Journal articles: 'Protein design'

1

Porebski, Benjamin T., and Ashley M. Buckle. "Consensus protein design." Protein Engineering Design and Selection 29, no. 7 (June 5, 2016): 245–51. http://dx.doi.org/10.1093/protein/gzw015.

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Sawyer, Nicholas, Elizabeth B. Speltz, and Lynne Regan. "NextGen protein design." Biochemical Society Transactions 41, no. 5 (September 23, 2013): 1131–36. http://dx.doi.org/10.1042/bst20130112.

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Protein engineering is at an exciting stage because designed protein–protein interactions are being used in many applications. For instance, three designed proteins are now in clinical trials. Although there have been many successes over the last decade, protein engineering still faces numerous challenges. Often, designs do not work as anticipated and they still require substantial redesign. The present review focuses on the successes, the challenges and the limitations of rational protein design today.

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Pierce, Niles A., and Erik Winfree. "Protein Design is NP-hard." Protein Engineering, Design and Selection 15, no. 10 (October 2002): 779–82. http://dx.doi.org/10.1093/protein/15.10.779.

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4

Regan, Lynne. "Protein design." Current Opinion in Biotechnology 2, no. 4 (August 1991): 544–50. http://dx.doi.org/10.1016/0958-1669(91)90079-k.

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Libertini, Giacinto, and Alberto Di Donato. "Computer-aided gene design." "Protein Engineering, Design and Selection" 5, no. 8 (1992): 821–25. http://dx.doi.org/10.1093/protein/5.8.821.

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Kuroda, D., H. Shirai, M. P. Jacobson, and H. Nakamura. "Computer-aided antibody design." Protein Engineering Design and Selection 25, no. 10 (June 2, 2012): 507–22. http://dx.doi.org/10.1093/protein/gzs024.

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7

Arnold, Frances H. "Protein design for non-aqueous solvents." "Protein Engineering, Design and Selection" 2, no. 1 (1988): 21–25. http://dx.doi.org/10.1093/protein/2.1.21.

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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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Sawyer, Nicholas, Danielle M. Williams, and Lynne Regan. "Protein goldendoodles: Designing new proteins." Biochemist 36, no. 1 (February 1, 2014): 28–33. http://dx.doi.org/10.1042/bio03601028.

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The goldendoodle (Figure 1) is a breed of dog created to combine the desirable features of the golden retriever (calm personality, good with people and an excellent service dog) with those of the poodle (low shedding and hypoallergenic). The result surpasses expectations: not only does the goldendoodle have a great personality and low shedding, but also the animal is exceedingly cute and in great demand. Protein design, the creation of novel proteins either de novo or by extensive mutagenesis of natural proteins, has likewise produced many ‘goldendoodle-esque’ proteins whose unprecedented combination of stability and function have revolutionized academic and clinical research. Here, we discuss the history of protein design and highlight some particularly successful protein designs of this type.

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Kim, Peter S. "Passing the first milestone in protein design." "Protein Engineering, Design and Selection" 2, no. 4 (1988): 249–50. http://dx.doi.org/10.1093/protein/2.4.249.

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Baumann, G., C. Froömmel, and C. Sander. "Polarity as a criterion in protein design." "Protein Engineering, Design and Selection" 2, no. 5 (1989): 329–34. http://dx.doi.org/10.1093/protein/2.5.329.

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Mena, Marco A., and Patrick S. Daugherty. "Automated design of degenerate codon libraries." Protein Engineering, Design and Selection 18, no. 12 (October 20, 2005): 559–61. http://dx.doi.org/10.1093/protein/gzi061.

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13

KONO, Hidetoshi, and Jeffery G. Saven. "Combinatorial Protein Design." Seibutsu Butsuri 43, no. 4 (2003): 186–91. http://dx.doi.org/10.2142/biophys.43.186.

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Kraemer-Pecore, Christina M., Andrew M. Wollacott, and John R. Desjarlais. "Computational protein design." Current Opinion in Chemical Biology 5, no. 6 (December 2001): 690–95. http://dx.doi.org/10.1016/s1367-5931(01)00267-8.

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15

Schafmeister, Christian E., and Robert M. Stroud. "Helical protein design." Current Opinion in Biotechnology 9, no. 4 (August 1998): 350–53. http://dx.doi.org/10.1016/s0958-1669(98)80006-2.

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Saven, Jeffery G. "Combinatorial protein design." Current Opinion in Structural Biology 12, no. 4 (August 2002): 453–58. http://dx.doi.org/10.1016/s0959-440x(02)00347-0.

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17

DeGrado, William F. "Introduction: Protein Design." Chemical Reviews 101, no. 10 (October 2001): 3025–26. http://dx.doi.org/10.1021/cr000663z.

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18

Street, Arthur G., and Stephen L. Mayo. "Computational protein design." Structure 7, no. 5 (May 1999): R105—R109. http://dx.doi.org/10.1016/s0969-2126(99)80062-8.

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19

Dahiyat, Bassil I., and Stephen L. Mayo. "Protein design automation." Protein Science 5, no. 5 (May 1996): 895–903. http://dx.doi.org/10.1002/pro.5560050511.

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20

Bjerre, Benjamin, Jakob Nissen, Mikkel Madsen, Jūratė Fahrig-Kamarauskaitė, Rasmus K. Norrild, Peter C. Holm, Mathilde K. Nordentoft, et al. "Improving folding properties of computationally designed proteins." Protein Engineering, Design and Selection 32, no. 3 (March 2019): 145–51. http://dx.doi.org/10.1093/protein/gzz025.

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Abstract While the field of computational protein design has witnessed amazing progression in recent years, folding properties still constitute a significant barrier towards designing new and larger proteins. In order to assess and improve folding properties of designed proteins, we have developed a genetics-based folding assay and selection system based on the essential enzyme, orotate phosphoribosyl transferase from Escherichia coli. This system allows for both screening of candidate designs with good folding properties and genetic selection of improved designs. Thus, we identified single amino acid substitutions in two failed designs that rescued poorly folding and unstable proteins. Furthermore, when these substitutions were transferred into a well-structured design featuring a complex folding profile, the resulting protein exhibited native-like cooperative folding with significantly improved stability. In protein design, a single amino acid can make the difference between folding and misfolding, and this approach provides a useful new platform to identify and improve candidate designs.

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21

Lewis, Steven M., and Brian A. Kuhlman. "Anchored Design of Protein-Protein Interfaces." PLoS ONE 6, no. 6 (June 17, 2011): e20872. http://dx.doi.org/10.1371/journal.pone.0020872.

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22

Ravikant, D. V. S., and Ron Elber. "Energy design for protein-protein interactions." Journal of Chemical Physics 135, no. 6 (August 14, 2011): 065102. http://dx.doi.org/10.1063/1.3615722.

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23

Schreiber, Gideon, and Sarel J. Fleishman. "Computational design of protein–protein interactions." Current Opinion in Structural Biology 23, no. 6 (December 2013): 903–10. http://dx.doi.org/10.1016/j.sbi.2013.08.003.

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24

Kortemme, Tanja, and David Baker. "Computational design of protein–protein interactions." Current Opinion in Chemical Biology 8, no. 1 (February 2004): 91–97. http://dx.doi.org/10.1016/j.cbpa.2003.12.008.

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25

Gibas, C., and S. Subramaniam. "Knowledge-based design of a soluble bacteriorhodopsin." Protein Engineering Design and Selection 10, no. 10 (October 1, 1997): 1175–90. http://dx.doi.org/10.1093/protein/10.10.1175.

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26

Mitra, Kakoli, Thomas A. Steitz, and Donald M. Engelman. "Rational design of `water-soluble' bacteriorhodopsin variants." Protein Engineering, Design and Selection 15, no. 6 (June 2002): 485–92. http://dx.doi.org/10.1093/protein/15.6.485.

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27

Schreiber, G., D. Reichmann, M. Cohen, Y. Pillip, O. Rahat, O. Dym, V. Potapov, V. Sobolev, and M. Edelman. "Protein–protein interaction: from mechanism to protein design." Acta Crystallographica Section A Foundations of Crystallography 63, a1 (August 22, 2007): s18. http://dx.doi.org/10.1107/s0108767307099606.

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28

Farmer, Tylar Seiya, Patrick Bohse, and Dianne Kerr. "Rational Design Protein Engineering Through Crowdsourcing." Journal of Student Research 6, no. 2 (December 31, 2018): 31–38. http://dx.doi.org/10.47611/jsr.v6i2.377.

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Two popular methods exist to engineer a protein: directed evolution and rational design. Directed evolution utilizes a controlled environment to create proteins through induced mutations and selection, while rational design makes desired changes to a protein by directly manipulating its amino acids. Directed evolution is currently more commonly used, since rational design relies on structural knowledge of the protein of interest, which is often unavailable. Utilizing crowdsourcing manpower and computational power to improve protein depictions allows rational design to be more easily used to perform the manipulation of proteins. Two free programs, “Folding@home and “Foldit”, allow anyone with a computer and internet access to contribute to protein engineering. Folding@home relies on one’s computational power, while Foldit relies on user intuition to improve protein models. Rational design has allowed protein engineers to create artificial proteins that can be applied to the treatment of illnesses, research of enzyme activity in a living system, genetic engineering, and biological warfare. Starting with an overview of protein engineering, this paper discusses the methods of rational design and directed evolutions and goes on to explain how computer based programs can help in the advancement of rational design as a protein engineering method. Furthermore, this paper discusses the application of computer based programs in medicine and genetic engineering and presents some ethical issues that may arise from using such technology. The paper concludes with an analysis of whether or not computer based programs for protein engineering is worth the investment.

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29

MacDonald, James T., and Paul S. Freemont. "Computational protein design with backbone plasticity." Biochemical Society Transactions 44, no. 5 (October 15, 2016): 1523–29. http://dx.doi.org/10.1042/bst20160155.

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The computational algorithms used in the design of artificial proteins have become increasingly sophisticated in recent years, producing a series of remarkable successes. The most dramatic of these is the de novo design of artificial enzymes. The majority of these designs have reused naturally occurring protein structures as ‘scaffolds’ onto which novel functionality can be grafted without having to redesign the backbone structure. The incorporation of backbone flexibility into protein design is a much more computationally challenging problem due to the greatly increased search space, but promises to remove the limitations of reusing natural protein scaffolds. In this review, we outline the principles of computational protein design methods and discuss recent efforts to consider backbone plasticity in the design process.

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30

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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31

Case, Martin A., and George L. McLendon. "Metal-Assembled Modular Proteins: Toward Functional Protein Design." Accounts of Chemical Research 37, no. 10 (October 2004): 754–62. http://dx.doi.org/10.1021/ar960245+.

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32

CAREY, R. I., K. H. ALTMANN, and M. MUTTER. "ChemInform Abstract: Protein Design: Template-Assembled Synthetic Proteins." ChemInform 22, no. 47 (August 22, 2010): no. http://dx.doi.org/10.1002/chin.199147308.

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33

Powell, M. J., and D. E. Hansen. "Catalytic antibodies—a new direction in enzyme design." "Protein Engineering, Design and Selection" 3, no. 2 (1989): 69–75. http://dx.doi.org/10.1093/protein/3.2.69.

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34

Mucsi, Z., Z. Gaspari, G. Orosz, and A. Perczel. "Structure-oriented rational design of chymotrypsin inhibitor models." Protein Engineering Design and Selection 16, no. 9 (September 1, 2003): 673–81. http://dx.doi.org/10.1093/protein/gzg090.

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35

Chockalingam, Karuppiah, Mark Blenner, and Scott Banta. "Design and application of stimulus-responsive peptide systems." Protein Engineering, Design and Selection 20, no. 4 (January 1, 2007): 155–61. http://dx.doi.org/10.1093/protein/gzm008.

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36

Grove, T. Z., M. Hands, and L. Regan. "Creating novel proteins by combining design and selection." Protein Engineering Design and Selection 23, no. 6 (March 19, 2010): 449–55. http://dx.doi.org/10.1093/protein/gzq015.

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37

Zielenkiewicz, Piotr, Szymon Kaczanowski, and Andrzej M. Kierzek. "Shuffling Alghorithm For Protein Design." Protein & Peptide Letters 6, no. 2 (April 1999): 99–104. http://dx.doi.org/10.2174/092986650602221108163150.

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Abstract: A new algorithm producing a large number of divergent sequences which can adopt a defined structure is proposed. The algorithm can be applied to proteins of any size. This is achieved by constraining the amino acid composition to the native one and minimising the pseudoenergy function by "shuffling" the sequence. The algorithm is tested by sequence design for five different protein structures. Modelling by homology with the use of resultant sequences produces "proper" protein structures as judged using several different methods

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38

Baker, David. "Protein folding, structure prediction and design." Biochemical Society Transactions 42, no. 2 (March 20, 2014): 225–29. http://dx.doi.org/10.1042/bst20130055.

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I describe how experimental studies of protein folding have led to advances in protein structure prediction and protein design. I describe the finding that protein sequences are not optimized for rapid folding, the contact order–protein folding rate correlation, the incorporation of experimental insights into protein folding into the Rosetta protein structure production methodology and the use of this methodology to determine structures from sparse experimental data. I then describe the inverse problem (protein design) and give an overview of recent work on designing proteins with new structures and functions. I also describe the contributions of the general public to these efforts through the Rosetta@home distributed computing project and the FoldIt interactive protein folding and design game.

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39

Handel, Tracy. "De novo design of an α/β barrel protein." "Protein Engineering, Design and Selection" 3, no. 4 (1990): 233–34. http://dx.doi.org/10.1093/protein/3.4.233.

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40

Defresne, Marianne, Sophie Barbe, and Thomas Schiex. "Protein Design with Deep Learning." International Journal of Molecular Sciences 22, no. 21 (October 29, 2021): 11741. http://dx.doi.org/10.3390/ijms222111741.

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Computational Protein Design (CPD) has produced impressive results for engineering new proteins, resulting in a wide variety of applications. In the past few years, various efforts have aimed at replacing or improving existing design methods using Deep Learning technology to leverage the amount of publicly available protein data. Deep Learning (DL) is a very powerful tool to extract patterns from raw data, provided that data are formatted as mathematical objects and the architecture processing them is well suited to the targeted problem. In the case of protein data, specific representations are needed for both the amino acid sequence and the protein structure in order to capture respectively 1D and 3D information. As no consensus has been reached about the most suitable representations, this review describes the representations used so far, discusses their strengths and weaknesses, and details their associated DL architecture for design and related tasks.

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41

Ferrando, Juan, and Lee A. Solomon. "Recent Progress Using De Novo Design to Study Protein Structure, Design and Binding Interactions." Life 11, no. 3 (March 10, 2021): 225. http://dx.doi.org/10.3390/life11030225.

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De novo protein design is a powerful methodology used to study natural functions in an artificial-protein context. Since its inception, it has been used to reproduce a plethora of reactions and uncover biophysical principles that are often difficult to extract from direct studies of natural proteins. Natural proteins are capable of assuming a variety of different structures and subsequently binding ligands at impressively high levels of both specificity and affinity. Here, we will review recent examples of de novo design studies on binding reactions for small molecules, nucleic acids, and the formation of protein-protein interactions. We will then discuss some new structural advances in the field. Finally, we will discuss some advancements in computational modeling and design approaches and provide an overview of some modern algorithmic tools being used to design these proteins.

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42

Niitsu, Ai, Jack W. Heal, Kerstin Fauland, Andrew R. Thomson, and Derek N. Woolfson. "Membrane-spanning α-helical barrels as tractable protein-design targets." Philosophical Transactions of the Royal Society B: Biological Sciences 372, no. 1726 (June 19, 2017): 20160213. http://dx.doi.org/10.1098/rstb.2016.0213.

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The rational ( de novo ) design of membrane-spanning proteins lags behind that for water-soluble globular proteins. This is due to gaps in our knowledge of membrane-protein structure, and experimental difficulties in studying such proteins compared to water-soluble counterparts. One limiting factor is the small number of experimentally determined three-dimensional structures for transmembrane proteins. By contrast, many tens of thousands of globular protein structures provide a rich source of ‘scaffolds’ for protein design, and the means to garner sequence-to-structure relationships to guide the design process. The α-helical coiled coil is a protein-structure element found in both globular and membrane proteins, where it cements a variety of helix–helix interactions and helical bundles. Our deep understanding of coiled coils has enabled a large number of successful de novo designs. For one class, the α-helical barrels—that is, symmetric bundles of five or more helices with central accessible channels—there are both water-soluble and membrane-spanning examples. Recent computational designs of water-soluble α-helical barrels with five to seven helices have advanced the design field considerably. Here we identify and classify analogous and more complicated membrane-spanning α-helical barrels from the Protein Data Bank. These provide tantalizing but tractable targets for protein engineering and de novo protein design. This article is part of the themed issue ‘Membrane pores: from structure and assembly, to medicine and technology’.

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43

Chang-Sheng, ZHANG, and LAI Lu-Hua. "Protein-Protein Interaction: Prediction, Design, and Modulation." Acta Physico-Chimica Sinica 28, no. 10 (2012): 2363–80. http://dx.doi.org/10.3866/pku.whxb201209172.

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44

J. Bienstock, Rachelle. "Computational Drug Design Targeting Protein-Protein Interactions." Current Drug Metabolism 18, no. 9 (March 1, 2012): 1240–54. http://dx.doi.org/10.2174/138920012799362891.

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45

Rognan, Didier. "Rational design of protein–protein interaction inhibitors." MedChemComm 6, no. 1 (2015): 51–60. http://dx.doi.org/10.1039/c4md00328d.

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46

J. Bienstock, Rachelle. "Computational Drug Design Targeting Protein-Protein Interactions." Current Pharmaceutical Design 18, no. 9 (March 1, 2012): 1240–54. http://dx.doi.org/10.2174/138161212799436449.

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47

Fry, David, Kuo-Sen Huang, Paola Di Lello, Peter Mohr, Klaus Müller, Sung-Sau So, Takeo Harada, Martin Stahl, Binh Vu, and Harald Mauser. "Design of Libraries Targeting Protein-Protein Interfaces." ChemMedChem 8, no. 5 (February 21, 2013): 726–32. http://dx.doi.org/10.1002/cmdc.201200540.

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48

Shakhnovich, E. I., and A. M. Gutin. "A new approach to the design of stable proteins." "Protein Engineering, Design and Selection" 6, no. 8 (1993): 793–800. http://dx.doi.org/10.1093/protein/6.8.793.

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49

Pan, S. J., W. L. Cheung, H. K. Fung, C. A. Floudas, and A. J. Link. "Computational design of the lasso peptide antibiotic microcin J25." Protein Engineering Design and Selection 24, no. 3 (November 23, 2010): 275–82. http://dx.doi.org/10.1093/protein/gzq108.

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50

Stura, Enrico A. "Protein crystallization for drug design in the last 50 years." Arbor 191, no. 772 (April 30, 2015): a222. http://dx.doi.org/10.3989/arbor.2015.772n2008.

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Journal articles on the topic 'Protein design'

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