Hierarchical coexistence of universality and diversity controls robustness and multi-functionality in intermediate filament protein networks

Proteins constitute the elementary building blocks of a vast variety of biological materials such as cellular protein networks, spider silk or bone, where they create extremely robust, multi-functional materials by self-organization of structures over many length- and time scales, from nano to macro. Some of the structural features are commonly found in a many different tissues, that is, they are highly conserved. Examples of such universal building blocks include alpha-helices, beta-sheets or tropocollagen molecules. In contrast, other features are highly specific to tissue types, such as particular filament assemblies, beta-sheet nanocrystals in spider silk or tendon fascicles. These examples illustrate that the coexistence of universality and diversity – in the following referred to as the universality-diversity paradigm (UDP) – is an overarching feature in protein materials. This paradigm is a paradox: How can a structure be universal and diverse at the same time? In protein materials, the coexistence of universality and diversity is enabled by utilizing hierarchies, which serve as an additional dimension beyond the 3D or 4D physical space. This may be crucial to understand how their structure and properties are linked, and how these materials are capable of combining seemingly disparate properties such as strength and robustness. Here we illustrate how the UDP enables to unify universal building blocks and highly diversified patterns through formation of hierarchical structures that lead to multi-functional, robust yet highly adapted structures. We illustrate these concepts in an analysis of three types of intermediate filament proteins, including vimentin, lamin and keratin

Fast scalable visualization techniques for interactive billion-particle walkthrough

This research develops a comprehensive framework for interactive walkthrough involving one billion particles in an immersive virtual environment to enable interrogative visualization of large atomistic simulation data. As a mixture of scientific and engineering approaches, the framework is based on four key techniques: adaptive data compression based on space-filling curves, octree-based visibility and occlusion culling, predictive caching based on machine learning, and scalable data reduction based on parallel and distributed processing. In terms of parallel rendering, this system combines functional parallelism, data parallelism, and temporal parallelism to improve interactivity. The visualization framework will be applicable not only to material simulation, but also to computational biology, applied mathematics, mechanical engineering, and nanotechnology, etc

Multiscale modeling and simulation of deformation and failure mechanisms of hierarchical alpha-helical protein materials

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 116-121).Alpha-helical (AH) protein structures are critical building blocks of life, representing the key constituents of biological materials such as cells, hair, hoof and wool, where they assemble to form hierarchical structures. AHs play an important mechanical role in biological processes such as mechanotransduction, cell mechanics, tissue mechanics and remodeling. Whereas the mechanics of engineered materials has been widely investigated, the deformation and failure mechanisms of biological protein materials remain largely unknown, partly due to a lack of understanding of how individual protein building blocks respond to mechanical load and how the hierarchical features participate in the function of the overall biological system. In this Thesis, we develop, calibrate, validate and apply two computational models to predict the elasticity, deformation, strength and failure mechanisms of AH protein arrangements and eukaryotic cells over multiple orders of magnitude in time- and lengthscales. Our AH protein model is based on the formulation of tensile double-well mesoscale potentials and intermolecular adhesion Lennard-Jones potentials derived directly from results of full atomistic simulations. We report a systematic analysis of the influence of key parameters on the strength properties and deformation mechanisms, including structural and chemical parameters, and compare it with theoretical strength models. We find a weakening effect as the length of AH proteins increases, followed by an asymptotic regime in which the strength remains constant. We also show that interprotein sliding is a dominating mechanism that persists for a variety of geometries and realistic biologically occurring amino acid sequences. The model reported here is generally applicable to other protein filaments that feature a serial array of domains that unfold under applied strain. Although simple, our coarse-grained cell model agrees well with experiments and illustrates how the multiscale approach developed here can be used to describe more complex biological structures. We further show that cytoskeletal intermediate filaments contribute to cell stiffness and deformation and thus play a significant role to maintain cell structural integrity in response to stress. These studies lay the foundation to improve our understanding of pathological pathways linked to AH proteins such as muscular dystrophies.by Jeremie Bertaud.S.M

Indium Arsenide/Gallium Arsenide Quantum Dots and Nanomesas: Multimillion-Atom Molecular Dynamics Solutions on Parallel Architectures.

Multimillion-atom molecular dynamics (MD) simulations have been performed to study the flat InAs overlayers with self-limiting thickness on GaAs square nanomesas. The in-plane lattice constant of InAs layers parallel to the InAs/GaAs(001) interface starts to exceed the InAs bulk value at 12th monolayer (ML) and the hydrostatic stresses in InAs layers become tensile above ∼12 th ML. As a result, it is not favorable to have InAs overlayers thicker than 12 ML. This may explain the experimental findings of the growth of flat InAs overlayers with self-limiting thickness of ∼11 ML on GaAs nanomesas. We have also examined the lateral size effects on the stress distribution and morphology of InAs/GaAs square nanomesas using parallel molecular dynamics. Two mesas with the same vertical size but different lateral sizes are simulated. For the smaller mesa, a single stress domain is observed in the InAs overlayer, whereas two stress domains are found in the larger mesa. This indicates the existence of a critical lateral size for domain formation in accordance with recent experimental findings. The InAs overlayer in the larger mesa is laterally constrained to the GaAs bulk lattice constant but vertically relaxed to the InAs bulk lattice constant, consistent with the Poisson effect. Moreover, we have calculated surface energies of GaAs and InAs for the (100), (110), and (111) orientations. Both MD and the conjugate gradient method are used and the results are in excellent agreement. Surface reconstructions on GaAs(100) and InAs(100) are studied via the conjugate gradient method. We have developed a new model for GaAs(100) and InAs(100) surface atoms. Not only this model reproduces well surface energies for the (100) orientation, it also yields (1 x 2) dimer lengths in accordance with Ab initio calculations. Finally, a series of molecular dynamics simulations are performed to investigate the behavior under load of several 〈001〉 and 〈011〉 symmetrical tilt grain boundaries (GBs) in diamond. These MD simulations are based on the bond-order analytic potential. Crack propagation in polycrystalline diamond samples under an applied load is simulated, and found to be predominantly transgranular rather than intergranular

Size-dependent mechanical properties of beta-structures in protein materials

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 199-217).Protein materials such as spider silk can be exceptionally strong, and they can stretch tremendously before failure. Notably, silks are made entirely of proteins, which owe their structure and stability to weak molecular interactions, in particular, hydrogen bonds (H-bonds). Beta-structures, a class of protein folds that employ dense arrays of H-bonds, are universal in strong protein materials such as silks, amyloids, muscle fibers and virulence factors. The biological recipe for creating strong, tough materials from weak bonds, however, has so far remained a secret. In this dissertation, size, geometry and deformation rate dependent properties of beta-structures are investigated, in order to provide a link between the nanostructure and mechanics of protein materials at multiple length scales. Large-scale molecular dynamics (MD) simulations show that beta-structures reinforce protein materials such as silk by forming H-bonded crystalline regions that cross-link polypeptide chains. A key finding is that superior strength and toughness can only be achieved if the size of the beta-sheet crystals is reduced to a few nanometers. Upon confinement into orderly nanocrystals, H-bond arrays achieve a strong character through cooperation under uniform shear deformation. Moreover, the size-dependent emergence of a molecular stick-slip failure mechanism enhances toughness of the material. Based on replica-exchange MD simulations, the first representative atomistic model for spider silk is proposed. The computational, bottom-up approach predicts a multi-phase material with beta-sheet nanocrystals dispersed within semi-amorphous domains, where the large-deformation and failure of silk is governed by the beta-structures. These findings explain a wide range of observations from single molecule experiments on proteins, as well as characterization studies on silks. Results illustrate how nano-scale confinement of weak bond clusters may lead to strong, tough polymer materials that self-assemble from common, simple building blocks.by Sinan Keten.Ph.D