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Journal articles on the topic 'Computational protein design'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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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 (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 (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 (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 prom

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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 (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 (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 (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 th

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7

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

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8

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

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9

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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10

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 (2011): 129–49. http://dx.doi.org/10.1146/annurev-physchem-032210-103509.

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11

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

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12

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

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13

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

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14

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

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15

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

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16

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

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17

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

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18

Scheck, Andreas, Stéphane Rosset, Michaël Defferrard, et al. "RosettaSurf—A surface-centric computational design approach." PLOS Computational Biology 18, no. 3 (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

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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 (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 (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 (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, et al. "Computational design of a protein crystal." Proceedings of the National Academy of Sciences 109, no. 19 (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 (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 (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 (2012): 1241–52. http://dx.doi.org/10.1002/pro.2128.

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29

Son, Ahrum, Jongham Park, Woojin Kim, et al. "Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence." Molecules 29, no. 19 (2024): 4626. http://dx.doi.org/10.3390/molecules29194626.

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The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design of proteins with unprecedented precision and functionality. Computational methods now play a crucial role in enhancing the stability, activity, and specificity of proteins for diverse applications in biotechnology and medicine. Techniques such as deep learning, reinforcement learning, and transfer learning have dramatically improved protein structure prediction, optimization of binding affinities, and enzyme design.

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30

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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31

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 (2011): 503–16. http://dx.doi.org/10.1093/protein/gzr006.

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32

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 (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

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33

Noguchi, Hiroki, Christine Addy, David Simoncini та ін. "Computational design of symmetrical eight-bladed β-propeller proteins". IUCrJ 6, № 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 even

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34

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 (2015): 167–74. http://dx.doi.org/10.1038/nsmb.2938.

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35

Brunette, TJ, Fabio Parmeggiani, Po-Ssu Huang, et al. "Exploring the repeat protein universe through computational protein design." Nature 528, no. 7583 (2015): 580–84. http://dx.doi.org/10.1038/nature16162.

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36

Vucinic, Jelena, David Simoncini, Manon Ruffini, Sophie Barbe, and Thomas Schiex. "Positive multistate protein design." Bioinformatics 36, no. 1 (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

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37

Bjerre, Benjamin, Jakob Nissen, Mikkel Madsen, et al. "Improving folding properties of computationally designed proteins." Protein Engineering, Design and Selection 32, no. 3 (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 am

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38

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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39

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 (2016): 327–38. http://dx.doi.org/10.1093/protein/gzw026.

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40

Simoncini, David, Kam Y. J. Zhang, Thomas Schiex, and Sophie Barbe. "A structural homology approach for computational protein design with flexible backbone." Bioinformatics 35, no. 14 (2018): 2418–26. http://dx.doi.org/10.1093/bioinformatics/bty975.

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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. Energy functions remain however imperfect and injecting relevant information from known structures in the design process should lead to improved designs. Results We introduce Shades, a data-driven CPD method that exploits local structural environments in known protein structures together with energy to g

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41

Glasgow, Anum A., Yao-Ming Huang, Daniel J. Mandell, et al. "Computational design of a modular protein sense-response system." Science 366, no. 6468 (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 interme

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42

Gutte, B., and S. Klauser. "Design of catalytic polypeptides and proteins." Protein Engineering, Design and Selection 31, no. 12 (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 wi

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43

Taechalertpaisarn, Jaru, Rui-Liang Lyu, Maritess Arancillo, et al. "Design criteria for minimalist mimics of protein–protein interface segments." Organic & Biomolecular Chemistry 17, no. 4 (2019): 908–15. http://dx.doi.org/10.1039/c8ob02901f.

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44

Son, Ahrum, Jongham Park, Woojin Kim, et al. "Integrating Computational Design and Experimental Approaches for Next-Generation Biologics." Biomolecules 14, no. 9 (2024): 1073. http://dx.doi.org/10.3390/biom14091073.

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Therapeutic protein engineering has revolutionized medicine by enabling the development of highly specific and potent treatments for a wide range of diseases. This review examines recent advances in computational and experimental approaches for engineering improved protein therapeutics. Key areas of focus include antibody engineering, enzyme replacement therapies, and cytokine-based drugs. Computational methods like structure-based design, machine learning integration, and protein language models have dramatically enhanced our ability to predict protein properties and guide engineering efforts

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45

Lu, Lei, Xuxu Gou, Sophia K. Tan, et al. "De novo design of drug-binding proteins with predictable binding energy and specificity." Science 384, no. 6691 (2024): 106–12. http://dx.doi.org/10.1126/science.adl5364.

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The de novo design of small molecule–binding proteins has seen exciting recent progress; however, high-affinity binding and tunable specificity typically require laborious screening and optimization after computational design. We developed a computational procedure to design a protein that recognizes a common pharmacophore in a series of poly(ADP-ribose) polymerase–1 inhibitors. One of three designed proteins bound different inhibitors with affinities ranging from <5 nM to low micromolar. X-ray crystal structures confirmed the accuracy of the designed protein-drug interactions. Molecular dy

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46

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 (2012): e52505. http://dx.doi.org/10.1371/journal.pone.0052505.

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47

Carlson, Heather A., and J. Andrew McCammon. "Accommodating Protein Flexibility in Computational Drug Design,." Molecular Pharmacology 57, no. 2 (2000): 213–18. https://doi.org/10.1016/s0026-895x(24)23192-8.

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48

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 (2014): 193–202. http://dx.doi.org/10.1016/j.jsb.2013.08.003.

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49

Mignon, David, Karen Druart, Eleni Michael, et al. "Physics-Based Computational Protein Design: An Update." Journal of Physical Chemistry A 124, no. 51 (2020): 10637–48. http://dx.doi.org/10.1021/acs.jpca.0c07605.

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50

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 (2005): 12724–29. http://dx.doi.org/10.1073/pnas.0506124102.

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