{"id":311,"date":"2021-04-18T17:47:49","date_gmt":"2021-04-18T17:47:49","guid":{"rendered":"http:\/\/dlab.clemson.edu\/?p=311"},"modified":"2025-04-25T21:06:31","modified_gmt":"2025-04-25T21:06:31","slug":"protein-design-and-protein-engineering","status":"publish","type":"post","link":"https:\/\/dlab.clemson.edu\/?p=311","title":{"rendered":"Protein Design and Protein Engineering"},"content":{"rendered":"

The ability to accurately manipulate a protein\u2019s structure, dynamics and function, and eventually to create de novo<\/em> proteins with specific functions is the holy grail of modern biology. The success in protein design & engineering requires the accurate knowledge of inter-residue interactions, protein folding, protein-ligand and protein-protein recognitions, and also the molecular mechanism of catalysis. For example, the design of an enzyme needs the sub-angstrom precision in strategically positioning residues important for catalysis. Therefore, computational modeling plays a crucial role in protein design. Despite some recent successes in protein design, we are still far from perfect, requiring better understanding of inter-molecular interactions and faster sampling approaches.<\/p>\n

\"Medusa-Protein\"<\/p>\n

We designed a molecular modeling suite, named Medusa<\/strong>. Medusa features physical-based force field to model inter-residue interactions, and a Monte-Carlo based simulated annealing approach to sample the sequence and sidechain structural spaces. The orientation of amino acid is usually modeled by rotamer library, a set of discrete sidechain conformations tabulated from the protein databank. <\/span>One of the salient features of Medusa algorithm is the continuous sampling of sub-rotamer space. As result, we were able to accurately model the sidechain packing, and estimate the interaction energies. The applications of Medusa include prediction of the optimal amino acid sequence compatible to the structure of a given protein or protein-ligand and protein-protein complex. Hence, Medusa can be used to design a novel<\/em> protein with low energy by itself (high stability), or that can bind to a specific partner, including small-molecule ligands and proteins (strong affinity), thus novel functions.<\/span><\/p>\n

In Medusa, protein backbone can be perturbed to model subtle protein backbone changes upon mutations or binding with partners. <\/span> We model the major protein backbone changes using discrete molecular dynamics (DMD<\/strong>), or event-driven molecular dynamics. We developed an all-atom protein model for DMD simulations, where the inter-atomic interactions were adopted from Medusa forcefield. Using all-atom DMD, we were able to fold a few proteins into their near-native states. Combining Medusa and all-atom DMD, we can efficiently sample both structural and sequence spaces, allowing us to design proteins with novel functions.<\/p>\n

 <\/p>\n

11. Y. Xing, N. Andrikopoulos, Z. Zhang, Y. Sun, P.C. Ke, F. Ding, \u201cModulating nanodroplet formation en route to fibrillization of amyloid peptides with designed flanking sequences\u201d, Biomacromolecules, 23(10):4179-4191, doi:10.1021\/acs.biomac.2c00642 (2022)<\/p>\n

10. Dagliyan, O., Shirvanyants, D., Karginov, A., F. Ding, L. Fee, Chandransekaran, S.N., Freisinger, C.M., Smolen, G., Huttenlocher, A., Hahn, K.M., and Dokholyan, N.V., \u201cRational design of a novel protein for allosteric control of kinases in living organisms\u201d, Proceedings of the National Academy of Sciences USA, in press (2013)<\/p>\n

9. A. Karginov, F. Ding, P. Kota, Dokholyan, N.V. and K. Hahn, \u201cEngineered allosteric regulation of kinases in living cells\u201d, Nature Biotechnology, 28:743-748, (2010) [download]<\/a><\/p>\n

8. M. P. Torres, M. J. Lee, F. Ding, C. Purbeck, B. Kuhlman, N. V. Dokholyan, and H. G. Dohlman, \u201cG Protein Mono-Ubiquitination By the RSP5 Ubiquitin Ligase\u201d, Journal of Biological Chemistry, 284: 8940-8950 (2009)[download]<\/a><\/p>\n

7. Yin, S., F. Ding and Dokholyan, N. V. \u201cModeling mutations in proteins using Medusa and discrete molecular dynamics\u201d in \u201cProtein Structure Prediction: Method and Algorithms\u201d, Editors: Rangwala, H. and Karypis, G. Wiley & Sons, (2009)<\/p>\n

6. S. Yin, F. Ding, and N. V. Dokholyan, \u201cComputational evaluation of protein stability change upon mutations using Eris.\u201d in \u201cIn Vitro Mutagenesis Protocols\u201d Editor: J. Braman. Humana Press (2009)<\/p>\n

5. F. Ding, D. Tsao, H. Nie, and N. V. Dokholyan, \u201cAb initio folding of proteins using all-atom discrete molecular dynamics\u201d Structure, 16: 1010-1018 (2008) [download]<\/a><\/p>\n

4. S. Yin, F. Ding, and N. V. Dokholyan, \u201cModeling backbone flexibility improves protein stability estimation\u201d, Structure, 15: 1567-1576 (2007) [download]<\/a><\/p>\n

3. S. Yin, F. Ding, and N. V. Dokholyan, \u201cEris: An automated estimator of protein stability\u201d Nature Methods, 4: 466-467 (2007) [download]<\/a>[service]<\/a><\/p>\n

2. V. V. Demidov, N. V. Dokholyan, C. Witte-Hoffman, P. Chalasani, H.-W. Yiu, F. Ding, Y. Yu, C. R. Cantor, N. E. Broude, \u201cFast complementation of split fluorescent protein triggered by DNA hybridization\u201d, Proceedings of the National Academy of Sciences USA, 103: 2052-2056 (2006).[download]<\/a><\/p>\n

<\/a>1. F. Ding and N. V. Dokholyan, \u201cEmergence of protein fold families through rational design\u201d Public Library of Science Computational Biology, 2: e85 (2006) [download]<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

The ability to accurately manipulate a protein\u2019s structure, dynamics and function, and eventually to create de novo proteins with specific functions is the holy grail of modern biology. The success in protein design & engineering requires the accurate knowledge of inter-residue interactions, protein folding, protein-ligand and protein-protein recognitions, and also the molecular mechanism of catalysis. … <\/p>\n

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