Molecular dynamics (MD) simulation is a valuable tool for characterizing the structural dynamics of folded proteins and should be similarly applicable to disordered proteins and proteins with both ...folded and disordered regions. It has been unclear, however, whether any physical model (force field) used in MD simulations accurately describes both folded and disordered proteins. Here, we select a benchmark set of 21 systems, including folded and disordered proteins, simulate these systems with six state-of-theart force fields, and compare the results to over 9,000 available experimental data points. We find that none of the tested force fields simultaneously provided accurate descriptions of folded proteins, of the dimensions of disordered proteins, and of the secondary structure propensities of disordered proteins. Guided by simulation results on a subset of our benchmark, however, we modified parameters of one force field, achieving excellent agreement with experiment for disordered proteins, while maintaining state-of-the-art accuracy for folded proteins. The resulting force field, a99SB-disp, should thus greatly expand the range of biological systems amenable to MD simulation. A similar approach could be taken to improve other force fields.
In this work, the Smooth Particle Hydrodynamics (SPH) method, a Lagrangian mesh-free numerical scheme, is adapted for the first time to resolve thermal–mechanical–material fields in a range of Laser ...Fusion Additive Manufacturing processes. The method is capable of simulating large-deformation, free-surface melting, flow, and re-solidification of metallic materials with complex physics and material geometries. A novel SPH formulation for modeling isothermally-incompressible fluids, which allows for the accurate simulation of thermally-driven, liquid-phase metal expansion/contraction, is presented and verified. Fundamental validation of the methodology is performed via comparison with spot laser welding experiments. The methodology is then used to investigate the specific Additive Manufacturing process of the Selective Laser Melting of Metallic, micro-scale particle beds. The physics of a track deposition process is explored through numerical experiments, and the influence of processing parameters on the quality of the finished melt-track is investigated. The unique abilities of using a Lagrangian mesh-free method, as opposed to mesh-based numerical schemes, to model this process are highlighted. The SPH method is found to be a viable and promising numerical tool for simulating laser fusion driven Additive Manufacturing processes.
Widely developed for clinical screening, electrocardiogram (ECG) recordings capture the cardiac electrical activity from the body surface. ECG analysis can therefore be a crucial first step to help ...diagnose, understand and predict cardiovascular disorders responsible for 30% of deaths worldwide. Computational techniques, and more specifically machine learning techniques and computational modelling are powerful tools for classification, clustering and simulation, and they have recently been applied to address the analysis of medical data, especially ECG data. This review describes the computational methods in use for ECG analysis, with a focus on machine learning and 3D computer simulations, as well as their accuracy, clinical implications and contributions to medical advances. The first section focuses on heartbeat classification and the techniques developed to extract and classify abnormal from regular beats. The second section focuses on patient diagnosis from whole recordings, applied to different diseases. The third section presents real-time diagnosis and applications to wearable devices. The fourth section highlights the recent field of personalized ECG computer simulations and their interpretation. Finally, the discussion section outlines the challenges of ECG analysis and provides a critical assessment of the methods presented. The computational methods reported in this review are a strong asset for medical discoveries and their translation to the clinical world may lead to promising advances.
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•IDPs are involved in neurodegenerative diseases.•Structure determination of the Aβ, tau and α-synuclein proteins is a challenge.•Computer simulation results free or guided by ...experimental data.•PEP-fold results on α-synuclein monomer.
Intrinsically disordered proteins (IDPs) play many biological roles in the human proteome ranging from vesicular transport, signal transduction to neurodegenerative diseases. The Aβ and tau proteins, and the α-synuclein protein, key players in Alzheimer’s and Parkinson’s diseases, respectively are fully disordered at the monomer level. The structural heterogeneity of the monomeric and oligomeric states and the high self-assembly propensity of these three IDPs have precluded experimental structural determination. Simulations have been used to determine the atomic structures of these IDPs. In this article, we review recent computer models to capture the equilibrium ensemble of Aβ, tau and α-synuclein proteins at different association steps in aqueous solution and present new results of the PEP-FOLD framework on α-synuclein monomer.
Researchers have explored the effectiveness of computer simulations for supporting science teaching and learning during the past four decades. The purpose of this paper is to provide a comprehensive, ...critical review of the literature on the impact of computer simulations on science teaching and learning, with the goal of summarizing what is currently known and providing guidance for future research. We report on the outcomes of 61 empirical studies dealing with the efficacy of, and implications for, computer simulations in science instruction. The overall findings suggest that simulations can be as effective, and in many ways more effective, than traditional (i.e. lecture-based, textbook-based and/or physical hands-on) instructional practices in promoting science content knowledge, developing process skills, and facilitating conceptual change. As with any other educational tool, the effectiveness of computer simulations is dependent upon the ways in which they are used. Thus, we outline specific research-based guidelines for best practice. Computer simulations are most effective when they (a) are used as supplements; (b) incorporate high-quality support structures; (c) encourage student reflection; and (d) promote cognitive dissonance. Used appropriately, computer simulations involve students in inquiry-based, authentic science explorations. Additionally, as educational technologies continue to evolve, advantages such as flexibility, safety, and efficiency deserve attention.
While computer simulations are a key element in understanding and doing science today, their nature and implications for science education have not been adequately explored in the relevant ...literature. In this article, (1) we provide an analysis of the methodology and epistemology of computer simulations, aiming to contribute to a sound and comprehensive account of the nature of computer simulations in science education, and (2) examine certain implications for science education, particularly in terms of contemporary educational goals relating to scientific literacy. We describe methodological elements relating to processes, techniques, and skills required for the construction and evaluation of scientific simulations, and we discuss epistemological views of their reliability and epistemic status based on the relevant philosophical views. We then examine implications of these elements for the use of simulations and especially for supporting scientific practices in the classroom and the corresponding educational goals. Concretely, we compare educational simulations with those used in scientific research and with laboratory experiments, we discuss the question of the reliability of simulations used in teaching or in public information, and we give examples of their use for supporting NOS understanding and reasoning abilities. Finally, in the context of the philosophical discourse about scientific realism, we examine implications of the epistemology of models that concern the conception of the relation between scientific claims and the real world, which constitutes a fundamental epistemological basis for teaching the nature of science.
Disordered systems like liquids, gels, glasses, or granular materials are not only ubiquitous in daily life and in industrial applications, but they are also crucial for the mechanical stability of ...cells or the transport of chemical and biological agents in living organisms. Despite the importance of these systems, their microscopic structure is understood only on a rudimentary level, thus in stark contrast to the case of gases and crystals. Since scattering experiments and analytical calculations usually give only structural information that is spherically averaged, the three-dimensional (3D) structure of disordered systems is basically unknown. Here, we introduce a simple method that allows probing of the 3D structure of such systems. Using computer simulations, we find that hard sphere-like liquids have on intermediate and large scales a simple structural order given by alternating layers with icosahedral and dodecahedral symmetries, while open network liquids like silica have a structural order with tetrahedral symmetry. These results show that liquids have a highly nontrivial 3D structure and that this structural information is encoded in nonstandard correlation functions.
Experimental and theoretical studies of diffusion in quinary Co-Cr-Fe-Mn-Ni and quaternary Co-Cr-Fe-Ni and Co-Fe-Mn-Ni FCC-structured high entropy alloys were performed. The diffusion couples, with ...thorium dioxide markers placed at initial joint positions, were annealed at temperature of 1350 K for 72 or 73 h. The concentration profiles obtained from the quinary system were used to determine tracer diffusivities of all components, by using a combinatorial approach, i.e. the Darken method with thermodynamic description provided by Miedema's scheme combined with the optimization method. The results showed good qualitative agreement with the tracer data from radiotracer experiments. The calculated thermodynamic factor, ranged from tens to hundreds of %, show importance of mixing enthalpy on interdiffusion kinetics. The values determined for 5-component system were then used a priori to simulate the concentration profiles for 4-component ones, showing very good agreement with the experimental data. The results indicate that change of components number did not influence the diffusion kinetics in the investigated systems and do not support existence of the sluggish diffusion effect.
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•Diffusion studies in quinary and quaternary high entropy alloys are presented.•Combinatorial approach is used for determination of diffusivities in quinary HEAs.•The determined values are used to simulate diffusion profiles in quaternary systems.•The quality of fit to experimental data is almost the same as for the quinary system.•The change of components number did not influence the diffusion kinetics.
Excitement at the prospect of using data-driven generative models to sample configurational ensembles of biomolecular systems stems from the extraordinary success of these models on a diverse set of ...high-dimensional sampling tasks. Unlike image generation or even the closely related problem of protein structure prediction, there are currently no data sources with sufficient breadth to parametrize generative models for conformational ensembles. To enable discovery, a fundamentally different approach to building generative models is required: models should be able to propose rare, albeit physical, conformations that may not arise in even the largest data sets. Here we introduce a modular strategy to generate conformations based on “backmapping” from a fixed protein backbone that (1) maintains conformational diversity of the side chains and (2) couples the side-chain fluctuations using global information about the protein conformation. Our model combines simple statistical models of side-chain conformations based on rotamer libraries with the now ubiquitous transformer architecture to sample with atomistic accuracy. Together, these ingredients provide a strategy for rapid data acquisition and hence a crucial ingredient for scalable physical simulation with generative neural networks.
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