Physics of Proteins: Intrinsically Disordered Proteins & Evolution

FOCUS · MAR-B71 · ID: 3112466

Presentations

  • How to Fold the Proteome (Mission Accomplished?)

    ORAL · Invited

    –

    Publication: Non-Refoldability is Pervasive Across the E. coli Proteome. PMID: 34308638
    A Proteome-Wide Map of Chaperone-Assisted Protein Refolding in a Cytosol-like Milieu. PMID: 36417429
    Intrinsically Disordered Regions Promote Protein Refoldability and Facilitate Retrieval from Biomolecular Condensates. 10.1101/2023.06.25.546465

    Presenters

    • Stephen Fried

      Johns Hopkins University

    Authors

    • Stephen Fried

      Johns Hopkins University

    View abstract →

  • Alpha helices are more evolutionarily robust to environmental perturbations than beta sheets: Bayesian theory for evolution

    ORAL

    –

    Publication: [1] Tobias Sikosek and Hue Sun Chan. Biophysics of protein
    evolution and evolutionary protein biophysics. Journal
    of The Royal Society Interface, 11(100):20140419, 2014.
    [2] Yu Xia and Michael Levitt. Roles of mutation and re-
    combination in the evolution of protein thermodynam-
    ics. Proceedings of the National Academy of Sciences,
    99(16):10382–10387, 2002.
    [3] Jesse D Bloom, Zhongyi Lu, David Chen, Alpan Raval,
    Ophelia S Venturelli, and Frances H Arnold. Evolution
    favors protein mutational robustness in sufficiently large
    populations. BMC biology, 5:1–21, 2007.
    [4] Vijay Jayaraman, Saacnicteh Toledo-Pati˜no, Lianet
    Noda-Garc´ıa, and Paola Laurino. Mechanisms of protein
    evolution. Protein Science, 31:e4362, 2022.
    [5] Jorge A Vila. Analysis of proteins in the light of muta-
    tions. European Biophysics Journal, pages 1–11, 2024.
    [6] Julia Hartling and Junhyong Kim. Mutational robust-
    ness and geometrical form in protein structures. Journal
    of Experimental Zoology Part B: Molecular and Develop-
    mental Evolution, 310(3):216–226, 2008.
    [7] Christian Schaefer, Avner Schlessinger, and Burkhard
    Rost. Protein secondary structure appears to be robust
    under in silico evolution while protein disorder appears
    not to be. Bioinformatics, 26(5):625–631, 2010.
    [8] Mary McLeod Rorick and Gunter P Wagner. Structural
    robustness confers evolvability in proteins. In 2010 AAAI
    Fall Symposium Series, 2010.
    [9] Mary M Rorick and G¨unter P Wagner. Protein structural
    modularity and robustness are associated with evolvabil-
    ity. Genome biology and evolution, 3:456–475, 2011.
    [10] Iain G Johnston, Kamaludin Dingle, Sam F Greenbury,
    Chico Q Camargo, Jonathan PK Doye, Sebastian E Ah-
    nert, and Ard A Louis. Symmetry and simplicity spon-
    taneously emerge from the algorithmic nature of evolu-
    tion. Proceedings of the National Academy of Sciences,
    119(11):e2113883119, 2022.
    [11] Qian-Yuan Tang, Tetsuhiro S Hatakeyama, and Kunihiko
    Kaneko. Functional sensitivity and mutational robust-
    ness of proteins. Physical Review Research, 2(3):033452,
    2020.
    [12] Qian-Yuan Tang and Kunihiko Kaneko. Dynamics-
    evolution correspondence in protein structures. Physical
    review letters, 127(9):098103, 2021.
    [13] Ayaka Sakata and Kunihiko Kaneko. Dimensional re-
    duction in evolving spin-glass model: correlation of
    phenotypic responses to environmental and mutational
    changes. Physical Review Letters, 124(21):218101, 2020.
    [14] Shintaro Nagata and Macoto Kikuchi. Emergence of co-
    operative bistability and robustness of gene regulatory
    networks. PLoS computational biology, 16(6):e1007969,
    2020.
    [15] Tadamune Kaneko and Macoto Kikuchi. Evolution en-
    hances mutational robustness and suppresses the emer-
    gence of a new phenotype: A new computational ap-
    proach for studying evolution. PLOS Computational Bi-
    ology, 18(1):e1009796, 2022.
    [16] Kunihiko Kaneko. Evolution of robustness to noise
    and mutation in gene expression dynamics. PLoS one,
    2(5):e434, 2007.
    [17] Stefano Ciliberti, Olivier C Martin, and Andreas Wag-
    ner. Robustness can evolve gradually in complex regula-
    tory gene networks with varying topology. PLoS compu-
    tational biology, 3(2):e15, 2007.
    [18] Kit Fun Lau and Ken A Dill. A lattice statistical mechan-
    ics model of the conformational and sequence spaces of
    proteins. Macromolecules, 22(10):3986–3997, 1989.
    [19] Helen M Berman, John Westbrook, Zukang Feng, Gary
    Gilliland, Talapady N Bhat, Helge Weissig, Ilya N
    Shindyalov, and Philip E Bourne. The protein data bank.
    Nucleic acids research, 28(1):235–242, 2000.
    [20] Hu Chen, Xin Zhou, and Zhong-Can Ou-Yang.
    Secondary-structure-favored hydrophobic-polar lattice
    model of protein folding. Physical Review E,
    64(4):041905, 2001.
    [21] Marek Cieplak and Jayanth R Banavar. Energy land-
    scape and dynamics of proteins: an exact analysis of a
    simplified lattice model. Physical Review E—Statistical,
    Nonlinear, and Soft Matter Physics, 88(4):040702, 2013.
    [22] Guangjie Shi, Thomas W¨ust, and David P Landau. Char-
    acterizing folding funnels with replica exchange wang-
    landau simulation of lattice proteins. Physical Review E,
    94(5):050402, 2016.
    [23] Erik Van Dijk, Patrick Varilly, Tuomas PJ Knowles,
    Daan Frenkel, and Sanne Abeln. Consistent treatment
    of hydrophobicity in protein lattice models accounts for
    cold denaturation. Physical review letters, 116(7):078101,
    2016.
    [24] Christian Holzgr¨afe, Anders Irb¨ack, and Carl Troein.
    Mutation-induced fold switching among lattice proteins.
    The Journal of chemical physics, 135(19), 2011.
    [25] Guangjie Shi, Thomas Vogel, Thomas W¨ust, Ying Wai
    Li, and David P Landau. Effect of single-site mutations
    on hydrophobic-polar lattice proteins. Physical Review
    E, 90(3):033307, 2014.
    [26] Shi-Jie Chen and Ken A Dill. Rna folding energy land-
    scapes. Proceedings of the National Academy of Sciences,
    97(2):646–651, 2000.
    [27] Tomoei Takahashi, George Chikenji, and Kei Tokita. Lat-
    tice protein design using bayesian learning. Physical Re-
    view E, 104:014404, 2021.
    [28] Valentino Bianco, Giancarlo Franzese, Christoph Del-
    lago, and Ivan Coluzza. Role of water in the selection of
    stable proteins at ambient and extreme thermodynamic
    conditions. Physical Review X, 7(2):021047, 2017.
    [29] Themis Lazaridis and Martin Karplus. Effective energy
    function for proteins in solution. Proteins: Structure,
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    [30] Tomoei Takahashi, George Chikenji, and Kei Tokita.
    The cavity method to protein design problem. Jour-
    nal of Statistical Mechanics: Theory and Experiment,
    2022(10):103403, 2022.
    [31] Ivan Coluzza. Computational protein design: a review.
    Journal of Physics: Condensed Matter, 29(14):143001,
    2017.
    [32] Simona Cocco, Christoph Feinauer, Matteo Figliuzzi,
    R´emi Monasson, and Martin Weigt. Inverse statistical
    physics of protein sequences: a key issues review. Re-
    ports on Progress in Physics, 81(3):032601, 2018.
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    12
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    Macromolecules, 22(12):4559–4573, 1989.
    [37] Khoi Tan Nguyen, St´ephanie V Le Clair, Shuji Ye, and
    Zhan Chen. Orientation determination of protein helical
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    [39] Gy¨orgy Abrus´an and Joseph A Marsh. Alpha helices
    are more robust to mutations than beta strands. PLoS
    computational biology, 12(12):e1005242, 2016.
    [40] George Chikenji, Macoto Kikuchi, and Yukito Iba. Multi-
    self-overlap ensemble for protein folding: ground state
    search and thermodynamics. Physical Review Letters,
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    [41] Nobu C Shirai and Macoto Kikuchi. Multicanonical sim-
    ulation of the domb-joyce model and the g¯o model: new
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    [42] Marc M´ezard and Giorgio Parisi. The bethe lattice
    spin glass revisited. The European Physical Journal B-
    Condensed Matter and Complex Systems, 20:217–233,
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    [43] Marc Mezard and Andrea Montanari. Information,
    physics, and computation. Oxford University Press, 2009.

    Presenters

    • Tomoei Takahashi

      The University of Tokyo

    Authors

    • Tomoei Takahashi

      The University of Tokyo

    • George Chikenji

      Nagoya University

    • Kei Tokita

      Nagoya University

    • Yoshiyuki Kabashima

      The University of Tokyo

    View abstract →

  • Understanding the Role of Excipients in the Stability of Biological Macromolecules

    ORAL

    –

    Publication: Planned Paper: Understanding the role of excipients in stability of biological molecules (in preparation)

    Presenters

    • Xianci Zeng

      University of Massachusetts Amherst

    Authors

    • Xianci Zeng

      University of Massachusetts Amherst

    • Idris Tohidian

      Michigan Technological University

    • Rohan Chaudhari

      Michigan Technological University

    • Jonathan Zajac

      University of Minnesota

    • Praveen Muralikrishnan

      University of Minnesota

    • Caryn L Heldt

      Michigan Technological University

    • Sapna Sarupria

      University of Minnesota

    • Sarah L Perry

      University of Massachusetts Amherst

    View abstract →