Journal articles: 'De novo Proteins'

1

Tamba, Y., Shah Md Masum, and M. Yamazaki. "De Novo Designed Membrane Proteins." Seibutsu Butsuri 43, supplement (2003): S167. http://dx.doi.org/10.2142/biophys.43.s167_2.

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2

Sander, Chris. "De novo design of proteins." Current Opinion in Structural Biology 1, no. 4 (August 1991): 630–37. http://dx.doi.org/10.1016/s0959-440x(05)80088-0.

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3

Kaplan, J., and W. F. DeGrado. "De novo design of catalytic proteins." Proceedings of the National Academy of Sciences 101, no. 32 (August 3, 2004): 11566–70. http://dx.doi.org/10.1073/pnas.0404387101.

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4

Moffet, David A., and Michael H. Hecht. "De Novo Proteins from Combinatorial Libraries." Chemical Reviews 101, no. 10 (October 2001): 3191–204. http://dx.doi.org/10.1021/cr000051e.

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5

Peacock, Anna FA. "Incorporating metals into de novo proteins." Current Opinion in Chemical Biology 17, no. 6 (December 2013): 934–39. http://dx.doi.org/10.1016/j.cbpa.2013.10.015.

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6

Dawson, William M., Guto G. Rhys, and Derek N. Woolfson. "Towards functional de novo designed proteins." Current Opinion in Chemical Biology 52 (October 2019): 102–11. http://dx.doi.org/10.1016/j.cbpa.2019.06.011.

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7

Skokowa, Julia, Mohammad Elgamacy, and Patrick Müller. "De Novo Design of Granulopoietic Proteins." Blood 136, Supplement 1 (November 5, 2020): 34–35. http://dx.doi.org/10.1182/blood-2020-138852.

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Protein therapeutics are clinically developed and used as minorly engineered forms of their natural templates. This direct adoption of natural proteins in therapeutic contexts very frequently faces major challenges, including instability, poor solubility, and aggregation, which may result in undesired clinical outcomes. In contrast to classical protein engineering techniques, de novo protein design enables the introduction of radical sequence and structure manipulations, which can be used to address these challenges. In this work, we test the utility of two different design strategies to design novel granulopoietic proteins, using structural information from human granulocyte-colony stimulating factor (hG-CSF) as a template. The two strategies are: (1) An epitope rescaffolding where we migrate a tertiary structural epitope to simpler, idealised, proteins scaffolds (Fig. 1A-C), and (2) a topological refactoring strategy, where we change the protein fold by rearranging connections across the secondary structures and optimised the designed sequence of the new fold (Fig. 1A,D,E). Testing only eight designs, we obtained novel granulopoietic proteins that bind to the G-CSF receptor, have nanomolar activity in cell-based assays, and were highly thermostable and protease-resistant. NMR structure determination showed three designs to match their designed coordinates within less than 2.5 Å. While the designs possessed starkly different sequence and structure from the native G-CSF, they showed very specific activity in differentiating primary human haematopoietic stem cells into fully mature granulocytes. Morever, one design shows significant and specific activity in vivo in zebrafish and mice. These results are prospectively directing us to investigate the role of dimerisation geometry of G-GCSF receptor on activation magnitude and downstream signalling pathways. More broadly, the results also motivate our ongoing work on to design other heamatopoietic agents. In conclusion, our findings highlight the utility of computational protein design as a highly effective and guided means for discovering nover receptor modulators, and to obtain new mechanistic information about the target molecule. Figure 1. Two different strategies to generate superfolding G-CSF designs. (A) X-ray structure of G-CSF (orange) bound to its cognate receptor (red) through its binding epitope (blue). According to the epitope rescaffolding strategy, (B) the critical binding epitope residues were disembodied and used as a geometric search query against the entire Protein Data Bank (PDB) to retrieve structurally compatible scaffolds. The top six compatible scaffolds structures are shown in cartoon representation. (C) The top two templates chosen for sequence design, were a de novo designed coiled-coil and a four-helix bundle with unknown function. The binding epitopes were grafted, and the scaffolds were optimised to rigidly host the guest epitope. (D-E) According to the topological refactoring strategy (D) the topology of the native G-CSF was rewired from around the fixed binding epitope, and then was further mutated to idealise the core residues (blue volume (E)) and residues distal from the binding epitope (orange crust (E)). Both strategies aimed at simplifying the topology, reducing the size, and rigidifying the bound epitope conformation through alternate means. Figure 1 Disclosures No relevant conflicts of interest to declare.

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D’Souza, Areetha, and Surajit Bhattacharjya. "De Novo-Designed β-Sheet Heme Proteins." Biochemistry 60, no. 6 (February 3, 2021): 431–39. http://dx.doi.org/10.1021/acs.biochem.0c00662.

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9

MORII, Hisayuki. "Binding Properties of de Novo Designed Proteins." Seibutsu Butsuri 32, no. 5 (1992): 239–42. http://dx.doi.org/10.2142/biophys.32.239.

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10

Hecht, Michael H., Aditi Das, Abigail Go, Luke H. Bradley, and Yinan Wei. "De novo proteins from designed combinatorial libraries." Protein Science 13, no. 7 (July 2004): 1711–23. http://dx.doi.org/10.1110/ps.04690804.

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11

Hecht, M. H. "De novo design of beta-sheet proteins." Proceedings of the National Academy of Sciences 91, no. 19 (September 13, 1994): 8729–30. http://dx.doi.org/10.1073/pnas.91.19.8729.

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12

Whitley, Paul, IngMarie Nilsson, and Gunnar von Heijne. "De novo design of integral membrane proteins." Nature Structural & Molecular Biology 1, no. 12 (December 1994): 858–62. http://dx.doi.org/10.1038/nsb1294-858.

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13

Hu, Ying, Aditi Das, Michael H. Hecht, and Giacinto Scoles. "Nanografting De Novo Proteins onto Gold Surfaces." Langmuir 21, no. 20 (September 2005): 9103–9. http://dx.doi.org/10.1021/la046857h.

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14

Ryan Cross. "Generate raises funds for de novo proteins." C&EN Global Enterprise 99, no. 42 (November 22, 2021): 19. http://dx.doi.org/10.1021/cen-09942-buscon15.

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15

Aubel, Margaux, Lars Eicholt, and Erich Bornberg-Bauer. "Assessing structure and disorder prediction tools for de novo emerged proteins in the age of machine learning." F1000Research 12 (March 29, 2023): 347. http://dx.doi.org/10.12688/f1000research.130443.1.

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Background: De novo protein coding genes emerge from scratch in the non-coding regions of the genome and have, per definition, no homology to other genes. Therefore, their encoded de novo proteins belong to the so-called "dark protein space". So far, only four de novo protein structures have been experimentally approximated. Low homology, presumed high disorder and limited structures result in low confidence structural predictions for de novo proteins in most cases. Here, we look at the most widely used structure and disorder predictors and assess their applicability for de novo emerged proteins. Since AlphaFold2 is based on the generation of multiple sequence alignments and was trained on solved structures of largely conserved and globular proteins, its performance on de novo proteins remains unknown. More recently, natural language models of proteins have been used for alignment-free structure predictions, potentially making them more suitable for de novo proteins than AlphaFold2. Methods: We applied different disorder predictors (IUPred3 short/long, flDPnn) and structure predictors, AlphaFold2 on the one hand and language-based models (Omegafold, ESMfold, RGN2) on the other hand, to four de novo proteins with experimental evidence on structure. We compared the resulting predictions between the different predictors as well as to the existing experimental evidence. Results: Results from IUPred, the most widely used disorder predictor, depend heavily on the choice of parameters and differ significantly from flDPnn which has been found to outperform most other predictors in a comparative assessment study recently. Similarly, different structure predictors yielded varying results and confidence scores for de novo proteins. Conclusions: We suggest that, while in some cases protein language model based approaches might be more accurate than AlphaFold2, the structure prediction of de novo emerged proteins remains a difficult task for any predictor, be it disorder or structure.

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16

Gutte, B., and S. Klauser. "Design of catalytic polypeptides and proteins." Protein Engineering, Design and Selection 31, no. 12 (December 1, 2018): 457–70. http://dx.doi.org/10.1093/protein/gzz009.

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Abstract The first part of this review article lists examples of complete, empirical de novo design that made important contributions to the development of the field and initiated challenging projects. The second part of this article deals with computational design of novel enzymes in native protein scaffolds; active designs were refined through random and site-directed mutagenesis producing artificial enzymes with nearly native enzyme- like activities against a number of non-natural substrates. Combining aspects of de novo design and biological evolution of nature’s enzymes has started and will accelerate the development of novel enzyme activities.

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17

Gunasekaran, K., C. Ramakrishnan, and P. Balaram. "Beta-hairpins in proteins revisited: lessons for de novo design." Protein Engineering Design and Selection 10, no. 10 (October 1, 1997): 1131–41. http://dx.doi.org/10.1093/protein/10.10.1131.

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18

TUCHSCHERER, G., V. STEINER, K. H. ALTMANN, and M. MUTTER. "ChemInform Abstract: De Novo Design of Proteins. Template-Assembled Synthetic Proteins ( TASP)." ChemInform 26, no. 25 (August 17, 2010): no. http://dx.doi.org/10.1002/chin.199525263.

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19

Skvortsov, V. S., A. V. Mikurova, and A. V. Rybina. "Use of de novo sequencing for proteins identification." Biomeditsinskaya Khimiya 63, no. 4 (2017): 341–50. http://dx.doi.org/10.18097/pbmc20176304341.

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Three de novo sequencing programs (Novor, PEAKS and PepNovo+) have been used for identification of 48 individual human proteins constituting the Universal Proteomics Standard Set 2 (UPS2) (“Sigma-Aldrich”, USA). Experimental data have been obtained by tandem mass spectrometry. The MS/MS was performed using pure UPS2 and UPS2 mixtures with E. coli extract and human plasma samples. Protein detection was based on identification of at least two peptides of 9 residues in length or one peptide containing at least 13 residues. Using these criteria 13 (Novor), 20 (PEAKS) and 11 (PepNovo+) proteins were detected in pure UPS2 sample. Protein identifications in mixed samples were comparable or worse. Better results (by ~20%) were obtained using prediction included high quality identified fragment (TAG) containing at least 7 residues and unidentified additional masses at N- and C-termini (PepNovo+). The latter approach confidently recognized mass-spectrometric artefacts (and probably PTM). Atypical mass changes missed in UNIMOD DB were found (PepNovo+) to be statistically significant at the C-terminus (+23.02, +26.04 and +27.03). Using peptides containing these modifications and milder detection threshold 41 of 48 UPS2 proteins were identified.

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20

Gibney, Brian R., and P. Leslie Dutton. "Histidine placement in de novo-designed heme proteins." Protein Science 8, no. 9 (1999): 1888–98. http://dx.doi.org/10.1110/ps.8.9.1888.

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21

Vitorino, Rui, Sofia Guedes, Fabio Trindade, Inês Correia, Gabriela Moura, Paulo Carvalho, Manuel A. S. Santos, and Francisco Amado. "De novo sequencing of proteins by mass spectrometry." Expert Review of Proteomics 17, no. 7-8 (August 2, 2020): 595–607. http://dx.doi.org/10.1080/14789450.2020.1831387.

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22

Singh, Arunima. "De novo design of small-molecule-binding proteins." Nature Methods 17, no. 11 (October 29, 2020): 1073. http://dx.doi.org/10.1038/s41592-020-00995-3.

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23

Tommos, Cecilia, Jack J. Skalicky, Denis L. Pilloud, A. Joshua Wand, and P. Leslie Dutton. "De Novo Proteins as Models of Radical Enzymes†." Biochemistry 38, no. 29 (July 1999): 9495–507. http://dx.doi.org/10.1021/bi990609g.

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24

West, M. W., W. Wang, J. Patterson, J. D. Mancias, J. R. Beasley, and M. H. Hecht. "De novo amyloid proteins from designed combinatorial libraries." Proceedings of the National Academy of Sciences 96, no. 20 (September 28, 1999): 11211–16. http://dx.doi.org/10.1073/pnas.96.20.11211.

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25

Sikorski, Andrzej, Andrzej Kolinski, and Jeffrey Skolnick. "Computer Simulations of De Novo Designed Helical Proteins." Biophysical Journal 75, no. 1 (July 1998): 92–105. http://dx.doi.org/10.1016/s0006-3495(98)77497-1.

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26

Benner, Steven A. "Predicting de novo the folded structure of proteins." Current Biology 2, no. 7 (July 1992): 376. http://dx.doi.org/10.1016/0960-9822(92)90077-n.

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27

Mutter, M., K. H. Altmann, G. Tuchscherer, and S. Vuilleumier. "Strategies for the de novo design of proteins." Tetrahedron 44, no. 3 (January 1988): 771–85. http://dx.doi.org/10.1016/s0040-4020(01)86116-0.

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28

Grzyb, J. "De Novo Designed Proteins - Perspective Materials for Nanotechnology." Acta Physica Polonica A 122, no. 2 (August 2012): 279–83. http://dx.doi.org/10.12693/aphyspola.122.279.

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29

Benner, Steven A. "Predicting de novo the folded structure of proteins." Current Opinion in Structural Biology 2, no. 3 (June 1992): 402–12. http://dx.doi.org/10.1016/0959-440x(92)90232-v.

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30

Moffet, David A., and Michael H. Hecht. "ChemInform Abstract: De Novo Proteins from Combinatorial Libraries." ChemInform 32, no. 52 (May 23, 2010): no. http://dx.doi.org/10.1002/chin.200152273.

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31

Rau, Harald K., and Wolfgang Haehnel. "Biomimetic constructs. De-novo design of redox proteins." Berichte der Bunsengesellschaft für physikalische Chemie 100, no. 12 (December 1996): 2052–56. http://dx.doi.org/10.1002/bbpc.19961001222.

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32

Lin, Yu-Ru, Nobuyasu Koga, Sergey M. Vorobiev, and David Baker. "Cyclic oligomer design with de novo αβ-proteins." Protein Science 26, no. 11 (October 25, 2017): 2187–94. http://dx.doi.org/10.1002/pro.3270.

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33

Hecht, Michael H., Kathleen M. Vogel, Thomas G. Spiro, Nina R. L. Rojas, Satwik Kamtekar, Cyrena T. Simons, Jeremy E. Mclean, and Ramy S. Farid. "De novo heme proteins from designed combinatorial libraries." Protein Science 6, no. 12 (December 31, 2008): 2512–24. http://dx.doi.org/10.1002/pro.5560061204.

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34

Rancurel, Corinne, Mahvash Khosravi, A. Keith Dunker, Pedro R. Romero, and David Karlin. "Overlapping Genes Produce Proteins with Unusual Sequence Properties and Offer Insight into De Novo Protein Creation." Journal of Virology 83, no. 20 (July 29, 2009): 10719–36. http://dx.doi.org/10.1128/jvi.00595-09.

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ABSTRACT It is widely assumed that new proteins are created by duplication, fusion, or fission of existing coding sequences. Another mechanism of protein birth is provided by overlapping genes. They are created de novo by mutations within a coding sequence that lead to the expression of a novel protein in another reading frame, a process called “overprinting.” To investigate this mechanism, we have analyzed the sequences of the protein products of manually curated overlapping genes from 43 genera of unspliced RNA viruses infecting eukaryotes. Overlapping proteins have a sequence composition globally biased toward disorder-promoting amino acids and are predicted to contain significantly more structural disorder than nonoverlapping proteins. By analyzing the phylogenetic distribution of overlapping proteins, we were able to confirm that 17 of these had been created de novo and to study them individually. Most proteins created de novo are orphans (i.e., restricted to one species or genus). Almost all are accessory proteins that play a role in viral pathogenicity or spread, rather than proteins central to viral replication or structure. Most proteins created de novo are predicted to be fully disordered and have a highly unusual sequence composition. This suggests that some viral overlapping reading frames encoding hypothetical proteins with highly biased composition, often discarded as noncoding, might in fact encode proteins. Some proteins created de novo are predicted to be ordered, however, and whenever a three-dimensional structure of such a protein has been solved, it corresponds to a fold previously unobserved, suggesting that the study of these proteins could enhance our knowledge of protein space.

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35

Simonsen, Anna Carina Wiborg, and Lis Rosendahl. "Origin of de Novo Synthesized Proteins in the Different Compartments of Pea-Rhizobium sp. Symbiosomes." Molecular Plant-Microbe Interactions® 12, no. 4 (April 1999): 319–27. http://dx.doi.org/10.1094/mpmi.1999.12.4.319.

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The origin of de novo synthesized proteins in the interface between pea and a Rhizobium sp. in root nodules has been determined. The symbiotic interface is defined as the peribacteroid space including proteins associated with either the symbiosome membrane or the bacteroid outer membrane. Two approaches have been used to study the origin of proteins in the symbiotic interface. First, to determine the localization of de novo synthesized plant-produced proteins in the symbiosomes, an in vitro protein translocation assay was established. To produce plant proteins poly A+ RNA was isolated from root nodules followed by in vitro translation in the presence of [35S]methionine. Subsequently, purified symbiosomes were incubated with the [35S]methionine-labeled plant proteins. The symbiosomes were subfractionated and de novo synthesized plant proteins in the different fractions were identified by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and fluorography. These import studies demonstrate the presence of plant proteins in the symbiosome membrane, peribacteroid space, and bacteroid membrane pellet. In contrast, no proteins of plant origin were detected in the bacteroid cytosol. Second, the presence of de novo synthesized bacteroid proteins in the interface was examined by incubation of symbiosomes isolated under micro-aerobic or aerobic conditions with [35S]methionine. Analysis of symbiosome compartments by SDS-PAGE and phosphor image revealed that proteins of bacteroid origin are primarily detected in the bacteroid cytosol and bacteroid membrane pellet. However, a few bacteroid-produced proteins are also observed in the symbiosome membrane. Together these data demonstrate that the majority of de novo synthesized proteins in the pea-Rhizobium sp. symbiotic interface are of plant origin.

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36

Bornberg-Bauer, Erich, Klara Hlouchova, and Andreas Lange. "Structure and function of naturally evolved de novo proteins." Current Opinion in Structural Biology 68 (June 2021): 175–83. http://dx.doi.org/10.1016/j.sbi.2020.11.010.

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37

Zhu-Qing, ZHANG. "Folding Mechanism of De novo Designed Proteins." Acta Physico-Chimica Sinica 28, no. 10 (2012): 2381–89. http://dx.doi.org/10.3866/pku.whxb201209144.

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38

Jaleel, Abdul, Gregory C. Henderson, Benjamin J. Madden, Katherine A. Klaus, Dawn M. Morse, Srinivas Gopala, and K. Sreekumaran Nair. "Identification of De Novo Synthesized and Relatively Older Proteins." Diabetes 59, no. 10 (July 9, 2010): 2366–74. http://dx.doi.org/10.2337/db10-0371.

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39

Mann, Samuel I., Animesh Nayak, George T. Gassner, Michael J. Therien, and William F. DeGrado. "De novo design of functional Mn-porphyrin binding proteins." Biophysical Journal 121, no. 3 (February 2022): 156a. http://dx.doi.org/10.1016/j.bpj.2021.11.1946.

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40

Sabath, Niv, Andreas Wagner, and David Karlin. "Evolution of Viral Proteins Originated De Novo by Overprinting." Molecular Biology and Evolution 29, no. 12 (July 19, 2012): 3767–80. http://dx.doi.org/10.1093/molbev/mss179.

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41

Quijano-Rubio, Alfredo, Umut Y. Ulge, Carl D. Walkey, and Daniel-Adriano Silva. "The advent of de novo proteins for cancer immunotherapy." Current Opinion in Chemical Biology 56 (June 2020): 119–28. http://dx.doi.org/10.1016/j.cbpa.2020.02.002.

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42

Bermeo, Sherry, Andrew Favor, Ya-Ting Chang, Andrew Norris, Scott E. Boyken, Yang Hsia, Hugh K. Haddox, et al. "De novo design of obligate ABC-type heterotrimeric proteins." Nature Structural & Molecular Biology 29, no. 12 (December 2022): 1266–76. http://dx.doi.org/10.1038/s41594-022-00879-4.

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AbstractThe de novo design of three protein chains that associate to form a heterotrimer (but not any of the possible two-chain heterodimers) and that can drive the assembly of higher-order branching structures is an important challenge for protein design. We designed helical heterotrimers with specificity conferred by buried hydrogen bond networks and large aromatic residues to enhance shape complementary packing. We obtained ten designs for which all three chains cooperatively assembled into heterotrimers with few or no other species present. Crystal structures of a helical bundle heterotrimer and extended versions, with helical repeat proteins fused to individual subunits, showed all three chains assembling in the designed orientation. We used these heterotrimers as building blocks to construct larger cyclic oligomers, which were structurally validated by electron microscopy. Our three-way junction designs provide new routes to complex protein nanostructures and enable the scaffolding of three distinct ligands for modulation of cell signaling.

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43

Faiella, Marina, Anindya Roy, Dayn Sommer, and Giovanna Ghirlanda. "De novo design of functional proteins: Toward artificial hydrogenases." Biopolymers 100, no. 6 (November 2013): 558–71. http://dx.doi.org/10.1002/bip.22420.

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44

Desjarlais, John R., and Tracy M. Handel. "De novo design of the hydrophobic cores of proteins." Protein Science 4, no. 10 (October 1995): 2006–18. http://dx.doi.org/10.1002/pro.5560041006.

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45

Gibb, Bruce C., Adam R. Mezo, and John C. Sherman. "Prototype for a new family of De Novo proteins." Tetrahedron Letters 36, no. 42 (October 1995): 7587–90. http://dx.doi.org/10.1016/0040-4039(95)01563-w.

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46

Altmann, Karl-Heinz, and Manfred Mutter. "A general strategy for the De novo design of proteins—Template assembled synthetic proteins." International Journal of Biochemistry 22, no. 9 (January 1990): 947–56. http://dx.doi.org/10.1016/0020-711x(90)90200-m.

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47

Sahtoe, Danny D., Adrian Coscia, Nur Mustafaoglu, Lauren M. Miller, Daniel Olal, Ivan Vulovic, Ta-Yi Yu, et al. "Transferrin receptor targeting by de novo sheet extension." Proceedings of the National Academy of Sciences 118, no. 17 (April 20, 2021): e2021569118. http://dx.doi.org/10.1073/pnas.2021569118.

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The de novo design of polar protein–protein interactions is challenging because of the thermodynamic cost of stripping water away from the polar groups. Here, we describe a general approach for designing proteins which complement exposed polar backbone groups at the edge of beta sheets with geometrically matched beta strands. We used this approach to computationally design small proteins that bind to an exposed beta sheet on the human transferrin receptor (hTfR), which shuttles interacting proteins across the blood–brain barrier (BBB), opening up avenues for drug delivery into the brain. We describe a design which binds hTfR with a 20 nM Kd, is hyperstable, and crosses an in vitro microfluidic organ-on-a-chip model of the human BBB. Our design approach provides a general strategy for creating binders to protein targets with exposed surface beta edge strands.

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48

Kuroda, Yutaka. "A strategy for the de novo design of helical proteins with stable folds." "Protein Engineering, Design and Selection" 8, no. 2 (1995): 97–101. http://dx.doi.org/10.1093/protein/8.2.97.

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49

Cohan, Megan C., Kiersten M. Ruff, and Rohit V. Pappu. "Information theoretic measures for quantifying sequence–ensemble relationships of intrinsically disordered proteins." Protein Engineering, Design and Selection 32, no. 4 (April 2019): 191–202. http://dx.doi.org/10.1093/protein/gzz014.

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Abstract Intrinsically disordered proteins (IDPs) contribute to a multitude of functions. De novo design of IDPs should open the door to modulating functions and phenotypes controlled by these systems. Recent design efforts have focused on compositional biases and specific sequence patterns as the design features. Analysis of the impact of these designs on sequence-function relationships indicates that individual sequence/compositional parameters are insufficient for describing sequence-function relationships in IDPs. To remedy this problem, we have developed information theoretic measures for sequence–ensemble relationships (SERs) of IDPs. These measures rely on prior availability of statistically robust conformational ensembles derived from all atom simulations. We show that the measures we have developed are useful for comparing sequence-ensemble relationships even when sequence is poorly conserved. Based on our results, we propose that de novo designs of IDPs, guided by knowledge of their SERs, should provide improved insights into their sequence–ensemble–function relationships.

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50

Bruno, Ludovica, Luca Mazzarella, Maarten Hoogenkamp, Arnulf Hertweck, Bradley S. Cobb, Stephan Sauer, Suzana Hadjur, et al. "Runx proteins regulate Foxp3 expression." Journal of Experimental Medicine 206, no. 11 (October 19, 2009): 2329–37. http://dx.doi.org/10.1084/jem.20090226.

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Runx proteins are essential for hematopoiesis and play an important role in T cell development by regulating key target genes, such as CD4 and CD8 as well as lymphokine genes, during the specialization of naive CD4 T cells into distinct T helper subsets. In regulatory T (T reg) cells, the signature transcription factor Foxp3 interacts with and modulates the function of several other DNA binding proteins, including Runx family members, at the protein level. We show that Runx proteins also regulate the initiation and the maintenance of Foxp3 gene expression in CD4 T cells. Full-length Runx promoted the de novo expression of Foxp3 during inducible T reg cell differentiation, whereas the isolated dominant-negative Runt DNA binding domain antagonized de novo Foxp3 expression. Foxp3 expression in natural T reg cells remained dependent on Runx proteins and correlated with the binding of Runx/core-binding factor β to regulatory elements within the Foxp3 locus. Our data show that Runx and Foxp3 are components of a feed-forward loop in which Runx proteins contribute to the expression of Foxp3 and cooperate with Foxp3 proteins to regulate the expression of downstream target genes.

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Journal articles on the topic 'De novo Proteins'

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