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Suppose that I have two proteins, protein A and protein B, and suppose that the sequence of amino acids of protein B is exactly the reverse of the sequence of protein A.
For example (these are made-up proteins):
protein A = [G,A,L,G,M,F,R] protein B = [R,F,M,G,L,A,G]
Will the 3D structure of protein B be somehow identical, or perhaps the mirror image, of the 3D structure of protein A?
No! Although there is a relationship, the protein would not fold properly since the C and N terminals are reversed consider the following:
as appose to :
These are completely different molecules!
http://www.ncbi.nlm.nih.gov/pubmed/1604320 http://www.pnas.org/content/95/13/7287.long http://www.pnas.org/content/111/32/11679
Co-transcriptional folding is encoded within RNA genes
Most of the existing RNA structure prediction programs fold a completely synthesized RNA molecule. However, within the cell, RNA molecules emerge sequentially during the directed process of transcription. Dedicated experiments with individual RNA molecules have shown that RNA folds while it is being transcribed and that its correct folding can also depend on the proper speed of transcription.
The main aim of this work is to study if and how co-transcriptional folding is encoded within the primary and secondary structure of RNA genes. In order to achieve this, we study the known primary and secondary structures of a comprehensive data set of 361 RNA genes as well as a set of 48 RNA sequences that are known to differ from the originally transcribed sequence units. We detect co-transcriptional folding by defining two measures of directedness which quantify the extend of asymmetry between alternative helices that lie 5' and those that lie 3' of the known helices with which they compete.
We show with statistical significance that co-transcriptional folding strongly influences RNA sequences in two ways: (1) alternative helices that would compete with the formation of the functional structure during co-transcriptional folding are suppressed and (2) the formation of transient structures which may serve as guidelines for the co-transcriptional folding pathway is encouraged.
These findings have a number of implications for RNA secondary structure prediction methods and the detection of RNA genes.
Much of our understanding of protein structure and mechanistic function has been derived from static high-resolution structures. As structural biology has continued to evolve it has become clear that high-resolution structures alone are unable to fully capture the mechanistic basis for protein structure and function in solution. Recently Hydrogen/Deuterium-exchange Mass Spectrometry (HDX-MS) has developed into a powerful and versatile tool for structural biologists that provides novel insights into protein structure and function. HDX-MS enables direct monitoring of a protein's structural fluctuations and conformational changes under native conditions in solution even as it is carrying out its functions. In this review, we focus on the use of HDX-MS to monitor these dynamic changes in proteins. We examine how HDX-MS has been applied to study protein structure and function in systems ranging from large, complex assemblies to intrinsically disordered proteins, and we discuss its use in probing conformational changes during protein folding and catalytic function.
Statement for a Broad Audience
The biophysical and structural characterization of proteins provides novel insight into their functionalities. Protein motions, ranging from small scale local fluctuations to larger concerted structural rearrangements, often determine protein function. Hydrogen/Deuterium-exchange Mass Spectrometry (HDX-MS) has proven a powerful biophysical tool capable of probing changes in protein structure and dynamic protein motions that are often invisible to most other techniques.
2.1. Theory behind the general strategy
A number of ideas and principles, borrowed in established and recent design of synthetic vaccines and petidomimetics, were used (see Ref. [ 3 ] for discussion and e.g. Refs. [  ,  ,  ,  ,  ,  ,  ]), as well as some of the ideas that lie behind the popular ZINC data base [ 70 ]. As discussed in Refs. [ 3 , 4 ], the present investigation started as a use case for the Hyperbolic Dirac Net (HDN) and particularly the associated Q-UEL language for automated inference [  ,  ,  ,  ]. The theory has been discussed elsewhere, e.g. in Refs. [  ,  ,  ,  ], which relate more to the practical and general uses of Q-UEL. These considerations are less important here because present studies can be reproduced by standard bioinformatics and molecular modeling means. Nonetheless, it is doubtful that the research for refs [ 3 , 4 ] could have been done and written up so rapidly without the aid of Q-UEL to interact with websites of the World Wide Web, gather knowledge, and facilitate use of the publically available bioinformatics tools [ 3 ].
2.2. Basic principles of epitope prediction for design of synthetic vaccines
The challenge is ultimately one of molecular recognition but in practice many key principles for hapten design relate to distinguishing types of naturally occurring epitope. By the term 𠇎pitope” in this paper is meant 𠇌ontinuous epitope”, though several smaller epitopes may be joined to represent a discontinuous epitope in which conformation and relative position in space can sometimes be important. While a synthetic construct implies the use of synthetic chemistry typically combined with a judicious carrier protein to which the peptide is linked chemically, constructs can also be obtained by cloning, using protein engineering principles [ 12 ]. The terms B-epitope and T-epitope relate to the traditional picture of a bone marrow B or thymus T response. B cell epitopes occur at the surface of the protein against which an immune system response is required. They are recognized by B cell receptors or antibodies in their native structure, and are concerned with the bone marrow response and antibody production. T epitopes may be buried inside protein structures and released by proteolysis, and are traditionally considered as concerned with a cellular response and immune system memory, i.e. active immunity. Continuous B cell epitope prediction is very similar to T cell epitope prediction. The focus is on B-epitopes here, though a B-epitope can also be (or overlap with) a T-epitope especially if it has a significant content of hydrophobic residues. Prediction of these has traditionally been based and has mainly been based on the amino acid properties such as hydrophilicity, charge, exposed surface area and secondary structure. There are many predictive algorithms available, but the present author prefers a more 𠇎xpert system” kind of approach that incudes experimental data, though the above biophysical considerations certainly still play a strong role (see below).
2.3. Some theoretical issues related to design of antagonists of COVID-19 infection
The previous paper [ 3 ] focused primarily on design of synthetic peptides as infection antagonists. However, partly for the reason of greater conformational flexibility discussed below, smaller less flexible organic molecules (i.e. with fewer rotatable bonds) are the traditional province of the synthetic chemist rather than use of an automated peptide synthesizer, and are preferred for pharmaceutical application. Consideration of peptides is more often considered as merely a useful intermediate step in more traditional pharmaceutical compound design. Biodegradability per se of peptides is not the main concern, since including d -amino acids in the design prevents proteolysis. In preliminary docking and simulation studies, the peptides do bind to 11β-hydroxysteroid dehydrogenase type 1, but less strongly and with several binding modes [ 3 ]. This weaker binding is not in itself a contraindication of the idea that these peptides bind at the same site as the more rigid non-peptide molecules, because it is an expected consequence of the much greater flexibility of peptides compared with molecules with, for example, multiple aromatic ring scaffolds. Conventional wisdom (e.g. Ref. [ 12 ]) frequently uses the rule-of-thumb that the total change in intramolecular (bond rotational) entropy of a peptide ligand is roughly TΔSTotal =ਁ.5 kcal mol 𢄡 per residue at 300 K, corresponding approximately to a 12-fold reduction in conformational freedom per residue on binding. Because van der Wall's and hydrogen bonding tend to be very roughly equivalent for peptides in water and in well bound forms, the water entropy effects known as hydrophobic effects (along with electrostatic forces) play an important role in determining the balance of energies and final outcome. KRSFIEDLLFNKV would thus cost about +19.5 kcal/mol entropic contribution to bind rigidly, primarily compensated by hydrophobic contacts at up to about 𢄡.7 kcal/mol in going from an aqueous to a non-polar environment, i.e. .1 kcal/mol for a 13 residue peptide or analogue of KRSFIEDLLFNKV. That example would not favor binding, but the proper calculation is in the details which should show balance that favors good binding if that is found to be the case experimentally. Despite the above comments, the flexibility of peptides does provide more opportunities to fit a specific binding site, i.e. they can show some accommodation and they are more tolerant to imperfections in the design process. However, this is also an argument for their importance as an intermediate step in the design of more conventional pharmaceutical agents.
Is protein folding symmetric with respect to reversing the sequence order? - Biology
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Is protein folding symmetric with respect to reversing the sequence order? - Biology
We thank Professor T. Parac-Vogt for allowing us to use the CD equipment, and the beamline scientists at the I04 beamline at Diamond Light Source and the NW12A beamline at Photon Factory Advanced Ring for their assistance.
A. R. D. Voet and L. Van Meervelt acknowledge the FWO (G0F9316N, G051917N and G0E4717N) for funding. J. R. H. Tame acknowledges support from OpenEye Scientific Software and the JSPS for funding. B. Mylemans and S. Wouters acknowledge the FWO for a PhD fellowship (ASP/17). T. Schiex and D. Simoncini acknowledge Agreenium for postdoctoral funding their work has been partially funded by the French Agence Nationale de la Recherche, Grant ANR-16-C40-0028 (to TS). K. Y. J. Zhang thanks JSPS for funding..
Abe, S., Tabe, H., Ijiri, H., Yamashita, K., Hirata, K., Atsumi, K., Shimoi, T., Akai, M., Mori, H., Kitagawa, S. & Ueno, T. (2017). ACS Nano , 11 , 2410. CrossRef CAS Google Scholar
Adams, P. D., Afonine, P. V., Bunkóczi, G., Chen, V. B., Davis, I. W., Echols, N., Headd, J. J., Hung, L.-W., Kapral, G. J., Grosse-Kunstleve, R. W., McCoy, A. J., Moriarty, N. W., Oeffner, R., Read, R. J., Richardson, D. C., Richardson, J. S., Terwilliger, T. C. & Zwart, P. H. (2010). Acta Cryst. D 66 , 213. Web of Science CrossRef CAS IUCr Journals Google Scholar
Allouche, D., André, I., Barbe, S., Davies, J., de Givry, S., Katsirelos, G., O'Sullivan, B., Prestwich, S., Schiex, T. & Traoré, S. (2014). Artif. Intell. 212 , 59. CrossRef Google Scholar
André, I., Bradley, P., Wang, C. & Baker, D. (2007). Proc. Natl Acad. Sci. USA , 104 , 17656. PubMed Google Scholar
Broom, A., Ma, S. M., Xia, K., Rafalia, H., Trainor, K., Colón, W., Gosavi, S. & Meiering, E. M. (2015). Proc. Natl Acad. Sci. USA , 112 , 14605. CrossRef CAS Google Scholar
Chen, V. B., Arendall, W. B., Headd, J. J., Keedy, D. A., Immormino, R. M., Kapral, G. J., Murray, L. W., Richardson, J. S. & Richardson, D. C. (2010). Acta Cryst. D 66 , 12. Web of Science CrossRef CAS IUCr Journals Google Scholar
Cooper, M. C., de Givry, S., Sanchez, M., Schiex, T., Zytnicki, M. & Werner, T. (2010). Artif. Intell. 174 , 449. CrossRef Google Scholar
Doyle, L., Hallinan, J., Bolduc, J., Parmeggiani, F., Baker, D., Stoddard, B. L. & Bradley, P. (2015). Nature (London) , 528 , 585. CrossRef CAS Google Scholar
Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. (2010). Acta Cryst. D 66 , 486. Web of Science CrossRef CAS IUCr Journals Google Scholar
Evans, P. R. & Murshudov, G. N. (2013). Acta Cryst. D 69 , 1204. Web of Science CrossRef CAS IUCr Journals Google Scholar
Gille, C., Fähling, M., Weyand, B., Wieland, T. & Gille, A. (2014). Nucleic Acids Res. 42 , W3–W6. CrossRef CAS Google Scholar
Guagnini, F., Antonik, P. M., Rennie, M. L., O'Byrne, P., Khan, A. R., Pinalli, R., Dalcanale, E. & Crowley, P. B. (2018). Angew. Chem. Int. Ed. 57 , 7126. CrossRef CAS Google Scholar
Hao, B., Oehlmann, S., Sowa, M., Harper, J. & Pavletich, N. (2007). Mol. Cell , 26 , 131. CrossRef CAS Google Scholar
Heddle, J. G., Yokoyama, T., Yamashita, I., Park, S.-Y. & Tame, J. R. H. (2006). Structure , 14 , 925. CrossRef CAS Google Scholar
Höcker, B., Lochner, A., Seitz, T., Claren, J. & Sterner, R. (2009). Biochemistry , 48 , 1145. Google Scholar
Huang, P.-S. S., Feldmeier, K., Parmeggiani, F., Fernandez Velasco, D. A., Höcker, B. & Baker, D. (2016). Nat. Chem. Biol. 12 , 29. CrossRef CAS Google Scholar
Hurley, B., O'Sullivan, B., Allouche, D., Katsirelos, G., Schiex, T., Zytnicki, M. & de Givry, S. (2016). Constraints , 21 , 413. CrossRef Google Scholar
Jurrus, E., Engel, D., Star, K., Monson, K., Brandi, J., Felberg, L. E., Brookes, D. H., Wilson, L., Chen, J., Liles, K., Chun, M., Li, P., Gohara, D. W., Dolinsky, T., Konecny, R., Koes, D. R., Nielsen, J. E., Head-Gordon, T., Geng, W., Krasny, R., Wei, G.-W., Holst, M. J., McCammon, J. A. & Baker, N. A. (2018). Protein Sci. 27 , 112. CrossRef CAS Google Scholar
Kabsch, W. (2010 a ). Acta Cryst. D 66 , 133. Web of Science CrossRef CAS IUCr Journals Google Scholar
Kabsch, W. (2010 b ). Acta Cryst. D 66 , 125. Web of Science CrossRef CAS IUCr Journals Google Scholar
Kabsch, W. & Sander, C. (1983). Biopolymers , 22 , 2577. CrossRef CAS PubMed Web of Science Google Scholar
Kopec, K. & Lupas, A. (2013). PLoS One , 8 , e77074. CrossRef Google Scholar
Lang, D., Thoma, R., Henn-Sax, M., Sterner, R. & Wilmanns, M. (2000). Science , 289 , 1546. Web of Science CrossRef PubMed CAS Google Scholar
Lebowitz, J., Lewis, M. S. & Schuck, P. (2002). Protein Sci. 11 , 2067. Web of Science CrossRef PubMed CAS Google Scholar
Lee, J. & Blaber, M. (2011). Proc. Natl Acad. Sci. USA , 108 , 126. Web of Science CrossRef CAS PubMed Google Scholar
Liang, M., Wang, L., Su, R., Qi, W., Wang, M., Yu, Y. & He, Z. (2013). Catal. Sci. Technol. 3 , 1910. CrossRef CAS Google Scholar
Lupas, A. N., Ponting, C. P. & Russell, R. B. (2001). J. Struct. Biol. 134 , 191. Web of Science CrossRef PubMed CAS Google Scholar
Main, E., Xiong, Y., Cocco, M., D'Andrea, L. & Regan, L. (2003). Structure , 11 , 497. Web of Science CrossRef PubMed CAS Google Scholar
McCoy, A. J., Grosse-Kunstleve, R. W., Adams, P. D., Winn, M. D., Storoni, L. C. & Read, R. J. (2007). J. Appl. Cryst. 40 , 658. Web of Science CrossRef CAS IUCr Journals Google Scholar
McGovern, R. E., Fernandes, H., Khan, A. R., Power, N. P. & Crowley, P. B. (2012). Nat. Chem. 4 , 527. CrossRef CAS Google Scholar
Nikkhah, M., Jawad-Alami, Z., Demydchuk, M., Ribbons, D. & Paoli, M. (2006). Biomol. Eng. 23 , 185. CrossRef CAS Google Scholar
O'Meara, M. J., Leaver-Fay, A., Tyka, M. D., Stein, A., Houlihan, K., DiMaio, F., Bradley, P., Kortemme, T., Baker, D., Snoeyink, J. & Kuhlman, B. (2015). J. Chem. Theory Comput. 11 , 609. CAS Google Scholar
Orengo, C. A., Jones, D. T. & Thornton, J. M. (1994). Nature (London) , 372 , 631. CrossRef CAS PubMed Web of Science Google Scholar
Pace, C. N. & Scholtz, J. M. (1997). Protein Structure: A Practical Approach , edited by T. E. Creighton, pp. 299. Oxford: IRL Press. Google Scholar
Paoli, M. (2001). Prog. Biophys. Mol. Biol. 76 , 103. Web of Science CrossRef PubMed CAS Google Scholar
Parmeggiani, F., Huang, P.-S., Vorobiev, S., Xiao, R., Park, K., Caprari, S., Su, M., Seetharaman, J., Mao, L., Janjua, H., Montelione, G. T., Hunt, J. & Baker, D. (2015). J. Mol. Biol. 427 , 563. CrossRef CAS Google Scholar
Parmeggiani, F., Pellarin, R., Larsen, A. P., Varadamsetty, G., Stumpp, M. T., Zerbe, O., Caflisch, A. & Plückthun, A. (2008). J. Mol. Biol. 376 , 1282. CrossRef PubMed CAS Google Scholar
Plückthun, A. (2015). Annu. Rev. Pharmacol. Toxicol. 55 , 489. Google Scholar
Pons, T., Gómez, R., Chinea, G. & Valencia, A. (2003). Curr. Med. Chem. 10 , 505. CrossRef CAS Google Scholar
Schapira, M., Tyers, M., Torrent, M. & Arrowsmith, C. (2017). Nat. Rev. Drug Discov. 16 , 773. CrossRef CAS Google Scholar
Scholtz, M., Grimsley, G. & Pace, N. (2009). Methods Enzymol. 466 , 549. CrossRef CAS Google Scholar
Schuck, P., Perugini, M., Gonzales, N., Howlett, G. & Schubert, D. (2002). Biophys. J. 82 , 1096. CrossRef CAS Google Scholar
Simoncini, D., Allouche, D., de Givry, S., Delmas, C., Barbe, S. & Schiex, T. (2015). J. Chem. Theory Comput. 11 , 5980. CrossRef CAS Google Scholar
Smock, R., Yadid, I., Dym, O., Clarke, J. & Tawfik, D. (2016). Cell , 164 , 476. CrossRef CAS Google Scholar
Söding, J. & Lupas, A. N. (2003). Bioessays , 25 , 837. Web of Science PubMed Google Scholar
Sontz, P. A., Bailey, J. B., Ahn, S. & Tezcan, F. (2015). J. Am. Chem. Soc. 137 , 11598. CrossRef CAS Google Scholar
Stumpp, M. T., Forrer, P., Binz, H. & Plückthun, A. (2003). J. Mol. Biol. 332 , 471. CrossRef CAS Google Scholar
Terada, D., Voet, A. R. D., Noguchi, H., Kamata, K., Ohki, M., Addy, C., Fujii, Y., Yamamoto, D., Ozeki, Y., Tame, J. R. H. & Zhang, K. Y. J. (2017). Sci. Rep. 7 , 5943. CrossRef Google Scholar
Touw, W., Baakman, C., Black, J., te Beek, T. A. H., Krieger, E., Joosten, R. & Vriend, G. (2015). Nucleic Acids Res. 43 , D364–D368. CrossRef CAS Google Scholar
Urvoas, A., Guellouz, A., Valerio-Lepiniec, M., Graille, M., Durand, D., Desravines, D. C., van Tilbeurgh, H., Desmadril, M. & Minard, P. (2010). J. Mol. Biol. 404 , 307. CrossRef CAS Google Scholar
Voet, A. R. D., Noguchi, H., Addy, C., Simoncini, D., Terada, D., Unzai, S., Park, S.-Y., Zhang, K. Y. J. & Tame, J. R. H. (2014). Proc. Natl Acad. Sci. USA , 111 , 15102. CrossRef CAS Google Scholar
Voet, A. R. D., Noguchi, H., Addy, C., Zhang, K. Y. J. & Tame, J. R. H. (2015). Angew. Chem. Int. Ed. 54 , 9857. CrossRef CAS Google Scholar
Voet, A. R. D., Simoncini, D., Tame, J. R. H. & Zhang, K. Y. J. (2017). Methods Mol. Biol. 1529 , 309. CrossRef CAS Google Scholar
Wei, H., Wang, Z., Zhang, J., House, S., Gao, Y.-G., Yang, L., Robinson, H., Tan, L., Xing, H., Hou, C., Robertson, I. M., Zuo, J.-M. & Lu, Y. (2011). Nat. Nanotechnol. 6 , 93. CrossRef CAS Google Scholar
Xia, X., Longo, L., Sutherland, M. & Blaber, M. (2016). Protein Sci. 25 , 1227. CrossRef CAS Google Scholar
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Seeman NC: Nanomaterials based on DNA. Annu Rev Biochem. 2010, 79: 65-87. 10.1146/annurev-biochem-060308-102244.
Goodman RP, Schaap IAT, Tardin CF, Erben CM, Berry RM, Schmidt CF, Turberfield AJ: Rapid chiral assembly of rigid DNA building blocks for molecular nanofabrication. Science. 2005, 310: 1661-1665. 10.1126/science.1120367.
He Y, Ye T, Su M, Zhang C, Ribbe AE, Jiang W, Mao CD: Hierarchical self-assembly of DNA into symmetric supramolecular polyhedra. Nature. 2008, 452: 198-U141. 10.1038/nature06597.
Douglas SM, Dietz H, Liedl T, Hogberg B, Graf F, Shih WM: Self-assembly of DNA into nanoscale three-dimensional shapes. Nature. 2009, 459: 414-418. 10.1038/nature08016.
Andersen ES, Dong M, Nielsen MM, Jahn K, Subramani R, Mamdouh W, Golas MM, Sander B, Stark H, Oliveira CL, Pedersen JS, Birkedal V, Besenbacher F, Gothelf KV, Kjems J: Self-assembly of a nanoscale DNA box with a controllable lid. Nature. 2009, 459: 73-76. 10.1038/nature07971.
Linko V, Dietz H: The enabled state of DNA nanotechnology. Curr Opin Biotechnol. 2013, 24: 555-561. 10.1016/j.copbio.2013.02.001.
Simmel FC: DNA-based assembly lines and nanofactories. Curr Opin Biotechnol. 2012, 23: 516-521. 10.1016/j.copbio.2011.12.024.
Zhang DY, Seelig G: Dynamic DNA nanotechnology using strand-displacement reactions. Nat Chem. 2011, 3: 103-113. 10.1038/nchem.957.
Pinheiro AV, Han D, Shih WM, Yan H: Challenges and opportunities for structural DNA nanotechnology. Nat Nanotechnol. 2011, 6: 763-772. 10.1038/nnano.2011.187.
Krishnan Y, Simmel FC: Nucleic acid based molecular devices. Angew Chem. 2011, 50: 3124-3156. 10.1002/anie.200907223.
Lo PK, Metera KL, Sleiman HF: Self-assembly of three-dimensional DNA nanostructures and potential biological applications. Curr Opin Chem Biol. 2010, 14: 597-607. 10.1016/j.cbpa.2010.08.002.
Aldaye FA, Palmer AL, Sleiman HF: Assembling materials with DNA as the guide. Science. 2008, 321: 1795-1799. 10.1126/science.1154533.
Seeman NC: Nucleic acid junctions and lattices. J Theor Biol. 1982, 99: 237-247. 10.1016/0022-5193(82)90002-9.
Chen JH, Seeman NC: Synthesis from DNA of a molecule with the connectivity of a cube. Nature. 1991, 350: 631-633. 10.1038/350631a0.
Ke Y, Ong LL, Shih WM, Yin P: Three-dimensional structures self-assembled from DNA bricks. Science. 2012, 338: 1177-1183. 10.1126/science.1227268.
Shih WM, Quispe JD, Joyce GF: A 1.7-kilobase single-stranded DNA that folds into a nanoscale octahedron. Nature. 2004, 427: 618-621. 10.1038/nature02307.
Ke Y, Sharma J, Liu M, Jahn K, Liu Y, Yan H: Scaffolded DNA origami of a DNA tetrahedron molecular container. Nano Lett. 2009, 9: 2445-2447. 10.1021/nl901165f.
Bell NA, Engst CR, Ablay M, Divitini G, Ducati C, Liedl T, Keyser UF: DNA origami nanopores. Nano Lett. 2012, 12: 512-517. 10.1021/nl204098n.
Graugnard E, Kellis DL, Bui H, Barnes S, Kuang W, Lee J, Hughes WL, Knowlton WB, Yurke B: DNA-controlled excitonic switches. Nano Lett. 2012, 12: 2117-2122. 10.1021/nl3004336.
Wickham SF, Endo M, Katsuda Y, Hidaka K, Bath J, Sugiyama H, Turberfield AJ: Direct observation of stepwise movement of a synthetic molecular transporter. Nat Nanotechnol. 2011, 6: 166-169. 10.1038/nnano.2010.284.
Qian L, Winfree E: Scaling up digital circuit computation with DNA strand displacement cascades. Science. 2011, 332: 1196-1201. 10.1126/science.1200520.
Qian L, Winfree E, Bruck J: Neural network computation with DNA strand displacement cascades. Nature. 2011, 475: 368-372. 10.1038/nature10262.
Yurke B, Turberfield AJ, Mills AP, Simmel FC, Neumann JL: A DNA-fuelled molecular machine made of DNA. Nature. 2000, 406: 605-608. 10.1038/35020524.
Omabegho T, Sha R, Seeman NC: A bipedal DNA Brownian motor with coordinated legs. Science. 2009, 324: 67-71. 10.1126/science.1170336.
Zhang C, Ko SH, Su M, Leng Y, Ribbe AE, Jiang W, Mao C: Symmetry controls the face geometry of DNA polyhedra. J Am Chem Soc. 2009, 131: 1413-1415. 10.1021/ja809666h.
Rothemund PW: Folding DNA to create nanoscale shapes and patterns. Nature. 2006, 440: 297-302. 10.1038/nature04586.
Han D, Pal S, Nangreave J, Deng Z, Liu Y, Yan H: DNA origami with complex curvatures in three-dimensional space. Science. 2011, 332: 342-346. 10.1126/science.1202998.
Douglas SM, Marblestone AH, Teerapittayanon S, Vazquez A, Church GM, Shih WM: Rapid prototyping of 3D DNA-origami shapes with caDNAno. Nucleic Acids Res. 2009, 37: 5001-5006. 10.1093/nar/gkp436.
Kuzuya A, Komiyama M: DNA origami: fold, stick, and beyond. Nanoscale. 2010, 2: 310-322. 10.1039/b9nr00246d.
Shih WM, Lin C: Knitting complex weaves with DNA origami. Curr Opin Struct Biol. 2010, 20: 276-282. 10.1016/j.sbi.2010.03.009.
Wei B, Dai M, Yin P: Complex shapes self-assembled from single-stranded DNA tiles. Nature. 2012, 485: 623-626. 10.1038/nature11075.
Liu Q, Song C, Wang ZG, Li N, Ding B: Precise organization of metal nanoparticles on DNA origami template. Methods. 2013, doi: 10.1016/j.ymeth.2013.10.006
Garibotti AV, Perez-Rentero S, Eritja R: Functionalization and self-assembly of DNA bidimensional arrays. Int J Mol Sci. 2011, 12: 5641-5651. 10.3390/ijms12095641.
Williams BA, Lund K, Liu Y, Yan H, Chaput JC: Self-assembled peptide nanoarrays: an approach to studying protein-protein interactions. Angew Chem. 2007, 46: 3051-3054. 10.1002/anie.200603919.
Lee JB, Roh YH, Um SH, Funabashi H, Cheng W, Cha JJ, Kiatwuthinon P, Muller DA, Luo D: Multifunctional nanoarchitectures from DNA-based ABC monomers. Nat Nanotechnol. 2009, 4: 430-436. 10.1038/nnano.2009.93.
Nakata E, Liew FF, Uwatoko C, Kiyonaka S, Mori Y, Katsuda Y, Endo M, Sugiyama H, Morii T: Zinc-finger proteins for site-specific protein positioning on DNA-origami structures. Angew Chem. 2012, 51: 2421-2424. 10.1002/anie.201108199.
Delebecque CJ, Lindner AB, Silver PA, Aldaye FA: Organization of intracellular reactions with rationally designed RNA assemblies. Science. 2011, 333: 470-474. 10.1126/science.1206938.
Conrado RJ, Wu GC, Boock JT, Xu H, Chen SY, Lebar T, Turnsek J, Tomsic N, Avbelj M, Gaber R, Koprivnjak T, Mori J, Glavnik V, Vovk I, Bencina M, Hodnik V, Anderluh G, Dueber JE, Jerala R, Delisa MP: DNA-guided assembly of biosynthetic pathways promotes improved catalytic efficiency. Nucleic Acids Res. 2012, 40: 1879-1889. 10.1093/nar/gkr888.
Li Z, Wei B, Nangreave J, Lin C, Liu Y, Mi Y, Yan H: A replicable tetrahedral nanostructure self-assembled from a single DNA strand. J Am Chem Soc. 2009, 131: 13093-13098. 10.1021/ja903768f.
Sobczak JP, Martin TG, Gerling T, Dietz H: Rapid folding of DNA into nanoscale shapes at constant temperature. Science. 2012, 338: 1458-1461. 10.1126/science.1229919.
Douglas SM, Bachelet I, Church GM: A logic-gated nanorobot for targeted transport of molecular payloads. Science. 2012, 335: 831-834. 10.1126/science.1214081.
Liu X, Xu Y, Yu T, Clifford C, Liu Y, Yan H, Chang Y: A DNA nanostructure platform for directed assembly of synthetic vaccines. Nano Lett. 2012, 12: 4254-4259. 10.1021/nl301877k.
Mohri K, Nishikawa M, Takahashi N, Shiomi T, Matsuoka N, Ogawa K, Endo M, Hidaka K, Sugiyama H, Takahashi Y, Takakura Y: Design and development of nanosized DNA assemblies in polypod-like structures as efficient vehicles for immunostimulatory CpG motifs to immune cells. ACS nano. 2012, 6: 5931-5940. 10.1021/nn300727j.
Li J, Pei H, Zhu B, Liang L, Wei M, He Y, Chen N, Li D, Huang Q, Fan C: Self-assembled multivalent DNA nanostructures for noninvasive intracellular delivery of immunostimulatory CpG oligonucleotides. ACS nano. 2011, 5: 8783-8789. 10.1021/nn202774x.
Kuhlman B, Dantas G, Ireton GC, Varani G, Stoddard BL, Baker D: Design of a novel globular protein fold with atomic-level accuracy. Science. 2003, 302: 1364-1368. 10.1126/science.1089427.
Fleishman SJ, Whitehead TA, Ekiert DC, Dreyfus C, Corn JE, Strauch EM, Wilson IA, Baker D: Computational design of proteins targeting the conserved stem region of influenza hemagglutinin. Science. 2011, 332: 816-821. 10.1126/science.1202617.
Regan L, Degrado WF: Characterization of a helical protein designed from 1st principles. Science. 1988, 241: 976-978. 10.1126/science.3043666.
Hecht MH, Richardson JS, Richardson DC, Ogden RC: Denovo design, expression, and characterization of felix - a 4-helix bundle protein of native-like sequence. Science. 1990, 249: 884-891. 10.1126/science.2392678.
Dill KA, MacCallum JL: The protein-folding problem, 50 years on. Science. 2012, 338: 1042-1046. 10.1126/science.1219021.
Levy ED, Boeri Erba E, Robinson CV, Teichmann SA: Assembly reflects evolution of protein complexes. Nature. 2008, 453: 1262-1265. 10.1038/nature06942.
Yeates TO: Nanobiotechnology: protein arrays made to order. Nat Nanotechnol. 2011, 6: 541-542. 10.1038/nnano.2011.127.
Lai YT, King NP, Yeates TO: Principles for designing ordered protein assemblies. Trends Cell Biol. 2012, 22: 653-661. 10.1016/j.tcb.2012.08.004.
Bozic S, Doles T, Gradisar H, Jerala R: New designed protein assemblies. Curr Opin Chem Biol. 2013, 17: 940-945. 10.1016/j.cbpa.2013.10.014.
Gradisar H, Bozic S, Doles T, Vengust D, Hafner-Bratkovic I, Mertelj A, Webb B, Sali A, Klavzar S, Jerala R: Design of a single-chain polypeptide tetrahedron assembled from coiled-coil segments. Nat Chem Biol. 2013, 9: 362-366. 10.1038/nchembio.1248.
Padilla JE, Colovos C, Yeates TO: Nanohedra: using symmetry to design self assembling protein cages, layers, crystals, and filaments. Proc Natl Acad Sci U S A. 2001, 98: 2217-2221. 10.1073/pnas.041614998.
Lai YT, Cascio D, Yeates TO: Structure of a 16-nm cage designed by using protein oligomers. Science. 2012, 336: 1129-10.1126/science.1219351.
Lai YT, Tsai KL, Sawaya MR, Asturias FJ, Yeates TO: Structure and flexibility of nanoscale protein cages designed by symmetric self-assembly. J Am Chem Soc. 2013, 135: 7738-7743. 10.1021/ja402277f.
Doles T, Bozic S, Gradisar H, Jerala R: Functional self-assembling polypeptide bionanomaterials. Biochem Soc Trans. 2012, 40: 629-634. 10.1042/BST20120025.
Sinclair JC, Davies KM, Venien-Bryan C, Noble ME: Generation of protein lattices by fusing proteins with matching rotational symmetry. Nat Nanotechnol. 2011, 6: 558-562. 10.1038/nnano.2011.122.
Grueninger D, Treiber N, Ziegler MO, Koetter JW, Schulze MS, Schulz GE: Designed protein-protein association. Science. 2008, 319: 206-209. 10.1126/science.1150421.
Leaver-Fay A, Tyka M, Lewis SM, Lange OF, Thompson J, Jacak R, Kaufman K, Renfrew PD, Smith CA, Sheffler W, Davis IW, Cooper S, Treuille A, Mandell DJ, Richter F, Ban YE, Fleishman SJ, Corn JE, Kim DE, Lyskov S, Berrondo M, Mentzer S, Popović Z, Havranek JJ, Karanicolas J, Das R, Meiler J, Kortemme T, Gray JJ, Kuhlman B: ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. Methods Enzymol. 2011, 487: 545-574.
King NP, Sheffler W, Sawaya MR, Vollmar BS, Sumida JP, Andre I, Gonen T, Yeates TO, Baker D: Computational design of self-assembling protein nanomaterials with atomic level accuracy. Science. 2012, 336: 1171-1174. 10.1126/science.1219364.
Apostolovic B, Danial M, Klok HA: Coiled coils: attractive protein folding motifs for the fabrication of self-assembled, responsive and bioactive materials. Chem Soc Rev. 2010, 39: 3541-3575. 10.1039/b914339b.
Woolfson DN, Ryadnov MG: Peptide-based fibrous biomaterials: some things old, new and borrowed. Curr Opin Chem Biol. 2006, 10: 559-567. 10.1016/j.cbpa.2006.09.019.
Bromley EH, Channon K, Moutevelis E, Woolfson DN: Peptide and protein building blocks for synthetic biology: from programming biomolecules to self-organized biomolecular systems. ACS Chem Biol. 2008, 3: 38-50. 10.1021/cb700249v.
Woolfson DN, Bartlett GJ, Bruning M, Thomson AR: New currency for old rope: from coiled-coil assemblies to alpha-helical barrels. Curr Opin Struct Biol. 2012, 22: 432-441. 10.1016/j.sbi.2012.03.002.
Ghadiri MR, Granja JR, Buehler LK: Artificial transmembrane ion channels from self-assembling peptide nanotubes. Nature. 1994, 369: 301-304. 10.1038/369301a0.
Woolfson DN: The design of coiled-coil structures and assemblies. Adv Protein Chem. 2005, 70: 79-112.
Mason JM, Muller KM, Arndt KM: Considerations in the design and optimization of coiled coil structures. Methods Mol Biol. 2007, 352: 35-70.
Gradisar H, Jerala R: De novo design of orthogonal peptide pairs forming parallel coiled-coil heterodimers. J Pept Sci. 2011, 17: 100-106. 10.1002/psc.1331.
Fletcher JM, Boyle AL, Bruning M, Bartlett GJ, Vincent TL, Zaccai NR, Armstrong CT, Bromley EH, Booth PJ, Brady RL, Thomson AR, Woolfson DN: A basis set of de novo coiled-coil Peptide oligomers for rational protein design and synthetic biology. ACS Synth Biol. 2012, 1: 240-250. 10.1021/sb300028q.
Thompson KE, Bashor CJ, Lim WA, Keating AE: SYNZIP protein interaction toolbox: in vitro and in vivo specifications of heterospecific coiled-coil interaction domains. ACS Synth Biol. 2012, 1: 118-129. 10.1021/sb200015u.
Zaccai NR, Chi B, Thomson AR, Boyle AL, Bartlett GJ, Bruning M, Linden N, Sessions RB, Booth PJ, Brady RL, Woolfson DN: A de novo peptide hexamer with a mutable channel. Nat Chem Biol. 2011, 7: 935-941. 10.1038/nchembio.692.
Thomas F, Boyle AL, Burton AJ, Woolfson DN: A Set of de novo designed parallel heterodimeric coiled coils with quantified dissociation constants in the micromolar to Sub-nanomolar regime. J Am Chem Soc. 2013, 135: 5161-5166. 10.1021/ja312310g.
Bromley EH, Sessions RB, Thomson AR, Woolfson DN: Designed alpha-helical tectons for constructing multicomponent synthetic biological systems. J Am Chem Soc. 2009, 131: 928-930. 10.1021/ja804231a.
Ryadnov MG, Woolfson DN: Engineering the morphology of a self-assembling protein fibre. Nat Mater. 2003, 2: 329-332. 10.1038/nmat885.
Dong H, Paramonov SE, Hartgerink JD: Self-assembly of alpha-helical coiled coil nanofibers. J Am Chem Soc. 2008, 130: 13691-13695. 10.1021/ja8037323.
Peng X, Jin J, Nakamura Y, Ohno T, Ichinose I: Ultrafast permeation of water through protein-based membranes. Nat Nanotechnol. 2009, 4: 353-357. 10.1038/nnano.2009.90.
Ueda M, Makino A, Imai T, Sugiyama J, Kimura S: Rational design of peptide nanotubes for varying diameters and lengths. J Pept Sci. 2011, 17: 94-99. 10.1002/psc.1304.
Knowles TP, Oppenheim TW, Buell AK, Chirgadze DY, Welland ME: Nanostructured films from hierarchical self-assembly of amyloidogenic proteins. Nat Nanotechnol. 2010, 5: 204-207. 10.1038/nnano.2010.26.
Gour N, Mondal S, Verma S: Synthesis and self-assembly of a neoglycopeptide: morphological studies and ultrasound-mediated DNA encapsulation. J Pept Sci. 2011, 17: 148-153. 10.1002/psc.1334.
Banwell EF, Abelardo ES, Adams DJ, Birchall MA, Corrigan A, Donald AM, Kirkland M, Serpell LC, Butler MF, Woolfson DN: Rational design and application of responsive alpha-helical peptide hydrogels. Nat Mater. 2009, 8: 596-600. 10.1038/nmat2479.
Banta S, Wheeldon IR, Blenner M: Protein engineering in the development of functional hydrogels. Annu Rev Biomed Eng. 2010, 12: 167-186. 10.1146/annurev-bioeng-070909-105334.
Fletcher JM, Harniman RL, Barnes FR, Boyle AL, Collins A, Mantell J, Sharp TH, Antognozzi M, Booth PJ, Linden N, Miles MJ, Sessions RB, Verkade P, Woolfson DN: Self-assembling cages from coiled-coil peptide modules. Science. 2013, 340: 595-599. 10.1126/science.1233936.
Raman S, Machaidze G, Lustig A, Aebi U, Burkhard P: Structure-based design of peptides that self-assemble into regular polyhedral nanoparticles. Nanomedicine. 2006, 2: 95-102. 10.1016/j.nano.2006.04.007.
Boyle AL, Bromley EH, Bartlett GJ, Sessions RB, Sharp TH, Williams CL, Curmi PM, Forde NR, Linke H, Woolfson DN: Squaring the circle in peptide assembly: from fibers to discrete nanostructures by de novo design. J Am Chem Soc. 2012, 134: 15457-15467. 10.1021/ja3053943.
Fowler PW, Pickup BT, Todorova TZ, Pisanski T: Fragment analysis of single-molecule conduction. J Chem Phys. 2009, 130: 174708-10.1063/1.3124828.
Der BS, Kuhlman B: Cages from coils. Nat Biotechnol. 2013, 31: 809-810. 10.1038/nbt.2670.
There is active research ongoing to uncover the mechanisms by which disease-associated proteins misfold, aggregate, and cause cellular toxicity. Continued progress in our ability to interrogate amyloid-forming proteins and their interactions with other cellular proteins provide confidence that novel therapies will be identified for multiple disease states. Therapeutic options now being explored include targeting misfolded protein-chaperone interactions at various points in the proteostatic pathway, promoting protein clearance, and large-scale rebalancing of proteostatic network. However, the identification and in vivo validation of new therapeutic compounds is impeded by the shortage of known disease drivers and the lack of reliable biomarkers for monitoring therapeutic responses in relevant animal models. However, the increase in cooperative research and collaboration among the drug discovery community (pharmaceutical companies, foundations, academia, contract research organizations, clinicians, regulatory agencies, advocacy groups and patients) is a positive shift that can help accelerate the identification of novel therapeutic modalities.
S1 Fig. Architecture of the deep 3D convolutional neural network model and hyperparameter search.
(A) The overall organization of the model begins with the input tensor and ends with a final layer that outputs ΔΔG prediction. The numbers in parentheses before the Flatten layer represent the dimensionality of the output from each layer in the format (width, height, depth, property channels). The number in parentheses starting from the Flatten layer represent the number of output features from each of the densely connected layers. This optimized architecture was determined through cross-validation. (B) Results from cross validating the sizes of the convolutional layers while keeping the size of the densely connected layer at 32 neurons. (C) Results from cross validating the size of the densely connected layer while keeping the sizes of the convolutional layers at (16, 24, 32). (D) Results from cross validating the dimensions of the input grid.
S2 Fig. Pairwise percent sequence identity of the proteins in the S2648 and VariBench data sets.
(A) A heatmap representation of the pairwise percent sequence identity matrix of the proteins in the S2648 data set. (B) A heatmap representation of the pairwise percent sequence identity matrix of the proteins in the VariBench data set. It is obvious from these two heatmaps that there is substantial pairwise homology (percent identity > 25%) in both S2648 and VariBench. The pairwise identity matrices were obtained using the Clustal Omega multiple sequence alignment program .
S3 Fig. CNNs trained using only direct mutations show a large prediction bias.
(A) Performance of an ensemble of ten networks trained using only the set of 1,744 direct mutations on predicting the ΔΔGs of the direct mutations in the blind test set The Pearson correlation coefficient (r) between predicted values and experimentally determined values is 0.47, and the root-mean-square deviation (σ) of predicted values from experimentally determined values is 1.38 kcal/mol. (B) Performance of the same ensemble of ten networks on predicting the ΔΔGs of the reverse mutations in the blind test set The Pearson correlation coefficient (r) between predicted values and experimentally determined values is -0.06, and the root-mean-square deviation (σ) of predicted values from experimentally determined values is 2.40 kcal/mol. (C) Direct versus reverse ΔΔG values of all the mutations in the blind test set predicted by the same ensemble of networks. (B) and (C) highlight that the models trained with only direct mutations have a large bias and, when compared to the models trained using the balanced data set, the necessity of adding reverse mutations to correct the bias. The dots are colored in gradient from blue to red such that blue represents the most accurate prediction and red represents the least accurate.
Figure 2. Design of acidic-(HhH)2. Symmetric-(HhH)2 is a fully symmetric protein constructed in a previous study to understand the properties of ancient protein forms.(27) Derived from a family of dsDNA binding proteins, it is positively charged at neutral pH. To generate a hyperacidic model protein for this study, all lysine residues in symmetric-(HhH)2 were substituted with glutamate (see Results for more details). The conserved loop residues between the two helices of the HhH motif (PGIGP) are underlined, and the linker residues (GSVE) between the two HhH motifs are rendered in italics in the sequence and colored magenta in the structural model. The C-terminus of acidic-(HhH)2 bears a 6xHis tag connected by a two-residue Leu-Glu linker (not shown).