1
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Jaeger HM, Murugan A, Nagel SR. Training physical matter to matter. SOFT MATTER 2024; 20:6695-6701. [PMID: 39140794 DOI: 10.1039/d4sm00629a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2024]
Abstract
Biological systems offer a great many examples of how sophisticated, highly adapted behavior can emerge from training. Here we discuss how training might be used to impart similarly adaptive properties in physical matter. As a special form of materials processing, training differs in important ways from standard approaches of obtaining sought after material properties. In particular, rather than designing or programming the local configurations and interactions of constituents, training uses externally applied stimuli to evolve material properties. This makes it possible to obtain different functionalities from the same starting material (pluripotency). Furthermore, training evolves a material in situ or under conditions similar to those during the intended use; thus, material performance can improve rather than degrade over time. We discuss requirements for trainability, outline recently developed training strategies for creating soft materials with multiple, targeted and adaptable functionalities, and provide examples where the concept of training has been applied to materials on length scales from the molecular to the macroscopic.
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Affiliation(s)
- Heinrich M Jaeger
- The James Franck Institute and Department of Physics, The University of Chicago, 929 E 57th St., Chicago, Illinois 60637, USA.
| | - Arvind Murugan
- The James Franck Institute and Department of Physics, The University of Chicago, 929 E 57th St., Chicago, Illinois 60637, USA.
| | - Sidney R Nagel
- The James Franck Institute and Department of Physics, The University of Chicago, 929 E 57th St., Chicago, Illinois 60637, USA.
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2
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Chalopin Y. GPCR Signaling: A Study of the Interplay Between Structure, Energy, and Function. Proteins 2024. [PMID: 39095933 DOI: 10.1002/prot.26724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Revised: 04/26/2024] [Accepted: 06/12/2024] [Indexed: 08/04/2024]
Abstract
G protein-coupled receptors (GPCRs) exemplify sophisticated allosteric communication, transducing extracellular signals through ligand-induced structural rearrangements that resonate through the molecular scaffold. Despite extensive study, the biophysical underpinnings of how conformational changes spread remain unclear. This work employs a novel physics-based framework to characterize the role of energy dissipation in directing intramolecular signaling pathways. By modeling each residue as a network of coupled oscillators, we generate a localization landscape depicting the vibrational energy distribution throughout the protein scaffold. Quantifying directional energy flux between residues reveals distinct pathways for energy and information transfer, illuminating sequences of allosteric communication. Our analysis of CB1 and CCR5 crystal structures unveils an anisotropic pattern of energy dissipation aligning with key functional dynamics, such as activation-related conformational changes. These anisotropic patterns of vibrational energy flow constitute pre-configured channels for allosteric signaling. Elucidating the relationship between structural topology and energy dissipation patterns provides key insights into the thermodynamic drivers of conformational signaling. This methodology significantly advances our mechanistic understanding of allostery in GPCRs and presents a broadly applicable approach for rationally dissecting allosteric communication pathways, with potential implications for structure-based drug design targeting these critical receptors.
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Affiliation(s)
- Yann Chalopin
- Structures, Properties and Modeling of Solids Laboratory Physics Department, CentraleSupélec/National Center for the Scientific Research, University of Paris-Saclay, Gif-sur-Yvette, France
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3
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Arieli M, Moshe M, Sharon E. Mechanical design principles in frustrated thin elastic sheets. SOFT MATTER 2024; 20:4414-4421. [PMID: 38767344 DOI: 10.1039/d4sm00166d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Using a geometric formalism of elasticity theory we develop a systematic theoretical framework for shaping and manipulating the energy landscape of slender solids, and consequently their mechanical response to external perturbations. We formally express global mechanical properties associated with non-Euclidean thin sheets in terms of their local rest lengths and rest curvatures, and we interpret the expressions as both forward and inverse problems for designing the desired mechanical properties. We show that by wisely designing geometric frustration, anomalous mechanical properties can be encoded into a material using accessible experimental techniques. To test the methodology we derive a family of ribbon-springs with extreme mechanical behavior such as tunable, anharmonic, and even vanishing rigidities. The presented formalism can be discretized, offering a new methodology for designing mechanical properties and thus opens a new pathway for the design of both continuum and discrete solids and structures.
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Affiliation(s)
- Michal Arieli
- Racah Institute of Physics, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel.
| | - Michael Moshe
- Racah Institute of Physics, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel.
| | - Eran Sharon
- Racah Institute of Physics, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel.
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4
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Rocklin DZ. Topological mechanics without the topology: A universal, model-free approach to mechanical response. Proc Natl Acad Sci U S A 2024; 121:e2322681121. [PMID: 38408262 PMCID: PMC10927589 DOI: 10.1073/pnas.2322681121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/28/2024] Open
Affiliation(s)
- D. Zeb Rocklin
- Department of Physics, Georgia Institute of Technology, Atlanta, GA30332
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5
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Chennakesavalu S, Manikandan SK, Hu F, Rotskoff GM. Adaptive nonequilibrium design of actin-based metamaterials: Fundamental and practical limits of control. Proc Natl Acad Sci U S A 2024; 121:e2310238121. [PMID: 38359294 PMCID: PMC10895351 DOI: 10.1073/pnas.2310238121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 11/13/2023] [Indexed: 02/17/2024] Open
Abstract
The adaptive and surprising emergent properties of biological materials self-assembled in far-from-equilibrium environments serve as an inspiration for efforts to design nanomaterials. In particular, controlling the conditions of self-assembly can modulate material properties, but there is no systematic understanding of either how to parameterize external control or how controllable a given material can be. Here, we demonstrate that branched actin networks can be encoded with metamaterial properties by dynamically controlling the applied force under which they grow and that the protocols can be selected using multi-task reinforcement learning. These actin networks have tunable responses over a large dynamic range depending on the chosen external protocol, providing a pathway to encoding "memory" within these structures. Interestingly, we obtain a bound that relates the dissipation rate and the rate of "encoding" that gives insight into the constraints on control-both physical and information theoretical. Taken together, these results emphasize the utility and necessity of nonequilibrium control for designing self-assembled nanostructures.
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Affiliation(s)
| | | | - Frank Hu
- Department of Chemistry, Stanford University, Stanford, CA94305
| | - Grant M. Rotskoff
- Department of Chemistry, Stanford University, Stanford, CA94305
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA94305
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6
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Stern M, Liu AJ, Balasubramanian V. Physical effects of learning. Phys Rev E 2024; 109:024311. [PMID: 38491658 DOI: 10.1103/physreve.109.024311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 01/31/2024] [Indexed: 03/18/2024]
Abstract
Interacting many-body physical systems ranging from neural networks in the brain to folding proteins to self-modifying electrical circuits can learn to perform diverse tasks. This learning, both in nature and in engineered systems, can occur through evolutionary selection or through dynamical rules that drive active learning from experience. Here, we show that learning in linear physical networks with weak input signals leaves architectural imprints on the Hessian of a physical system. Compared to a generic organization of the system components, (a) the effective physical dimension of the response to inputs decreases, (b) the response of physical degrees of freedom to random perturbations (or system "susceptibility") increases, and (c) the low-eigenvalue eigenvectors of the Hessian align with the task. Overall, these effects embody the typical scenario for learning processes in physical systems in the weak input regime, suggesting ways of discovering whether a physical network may have been trained.
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Affiliation(s)
- Menachem Stern
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Andrea J Liu
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, New York 10010, USA
| | - Vijay Balasubramanian
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, New Mexico 87501, USA
- Theoretische Natuurkunde, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium
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7
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Tah I, Haertter D, Crawford JM, Kiehart DP, Schmidt CF, Liu AJ. Minimal vertex model explains how the amnioserosa avoids fluidization during Drosophila dorsal closure. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.20.572544. [PMID: 38187730 PMCID: PMC10769242 DOI: 10.1101/2023.12.20.572544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Dorsal closure is a process that occurs during embryogenesis of Drosophila melanogaster . During dorsal closure, the amnioserosa (AS), a one-cell thick epithelial tissue that fills the dorsal opening, shrinks as the lateral epidermis sheets converge and eventually merge. During this process, the aspect ratio of amnioserosa cells increases markedly. The standard 2-dimensional vertex model, which successfully describes tissue sheet mechanics in multiple contexts, would in this case predict that the tissue should fluidize via cell neighbor changes. Surprisingly, however, the amnioserosa remains an elastic solid with no such events. We here present a minimal extension to the vertex model that explains how the amnioserosa can achieve this unexpected behavior. We show that continuous shrinkage of the preferred cell perimeter and cell perimeter polydispersity lead to the retention of the solid state of the amnioserosa. Our model accurately captures measured cell shape and orientation changes and predicts non-monotonic junction tension that we confirm with laser ablation experiments. Significance Statement During embryogenesis, cells in tissues can undergo significant shape changes. Many epithelial tissues fluidize, i.e. cells exchange neighbors, when the average cell aspect ratio increases above a threshold value, consistent with the standard vertex model. During dorsal closure in Drosophila melanogaster , however, the amnioserosa tissue remains solid even as the average cell aspect ratio increases well above threshold. We introduce perimeter polydispersity and allow the preferred cell perimeters, usually held fixed in vertex models, to decrease linearly with time as seen experimentally. With these extensions to the standard vertex model, we capture experimental observations quantitatively. Our results demonstrate that vertex models can describe the behavior of the amnioserosa in dorsal closure by allowing normally fixed parameters to vary with time.
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8
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Tah I, Haertter D, Crawford JM, Kiehart DP, Schmidt CF, Liu AJ. Minimal vertex model explains how the amnioserosa avoids fluidization during Drosophila dorsal closure. ARXIV 2023:arXiv:2312.12926v1. [PMID: 38196754 PMCID: PMC10775355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
Dorsal closure is a process that occurs during embryogenesis of Drosophila melanogaster. During dorsal closure, the amnioserosa (AS), a one-cell thick epithelial tissue that fills the dorsal opening, shrinks as the lateral epidermis sheets converge and eventually merge. During this process, the aspect ratio of amnioserosa cells increases markedly. The standard 2-dimensional vertex model, which successfully describes tissue sheet mechanics in multiple contexts, would in this case predict that the tissue should fluidize via cell neighbor changes. Surprisingly, however, the amnioserosa remains an elastic solid with no such events. We here present a minimal extension to the vertex model that explains how the amnioserosa can achieve this unexpected behavior. We show that continuous shrink-age of the preferred cell perimeter and cell perimeter polydispersity lead to the retention of the solid state of the amnioserosa. Our model accurately captures measured cell shape and orientation changes and predicts non-monotonic junction tension that we confirm with laser ablation experiments.
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Affiliation(s)
- Indrajit Tah
- Speciality Glass Division, CSIR-Central Glass and Ceramic Research Institute, Kolkata, India
- Department of Physics and Astronomy, University of Pennsylvania, PA, USA
| | - Daniel Haertter
- Institute of Pharmacology and Toxicology, University Medical Center and Campus Institute Data Science (CIDAS), University of Göttingen, Germany
- Department of Physics and Soft Matter Center, Duke University, Durham, NC, USA
| | | | | | | | - Andrea J. Liu
- Department of Physics and Astronomy, University of Pennsylvania, PA, USA
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9
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Zheng X, Zhang X, Chen TT, Watanabe I. Deep Learning in Mechanical Metamaterials: From Prediction and Generation to Inverse Design. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2302530. [PMID: 37332101 DOI: 10.1002/adma.202302530] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 05/27/2023] [Indexed: 06/20/2023]
Abstract
Mechanical metamaterials are meticulously designed structures with exceptional mechanical properties determined by their microstructures and constituent materials. Tailoring their material and geometric distribution unlocks the potential to achieve unprecedented bulk properties and functions. However, current mechanical metamaterial design considerably relies on experienced designers' inspiration through trial and error, while investigating their mechanical properties and responses entails time-consuming mechanical testing or computationally expensive simulations. Nevertheless, recent advancements in deep learning have revolutionized the design process of mechanical metamaterials, enabling property prediction and geometry generation without prior knowledge. Furthermore, deep generative models can transform conventional forward design into inverse design. Many recent studies on the implementation of deep learning in mechanical metamaterials are highly specialized, and their pros and cons may not be immediately evident. This critical review provides a comprehensive overview of the capabilities of deep learning in property prediction, geometry generation, and inverse design of mechanical metamaterials. Additionally, this review highlights the potential of leveraging deep learning to create universally applicable datasets, intelligently designed metamaterials, and material intelligence. This article is expected to be valuable not only to researchers working on mechanical metamaterials but also those in the field of materials informatics.
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Affiliation(s)
- Xiaoyang Zheng
- Center for Basic Research on Materials, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, 305-0047, Japan
- Graduate School of Pure and Applied Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8573, Japan
| | - Xubo Zhang
- Graduate School of Pure and Applied Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8573, Japan
| | - Ta-Te Chen
- Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan
- National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, 305-0047, Japan
| | - Ikumu Watanabe
- Center for Basic Research on Materials, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, 305-0047, Japan
- Graduate School of Pure and Applied Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8573, Japan
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10
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Maji A, Dasbiswas K, Rabin Y. Shape transitions in a network model of active elastic shells. SOFT MATTER 2023; 19:7216-7226. [PMID: 37724013 DOI: 10.1039/d3sm01041d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/20/2023]
Abstract
Morphogenesis involves the transformation of initially simple shapes, such as multicellular spheroids, into more complex 3D shapes. These shape changes are governed by mechanical forces including molecular motor-generated forces as well as hydrostatic fluid pressure, both of which are actively regulated in living matter through mechano-chemical feedback. Inspired by autonomous, biophysical shape change, such as occurring in the model organism hydra, we introduce a minimal, active, elastic model featuring a network of springs in a globe-like spherical shell geometry. In this model there is coupling between activity and the shape of the shell: if the local curvature of a filament represented by a spring falls below a critical value, its elastic constant is actively changed. This results in deformation of the springs that changes the shape of the shell. By combining excitation of springs and pressure regulation, we show that the shell undergoes a transition from spheroidal to either elongated ellipsoidal or a different spheroidal shape, depending on pressure. There exists a critical pressure at which there is an abrupt change from ellipsoids to spheroids, showing that pressure is potentially a sensitive switch for material shape. We thus offer biologically inspired design principles for autonomous shape transitions in active elastic shells.
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Affiliation(s)
- Ajoy Maji
- Faculty of Biomedical Engineering, Technion-Israel Institute of Technology, 32000 Haifa, Israel
| | - Kinjal Dasbiswas
- Department of Physics, University of California, Merced, Merced, CA 95343, USA
| | - Yitzhak Rabin
- Department of Physics, Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 5290002, Israel.
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11
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Yang T, Hathcock D, Chen Y, McEuen PL, Sethna JP, Cohen I, Griniasty I. Bifurcation instructed design of multistate machines. Proc Natl Acad Sci U S A 2023; 120:e2300081120. [PMID: 37579174 PMCID: PMC10450659 DOI: 10.1073/pnas.2300081120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 07/23/2023] [Indexed: 08/16/2023] Open
Abstract
We propose a design paradigm for multistate machines where transitions from one state to another are organized by bifurcations of multiple equilibria of the energy landscape describing the collective interactions of the machine components. This design paradigm is attractive since, near bifurcations, small variations in a few control parameters can result in large changes to the system's state providing an emergent lever mechanism. Further, the topological configuration of transitions between states near such bifurcations ensures robust operation, making the machine less sensitive to fabrication errors and noise. To design such machines, we develop and implement a new efficient algorithm that searches for interactions between the machine components that give rise to energy landscapes with these bifurcation structures. We demonstrate a proof of concept for this approach by designing magnetoelastic machines whose motions are primarily guided by their magnetic energy landscapes and show that by operating near bifurcations we can achieve multiple transition pathways between states. This proof of concept demonstration illustrates the power of this approach, which could be especially useful for soft robotics and at the microscale where typical macroscale designs are difficult to implement.
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Affiliation(s)
- Teaya Yang
- Laboratory of Atomic and Solid State Physics, Cornell University, Ithaca, NY14853
| | - David Hathcock
- Laboratory of Atomic and Solid State Physics, Cornell University, Ithaca, NY14853
| | - Yuchao Chen
- Laboratory of Atomic and Solid State Physics, Cornell University, Ithaca, NY14853
| | - Paul L. McEuen
- Laboratory of Atomic and Solid State Physics, Cornell University, Ithaca, NY14853
- Kavli Institute at Cornell for Nanoscale Science, Cornell University, Ithaca, NY14853
| | - James P. Sethna
- Laboratory of Atomic and Solid State Physics, Cornell University, Ithaca, NY14853
| | - Itai Cohen
- Laboratory of Atomic and Solid State Physics, Cornell University, Ithaca, NY14853
- Kavli Institute at Cornell for Nanoscale Science, Cornell University, Ithaca, NY14853
| | - Itay Griniasty
- Laboratory of Atomic and Solid State Physics, Cornell University, Ithaca, NY14853
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12
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Falk MJ, Wu J, Matthews A, Sachdeva V, Pashine N, Gardel ML, Nagel SR, Murugan A. Learning to learn by using nonequilibrium training protocols for adaptable materials. Proc Natl Acad Sci U S A 2023; 120:e2219558120. [PMID: 37364104 PMCID: PMC10319023 DOI: 10.1073/pnas.2219558120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 05/25/2023] [Indexed: 06/28/2023] Open
Abstract
Evolution in time-varying environments naturally leads to adaptable biological systems that can easily switch functionalities. Advances in the synthesis of environmentally responsive materials therefore open up the possibility of creating a wide range of synthetic materials which can also be trained for adaptability. We consider high-dimensional inverse problems for materials where any particular functionality can be realized by numerous equivalent choices of design parameters. By periodically switching targets in a given design algorithm, we can teach a material to perform incompatible functionalities with minimal changes in design parameters. We exhibit this learning strategy for adaptability in two simulated settings: elastic networks that are designed to switch deformation modes with minimal bond changes and heteropolymers whose folding pathway selections are controlled by a minimal set of monomer affinities. The resulting designs can reveal physical principles, such as nucleation-controlled folding, that enable such adaptability.
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Affiliation(s)
- Martin J. Falk
- Department of Physics, The University of Chicago, Chicago, IL60637
| | - Jiayi Wu
- Department of Physics, The University of Chicago, Chicago, IL60637
| | - Ayanna Matthews
- Graduate Program in Biophysical Sciences, The University of Chicago, Chicago, IL60637
| | - Vedant Sachdeva
- Graduate Program in Biophysical Sciences, The University of Chicago, Chicago, IL60637
| | - Nidhi Pashine
- School of Engineering and Applied Science, Yale University, New Haven, CT06511
| | - Margaret L. Gardel
- Department of Physics, The University of Chicago, Chicago, IL60637
- James Franck Institute, The University of Chicago, Chicago, IL60637
- Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL60637
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, IL60637
| | - Sidney R. Nagel
- Department of Physics, The University of Chicago, Chicago, IL60637
- James Franck Institute, The University of Chicago, Chicago, IL60637
| | - Arvind Murugan
- Department of Physics, The University of Chicago, Chicago, IL60637
- James Franck Institute, The University of Chicago, Chicago, IL60637
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13
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Mendels D, Byléhn F, Sirk TW, de Pablo JJ. Systematic modification of functionality in disordered elastic networks through free energy surface tailoring. SCIENCE ADVANCES 2023; 9:eadf7541. [PMID: 37285442 DOI: 10.1126/sciadv.adf7541] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 05/01/2023] [Indexed: 06/09/2023]
Abstract
A combined machine learning-physics-based approach is explored for molecular and materials engineering. Specifically, collective variables, akin to those used in enhanced sampled simulations, are constructed using a machine learning model trained on data gathered from a single system. Through the constructed collective variables, it becomes possible to identify critical molecular interactions in the considered system, the modulation of which enables a systematic tailoring of the system's free energy landscape. To explore the efficacy of the proposed approach, we use it to engineer allosteric regulation and uniaxial strain fluctuations in a complex disordered elastic network. Its successful application in these two cases provides insights regarding how functionality is governed in systems characterized by extensive connectivity and points to its potential for design of complex molecular systems.
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Affiliation(s)
- Dan Mendels
- Pritzker School of Molecular Engineering, University of Chicago, 5640 S. Ellis Avenue, Chicago, IL 60637 USA
| | - Fabian Byléhn
- Pritzker School of Molecular Engineering, University of Chicago, 5640 S. Ellis Avenue, Chicago, IL 60637 USA
| | - Timothy W Sirk
- Polymers Branch, U.S. CCDC Army Research Laboratory, Aberdeen Proving Ground, MD 21005, USA
| | - Juan J de Pablo
- Pritzker School of Molecular Engineering, University of Chicago, 5640 S. Ellis Avenue, Chicago, IL 60637 USA
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14
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Bhattacharyya K, Zwicker D, Alim K. Memory capacity of adaptive flow networks. Phys Rev E 2023; 107:034407. [PMID: 37073018 DOI: 10.1103/physreve.107.034407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 02/09/2023] [Indexed: 04/20/2023]
Abstract
Biological flow networks adapt their network morphology to optimize flow while being exposed to external stimuli from different spatial locations in their environment. These adaptive flow networks retain a memory of the stimulus location in the network morphology. Yet, what limits this memory and how many stimuli can be stored are unknown. Here, we study a numerical model of adaptive flow networks by applying multiple stimuli subsequently. We find strong memory signals for stimuli imprinted for a long time into young networks. Consequently, networks can store many stimuli for intermediate stimulus duration, which balance imprinting and aging.
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Affiliation(s)
- Komal Bhattacharyya
- Max Planck Institute for Dynamics and Self-Organisation, 37077 Göttingen, Germany
| | - David Zwicker
- Max Planck Institute for Dynamics and Self-Organisation, 37077 Göttingen, Germany
| | - Karen Alim
- Max Planck Institute for Dynamics and Self-Organisation, 37077 Göttingen, Germany
- Center for Protein Assemblies and Department of Bioscience, School of Natural Sciences, Technische Universität München, 85748 Garching, Germany
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15
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Pashine N, Nasab AM, Kramer-Bottiglio R. Reprogrammable allosteric metamaterials from disordered networks. SOFT MATTER 2023; 19:1617-1623. [PMID: 36752560 DOI: 10.1039/d2sm01284g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Prior works on disordered mechanical metamaterial networks-consisting of fixed nodes connected by discrete bonds-have shown that auxetic and allosteric responses can be achieved by pruning a specific set of the bonds from an originally random network. However, bond pruning is irreversible and yields a single bulk response. Using material stiffness as a tunable design parameter, we create metamaterial networks where allosteric responses are achieved without bond removal. Such systems are experimentally realized through variable stiffness bonds that can strengthen and weaken on-demand. In a disordered mechanical network with variable stiffness bonds, different subsets of bonds can be strategically softened to achieve different bulk responses, enabling a multiplicity of reprogrammable input/output allosteric responses.
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Affiliation(s)
- Nidhi Pashine
- School of Engineering & Applied Science, Yale University, New Haven, CT, 06520, USA.
| | - Amir Mohammadi Nasab
- School of Engineering & Applied Science, Yale University, New Haven, CT, 06520, USA.
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16
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Li Z, Li X, Zhang H, Huang D, Zhang L. The prediction of contact force networks in granular materials based on graph neural networks. J Chem Phys 2023; 158:054905. [PMID: 36754816 DOI: 10.1063/5.0122695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
The contact force network, usually organized inhomogeneously by the inter-particle forces on the bases of the contact network topologies, is essential to the rigidity and stability in amorphous solids. How to capture such a "backbone" is crucial to the understanding of various anomalous properties or behaviors in those materials, which remains a central challenge presently in physics, engineering, or material science. Here, we use a novel graph neural network to predict the contact force network in two-dimensional granular materials under uniaxial compression. With the edge classification model in the framework of the deep graph library, we show that the inter-particle contact forces can be accurately estimated purely from the knowledge of the static microstructures, which can be acquired from a discrete element method or directly visualized from experimental methods. By testing the granular packings with different structural disorders and pressure, we further demonstrate the robustness of the optimized graph neural network to changes in various model parameters. Our research tries to provide a new way of extracting the information about the inter-particle forces, which substantially improves the efficiency and reduces the costs compared to the traditional experiments.
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Affiliation(s)
- Zirui Li
- School of Automation, Central South University, Changsha 410083, China
| | - Xingqiao Li
- School of Automation, Central South University, Changsha 410083, China
| | - Hang Zhang
- School of Automation, Central South University, Changsha 410083, China
| | - Duan Huang
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Ling Zhang
- School of Automation, Central South University, Changsha 410083, China
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17
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Arinze C, Stern M, Nagel SR, Murugan A. Learning to self-fold at a bifurcation. Phys Rev E 2023; 107:025001. [PMID: 36932611 DOI: 10.1103/physreve.107.025001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 12/23/2022] [Indexed: 02/10/2023]
Abstract
Disordered mechanical systems can deform along a network of pathways that branch and recombine at special configurations called bifurcation points. Multiple pathways are accessible from these bifurcation points; consequently, computer-aided design algorithms have been sought to achieve a specific structure of pathways at bifurcations by rationally designing the geometry and material properties of these systems. Here, we explore an alternative physical training framework in which the topology of folding pathways in a disordered sheet is changed in a desired manner due to changes in crease stiffnesses induced by prior folding. We study the quality and robustness of such training for different "learning rules," that is, different quantitative ways in which local strain changes the local folding stiffness. We experimentally demonstrate these ideas using sheets with epoxy-filled creases whose stiffnesses change due to folding before the epoxy sets. Our work shows how specific forms of plasticity in materials enable them to learn nonlinear behaviors through their prior deformation history in a robust manner.
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Affiliation(s)
- Chukwunonso Arinze
- Department of Physics, University of Chicago, Chicago, Illinois 60637, USA
| | - Menachem Stern
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Sidney R Nagel
- Department of Physics, University of Chicago, Chicago, Illinois 60637, USA
| | - Arvind Murugan
- Department of Physics, University of Chicago, Chicago, Illinois 60637, USA
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18
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Tripathy M, Srivastava A, Sastry S, Rao M. Protein as evolvable functionally constrained amorphous matter. J Biosci 2022. [DOI: 10.1007/s12038-022-00313-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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19
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Chen S, Giardina F, Choi GPT, Mahadevan L. Modular representation and control of floppy networks. Proc Math Phys Eng Sci 2022. [DOI: 10.1098/rspa.2022.0082] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Geometric graph models of systems as diverse as proteins, DNA assemblies, architected materials and robot swarms are useful abstract representations of these objects that also unify ways to study their properties and control them in space and time. While much work has been done in the context of characterizing the behaviour of these networks close to critical points associated with bond and rigidity percolation, isostaticity, etc., much less is known about floppy, underconstrained networks that are far more common in nature and technology. Here, we combine geometric rigidity and algebraic sparsity to provide a framework for identifying the zero energy floppy modes via a representation that illuminates the underlying hierarchy and modularity of the network and thence the control of its nestedness and locality. Our framework allows us to demonstrate a range of applications of this approach that include robotic reaching tasks with motion primitives, and predicting the linear and nonlinear response of elastic networks based solely on infinitesimal rigidity and sparsity, which we test using physical experiments. Our approach is thus likely to be of use broadly in dissecting the geometrical properties of floppy networks using algebraic sparsity to optimize their function and performance.
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Affiliation(s)
- Siheng Chen
- School of Engineering and Applied Sciences, Harvard University, Cambridge MA 02138, USA
| | - Fabio Giardina
- School of Engineering and Applied Sciences, Harvard University, Cambridge MA 02138, USA
| | - Gary P. T. Choi
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge MA 02139, USA
| | - L. Mahadevan
- School of Engineering and Applied Sciences, Harvard University, Cambridge MA 02138, USA
- Department of Physics, Harvard University, Cambridge MA 02138, USA
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge MA 02138, USA
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20
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Bhattacharyya K, Zwicker D, Alim K. Memory Formation in Adaptive Networks. PHYSICAL REVIEW LETTERS 2022; 129:028101. [PMID: 35867448 DOI: 10.1103/physrevlett.129.028101] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 06/14/2022] [Accepted: 06/15/2022] [Indexed: 06/15/2023]
Abstract
The continuous adaptation of networks like our vasculature ensures optimal network performance when challenged with changing loads. Here, we show that adaptation dynamics allow a network to memorize the position of an applied load within its network morphology. We identify that the irreversible dynamics of vanishing network links encode memory. Our analytical theory successfully predicts the role of all system parameters during memory formation, including parameter values which prevent memory formation. We thus provide analytical insight on the theory of memory formation in disordered systems.
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Affiliation(s)
- Komal Bhattacharyya
- Max Planck Institute for Dynamics and Self-Organisation, Göttingen 37077, Germany
| | - David Zwicker
- Max Planck Institute for Dynamics and Self-Organisation, Göttingen 37077, Germany
| | - Karen Alim
- Max Planck Institute for Dynamics and Self-Organisation, Göttingen 37077, Germany
- Center for Protein Assemblies (CPA), Physik-Department, Technische Universität München, Garching 85748, Germany
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21
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Hagh VF, Nagel SR, Liu AJ, Manning ML, Corwin EI. Transient learning degrees of freedom for introducing function in materials. Proc Natl Acad Sci U S A 2022; 119:e2117622119. [PMID: 35512090 PMCID: PMC9171605 DOI: 10.1073/pnas.2117622119] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 03/08/2022] [Indexed: 11/24/2022] Open
Abstract
SignificanceMany protocols used in material design and training have a common theme: they introduce new degrees of freedom, often by relaxing away existing constraints, and then evolve these degrees of freedom based on a rule that leads the material to a desired state at which point these new degrees of freedom are frozen out. By creating a unifying framework for these protocols, we can now understand that some protocols work better than others because the choice of new degrees of freedom matters. For instance, introducing particle sizes as degrees of freedom to the minimization of a jammed particle packing can lead to a highly stable state, whereas particle stiffnesses do not have nearly the same impact.
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Affiliation(s)
- Varda F. Hagh
- James Franck Institute, University of Chicago, Chicago, IL 60637
- Department of Physics and Materials Science Institute, University of Oregon, Eugene, OR 97403
| | - Sidney R. Nagel
- James Franck Institute, University of Chicago, Chicago, IL 60637
| | - Andrea J. Liu
- Department of Physics, University of Pennsylvania, Philadelphia, PA 19104
| | - M. Lisa Manning
- Department of Physics, Syracuse University, Syracuse, NY 13244
- BioInspired Institute, Syracuse University, Syracuse, NY 13244
| | - Eric I. Corwin
- Department of Physics and Materials Science Institute, University of Oregon, Eugene, OR 97403
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22
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Tauber J, van der Gucht J, Dussi S. Stretchy and disordered: Toward understanding fracture in soft network materials via mesoscopic computer simulations. J Chem Phys 2022; 156:160901. [PMID: 35490006 DOI: 10.1063/5.0081316] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
Soft network materials exist in numerous forms ranging from polymer networks, such as elastomers, to fiber networks, such as collagen. In addition, in colloidal gels, an underlying network structure can be identified, and several metamaterials and textiles can be considered network materials as well. Many of these materials share a highly disordered microstructure and can undergo large deformations before damage becomes visible at the macroscopic level. Despite their widespread presence, we still lack a clear picture of how the network structure controls the fracture processes of these soft materials. In this Perspective, we will focus on progress and open questions concerning fracture at the mesoscopic scale, in which the network architecture is clearly resolved, but neither the material-specific atomistic features nor the macroscopic sample geometries are considered. We will describe concepts regarding the network elastic response that have been established in recent years and turn out to be pre-requisites to understand the fracture response. We will mostly consider simulation studies, where the influence of specific network features on the material mechanics can be cleanly assessed. Rather than focusing on specific systems, we will discuss future challenges that should be addressed to gain new fundamental insights that would be relevant across several examples of soft network materials.
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Affiliation(s)
- Justin Tauber
- Physical Chemistry and Soft Matter, Wageningen University, Wageningen, The Netherlands
| | - Jasper van der Gucht
- Physical Chemistry and Soft Matter, Wageningen University, Wageningen, The Netherlands
| | - Simone Dussi
- Physical Chemistry and Soft Matter, Wageningen University, Wageningen, The Netherlands
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23
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Wycoff JF, Dillavou S, Stern M, Liu AJ, Durian DJ. Desynchronous learning in a physics-driven learning network. J Chem Phys 2022; 156:144903. [DOI: 10.1063/5.0084631] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
In a neuron network, synapses update individually using local information, allowing for entirely decentralized learning. In contrast, elements in an artificial neural network are typically updated simultaneously using a central processor. Here, we investigate the feasibility and effect of desynchronous learning in a recently introduced decentralized, physics-driven learning network. We show that desynchronizing the learning process does not degrade the performance for a variety of tasks in an idealized simulation. In experiment, desynchronization actually improves the performance by allowing the system to better explore the discretized state space of solutions. We draw an analogy between desynchronization and mini-batching in stochastic gradient descent and show that they have similar effects on the learning process. Desynchronizing the learning process establishes physics-driven learning networks as truly fully distributed learning machines, promoting better performance and scalability in deployment.
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Affiliation(s)
- J. F. Wycoff
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - S. Dillavou
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - M. Stern
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - A. J. Liu
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - D. J. Durian
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
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24
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Coli GM, Boattini E, Filion L, Dijkstra M. Inverse design of soft materials via a deep learning-based evolutionary strategy. SCIENCE ADVANCES 2022; 8:eabj6731. [PMID: 35044828 PMCID: PMC8769546 DOI: 10.1126/sciadv.abj6731] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 11/22/2021] [Indexed: 05/19/2023]
Abstract
Colloidal self-assembly—the spontaneous organization of colloids into ordered structures—has been considered key to produce next-generation materials. However, the present-day staggering variety of colloidal building blocks and the limitless number of thermodynamic conditions make a systematic exploration intractable. The true challenge in this field is to turn this logic around and to develop a robust, versatile algorithm to inverse design colloids that self-assemble into a target structure. Here, we introduce a generic inverse design method to efficiently reverse-engineer crystals, quasicrystals, and liquid crystals by targeting their diffraction patterns. Our algorithm relies on the synergetic use of an evolutionary strategy for parameter optimization, and a convolutional neural network as an order parameter, and provides a way forward for the inverse design of experimentally feasible colloidal interactions, specifically optimized to stabilize the desired structure.
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25
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Scholl D, Deniz AA. Conformational Freedom and Topological Confinement of Proteins in Biomolecular Condensates. J Mol Biol 2022; 434:167348. [PMID: 34767801 PMCID: PMC8748313 DOI: 10.1016/j.jmb.2021.167348] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 11/01/2021] [Accepted: 11/02/2021] [Indexed: 01/17/2023]
Abstract
The emergence of biomolecular condensation and liquid-liquid phase separation (LLPS) introduces a new layer of complexity into our understanding of cell and molecular biology. Evidence steadily grows indicating that condensates are not only implicated in physiology but also human disease. Macro- and mesoscale characterization of condensates as a whole have been instrumental in understanding their biological functions and dysfunctions. By contrast, the molecular level characterization of condensates and how condensates modify the properties of the molecules that constitute them thus far remain comparably scarce. In this minireview we summarize and discuss the findings of several recent studies that have focused on structure, dynamics, and interactions of proteins undergoing condensation. The mechanistic insights they provide help us identify the relevant properties nature and scientists can leverage to modulate the behavior of condensate systems. We also discuss the unique environment of the droplet surface and speculate on effects of topological constraints and physical exclusion on condensate properties.
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Affiliation(s)
- Daniel Scholl
- Department of Integrative and Computational Biology, The Scripps Research Institute, 10550 N. Torrey Pines Rd., La Jolla, CA 92037, United States. https://twitter.com/@DanielScholl_be
| | - Ashok A Deniz
- Department of Integrative and Computational Biology, The Scripps Research Institute, 10550 N. Torrey Pines Rd., La Jolla, CA 92037, United States.
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26
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Shen X, Fang C, Jin Z, Tong H, Tang S, Shen H, Xu N, Lo JHY, Xu X, Xu L. Achieving adjustable elasticity with non-affine to affine transition. NATURE MATERIALS 2021; 20:1635-1642. [PMID: 34211155 DOI: 10.1038/s41563-021-01046-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 05/28/2021] [Indexed: 06/13/2023]
Abstract
For various engineering and industrial applications it is desirable to realize mechanical systems with broadly adjustable elasticity to respond flexibly to the external environment. Here we discover a topology-correlated transition between affine and non-affine regimes in elasticity in both two- and three-dimensional packing-derived networks. Based on this transition, we numerically design and experimentally realize multifunctional systems with adjustable elasticity. Within one system, we achieve solid-like affine response, liquid-like non-affine response and a continuous tunability in between. Moreover, the system also exhibits a broadly tunable Poisson's ratio from positive to negative values, which is of practical interest for energy absorption and for fracture-resistant materials. Our study reveals a fundamental connection between elasticity and network topology, and demonstrates its practical potential for designing mechanical systems and metamaterials.
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Affiliation(s)
- Xiangying Shen
- Department of Physics, The Chinese University of Hong Kong, Hong Kong, China
- The Beijing Computational Science Research Center, Beijing, China
- Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China
| | - Chenchao Fang
- Department of Physics, The Chinese University of Hong Kong, Hong Kong, China
- Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China
| | - Zhipeng Jin
- Department of Physics, The Chinese University of Hong Kong, Hong Kong, China
- Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China
| | - Hua Tong
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai, China
- Department of Physics, University of Science and Technology of China, Hefei, China
| | - Shixiang Tang
- Department of Physics, The Chinese University of Hong Kong, Hong Kong, China
| | - Hongchuan Shen
- Department of Physics, The Chinese University of Hong Kong, Hong Kong, China
| | - Ning Xu
- Hefei National Laboratory for Physical Sciences at the Microscale and Department of Physics, University of Science and Technology of China, Hefei, China
| | - Jack Hau Yung Lo
- Department of Physics, The Chinese University of Hong Kong, Hong Kong, China.
| | - Xinliang Xu
- The Beijing Computational Science Research Center, Beijing, China.
- Department of Physics, Beijing Normal University, Beijing, China.
| | - Lei Xu
- Department of Physics, The Chinese University of Hong Kong, Hong Kong, China.
- Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China.
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27
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Gomez D, Peña Ccoa WJ, Singh Y, Rojas E, Hocky GM. Molecular Paradigms for Biological Mechanosensing. J Phys Chem B 2021; 125:12115-12124. [PMID: 34709040 DOI: 10.1021/acs.jpcb.1c06330] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Many proteins in living cells are subject to mechanical forces, which can be generated internally by molecular machines, or externally, e.g., by pressure gradients. In general, these forces fall in the piconewton range, which is similar in magnitude to forces experienced by a molecule due to thermal fluctuations. While we would naively expect such moderate forces to produce only minimal changes, a wide variety of "mechanosensing" proteins have evolved with functions that are responsive to forces in this regime. The goal of this article is to provide a physical chemistry perspective on protein-based molecular mechanosensing paradigms used in living systems, and how these paradigms can be explored using novel computational methods.
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Affiliation(s)
- David Gomez
- Department of Biology, New York University, New York, New York 10003, United States.,Department of Chemistry, New York University, New York, New York 10003, United States
| | - Willmor J Peña Ccoa
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Yuvraj Singh
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Enrique Rojas
- Department of Biology, New York University, New York, New York 10003, United States
| | - Glen M Hocky
- Department of Chemistry, New York University, New York, New York 10003, United States
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28
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Abstract
Mechanical mechanisms have been used to process information for millennia, with famous examples ranging from the Antikythera mechanism of the Ancient Greeks to the analytical machines of Charles Babbage. More recently, electronic forms of computation and information processing have overtaken these mechanical forms, owing to better potential for miniaturization and integration. However, several unconventional computing approaches have recently been introduced, which blend ideas of information processing, materials science and robotics. This has raised the possibility of new mechanical computing systems that augment traditional electronic computing by interacting with and adapting to their environment. Here we discuss the use of mechanical mechanisms, and associated nonlinearities, as a means of processing information, with a view towards a framework in which adaptable materials and structures act as a distributed information processing network, even enabling information processing to be viewed as a material property, alongside traditional material properties such as strength and stiffness. We focus on approaches to abstract digital logic in mechanical systems, discuss how these systems differ from traditional electronic computing, and highlight the challenges and opportunities that they present.
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29
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Škrbić T, Maritan A, Giacometti A, Rose GD, Banavar JR. Building blocks of protein structures: Physics meets biology. Phys Rev E 2021; 104:014402. [PMID: 34412233 DOI: 10.1103/physreve.104.014402] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 03/22/2021] [Indexed: 12/11/2022]
Abstract
The native state structures of globular proteins are stable and well packed indicating that self-interactions are favored over protein-solvent interactions under folding conditions. We use this as a guiding principle to derive the geometry of the building blocks of protein structures-α helices and strands assembled into β sheets-with no adjustable parameters, no amino acid sequence information, and no chemistry. There is an almost perfect fit between the dictates of mathematics and physics and the rules of quantum chemistry. Protein evolution is facilitated by sequence-independent platforms, which can elaborate sequence-dependent functional diversity. Our work highlights the vital role of discreteness in life and may have implications for the creation of artificial life and on the nature of life elsewhere in the cosmos.
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Affiliation(s)
- Tatjana Škrbić
- Department of Physics and Institute for Fundamental Science, University of Oregon, Eugene, Oregon 97403, USA.,Dipartimento di Scienze Molecolari e Nanosistemi, Università Ca' Foscari Venezia, Campus Scientifico, Edificio Alfa, via Torino 155, 30170 Venezia Mestre, Italy
| | - Amos Maritan
- Dipartimento di Fisica e Astronomia, Università di Padova and INFN, via Marzolo 8, 35131 Padova, Italy
| | - Achille Giacometti
- Dipartimento di Scienze Molecolari e Nanosistemi, Università Ca' Foscari Venezia, Campus Scientifico, Edificio Alfa, via Torino 155, 30170 Venezia Mestre, Italy.,European Center for Living Technologies (ECLT), Ca' Bottacin, Dorsoduro 3911, Calle Crosera, 30123 Venezia, Italy
| | - George D Rose
- T. C. Jenkins Department of Biophysics, Johns Hopkins University, 3400 North Charles Street, Baltimore, Maryland 21218-2683, USA
| | - Jayanth R Banavar
- Department of Physics and Institute for Fundamental Science, University of Oregon, Eugene, Oregon 97403, USA
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30
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Singh N, van Hecke M. Design of Pseudo-Mechanisms and Multistable Units for Mechanical Metamaterials. PHYSICAL REVIEW LETTERS 2021; 126:248002. [PMID: 34213946 DOI: 10.1103/physrevlett.126.248002] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 05/26/2021] [Indexed: 06/13/2023]
Abstract
Mechanisms-collections of rigid elements coupled by perfect hinges which exhibit a zero-energy motion-motivate the design of a variety of mechanical metamaterials. We enlarge this design space by considering pseudo-mechanisms, collections of elastically coupled elements that exhibit motions with very low energy costs. We show that their geometric design generally is distinct from those of true mechanisms, thus opening up a large and virtually unexplored design space. We further extend this space by designing building blocks with bistable and tristable energy landscapes, realize these by 3D printing, and show how these form unit cells for multistable metamaterials.
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Affiliation(s)
- Nitin Singh
- AMOLF, Science Park 104, 1098 XG Amsterdam, Netherlands
| | - Martin van Hecke
- AMOLF, Science Park 104, 1098 XG Amsterdam, Netherlands
- Huygens-Kamerlingh Onnes Lab, Leiden University, PObox 9504, 2300 RA Leiden, Netherlands
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31
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Huang Q, Song P, Chen Y, Liu Z, Lai L. Allosteric Type and Pathways Are Governed by the Forces of Protein-Ligand Binding. J Phys Chem Lett 2021; 12:5404-5412. [PMID: 34080881 DOI: 10.1021/acs.jpclett.1c01253] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Allostery is central to many cellular processes, by up- or down-regulating target function. However, what determines the allosteric type remains elusive and currently it is impossible to predict whether the allosteric compounds would activate or inhibit target function before experimental studies. We demonstrated that the allosteric type and allosteric pathways are governed by the forces imposed by ligand binding to target protein using the anisotropic network model and developed an allosteric type prediction method (AlloType). AlloType correctly predicted 13 of the 16 allosteric systems in the data set with experimentally determined protein and complex structures as well as verified allosteric types, which was also used to identify allosteric pathways. When applied to glutathione peroxidase 4, a protein with no complex structure information, AlloType could still be able to predict the allosteric type of the recently reported allosteric activators, demonstrating its potential application in designing specific allosteric drugs and uncovering allosteric mechanisms.
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Affiliation(s)
- Qiaojing Huang
- Beijing National Laboratory for Molecular Sciences (BNLMS), State Key Laboratory for Structural Chemistry of Unstable and Stable Species, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Pengbo Song
- Beijing National Laboratory for Molecular Sciences (BNLMS), State Key Laboratory for Structural Chemistry of Unstable and Stable Species, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Yixin Chen
- Beijing National Laboratory for Molecular Sciences (BNLMS), State Key Laboratory for Structural Chemistry of Unstable and Stable Species, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Zhirong Liu
- Beijing National Laboratory for Molecular Sciences (BNLMS), State Key Laboratory for Structural Chemistry of Unstable and Stable Species, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Luhua Lai
- Beijing National Laboratory for Molecular Sciences (BNLMS), State Key Laboratory for Structural Chemistry of Unstable and Stable Species, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
- Center for Quantitative Biology, Peking University, Beijing 100871, China
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China
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32
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Hexner D. Training nonlinear elastic functions: nonmonotonic, sequence dependent and bifurcating. SOFT MATTER 2021; 17:4407-4412. [PMID: 33908450 DOI: 10.1039/d0sm02189j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The elastic behavior of materials operating in the linear regime is constrained, by definition, to operations that are linear in the imposed deformation. Although the nonlinear regime holds promise for new functionality, the design in this regime is challenging. In this paper, we demonstrate that a recent approach based on training [Hexner et al., PNAS 2020, 201922847] allows responses that are inherently non-linear. By applying designer strains, a disordered solid evolves through plastic deformations that alter its response. We show examples of elaborate nonlinear training paths that lead to the following functions: (1) frequency conversion, (2) logic gate and (3) expansion or contraction along one axis, depending on the sequence of imposed transverse compressions. We study the convergence rate and find that it depends on the trained function.
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Affiliation(s)
- Daniel Hexner
- Faculty of Mechanical Engineering, Technion, 320000 Haifa, Israel.
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33
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Ronellenfitsch H. Optimal Elasticity of Biological Networks. PHYSICAL REVIEW LETTERS 2021; 126:038101. [PMID: 33543959 DOI: 10.1103/physrevlett.126.038101] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 12/24/2020] [Indexed: 06/12/2023]
Abstract
Reinforced elastic sheets surround us in daily life, from concrete shell buildings to biological structures such as the arthropod exoskeleton or the venation network of dicotyledonous plant leaves. Natural structures are often highly optimized through evolution and natural selection, leading to the biologically and practically relevant problem of understanding and applying the principles of their design. Inspired by the hierarchically organized scaffolding networks found in plant leaves, here we model networks of bending beams that capture the discrete and nonuniform nature of natural materials. Using the principle of maximal rigidity under natural resource constraints, we show that optimal discrete beam networks reproduce the structural features of real leaf venation. Thus, in addition to its ability to efficiently transport water and nutrients, the venation network also optimizes leaf rigidity using the same hierarchical reticulated network topology. We study the phase space of optimal mechanical networks, providing concrete guidelines for the construction of elastic structures. We implement these natural design rules by fabricating efficient, biologically inspired metamaterials.
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Affiliation(s)
- Henrik Ronellenfitsch
- Department of Mathematics, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, Massachusetts 02139, USA
- Physics Department, Williams College, 33 Lab Campus Drive, Williamstown, Massachusetts 01267, USA
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34
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Rocks JW, Liu AJ, Katifori E. Hidden Topological Structure of Flow Network Functionality. PHYSICAL REVIEW LETTERS 2021; 126:028102. [PMID: 33512186 DOI: 10.1103/physrevlett.126.028102] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 07/01/2020] [Accepted: 10/29/2020] [Indexed: 06/12/2023]
Abstract
The ability to reroute and control flow is vital to the function of venation networks across a wide range of organisms. By modifying individual edges in these networks, either by adjusting edge conductances or creating and destroying edges, organisms robustly control the propagation of inputs to perform specific tasks. However, a fundamental disconnect exists between the structure and function: networks with different local architectures can perform the same functions. Here, we answer the question of how changes at the level of individual edges collectively create functionality at the scale of an entire network. Using persistent homology, we analyze networks tuned to perform complex tasks. We find that the responses of such networks encode a hidden topological structure composed of sectors of nearly uniform pressure. Although these sectors are not apparent in the underlying network structure, they correlate strongly with the tuned function. The connectivity of these sectors, rather than that of individual nodes, provides a quantitative relationship between structure and function in flow networks.
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Affiliation(s)
- Jason W Rocks
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Andrea J Liu
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Eleni Katifori
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
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35
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Abstract
We consider disordered solids in which the microscopic elements can deform plastically in response to stresses on them. We show that by driving the system periodically, this plasticity can be exploited to train in desired elastic properties, both in the global moduli and in local "allosteric" interactions. Periodic driving can couple an applied "source" strain to a "target" strain over a path in the energy landscape. This coupling allows control of the system's response, even at large strains well into the nonlinear regime, where it can be difficult to achieve control simply by design.
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36
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Yan L, Wang S. Shaping Polyclonal Responses via Antigen-Mediated Antibody Interference. iScience 2020; 23:101568. [PMID: 33083735 PMCID: PMC7530306 DOI: 10.1016/j.isci.2020.101568] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 06/29/2020] [Accepted: 09/14/2020] [Indexed: 12/05/2022] Open
Abstract
Broadly neutralizing antibodies (bnAbs) recognize conserved features of rapidly mutating pathogens and confer universal protection, but they emerge rarely in natural infection. Increasing evidence indicates that seemingly passive antibodies may interfere with natural selection of B cells. Yet, how such interference modulates polyclonal responses is unknown. Here we provide a framework for understanding the role of antibody interference—mediated by multi-epitope antigens—in shaping B cell clonal makeup and the fate of bnAb lineages. We find that, under heterogeneous interference, clones with different intrinsic fitness can collectively persist. Furthermore, antagonism among fit clones (specific for variable epitopes) promotes expansion of unfit clones (targeting conserved epitopes), at the cost of repertoire potency. This trade-off, however, can be alleviated by synergy toward the unfit. Our results provide a physical basis for antigen-mediated clonal interactions, stress system-level impacts of molecular synergy and antagonism, and offer principles to amplify naturally rare clones. Multi-epitope antigens mediate antibody interference that couples B cell lineages Trade-off exists between repertoire potency and persistence of broad lineages Antigen-mediated synergy toward intrinsically unfit clones alleviates the trade-off Amplifying rare clones by leveraging molecular interference structure
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Affiliation(s)
- Le Yan
- Chan Zuckerberg Biohub, 499 Illinois Street, San Francisco, CA 94158, USA
| | - Shenshen Wang
- Department of Physics and Astronomy, University of California, Los Angeles, Los Angeles, CA 90095, USA
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37
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Abstract
Living systems evolve one mutation at a time, but a single mutation can alter the effect of subsequent mutations. The underlying mechanistic determinants of such epistasis are unclear. Here, we demonstrate that the physical dynamics of a biological system can generically constrain epistasis. We analyze models and experimental data on proteins and regulatory networks. In each, we find that if the long-time physical dynamics is dominated by a slow, collective mode, then the dimensionality of mutational effects is reduced. Consequently, epistatic coefficients for different combinations of mutations are no longer independent, even if individually strong. Such epistasis can be summarized as resulting from a global nonlinearity applied to an underlying linear trait, that is, as global epistasis. This constraint, in turn, reduces the ruggedness of the sequence-to-function map. By providing a generic mechanistic origin for experimentally observed global epistasis, our work suggests that slow collective physical modes can make biological systems evolvable.
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Affiliation(s)
- Kabir Husain
- Department of Physics, University of Chicago, Chicago, IL
| | - Arvind Murugan
- Department of Physics, University of Chicago, Chicago, IL
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38
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Bonfanti S, Guerra R, Font-Clos F, Rayneau-Kirkhope D, Zapperi S. Automatic design of mechanical metamaterial actuators. Nat Commun 2020; 11:4162. [PMID: 32820158 PMCID: PMC7441157 DOI: 10.1038/s41467-020-17947-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 07/23/2020] [Indexed: 02/06/2023] Open
Abstract
Mechanical metamaterial actuators achieve pre-determined input–output operations exploiting architectural features encoded within a single 3D printed element, thus removing the need for assembling different structural components. Despite the rapid progress in the field, there is still a need for efficient strategies to optimize metamaterial design for a variety of functions. We present a computational method for the automatic design of mechanical metamaterial actuators that combines a reinforced Monte Carlo method with discrete element simulations. 3D printing of selected mechanical metamaterial actuators shows that the machine-generated structures can reach high efficiency, exceeding human-designed structures. We also show that it is possible to design efficient actuators by training a deep neural network which is then able to predict the efficiency from the image of a structure and to identify its functional regions. The elementary actuators devised here can be combined to produce metamaterial machines of arbitrary complexity for countless engineering applications. Efficient strategies to optimize metamaterial design for specific applications are urgently needed despite the rapid progress in this area. Here the authors propose a computational method combining an optimization algorithm with discrete element simulations for the automatic design of mechanical metamaterial actuators.
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Affiliation(s)
- Silvia Bonfanti
- Center for Complexity and Biosystems, Department of Physics, University of Milan, via Celoria 16, Milano, 20133, Italy
| | - Roberto Guerra
- Center for Complexity and Biosystems, Department of Physics, University of Milan, via Celoria 16, Milano, 20133, Italy
| | - Francesc Font-Clos
- Center for Complexity and Biosystems, Department of Physics, University of Milan, via Celoria 16, Milano, 20133, Italy
| | - Daniel Rayneau-Kirkhope
- Center for Complexity and Biosystems, Department of Physics, University of Milan, via Celoria 16, Milano, 20133, Italy
| | - Stefano Zapperi
- Center for Complexity and Biosystems, Department of Physics, University of Milan, via Celoria 16, Milano, 20133, Italy. .,CNR - Consiglio Nazionale delle Ricerche, Istituto di Chimica della Materia Condensata e di Tecnologie per l'Energia, Via R. Cozzi 53, Milano, 20125, Italy.
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39
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Wang J, Jain A, McDonald LR, Gambogi C, Lee AL, Dokholyan NV. Mapping allosteric communications within individual proteins. Nat Commun 2020; 11:3862. [PMID: 32737291 PMCID: PMC7395124 DOI: 10.1038/s41467-020-17618-2] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2019] [Accepted: 06/30/2020] [Indexed: 02/05/2023] Open
Abstract
Allostery in proteins influences various biological processes such as regulation of gene transcription and activities of enzymes and cell signaling. Computational approaches for analysis of allosteric coupling provide inexpensive opportunities to predict mutations and to design small-molecule agents to control protein function and cellular activity. We develop a computationally efficient network-based method, Ohm, to identify and characterize allosteric communication networks within proteins. Unlike previously developed simulation-based approaches, Ohm relies solely on the structure of the protein of interest. We use Ohm to map allosteric networks in a dataset composed of 20 proteins experimentally identified to be allosterically regulated. Further, the Ohm allostery prediction for the protein CheY correlates well with NMR CHESCA studies. Our webserver, Ohm.dokhlab.org, automatically determines allosteric network architecture and identifies critical coupled residues within this network.
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Affiliation(s)
- Jian Wang
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, 17033-0850, USA
| | - Abha Jain
- Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599-7363, USA
| | - Leanna R McDonald
- Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599-7363, USA
| | - Craig Gambogi
- Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599-7363, USA
| | - Andrew L Lee
- Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599-7363, USA
| | - Nikolay V Dokholyan
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, 17033-0850, USA.
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Departments of Biochemistry & Molecular Biology, Penn State College of Medicine, Hershey, PA, 17033-0850, USA.
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40
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Abstract
Mechanical metamaterials are usually designed to show desired responses to prescribed forces. In some applications, the desired force-response relationship is hard to specify exactly, but examples of forces and desired responses are easily available. Here, we propose a framework for supervised learning in thin, creased sheets that learn the desired force-response behavior by physically experiencing training examples and then, crucially, respond correctly (generalize) to previously unseen test forces. During training, we fold the sheet using training forces, prompting local crease stiffnesses to change in proportion to their experienced strain. We find that this learning process reshapes nonlinearities inherent in folding a sheet so as to show the correct response for previously unseen test forces. We show the relationship between training error, test error, and sheet size (model complexity) in learning sheets and compare them to counterparts in machine-learning algorithms. Our framework shows how the rugged energy landscape of disordered mechanical materials can be sculpted to show desired force-response behaviors by a local physical learning process.
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41
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Xie J, Lai L. Protein topology and allostery. Curr Opin Struct Biol 2020; 62:158-165. [DOI: 10.1016/j.sbi.2020.01.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Revised: 01/12/2020] [Accepted: 01/16/2020] [Indexed: 01/07/2023]
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42
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Lake PT, Davidson RB, Klem H, Hocky GM, McCullagh M. Residue-Level Allostery Propagates through the Effective Coarse-Grained Hessian. J Chem Theory Comput 2020; 16:3385-3395. [PMID: 32251581 DOI: 10.1021/acs.jctc.9b01149] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The long-ranged coupling between residues that gives rise to allostery in a protein is built up from short-ranged physical interactions. Computational tools used to predict this coupling and its functional relevance have relied on the application of graph theoretical metrics to residue-level correlations measured from all-atom molecular dynamics simulations. The short-ranged interactions that yield these long-ranged residue-level correlations are quantified by the effective coarse-grained Hessian. Here we compute an effective harmonic coarse-grained Hessian from simulations of a benchmark allosteric protein, IGPS, and demonstrate the improved locality of this graph Laplacian over two other connectivity matrices. Additionally, two centrality metrics are developed that indicate the direct and indirect importance of each residue at producing the covariance between the effector binding pocket and the active site. The residue importance indicated by these two metrics is corroborated by previous mutagenesis experiments and leads to unique functional insights; in contrast to previous computational analyses, our results suggest that fP76-hK181 is the most important contact for conveying direct allosteric paths across the HisF-HisH interface. The connectivity around fD98 is found to be important at affecting allostery through indirect means.
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Affiliation(s)
- Peter T Lake
- Department of Chemistry, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Russell B Davidson
- Department of Chemistry, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Heidi Klem
- Department of Chemistry, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Glen M Hocky
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Martin McCullagh
- Department of Chemistry, Colorado State University, Fort Collins, Colorado 80523, United States
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43
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Wang M, Grosberg AY. Three-body problem for Langevin dynamics with different temperatures. Phys Rev E 2020; 101:032131. [PMID: 32290012 DOI: 10.1103/physreve.101.032131] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Accepted: 02/20/2020] [Indexed: 11/07/2022]
Abstract
A mixture of Brownian particles at different temperatures has been a useful model for studying the out-of-equilibrium properties of systems made up of microscopic components with differing levels of activity. This model was previously studied analytically for two-particle interactions in the dilute limit, yielding a Boltzmann-like two-particle distribution with an effective temperature. Like the Newtonian two- and three-body problems, we ask here whether the two-particle results can be extended to three-particle interactions to get the three-particle distributions. By considering the special solvable case of pairwise quadratic interactions, we show that, unlike the two-particle distribution, the three-particle distribution cannot in general be Boltzmann-like with an effective temperature. We instead find that the steady-state distribution of any two particles in a triplet depends on the properties of and interactions with the third particle, leading to some unexpected behaviors not present in equilibrium.
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Affiliation(s)
- Michael Wang
- Department of Physics and Center for Soft Matter Research, New York University, 726 Broadway, New York, New York 10003, USA
| | - Alexander Y Grosberg
- Department of Physics and Center for Soft Matter Research, New York University, 726 Broadway, New York, New York 10003, USA
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44
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Loutchko D, Flechsig H. Allosteric communication in molecular machines via information exchange: what can be learned from dynamical modeling. Biophys Rev 2020; 12:443-452. [PMID: 32198636 DOI: 10.1007/s12551-020-00667-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 02/25/2020] [Indexed: 02/07/2023] Open
Abstract
Allosteric regulation is crucial for the operation of protein machines and molecular motors. A major challenge is to characterize and quantify the information exchange underlying allosteric communication between remote functional sites in a protein, and to identify the involved relevant pathways. We review applications of two topical approaches of dynamical protein modeling, a kinetic-based single-molecule stochastic model, which employs information thermodynamics to quantify allosteric interactions, and structure-based coarse-grained modeling to characterize intra-molecular couplings in terms of conformational motions and propagating mechanical strain. Both descriptions resolve the directionality of allosteric responses within a protein, emphasizing the concept of causality as the principal hallmark of protein allostery. We discuss the application of techniques from information thermodynamics to dynamic protein elastic networks and evolutionary designed model structures, and the ramifications for protein allostery.
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Affiliation(s)
- Dimitri Loutchko
- Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan
| | - Holger Flechsig
- Nano Life Science Institute (WPI-NanoLSI), Kanazawa University, Kakuma-machi, Kanazawa, Ishikawa, 920-1192, Japan.
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45
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Chen S, Choi GPT, Mahadevan L. Deterministic and stochastic control of kirigami topology. Proc Natl Acad Sci U S A 2020; 117:4511-4517. [PMID: 32054786 PMCID: PMC7060672 DOI: 10.1073/pnas.1909164117] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Kirigami, the creative art of paper cutting, is a promising paradigm for mechanical metamaterials. However, to make kirigami-inspired structures a reality requires controlling the topology of kirigami to achieve connectivity and rigidity. We address this question by deriving the maximum number of cuts (minimum number of links) that still allow us to preserve global rigidity and connectivity of the kirigami. A deterministic hierarchical construction method yields an efficient topological way to control both the number of connected pieces and the total degrees of freedom. A statistical approach to the control of rigidity and connectivity in kirigami with random cuts complements the deterministic pathway, and shows that both the number of connected pieces and the degrees of freedom show percolation transitions as a function of the density of cuts (links). Together, this provides a general framework for the control of rigidity and connectivity in planar kirigami.
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Affiliation(s)
- Siheng Chen
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138
| | - Gary P T Choi
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138
| | - L Mahadevan
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138;
- Department of Physics, Harvard University, Cambridge, MA 02138
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138
- Kavli Institute for Bionano Science and Technology, Harvard University, Cambridge, MA 02138
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46
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Bravi B, Ravasio R, Brito C, Wyart M. Direct coupling analysis of epistasis in allosteric materials. PLoS Comput Biol 2020; 16:e1007630. [PMID: 32119660 PMCID: PMC7067494 DOI: 10.1371/journal.pcbi.1007630] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 03/12/2020] [Accepted: 01/03/2020] [Indexed: 11/22/2022] Open
Abstract
In allosteric proteins, the binding of a ligand modifies function at a distant active site. Such allosteric pathways can be used as target for drug design, generating considerable interest in inferring them from sequence alignment data. Currently, different methods lead to conflicting results, in particular on the existence of long-range evolutionary couplings between distant amino-acids mediating allostery. Here we propose a resolution of this conundrum, by studying epistasis and its inference in models where an allosteric material is evolved in silico to perform a mechanical task. We find in our model the four types of epistasis (Synergistic, Sign, Antagonistic, Saturation), which can be both short or long-range and have a simple mechanical interpretation. We perform a Direct Coupling Analysis (DCA) and find that DCA predicts well the cost of point mutations but is a rather poor generative model. Strikingly, it can predict short-range epistasis but fails to capture long-range epistasis, in consistence with empirical findings. We propose that such failure is generic when function requires subparts to work in concert. We illustrate this idea with a simple model, which suggests that other methods may be better suited to capture long-range effects. Allostery in proteins is the property of highly specific responses to ligand binding at a distant site. To inform protocols of de novo drug design, it is fundamental to understand the impact of mutations on allosteric regulation and whether it can be predicted from evolutionary correlations. In this work we consider allosteric architectures artificially evolved to optimize the cooperativity of binding at allosteric and active site. We first characterize the emergent pattern of epistasis as well as the underlying mechanical phenomena, finding the four types of epistasis (Synergistic, Sign, Antagonistic, Saturation), which can be both short or long-range. The numerical evolution of these allosteric architectures allows us to benchmark Direct Coupling Analysis, a method which relies on co-evolution in sequence data to infer direct evolutionary couplings, in connection to allostery. We show that Direct Coupling Analysis predicts quantitatively point mutation costs but underestimates strong long-range epistasis. We provide an argument, based on a simplified model, illustrating the reasons for this discrepancy. Our analysis suggests neural networks as more promising tool to measure epistasis.
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Affiliation(s)
- Barbara Bravi
- Institute of Physics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- * E-mail: (BB); (MW)
| | - Riccardo Ravasio
- Institute of Physics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Carolina Brito
- Instituto de Fìsica, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Matthieu Wyart
- Institute of Physics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- * E-mail: (BB); (MW)
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47
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Banerjee S, Gardel ML, Schwarz US. The Actin Cytoskeleton as an Active Adaptive Material. ANNUAL REVIEW OF CONDENSED MATTER PHYSICS 2020; 11:421-439. [PMID: 33343823 PMCID: PMC7748259 DOI: 10.1146/annurev-conmatphys-031218-013231] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Actin is the main protein used by biological cells to adapt their structure and mechanics to their needs. Cellular adaptation is made possible by molecular processes that strongly depend on mechanics. The actin cytoskeleton is also an active material that continuously consumes energy. This allows for dynamical processes that are possible only out of equilibrium and opens up the possibility for multiple layers of control that have evolved around this single protein.Here we discuss the actin cytoskeleton from the viewpoint of physics as an active adaptive material that can build structures superior to man-made soft matter systems. Not only can actin be used to build different network architectures on demand and in an adaptive manner, but it also exhibits the dynamical properties of feedback systems, like excitability, bistability, or oscillations. Therefore, it is a prime example of how biology couples physical structure and information flow and a role model for biology-inspired metamaterials.
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Affiliation(s)
- Shiladitya Banerjee
- Department of Physics and Astronomy and Institute for the Physics of Living Systems, University College London, London WC1E 6BT, United Kingdom
- Department of Physics, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
| | - Margaret L Gardel
- Department of Physics, James Franck Institute, and Institute for Biophysical Dynamics, University of Chicago, Chicago, Illinois 60637, USA
| | - Ulrich S Schwarz
- Institute for Theoretical Physics and BioQuant, Heidelberg University, 69120 Heidelberg, Germany
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48
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Rivoire O. Parsimonious evolutionary scenario for the origin of allostery and coevolution patterns in proteins. Phys Rev E 2020; 100:032411. [PMID: 31640027 DOI: 10.1103/physreve.100.032411] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Indexed: 12/16/2022]
Abstract
Proteins display generic properties that are challenging to explain by direct selection, notably allostery, the capacity to be regulated through long-range effects, and evolvability, the capacity to adapt to new selective pressures. An evolutionary scenario is proposed where proteins acquire these two features indirectly as a by-product of their selection for a more fundamental property, exquisite discrimination, the capacity to bind discriminatively very similar ligands. Achieving this task is shown to typically require proteins to undergo a conformational change. We argue that physical and evolutionary constraints impel this change to be controlled by a group of sites extending from the binding site. Proteins can thus acquire a latent potential for allosteric regulation and evolutionary adaptation because of long-range effects that initially arise as evolutionary spandrels. This scenario accounts for the groups of conserved and coevolving residues observed in multiple sequence alignments. However, we propose that most pairs of coevolving and contacting residues inferred from such alignments have a different origin, related to thermal stability. A physical model is presented that illustrates this evolutionary scenario and its implications. The scenario can be implemented in experiments of protein evolution to directly test its predictions.
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Affiliation(s)
- Olivier Rivoire
- Center for Interdisciplinary Research in Biology, Collège de France, Centre National de la Recherche Scientifique, INSERM, PSL Research University, 75005 Paris, France
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49
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Pashine N, Hexner D, Liu AJ, Nagel SR. Directed aging, memory, and nature's greed. SCIENCE ADVANCES 2019; 5:eaax4215. [PMID: 32064313 PMCID: PMC6989340 DOI: 10.1126/sciadv.aax4215] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Accepted: 09/13/2019] [Indexed: 05/05/2023]
Abstract
Disordered materials are often out of equilibrium and evolve very slowly in a rugged and tortuous energy landscape. This slow evolution, referred to as aging, is deemed undesirable as it often leads to material degradation. However, we show that aging also encodes a memory of the stresses imposed during preparation. Because of inhomogeneous local stresses, the material itself decides how to evolve by modifying stressed regions differently from those under less stress. Because material evolution occurs in response to stresses, aging can be "directed" to produce sought-after responses and unusual functionalities that do not inherently exist. Aging obeys a natural "greedy algorithm" as, at each instant, the material simply follows the path of most rapid and accessible relaxation. Our experiments and simulations illustrate directed aging in examples in which the material's elasticity transforms as desired because of an imposed deformation.
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Affiliation(s)
- Nidhi Pashine
- Department of Physics and The James Franck and Enrico Fermi Institutes, University of Chicago, Chicago, IL 60637, USA
- Corresponding author.
| | - Daniel Hexner
- Department of Physics and The James Franck and Enrico Fermi Institutes, University of Chicago, Chicago, IL 60637, USA
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Andrea J. Liu
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sidney R. Nagel
- Department of Physics and The James Franck and Enrico Fermi Institutes, University of Chicago, Chicago, IL 60637, USA
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50
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Ravasio R, Flatt SM, Yan L, Zamuner S, Brito C, Wyart M. Mechanics of Allostery: Contrasting the Induced Fit and Population Shift Scenarios. Biophys J 2019; 117:1954-1962. [PMID: 31653447 PMCID: PMC7031744 DOI: 10.1016/j.bpj.2019.10.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 09/27/2019] [Accepted: 10/03/2019] [Indexed: 12/11/2022] Open
Abstract
In allosteric proteins, binding a ligand can affect function at a distant location, for example, by changing the binding affinity of a substrate at the active site. The induced fit and population shift models, which differ by the assumed number of stable configurations, explain such cooperative binding from a thermodynamic viewpoint. Yet, understanding what mechanical principles constrain these models remains a challenge. Here, we provide an empirical study on 34 proteins supporting the idea that allosteric conformational change generally occurs along a soft elastic mode presenting extended regions of high shear. We argue, based on a detailed analysis of how the energy profile along such a mode depends on binding, that in the induced fit scenario, there is an optimal stiffness ka∗ ∼ 1/N for cooperative binding, where N is the number of residues. We find that the population shift scenario is more robust to mutations affecting stiffness because binding becomes more and more cooperative with stiffness up to the same characteristic value ka∗, beyond which cooperativity saturates instead of decaying. We numerically confirm these findings in a nonlinear mechanical model. Dynamical considerations suggest that a stiffness of order ka∗ is favorable in that scenario as well, supporting that for proper function, proteins must evolve a functional elastic mode that is softer as their size increases. In consistency with this view, we find a fair anticorrelation between the stiffness of the allosteric response and protein size in our data set.
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Affiliation(s)
- Riccardo Ravasio
- Institute of Physics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
| | - Solange Marie Flatt
- Institute of Physics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Le Yan
- Kavli Institute for Theoretical Physics, University of California, Santa Barbara, California
| | - Stefano Zamuner
- Institute of Physics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Carolina Brito
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, State of Rio Grande do Sul, Brazil
| | - Matthieu Wyart
- Institute of Physics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
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