"Anatomy Is a Living Wholeness of Function…Not Static Academic Structure".

Thin films of amorphous alumina (a-Al2O3) have recently been found to deform permanently up to 100% elongation without fracture at room temperature. If the underlying ductile deformation mechanism can be understood at the nanoscale and exploited in bulk samples, it could help to facilitate the design of damage-tolerant glassy materials, the holy grail within glass science. Here, based on atomistic simulations and classification-based machine learning, we reveal that the propensity of a-Al2O3 to exhibit nanoscale ductility is encoded in its static (nonstrained) structure. By considering the fracture response of a series of a-Al2O3 systems quenched under varying pressure, we demonstrate that the degree of nanoductility is correlated with the number of bond switching events, specifically the fraction of 5- and 6-fold coordinated Al atoms, which are able to decrease their coordination numbers under stress. In turn, we find that the tendency for bond switching can be predicted based on a nonintuitive structural descriptor calculated based on the static structure, namely, the recently developed "softness" metric as determined from machine learning. Importantly, the softness metric is here trained from the spontaneous dynamics of the system (i.e., under zero strain) but, interestingly, is able to readily predict the fracture behavior of the glass (i.e., under strain). That is, lower softness facilitates Al bond switching and the local accumulation of high-softness regions leads to rapid crack propagation. These results are helpful for designing glass formulations with improved resistance to fracture.

In strained monoatomic chains with Lennard-Jones interactions, we revealed a stable static non-homogeneous structure appearing as a result of a certain phase transition. Positions of individual particles in this structure form an exact arithmetic progression whose difference depends on the value of the strain. For N-particle chain, this structure is characterized by one long and N-1 short interatomic distances (bonds). In the vicinity of the static structure, we found discrete breathers of new type which essentially differ from the traditional breathers in the form of Sievers-Takeno and Page modes. It is well known that these modes possess some staggered structures and demonstrate exponential decay of the particle amplitudes from the core to their tails. In contrast to such properties, our breathers are characterised by smooth decay and amplitudes of the particles form approximately a decreasing arithmetic progression. Core of these breathers is located on two particles with long bond in static structure. Our breathers demonstrate soft type of nonlinearity (the frequency decreases with increasing of amplitudes) and they are stable dynamical objects for amplitudes up to 20%-30% of interparticle distance of the strained equidistant chain. For infinitely small amplitudes these breathers tend to the above described static non-homogeneous structure. We studied dependence of their properties on amplitude, strain and the number of particles in the chain. There exist a reason to suppose that the above static and dynamical structures can exist in real monoatomic chains consisting of carbon, boron, and other atoms.

Critical nodes of software systems are the nodes with the ability to transmit fault rapidly in the whole system. As a result, they have a significant impact on software reliability. To evaluate and identify the critical nodes, previous research focused on the static structure of software network which is inadequate since dynamic information affects the evaluation result as well. We propose a comprehensive method combining both static and dynamic information to evaluate critical nodes in our work. Static information consists of parameters measuring software structure. The importance of one node differs in different structures. In our evaluation model, we choose betweenness centrality to measure static structure because it can describe the importance of one node in static topological structure in complex network theory. For dynamic information, we focus on the execution frequency in specific software operation profile and user related operation. Combining the static and dynamic evaluating results, the comprehensive model is obtained. Using this evaluation model, we can obtain the critical nodes for one software system corresponding to different service states which indicate some sets of operating states. Protection strategy can be applied to these nodes pertinently to achieve efficient system reliability improvement.

Complex molecular simulation methods are typically required to calculate the thermodynamic properties of biochemical systems. One example thereof is the thermodynamic profiling of (de)solvation of proteins, which is an essential driving force for protein-ligand and protein-protein binding. The thermodynamic state of water molecules depends on its enthalpic and entropic components; the latter is governed by dynamic properties of the molecule. Here, we developed, to the best of our knowledge, two novel machine learning methods based on deep neural networks that are able to generate the converged thermodynamic state of dynamic water molecules in the heterogeneous protein environment based solely on the information of the static protein structure. The applicability of our machine learning methods to predict the hydration information is demonstrated in two different studies, the qualitative analysis and quantitative prediction of structure-activity relationships, and the prediction of protein-ligand binding modes.

Finding Gas Diffusion Pathways in Proteins: Application to O2 and H2 Transport in CpI [FeFe]-Hydrogenase and the Role of Packing Defects

Reflections

Computational studies of organic systems are frequently limited to static pictures that closely align with textbook style presentations of reaction mechanisms and isomerization processes. Of course, in reality chemical systems are dynamic entities where a multitude of molecular conformations exists on incredibly complex potential energy surfaces (PES). Here, we borrow a computational technique originally conceived to be used in the context of biological simulations, together with empirical force fields, and apply it to organic chemical problems. Replica‐exchange molecular dynamics (REMD) permits thorough exploration of the PES. We combined REMD with density functional tight binding (DFTB), thereby establishing the level of accuracy necessary to analyze small molecular systems. Through the study of four prototypical problems: isomer identification, reaction mechanisms, temperature‐dependent rotational processes, and catalysis, we reveal new insights and chemistry that likely would be missed using static electronic structure computations. The REMD‐DFTB methodology at the heart of this study is powered by i‐PI, which efficiently handles the interface between the DFTB and REMD codes. © 2015 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.

Four hydrates with d(001) = 8.4, 8.6, and 10 Å (two types) were synthesized by intercalating kaolinite with dimethylsulfoxide and treating the intercalated clay with fluoride ions. X-ray powder diffraction, infrared spectroscopy, differential scanning calorimetry, thermal gravimetric analysis, and kinetics of dehydration experiments have led to the identification of two types of interlayer water. One type of water (hole water) is situated in the ditrigonal holes of the silica tetrahedral surface; the second type (associated water) forms a discontinuous layer of mobile water. The 8.4-Å and 8.6-Å hydrates have only hole water, whereas the two synthetic 10-Å hydrates and halloysite(10Å) contain both hole and associated water. The hole water is probably hydrogen bonded to the basal oxygens of the silica tetrahedra or, in the 8-Å hydrates when fluorine exchanges for inner-surface hydroxyls, the water molecules may reorient and form stronger hydrogen bonds to the fluorine. Associated water forms water-water hydrogen bonds approximately equal in strength to liquid water but is less strongly bonded to the clay surfaces than hole water. At room temperature the hole and associated water in the 10-Å hydrates do not form an ice-like structure.

Resolving biomolecular motion and interactions by R2 and R1ρ relaxation dispersion NMR.

Prediction of three-dimensional structures and structural flexibilities of wild-type and mutant cytochrome P450 1A2 using molecular dynamics simulations

Natural history data for young subjects with Sanfilippo syndrome type B (MPS IIIB)

This paper studies first-in-first-out (FIFO) indexes, each of which manages a dataset where objects are deleted in the same order as their insertions. We give a technique that converts a static data structure to a FIFO index for all decomposable problems, provided that the static structure can be constructed efficiently. We present FIFO access methods to solve several problems including half-plane search, nearest neighbor search, and extreme-point search. All of our structures consume linear space, and have optimal or near-optimal query cost.

The emergence of deep learning, particularly AlphaFold, has revolutionized static protein structure prediction, marking a transformative milestone in structural biology. However, protein function is not solely determined by static three-dimensional structures but is fundamentally governed by dynamic transitions between multiple conformational states. This shift from static to multi-state representations is crucial for understanding the mechanistic basis of protein function and regulation. This review outlines the fundamental concepts of protein dynamic conformations, surveys recent computational advances in modeling these dynamics in the post-AlphaFold era, and highlights key challenges, including data limitations, methodological constraints, and evaluation metrics. We also discuss potential strategies to address these challenges and explore future research directions to deepen our understanding of protein dynamics and their functional implications. This work aims to provide insights and perspectives to facilitate the ongoing development of protein conformation studies in the era of artificial intelligence-driven structural biology.

Ion channels are proteins that form pores of nanoscopic dimensions in cell membranes. As a consequence of advance in protein crystallography we now know the three-dimensional structures of a number of ion channels. However, X-ray diffraction techniques yield an essentially static (time- and space-averaged) structure of an ion channel, in an environment often somewhat distantly related to that which the protein experiences when in a cell membrane. Thus, additional techniques are required to fully understand the relationship between channel structure and function. Potassium (K) channels (Yellen, 2002) provide an opportunity to explore the relationship between membrane protein structure, dynamics, and function. Furthermore, K channels are of considerable physiological and biomedical interest. They regulate K + ion flux across cell membranes. K channel regulation is accomplished by a conformational change that allows the protein to switch between two alternative (closed vs. open) conformations, a process known as gating. Gating is an inherently dynamic process that cannot be fully characterized by static structures alone. The elucidation of the structures of several K + channels (Mackinnon, 2003;

We present calculations for the static structure and ordering properties of two lithium-based $s\ensuremath{-}p$ bonded liquid alloys, Li-Na and Li-Mg. Our theoretical approach is based on the neutral pseudoatom method to derive the interatomic pair potentials, and on the modified-hypernetted-chain theory of liquids to obtain the liquid static structure, leading to a whole combination that is free of adjustable parameters. The study is complemented by performing molecular dynamics simulations which, besides checking the theoretical static structural results, also allow a calculation of some dynamical properties. The obtained results are compared with the available experimental data.