q, Zhang L, Zhao W. USP1-UAF1 deubiquitinase complex stabilizes TBK1 and enhances antiviral responses. J Exp Med. 2017;214:3553–3563. PubMed PMC

Kolesnikova O, Radu L, Poterszman A. TFIIH: A multi-subunit complex at the cross-roads of transcription and DNA repair. Adv Protein Chem Struct Biol. 2019;115:21–67. PubMed

Fisher RP. Cdk7: a kinase at the core of transcription and in the crosshairs of cancer drug discovery. Transcription. 2019;10:47–56. PubMed PMC

Norwood SJ, Shaw DJ, Cowieson NP, Owen DJ, Teasdale RD, Collins BM. Assembly and solution structure of the core retromer protein complex. Traffic. 2011;12:56–71. PubMed

Archbold JK, Whitten AE, Hu S-H, Collins BM, Martin JL. SNARE-ing the structures of Sec1/Munc18 proteins. Curr Opin Struct Biol. 2014;29:44–51. PubMed

Jiang Q, Liu F, Miao C, Li Q, Zhang Z, Xiao P, Su L, Yu K, Chen X, Zhang F, Chakravarti A, Li L. RET somatic mutations are underrecognized in Hirschsprung disease. Genet Med. 2018;20:770–777. PubMed PMC

Tomuschat C, Puri P. RET gene is a major risk factor for Hirschsprung’s disease: a meta-analysis. Pediatr Surg Int. 2015;31:701–710. PubMed

Djender S, Schneider A, Beugnet A, Crepin R, Desrumeaux KE, Romani C, Moutel S, Perez F, de Marco A. Bacterial cytoplasm as an effective cell compartment for producing functional VHH-based affinity reagents and Camelidae IgG-like recombinant antibodies. Microb Cell Fact. 2014;13:140. PubMed PMC

Arnold K, Bordoli L, Kopp J, Schwede T. The SWISS-MODEL workspace: A web-based environment for protein structure homology modelling. Bioinformatics. 2006;22:195–201. PubMed

Lolli G, Lowe ED, Brown NR, Johnson LN. The crystal structure of human CDK7 and its protein recognition properties. Structure. 2004;12:2067–2079. PubMed

Abdulrahman W, Iltis I, Radu L, Braun C, Maglott-Roth A, Giraudon C, Egly J-M, Poterszman A. ARCH domain of XPD, an anchoring platform for CAK that conditions TFIIH DNA repair and transcription activities. Proc Natl Acad Sci U S A. 2013;110:E633–42. PubMed PMC

Bandyopadhyay B, Goldenzweig A, Unger T, Adato O, Fleishman SJ, Unger R, Horovitz A. Local energetic frustration affects the dependence of green fluorescent protein folding on the chaperonin GroEL. J Biol Chem. 2017;292:20583–20591. PubMed PMC

Bershtein S, Segal M, Bekerman R, Tokuriki N, Tawfik DS. Robustness-epistasis link shapes the fitness landscape of a randomly drifting protein. Nature. 2006;444:929–932. PubMed

Meiering EM, Serrano L, Fersht AR. Effect of active site residues in barnase on activity and stability. J Mol Biol. 1992;225:585–589. PubMed

Weinstein J, Khersonsky O, Fleishman SJ. Practically useful protein-design methods combining phylogenetic and atomistic calculations. Curr Opin Struct Biol. 2020;63:58–64. PubMed PMC

Khersonsky O, Fleishman SJ. Why reinvent the wheel? Building new proteins based on ready-made parts. Protein Sci. 2016;25:1179–1187. PubMed PMC

Lapidoth G, Khersonsky O, Lipsh R, Dym O, Albeck S, Rogotner S, Fleishman SJ. Highly active enzymes by automated combinatorial backbone assembly and sequence design. Nat Commun. 2018;9:2780. PubMed PMC

Khersonsky O, Lipsh R, Avizemer Z, Ashani Y, Goldsmith M, Leader H, Dym O, Rogotner S, Trudeau DL, Prilusky J, Amengual-Rigo P, Guallar V, Tawfik DS, Fleishman SJ. Automated Design of Efficient and Functionally Diverse Enzyme Repertoires. Mol Cell. 2018;72:178–186.:e5. PubMed PMC

Netzer R, Listov D, Lipsh R, Dym O, Albeck S, Knop O, Kleanthous C, Fleishman SJ. Ultrahigh specificity in a network of computationally designed protein-interaction pairs. Nat Commun. 2018;9:5286. PubMed PMC

Warszawski S, Borenstein Katz A, Lipsh R, Khmelnitsky L, Ben Nissan G, Javitt G, Dym O, Unger T, Knop O, Albeck S, Diskin R, Fass D, Sharon M, Fleishman SJ. Optimizing antibody affinity and stability by the automated design of the variable light-heavy chain interfaces. PLoS Comput Biol. 2019;15:e1007207. PubMed PMC

Moore JC, Rodriguez-Granillo A, Crespo A, Govindarajan S, Welch M, Hiraga K, Lexa K, Marshall N, Truppo MD. “Site and Mutation”-Specific Predictions Enable Minimal Directed Evolution Libraries. ACS Synth Biol. 2018 doi: 10.1021/acssynbio.7b00359. PubMed DOI

Pavelka A, Chovancova E, Damborsky J. HotSpot Wizard: a web server for identification of hot spots in protein engineering. Nucleic Acids Res. 2009;37:W376–83. PubMed PMC

Fleishman SJ, Whitehead TA, Strauch EM, Corn JE, Qin S, Zhou HX, Mitchell JC, Demerdash ON, Takeda-Shitaka M, Terashi G, Moal IH, et al. Community-wide assessment of protein-interface modeling suggests improvements to design methodology. J Mol Biol. 2011;414:289–302. PubMed PMC

Moretti R, Fleishman SJ, Agius R, Torchala M, Bates PA, Kastritis PL, Rodrigues JPGLM, Trellet M, Bonvin AMJJ, Cui M, Rooman M, et al. Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions. Proteins. 2013;81:1980–1987. PubMed PMC

Weinstein JJ, Goldenzweig A, Hoch S-Y, Fleishman SJ. PROSS 2: a new server for the design of stable and highly expressed protein variants. Bioinformatics. 2020 doi: 10.1093/bioinformatics/btaa1071. PubMed DOI PMC

Advancing Enzyme's Stability and Catalytic Efficiency through Synergy of Force-Field Calculations, Evolutionary Analysis, and Machine Learning