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Journal articles on the topic 'Computational protein design'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 September 2023

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1

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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2

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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3

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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4

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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5

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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6

Kundert, Kale, and Tanja Kortemme. "Computational design of structured loops for new protein functions." Biological Chemistry 400, no. 3 (February 25, 2019): 275–88. http://dx.doi.org/10.1515/hsz-2018-0348.

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Abstract The ability to engineer the precise geometries, fine-tuned energetics and subtle dynamics that are characteristic of functional proteins is a major unsolved challenge in the field of computational protein design. In natural proteins, functional sites exhibiting these properties often feature structured loops. However, unlike the elements of secondary structures that comprise idealized protein folds, structured loops have been difficult to design computationally. Addressing this shortcoming in a general way is a necessary first step towards the routine design of protein function. In this perspective, we will describe the progress that has been made on this problem and discuss how recent advances in the field of loop structure prediction can be harnessed and applied to the inverse problem of computational loop design.

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7

Frappier, Vincent, and Amy E. Keating. "Data-driven computational protein design." Current Opinion in Structural Biology 69 (August 2021): 63–69. http://dx.doi.org/10.1016/j.sbi.2021.03.009.

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8

Samish, Ilan, Christopher M. MacDermaid, Jose Manuel Perez-Aguilar, and Jeffery G. Saven. "Theoretical and Computational Protein Design." Annual Review of Physical Chemistry 62, no. 1 (May 5, 2011): 129–49. http://dx.doi.org/10.1146/annurev-physchem-032210-103509.

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9

Coluzza, Ivan. "Computational protein design: a review." Journal of Physics: Condensed Matter 29, no. 14 (February 27, 2017): 143001. http://dx.doi.org/10.1088/1361-648x/aa5c76.

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10

Desjarlais, John R., and Stephen L. Mayo. "Editorial overview: Computational protein design." Current Opinion in Structural Biology 12, no. 4 (August 2002): 429–30. http://dx.doi.org/10.1016/s0959-440x(02)00343-3.

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11

Park, Sheldon, Xi Yang, and Jeffery G. Saven. "Advances in computational protein design." Current Opinion in Structural Biology 14, no. 4 (August 2004): 487–94. http://dx.doi.org/10.1016/j.sbi.2004.06.002.

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12

Vizcarra, Christina L., and Stephen L. Mayo. "Electrostatics in computational protein design." Current Opinion in Chemical Biology 9, no. 6 (December 2005): 622–26. http://dx.doi.org/10.1016/j.cbpa.2005.10.014.

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13

Havranek, James J. "Specificity in Computational Protein Design." Journal of Biological Chemistry 285, no. 41 (July 29, 2010): 31095–99. http://dx.doi.org/10.1074/jbc.r110.157685.

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14

Lippow, Shaun M., and Bruce Tidor. "Progress in computational protein design." Current Opinion in Biotechnology 18, no. 4 (August 2007): 305–11. http://dx.doi.org/10.1016/j.copbio.2007.04.009.

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15

Hwang, Inseong, and Sheldon Park. "Computational design of protein therapeutics." Drug Discovery Today: Technologies 5, no. 2-3 (September 2008): e43-e48. http://dx.doi.org/10.1016/j.ddtec.2008.11.004.

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16

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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17

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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18

Scheck, Andreas, Stéphane Rosset, Michaël Defferrard, Andreas Loukas, Jaume Bonet, Pierre Vandergheynst, and Bruno E. Correia. "RosettaSurf—A surface-centric computational design approach." PLOS Computational Biology 18, no. 3 (March 16, 2022): e1009178. http://dx.doi.org/10.1371/journal.pcbi.1009178.

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Proteins are typically represented by discrete atomic coordinates providing an accessible framework to describe different conformations. However, in some fields proteins are more accurately represented as near-continuous surfaces, as these are imprinted with geometric (shape) and chemical (electrostatics) features of the underlying protein structure. Protein surfaces are dependent on their chemical composition and, ultimately determine protein function, acting as the interface that engages in interactions with other molecules. In the past, such representations were utilized to compare protein structures on global and local scales and have shed light on functional properties of proteins. Here we describe RosettaSurf, a surface-centric computational design protocol, that focuses on the molecular surface shape and electrostatic properties as means for protein engineering, offering a unique approach for the design of proteins and their functions. The RosettaSurf protocol combines the explicit optimization of molecular surface features with a global scoring function during the sequence design process, diverging from the typical design approaches that rely solely on an energy scoring function. With this computational approach, we attempt to address a fundamental problem in protein design related to the design of functional sites in proteins, even when structurally similar templates are absent in the characterized structural repertoire. Surface-centric design exploits the premise that molecular surfaces are, to a certain extent, independent of the underlying sequence and backbone configuration, meaning that different sequences in different proteins may present similar surfaces. We benchmarked RosettaSurf on various sequence recovery datasets and showcased its design capabilities by generating epitope mimics that were biochemically validated. Overall, our results indicate that the explicit optimization of surface features may lead to new routes for the design of functional proteins.

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19

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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20

Alvizo, Oscar, Benjamin D. Allen, and Stephen L. Mayo. "Computational protein design promises to revolutionize protein engineering." BioTechniques 42, no. 1 (January 2007): 31–39. http://dx.doi.org/10.2144/000112336.

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21

Zhou, Alice Qinhua, Corey S. O'Hern, and Lynne Regan. "Novel Computational Methods to Design Protein-Protein Interactions." Biophysical Journal 106, no. 2 (January 2014): 654a—655a. http://dx.doi.org/10.1016/j.bpj.2013.11.3622.

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22

Parikesit, A. A., and U. S. F. Tambunan. "COMPUTATIONAL PROTEIN DESIGN IN GREEN CHEMISTRY." Rasayan Journal of Chemistry 11, no. 3 (2018): 1133–38. http://dx.doi.org/10.31788/rjc.2018.1133038.

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23

Park, Sheldon, Xiaoran Fu Stowell, Wei Wang, Xi Yang, and Jeffery G. Saven. "7 Computational protein design and discovery." Annu. Rep. Prog. Chem., Sect. C: Phys. Chem. 100 (2004): 195–236. http://dx.doi.org/10.1039/b313669h.

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24

Lanci, C. J., C. M. MacDermaid, S. g. Kang, R. Acharya, B. North, X. Yang, X. J. Qiu, W. F. DeGrado, and J. G. Saven. "Computational design of a protein crystal." Proceedings of the National Academy of Sciences 109, no. 19 (April 25, 2012): 7304–9. http://dx.doi.org/10.1073/pnas.1112595109.

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25

Pantazes, Robert J., Matthew J. Grisewood, and Costas D. Maranas. "Recent advances in computational protein design." Current Opinion in Structural Biology 21, no. 4 (August 2011): 467–72. http://dx.doi.org/10.1016/j.sbi.2011.04.005.

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26

Norn, Christoffer H., and Ingemar André. "Computational design of protein self-assembly." Current Opinion in Structural Biology 39 (August 2016): 39–45. http://dx.doi.org/10.1016/j.sbi.2016.04.002.

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27

Mandell, Daniel J., and Tanja Kortemme. "Backbone flexibility in computational protein design." Current Opinion in Biotechnology 20, no. 4 (August 2009): 420–28. http://dx.doi.org/10.1016/j.copbio.2009.07.006.

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28

Davey, James A., and Roberto A. Chica. "Multistate approaches in computational protein design." Protein Science 21, no. 9 (August 10, 2012): 1241–52. http://dx.doi.org/10.1002/pro.2128.

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29

Hearst, David P., and Fred E. Cohensup. "GRAFTER: a computational aid for the design of novel proteins." "Protein Engineering, Design and Selection" 7, no. 12 (1994): 1411–21. http://dx.doi.org/10.1093/protein/7.12.1411.

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30

Morin, A., K. W. Kaufmann, C. Fortenberry, J. M. Harp, L. S. Mizoue, and J. Meiler. "Computational design of an endo-1,4- -xylanase ligand binding site." Protein Engineering Design and Selection 24, no. 6 (February 24, 2011): 503–16. http://dx.doi.org/10.1093/protein/gzr006.

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31

Park, Keunwan, Betty W. Shen, Fabio Parmeggiani, Po-Ssu Huang, Barry L. Stoddard, and David Baker. "Control of repeat-protein curvature by computational protein design." Nature Structural & Molecular Biology 22, no. 2 (January 12, 2015): 167–74. http://dx.doi.org/10.1038/nsmb.2938.

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32

Brunette, TJ, Fabio Parmeggiani, Po-Ssu Huang, Gira Bhabha, Damian C. Ekiert, Susan E. Tsutakawa, Greg L. Hura, John A. Tainer, and David Baker. "Exploring the repeat protein universe through computational protein design." Nature 528, no. 7583 (December 2015): 580–84. http://dx.doi.org/10.1038/nature16162.

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33

Guffy, Sharon L., Bryan S. Der, and Brian Kuhlman. "Probing the minimal determinants of zinc binding with computational protein design." Protein Engineering Design and Selection 29, no. 8 (June 29, 2016): 327–38. http://dx.doi.org/10.1093/protein/gzw026.

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34

Sarkar, Sharanya, Khushboo Gulati, Manikyaprabhu Kairamkonda, Amit Mishra, and Krishna Mohan Poluri. "Elucidating Protein-protein Interactions Through Computational Approaches and Designing Small Molecule Inhibitors Against them for Various Diseases." Current Topics in Medicinal Chemistry 18, no. 20 (December 31, 2018): 1719–36. http://dx.doi.org/10.2174/1568026618666181025114903.

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Background: To carry out wide range of cellular functionalities, proteins often associate with one or more proteins in a phenomenon known as Protein-Protein Interaction (PPI). Experimental and computational approaches were applied on PPIs in order to determine the interacting partners, and also to understand how an abnormality in such interactions can become the principle cause of a disease. Objective: This review aims to elucidate the case studies where PPIs involved in various human diseases have been proven or validated with computational techniques, and also to elucidate how small molecule inhibitors of PPIs have been designed computationally to act as effective therapeutic measures against certain diseases. Results: Computational techniques to predict PPIs are emerging rapidly in the modern day. They not only help in predicting new PPIs, but also generate outputs that substantiate the experimentally determined results. Moreover, computation has aided in the designing of novel inhibitor molecules disrupting the PPIs. Some of them are already being tested in the clinical trials. Conclusion: This review delineated the classification of computational tools that are essential to investigate PPIs. Furthermore, the review shed light on how indispensable computational tools have become in the field of medicine to analyze the interaction networks and to design novel inhibitors efficiently against dreadful diseases in a shorter time span.

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35

Vucinic, Jelena, David Simoncini, Manon Ruffini, Sophie Barbe, and Thomas Schiex. "Positive multistate protein design." Bioinformatics 36, no. 1 (June 14, 2019): 122–30. http://dx.doi.org/10.1093/bioinformatics/btz497.

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Abstract Motivation Structure-based computational protein design (CPD) plays a critical role in advancing the field of protein engineering. Using an all-atom energy function, CPD tries to identify amino acid sequences that fold into a target structure and ultimately perform a desired function. The usual approach considers a single rigid backbone as a target, which ignores backbone flexibility. Multistate design (MSD) allows instead to consider several backbone states simultaneously, defining challenging computational problems. Results We introduce efficient reductions of positive MSD problems to Cost Function Networks with two different fitness definitions and implement them in the Pompd (Positive Multistate Protein design) software. Pompd is able to identify guaranteed optimal sequences of positive multistate full protein redesign problems and exhaustively enumerate suboptimal sequences close to the MSD optimum. Applied to nuclear magnetic resonance and back-rubbed X-ray structures, we observe that the average energy fitness provides the best sequence recovery. Our method outperforms state-of-the-art guaranteed computational design approaches by orders of magnitudes and can solve MSD problems with sizes previously unreachable with guaranteed algorithms. Availability and implementation https://forgemia.inra.fr/thomas.schiex/pompd as documented Open Source. Supplementary information Supplementary data are available at Bioinformatics online.

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36

Tian, Pu. "Computational protein design, from single domain soluble proteins to membrane proteins." Chemical Society Reviews 39, no. 6 (2010): 2071. http://dx.doi.org/10.1039/b810924a.

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37

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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38

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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39

Glasgow, Anum A., Yao-Ming Huang, Daniel J. Mandell, Michael Thompson, Ryan Ritterson, Amanda L. Loshbaugh, Jenna Pellegrino, et al. "Computational design of a modular protein sense-response system." Science 366, no. 6468 (November 21, 2019): 1024–28. http://dx.doi.org/10.1126/science.aax8780.

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Sensing and responding to signals is a fundamental ability of living systems, but despite substantial progress in the computational design of new protein structures, there is no general approach for engineering arbitrary new protein sensors. Here, we describe a generalizable computational strategy for designing sensor-actuator proteins by building binding sites de novo into heterodimeric protein-protein interfaces and coupling ligand sensing to modular actuation through split reporters. Using this approach, we designed protein sensors that respond to farnesyl pyrophosphate, a metabolic intermediate in the production of valuable compounds. The sensors are functional in vitro and in cells, and the crystal structure of the engineered binding site closely matches the design model. Our computational design strategy opens broad avenues to link biological outputs to new signals.

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40

Noguchi, Hiroki, Christine Addy, David Simoncini, Staf Wouters, Bram Mylemans, Luc Van Meervelt, Thomas Schiex, Kam Y. J. Zhang, Jeremy R. H. Tame, and Arnout R. D. Voet. "Computational design of symmetrical eight-bladed β-propeller proteins." IUCrJ 6, no. 1 (January 1, 2019): 46–55. http://dx.doi.org/10.1107/s205225251801480x.

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β-Propeller proteins form one of the largest families of protein structures, with a pseudo-symmetrical fold made up of subdomains called blades. They are not only abundant but are also involved in a wide variety of cellular processes, often by acting as a platform for the assembly of protein complexes. WD40 proteins are a subfamily of propeller proteins with no intrinsic enzymatic activity, but their stable, modular architecture and versatile surface have allowed evolution to adapt them to many vital roles. By computationally reverse-engineering the duplication, fusion and diversification events in the evolutionary history of a WD40 protein, a perfectly symmetrical homologue called Tako8 was made. If two or four blades of Tako8 are expressed as single polypeptides, they do not self-assemble to complete the eight-bladed architecture, which may be owing to the closely spaced negative charges inside the ring. A different computational approach was employed to redesign Tako8 to create Ika8, a fourfold-symmetrical protein in which neighbouring blades carry compensating charges. Ika2 and Ika4, carrying two or four blades per subunit, respectively, were found to assemble spontaneously into a complete eight-bladed ring in solution. These artificial eight-bladed rings may find applications in bionanotechnology and as models to study the folding and evolution of WD40 proteins.

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41

Malisi, Christoph, Marcel Schumann, Nora C. Toussaint, Jorge Kageyama, Oliver Kohlbacher, and Birte Höcker. "Binding Pocket Optimization by Computational Protein Design." PLoS ONE 7, no. 12 (December 27, 2012): e52505. http://dx.doi.org/10.1371/journal.pone.0052505.

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42

Allison, Brittany, Steven Combs, Sam DeLuca, Gordon Lemmon, Laura Mizoue, and Jens Meiler. "Computational design of protein-small molecule interfaces." Journal of Structural Biology 185, no. 2 (February 2014): 193–202. http://dx.doi.org/10.1016/j.jsb.2013.08.003.

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43

Mignon, David, Karen Druart, Eleni Michael, Vaitea Opuu, Savvas Polydorides, Francesco Villa, Thomas Gaillard, Nicolas Panel, Georgios Archontis, and Thomas Simonson. "Physics-Based Computational Protein Design: An Update." Journal of Physical Chemistry A 124, no. 51 (November 10, 2020): 10637–48. http://dx.doi.org/10.1021/acs.jpca.0c07605.

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44

Bolon, D. N., R. A. Grant, T. A. Baker, and R. T. Sauer. "Specificity versus stability in computational protein design." Proceedings of the National Academy of Sciences 102, no. 36 (August 29, 2005): 12724–29. http://dx.doi.org/10.1073/pnas.0506124102.

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45

Macdonald, Gareth John. "AI and Computational Design Advance Protein Engineering." Genetic Engineering & Biotechnology News 43, no. 2 (February 1, 2023): 26–28. http://dx.doi.org/10.1089/gen.43.02.10.

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46

Jeliazkov, Jeliazko R., Aaron C. Robinson, James M. Berger, Bertrand García-Moreno E., and Jeffrey J. Gray. "Computational Design of High-Resolution Protein Crystals." Biophysical Journal 114, no. 3 (February 2018): 575a. http://dx.doi.org/10.1016/j.bpj.2017.11.3146.

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47

Allouche, David, Isabelle André, Sophie Barbe, Jessica Davies, Simon de Givry, George Katsirelos, Barry O'Sullivan, Steve Prestwich, Thomas Schiex, and Seydou Traoré. "Computational protein design as an optimization problem." Artificial Intelligence 212 (July 2014): 59–79. http://dx.doi.org/10.1016/j.artint.2014.03.005.

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48

Traoré, Seydou, Kyle E. Roberts, David Allouche, Bruce R. Donald, Isabelle André, Thomas Schiex, and Sophie Barbe. "Fast search algorithms for computational protein design." Journal of Computational Chemistry 37, no. 12 (February 2, 2016): 1048–58. http://dx.doi.org/10.1002/jcc.24290.

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49

Suárez, María, Pablo Tortosa, and Alfonso Jaramillo. "PROTDES: CHARMM toolbox for computational protein design." Systems and Synthetic Biology 2, no. 3-4 (December 2008): 105–13. http://dx.doi.org/10.1007/s11693-009-9026-7.

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50

Jha, Ramesh K., Andrew Leaver-Fay, Shuangye Yin, Yibing Wu, Glenn L. Butterfoss, Thomas Szyperski, Nikolay V. Dokholyan, and Brian Kuhlman. "Computational Design of a PAK1 Binding Protein." Journal of Molecular Biology 400, no. 2 (July 2010): 257–70. http://dx.doi.org/10.1016/j.jmb.2010.05.006.

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