Structure-Function Relationship of Archaeal Rhodopsin Proteins Analyzed by Continuum Electrostatics
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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. -
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. -
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” -
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. -
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 -
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. -
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. -
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. -
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. -
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.