Learning to Speak the Language of Proteins

Total Page:16

File Type:pdf, Size:1020Kb

Learning to Speak the Language of Proteins P ERSPECTIVES ic crystal quantum cas- 450 cade lasers are likely to CW Radiation cone ω BZ boundary InP be very desirable, for ex- Pulsed ample, in imaging appli- CW GaAs cations. More important- 300 Pulsed ly, substantial efforts are currently invested in pur- suing the realization of quantum cascade lasers mperature (K) 150 Te ω = ω at near-infrared telecom- 0 munication wavelengths. k Their property of being 0 y easily modulated at very 051020 50 100 k high speed would be very Wavelength (µm) x useful in high-throughput Quantum cascade successes. (Left) Maximum operating temperatures of the best existing quantum cascade lasers as data transmission. In this a function of their emission wavelength. (Right) Frequency ω of a representative waveguide mode (red line) as a func- respect, device integration tion of its wave vector kxy.The photonic crystal structure restricts the allowed wave vectors to the Brillouin zone (BZ), ω and vertical coupling of whose extension depends on the crystal periodicity. The portion of the (kxy) curve that, in the absence of the photon- the light would be as es- ic crystal, would be outside the BZ (dashed line) is “reflected” into the BZ, creating a photonic band. With this trick, a ω sential as for convention- guided mode of frequency 0 (yellow dot), which would otherwise have a large wave vector, can instead be formed in- ω al interband lasers. side the cone > c kxy (the “radiation cone”). This is necessary for obtaining radiation orthogonal to the x-y plane. Finally, photonic crys- tals could serve as waveguides in future fabrication technology. And, as we can see References cascade lasers realized in structures with from (1), their mainly planar emission is a 1. R. Colombelli et al., Science 302, 1374 (2003); published online 30 October 2003 (10.1126/ lower dimensionality, such as nanowhisk- perfect match for simple two-dimensional science.1090561). ers (11), which should provide better configurations. 2. J. Faist et al., Science 264, 553 (1994). threshold and temperature performances. But quantum cascade lasers offer even 3. F. Capasso et al., Phys. Today 55 No. 5, 34 (2002). One should also ask the reverse ques- more. As Colombelli et al. show (1), they 4. E. Yablonovitch, Phys. Rev. Lett. 58, 2059 (1987). 5. J. D. Joannopoulos, P. R. Villeneuve, S. H. Fan, Nature on June 17, 2008 tion: What can quantum cascade lasers can operate using surface-plasmon waves. 386, 143 (1997). give to photonic crystal science? They may Surface-plasmon photonic crystals may 6. M. Beck et al., Science 295, 301 (2002). provide the ideal laboratory system for de- enable the miniaturization of photonic cir- 7. R. Köhler et al., Nature 417, 156 (2002). 8. E. Cubukcu et al., Nature 423, 604 (2003). veloping new physics and device concepts. cuits, with great promise for subwave- 9. P. Russel, Science 299, 358 (2003). Because only electrons are involved, they length optics, data storage, and mi- 10. H. Benisty et al., in Photonic Crystals and Light do not suffer from surface recombination, croscopy (12). The availability of sources Localization,C.M.Soukoulis, Ed. (Kluwer, Dordrecht, emitting in surface-plasmon photonic Netherlands, 2001), pp. 117–128. and injection photonic crystal devices are 11. L. Samuelson, Mater. Today 6, 22 (2003). easily made. Furthermore, they can operate crystal modes will further fuel this area of 12. W. L. Barnes, A. Dereux, T. W. Ebbesen, Nature 424, at long wavelengths, greatly simplifying research. 824 (2003). www.sciencemag.org STRUCTURAL BIOLOGY Then, they synthesized their new protein and analyzed its unique folded structure by Learning to Speak nuclear magnetic resonance spectroscopy and x-ray crystallography. The final x-ray structure of the protein turned out to be an the Language of Proteins almost exact copy of the structure modeled Downloaded from by the computer. The Kuhlman et al. work David T. Jones is a clear demonstration that structural bi- ologists have mastered the first stage of nderstanding the folding of proteins learner develops basic conversational learning the language of protein folding. can be likened to learning to speak a skills. Finally, after a great deal of practice, As with most languages, the language Unew language. Learning a language the learner begins to understand the actual of proteins is based on a relatively simple can be a long and difficult process. For syntax of the language, and the process is alphabet. In the case of most naturally oc- both young children and travelers in for- almost complete. Applying this analogy to curring proteins, an alphabet of just 20 let- eign countries, the first breakthrough in the report on page 1364 of this issue, we ters corresponding to 20 amino acids is learning a language is the ability to speak a can say that Kuhlman et al. (1) have spoken enough to construct an astonishing variety few important phrases, such as “I’m hun- a new sentence in the complex language of of protein structures that carry out a stag- gry!” or “Where is the railway station?” protein folding and have been understood gering array of biochemical processes. The and to be clearly understood. Eventually, perfectly. so-called protein-folding problem that has by uttering more and more of these phras- These investigators have successfully preoccupied structural biologists for more es and listening to the resulting replies, the created a new protein from scratch. Using than four decades can be most simply ex- an iterative computer program that opti- plained as the problem of discovering how mizes both sequence design and structure simple strings of 20 amino acids can en- The author is in the Department of Computer Science prediction, they have created a 93-residue code the complex three-dimensional (3D) and Department of Biochemistry and Molecular α β Biology, University College, London WC1E 6BT, UK. / protein that they call Top7, which has folded structures of proteins. Solving the CREDIT: LEFT PANEL, S.AND J. BLASER FAIST, NEUCHÂTEL UNIVERSITY E-mail: [email protected] a unique, as yet unobserved, topology. protein-folding problem implies that we www.sciencemag.org SCIENCE VOL 302 21 NOVEMBER 2003 1347 P ERSPECTIVES Kuhlman et al. (1): would have the ability to read any amino Novel protein fold acid sequence and deduce the correct na- tive folded structure for that protein. Even with recent progress (2), a complete solu- 93 tion to this problem lies somewhere in the future. Despite the difficulty in going di- rectly from sequence to structure, a number of researchers have been interested in the Harbury et al. (9):) inverse problem (3–5): deducing an amino Right-handed acid sequence that will, when synthesized, coiled coil self-assemble into a single desired 3D structure. Unlike the protein-folding prob- lem, which has only one desired solution (the native folded state of the protein), the Dahiyyy(at and Mayo (8):) Protein chain length Zinc finger motif inverse case is likely to have many solu- 35 tions. There are many examples of pairs of proteins that fold in a strikingly similar way, but which have no evident similarity in their amino acid sequences (6). 28 Statistically at least, solving the inverse protein-folding problem with its many po- tential solutions should be easier than solv- ing the protein-folding problem, which has 1997 1998 2003 usually just a single correct solution. Year The earliest methods for designing pro- Protein design: The future is now. A timeline showing progress over the last 6 years in designing teins focused on the problem of building a new proteins using computer programs and then validating their synthesized versions experimen- hydrophobic core for a fixed backbone tally with techniques such as x-ray crystallography and NMR spectroscopy (1). [Images of structures structure. Because even single mutations were prepared using Swiss-PdbViewer.] usually result in conformational changes in on June 17, 2008 the protein backbone, these very restricted the target structure in atomic detail. steps together with essential experimental models were never likely to be successful. Despite this impressive result, the design of validation. We need to know whether the Probably the most useful idea to come out complete proteins has been restricted to methodology of Kuhlman and colleagues of these early methods was that of the side- short polypeptides with no complex fold- can be applied successfully to other design chain rotamer (7). Although most side ing topology. By designing a large complex problems, and we may in fact learn more chains have a lot of conformational flexi- folded protein from scratch and validating from failed attempts to apply their methods bility, they nonetheless have conformation- its structure in atomic detail, Kuhlman and than from the successes. The cycle of hy- al preferences, which can be expressed as a colleagues have taken the next leap forward pothesis, experimentation, and comparison relatively short list of likely conformations (see the figure). is the cornerstone of most scientific re- www.sciencemag.org for each of the 20 amino acids. Despite the It is tempting to lapse into futurology and search. Combining new protein design al- limitations of the fixed backbone assump- hold up the design of Top7 as the first step gorithms with structural genomics tech- tion, a number of groups used it to design toward a new world of disease treatments niques and high-throughput structure de- plausible protein sequences, which were at and manufacturing processes. However, termination should drive progress in under- least partially validated experimentally, and such a prospect may not be borne out by our standing how proteins fold and, perhaps, in one or two cases, fully validated. Most existing knowledge of the protein world. even how they evolve.
Recommended publications
  • Recent Advances in Automated Protein Design and Its Future
    EXPERT OPINION ON DRUG DISCOVERY 2018, VOL. 13, NO. 7, 587–604 https://doi.org/10.1080/17460441.2018.1465922 REVIEW Recent advances in automated protein design and its future challenges Dani Setiawana, Jeffrey Brenderb and Yang Zhanga,c aDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA; bRadiation Biology Branch, Center for Cancer Research, National Cancer Institute – NIH, Bethesda, MD, USA; cDepartment of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA ABSTRACT ARTICLE HISTORY Introduction: Protein function is determined by protein structure which is in turn determined by the Received 25 October 2017 corresponding protein sequence. If the rules that cause a protein to adopt a particular structure are Accepted 13 April 2018 understood, it should be possible to refine or even redefine the function of a protein by working KEYWORDS backwards from the desired structure to the sequence. Automated protein design attempts to calculate Protein design; automated the effects of mutations computationally with the goal of more radical or complex transformations than protein design; ab initio are accessible by experimental techniques. design; scoring function; Areas covered: The authors give a brief overview of the recent methodological advances in computer- protein folding; aided protein design, showing how methodological choices affect final design and how automated conformational search protein design can be used to address problems considered beyond traditional protein engineering, including the creation of novel protein scaffolds for drug development. Also, the authors address specifically the future challenges in the development of automated protein design. Expert opinion: Automated protein design holds potential as a protein engineering technique, parti- cularly in cases where screening by combinatorial mutagenesis is problematic.
    [Show full text]
  • Structure-Function Relationship of Archaeal Rhodopsin Proteins Analyzed by Continuum Electrostatics
    Structure-Function Relationship of Archaeal Rhodopsin Proteins analyzed by Continuum Electrostatics DISSERTATION submitted to the Faculty of Biology, Chemistry and Geoscience of the University of Bayreuth, Germany for obtaining the degree of Doctor of Natural Sciences presented by Edda Kloppmann born in Luneburg,¨ Germany Bayreuth 2010 Vollstandiger¨ Abdruck der von der Fakultat¨ fur¨ Biologie, Chemie und Geowissenschaften der Universitat¨ Bayreuth genehmigten Disserta- tion zur Erlangung des akademischen Grades Doktor der Naturwis- ” senschaften (Dr. rer. nat.)“. Die vorliegende Arbeit wurde im Zeitraum von November 2002 bis November 2003 am IWR Heidelberg und von Dezember 2003 bis April 2007 am Lehrstuhl fur¨ Biopolymere an der Universitat¨ Bayreuth unter der Leitung von Professor G. Matthias Ullmann erstellt. Hiermit erklare¨ ich, dass ich die vorliegende Arbeit selbststandig¨ ver- fasst und keine anderen als die von mir angegebenen Quellen und Hilfsmittel verwendet habe. Ferner erklare¨ ich, dass ich nicht bereits anderweitig mit oder ohne Er- folg versucht habe, eine Dissertation einzureichen oder mich der Dok- torprufung¨ zu unterziehen. Bayreuth, den 13.01.2010 Prufungsausschuss:¨ 1. Gutachter: Prof. Dr. G. Matthias Ullmann 2. Gutachter: Prof. Dr. Franz X. Schmid Prufungsvorsitz:¨ Prof. Dr. Thomas Hellweg Prof. Dr. Benedikt Westermann Einreichung des Promotionsgesuchs: 13.01.2010 Kolloqium: 04.05.2010 Structure-Function Relationship of Archaeal Rhodopsin Proteins analyzed by Continuum Electrostatics ABSTRACT Rhodopsin proteins perform two cellular key functions: signaling of external stimuli and ion transport. Examples of both functional types are found in the family of archaeal rho- dopsins, namely the proton pump bacteriorhodopsin, the chloride pump halorhodopsin and the photoreceptor sensory rhodopsin II. For these three membrane proteins, high- resolution X-ray structures are available, allowing a theoretical investigation in atomic detail.
    [Show full text]
  • Precise Assembly of Complex Beta Sheet Topologies from De Novo Designed Building Blocks
    SHORT REPORT Precise assembly of complex beta sheet topologies from de novo designed building blocks Indigo Chris King1*†, James Gleixner1, Lindsey Doyle2, Alexandre Kuzin3, John F Hunt3, Rong Xiao4, Gaetano T Montelione4, Barry L Stoddard2, Frank DiMaio1, David Baker1 1Institute for Protein Design, University of Washington, Seattle, United States; 2Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, United States; 3Biological Sciences, Northeast Structural Genomics Consortium, Columbia University, New York, United States; 4Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Northeast Structural Genomics Consortium, Rutgers, The State University of New Jersey, Piscataway, United States Abstract Design of complex alpha-beta protein topologies poses a challenge because of the large number of alternative packing arrangements. A similar challenge presumably limited the emergence of large and complex protein topologies in evolution. Here, we demonstrate that protein topologies with six and seven-stranded beta sheets can be designed by insertion of one de novo designed beta sheet containing protein into another such that the two beta sheets are merged to form a single extended sheet, followed by amino acid sequence optimization at the newly formed strand-strand, strand-helix, and helix-helix interfaces. Crystal structures of two such *For correspondence: chrisk1@ uw.edu designs closely match the computational design models. Searches for similar structures in the SCOP protein domain database yield only weak matches with different beta sheet connectivities. A † Present address: Molecular similar beta sheet fusion mechanism may have contributed to the emergence of complex beta Engineering and Sciences, sheets during natural protein evolution. University of Washington, DOI: 10.7554/eLife.11012.001 Seattle, United States Competing interests: The authors declare that no competing interests exist.
    [Show full text]
  • New Fold Prediction Be Easily Detected from the Sequence
    09/07/02 Methods for protein structure prediction Computergestützte Strukturbiologie Methods are distinguished according to the relationship between (Strukturelle Bioinformatik) the target protein(s) and proteins of known structure: • Comparative modeling: A clear evolutionary relationship between the target and a protein of known structure can New fold prediction be easily detected from the sequence. • Fold recognition: The structure of the target turns out to be related to that of a protein of known structure although the relationship is difficult, or impossible, to detect from the sequences. Sommersemester 2009 • New fold prediction: Neither the sequence nor the structure of the target protein are similar to that of a known protein. Peter Güntert Scheme of protein CASP: Fragment-based predictions structure predicition Degenerate sequence-to-structure Fragment-based approaches relationship • Rosetta (David Baker) • Fragfold (David Jones) 1 09/07/02 Steps of fragment-based structure Fragment prediction search • Split sequence into fragments • For each fragment, search the database of known structures for regions with a similar sequence (“neighbors”) • Use an optimization technique to find the best combination of fragments Distance between target and template ROSETTA: fragment sequences in Rosetta Distance between 9 20 fragments dist = ∑∑S(aa,i) − X (aa,i) i=1 aa=1 • S(aa,i) and X(aa,i) are the frequencies of the amino acid in position i = 1,…,9 of the target and template nine-residue sequences or alignments. • The 25 closest “neighbors”
    [Show full text]
  • Introduction of a Polar Core Into the De Novo Designed Protein Top7
    Introduction of a polar core into the de novo designed protein Top7 Benjamin Basanta,1,2,3 Kui K. Chan,4 Patrick Barth,5,6,7 Tiffany King,8 Tobin R. Sosnick,8,9 James R. Hinshaw,10 Gaohua Liu,11,12 John K. Everett,11,12 Rong Xiao,11,12 Gaetano T. Montelione,11,12,13 and David Baker1,2,14* 1Department of Biochemistry, University of Washington, Seattle, Washington 98195 2Institute for Protein Design, University of Washington, Seattle, Washington 98195 3Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, Washington 98195, USA 4Enzyme Engineering, EnzymeWorks, California 92121 5Structural and Computational Biology and Molecular Biophysics Graduate Program, Baylor College of Medicine, Houston, Texas 77030 6Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas 77030 7Department of Pharmacology Baylor College of Medicine, Houston, Texas 77030 8Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, Illinois 60637 9Institute for Biophysical Dynamics, University of Chicago, Chicago, Illinois 60637 10Department of Chemistry, University of Chicago, Chicago, Illinois 60637 11Department of Molecular Biology and Biochemistry, Center of Advanced Biotechnology and Medicine, The State University of New Jersey, Piscataway, New Jersey 08854 12Northeast Structural Genomics Consortium, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854 13Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, the State University of New Jersey, Piscataway, New Jersey 08854 14Howard Hughes Medical Institute, University of Washington, Seattle, Washington 98195 Received 2 October 2015; Revised 4 February 2016; Accepted 8 February 2016 DOI: 10.1002/pro.2899 Published online 00 Month 2016 proteinscience.org Abstract: Design of polar interactions is a current challenge for protein design.
    [Show full text]
  • Protein Sequence Design by Explicit Energy Landscape Optimization
    bioRxiv preprint doi: https://doi.org/10.1101/2020.07.23.218917; this version posted July 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Protein sequence design by explicit energy landscape optimization 1,2,# 1,2,# 1,2,5 6 1,2 1,2 ​ ​ ​ ​ ​ ​ Christoffer Norn , Basile I. M. Wicky , David Juergens , Sirui Liu , David Kim , Brian Koepnick , Ivan ​ ​ ​ ​ ​ ​ ​ 1,2 7 1,2,4,* 3,6,* Anishchenko ,​ Foldit Players ,​ David Baker ,​ Sergey Ovchinnikov ​ ​ ​ ​ 1 ​ Department of Biochemistry, University of Washington, Seattle, WA 98105 2 ​ Institute for Protein Design, University of Washington, Seattle, WA 98105 3 ​ John Harvard Distinguished Science Fellowship Program, Harvard University, Cambridge, MA 02138 4 ​ Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105 5 Molecular​ Engineering and Sciences Institute, University of Washington, Seattle, WA 98105 6 FAS​ Division of Science, Harvard University, Cambridge, MA 02138 7 A​ list of participants will appear in the supplementary information #These authors contributed equally. *Co-corresponding authors: [email protected], [email protected] Abstract The protein design problem is to identify an amino acid sequence which folds to a desired structure. Given Anfinsen’s thermodynamic hypothesis of folding, this can be recast as finding an amino acid sequence for which the lowest
    [Show full text]
  • Deep Learning Techniques Have Significantly Impacted Protein
    Available online at www.sciencedirect.com ScienceDirect Deep learning techniques have significantly impacted protein structure prediction and protein design 1 1,2 Robin Pearce and Yang Zhang Protein structure prediction and design can be regarded as two diagnosis, has recently made a significant impact on inverse processes governed by the same folding principle. protein structure prediction and design. In this review, Although progress remained stagnant over the past two we will highlight methods used for protein structure decades, the recent application of deep neural networks to prediction and protein design, as well as the impact spatial constraint prediction and end-to-end model training has brought about by deep learning on these fields, where significantly improved the accuracy of protein structure a particular emphasis will be put on developments that prediction, largely solving the problem at the fold level for have occurred within the past few years. single-domain proteins. The field of protein design has also witnessed dramatic improvement, where noticeable examples Protein structure prediction and impact have shown that information stored in neural-network models brought about by deep learning can be used to advance functional protein design. Thus, The goal of protein structure prediction is to use compu- incorporation of deep learning techniques into different steps of tational methods to determine the spatial location of protein folding and design approaches represents an exciting every atom in a protein molecule starting from its amino future direction and should continue to have a transformative acid sequence. Depending on whether a template struc- impact on both fields. ture is used, protein structure prediction approaches can be generally categorized as either template-based model- Addresses ing (TBM) or template-free modeling (FM) methods.
    [Show full text]
  • Computer-Based Design of B-Sheet Containing Proteins
    COMPUTER-BASED DESIGN OF B-SHEET CONTAINING PROTEINS Doo Nam Kim A dissertation submitted to the faculty at the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Biochemistry and Biophysics Chapel Hill 2016 Approved by: Brian Kuhlman Qi Zhang Andrew Lee Harold Erickson Jane Richardson © 2016 Doo Nam Kim ALL RIGHTS RESERVED ii ABSTRACT Doo Nam Kim: Computer-based Design of β-sheet Containing Proteins (Under the direction of Brian Kuhlman) Protein design is an excellent test of the minimal determinants of protein structure. Although 70% of naturally occurring proteins contain β-sheets, most previous design efforts have been limited to α-helix bundle proteins or the redesign of naturally occurring proteins. Here, we test and develop computer- based methods for designing proteins rich in β-strands. The molecular modeling program Rosetta was used for three separate design tasks: (1) the design of α/β and α+β proteins with a new method called SEWING, which builds proteins from pieces of naturally occurring proteins, (2) the stabilization of β- sheet proteins via the redesign of surface-facing residues, and (3) the de novo design of β-sandwich proteins. This research showed that it is possible to extend the SEWING method to non-α-helix proteins, allowing the incorporation of structural features found in nature, and that it is possible to dramatically boost protein thermal stability (> 25oC) with the redesign β-sheet surfaces. However, we also found that the de novo design of β-sandwich proteins still remains an elusive goal.
    [Show full text]
  • Protein Structure Determination Using Chemical Shifts Arxiv:1405.3642V1 [Physics.Chem-Ph] 24 Apr 2014
    FACULTY OF SCIENCE UNIVERSITY OF COPENHAGEN PhD Thesis Anders S. Christensen Protein Structure Determination Using Chemical Shifts arXiv:1405.3642v1 [physics.chem-ph] 24 Apr 2014 Academic supervisor: Jan H. Jensen November 13, 2017 Acknowledgements This thesis represents the work I have carried out as a PhD student under the supervision of Professor Jan H. Jensen in his group of Biocomputational Chemistry. Thank you to all who have supported me during my work at the third floor of C-building at the H.C. Ørsteds Institute. I would especially like to thank the following people: • Thank you to my supervisor, Jan "Yoda" Jensen for introducing me to the exciting fields of quantum chemistry and biocomputational chemistry, teaching me everything I know (and more), for your patience, and the inspiration you bring to everyone around you. • Thank you to Jens Breinholt at Novo Nordisk for supporting me with my work { I sincerely hope, that my work will very soon become practically useable. And thank you to the Novo Nordisk STAR PhD program for financial support, and for giving me the opportunity to carry out this study. • Thank you to all of our collaborators at the Biocenter, who always have been very support- ive. Especially, Thomas Hamelryck (in the presence of whom everything is trivially solved using Bayes' theorm) for helping me out with Bayesian theory, your great ideas and more, Wouter Boomsma for being seemingly all-knowing in what concerns PHAISTOS and always being exceptionally helpful, Simon Olsson for helping out with the implementation of the Jeffrey's prior code, and Kresten Lindorff-Larsen for always being encouraging and sharing your knowledge in this field.
    [Show full text]
  • Toward De Novo Design of Immune Silent Protein and Peptide Therapeutics Lance Stewart, Ph.D
    Toward de novo design of immune silent protein and peptide therapeutics Lance Stewart, Ph.D. MBA Chief Strategy and Operations Officer, UW IPD FDA – CERSI Collaborative Workshop “Predictive Immunogenicity for Better Clinical Outcomes” October 3 and 4, 2018 10-4-18 1 Design a new world of synthetic proteins • Founded in 2012 by Dr. David Baker • Organized within the School of Medicine, Biochemistry • ~140 Person Umbrella Organization • Faculty PIs (Baker, DiMaio, King, Gu, Bradley) • WRF Innovation Fellows (21) • Translational Investigators (1) • Research Staff (17) • Postdocs (27) • Graduate Students (36) • Admin (6) • Undergrads (27) • High School Student (1) • Using 10% of all UW Internet Traffic 10-4-18 2 PROTEIN STRUCTURE PREDICTION Amino Acid Sequence Protein Tertiary Structure PROTEIN DESIGN 3 The folded states of proteins are likely global energy minima for their sequences Unfolded Protein Structure Prediction: find lowest energy structure for fixed sequence Energy Protein Design: find a sequence for which desired structure has lowest energy Sample structures and sequences, and evaluate Native state energies using Rosetta molecular modeling suite 4 15 Years Ago (2003): First De Novo Design • First computational de novo design of a novel protein fold (Top 7) with atomic level accuracy. Model X-ray Structure Kuhlman et al, Science 2003 10-4-18 5 Today: The Coming of Age of De Novo Protein Design • Designed completely from scratch • Sequence unique from existing proteins in nature • Experimentally verified by high-resolution structure Top7 Kuhlman, Baker 1988 1997 2003 2011 2012 2013 2014 2015 2016 FSD-1 Mayo Coiled Coils: Degrado, Reagan, Kim, Harbury, Eisenberg, Alber, Woolfson Huang PS*, Boyken SE*, and Baker D.
    [Show full text]