Kristina M. Herman

The nucleotide state and rates of transitions between states regulate the dynamics of ATPases. Slow inorganic phosphate (P _i ) release following ATP hydrolysis is often rate limiting and associated with key conformational changes. Actin filaments offer a unique opportunity to understand the fundamentals of P _i release because identical subunits at filament ends and the interior release P _i at markedly different rates. The molecular origin of this difference is debated, so we used extensive all-atom molecular dynamics simulations to characterize P _i release from different subunits within an actin filament. The calculated dissociation rates of P _i from ADP-Mg ²⁺ in the active site correlate with experimentally measured P _i release rates and scale inversely with the numbers of water molecules in the cavity surrounding the γ-phosphate. Simulations show that egress of P _i through the protein channels, including through the N111-R177 backdoor, is not rate limiting and that subunits at the filament ends use alternative egress pathways.

The actomyosin cytoskeleton plays important roles in cell-cell adhesion by generating and responding to forces. Actin-binding proteins support actomyosin networks by reinforcing actin filaments, promoting actin remodeling, and transmitting forces to transmembrane adhesion proteins. One family of actin-binding proteins, LIM domain-containing proteins, is recruited to strained actin filaments. Here, we investigate two members of the LIM domain family, Zyxin and Lipoma-Preferred Partner (LPP), using the embryonic epithelium of Xenopus laevis , where actin-associated cell-cell junctions connect cells to promote tissue integrity and barrier function. Specifically, we compare Zyxin and LPP’s response to increased tension at tricellular junctions (TCJs), sites of heightened mechanical strain within the tissue. Upon increased tension, Zyxin and LPP mechanoaccumulate to different extents relative to F-actin, suggesting distinct mechanisms. Our results demonstrate that Zyxin’s and LPP’s LIM domain-containing regions (LCRs) are sufficient for mechanoaccumulation as well as responsible for the difference in mechanoaccumulation, while their N-termini play a regulatory role in their mechanosensitive responses. Docking simulations show that a hydrophobic interaction between the LCR and the “crack site” that forms in F-actin filaments under high force may underlie the LCR’s sensing of strained F-actin. Additionally, the docking simulations reveal that previously-identified conserved residues are present at the binding interface. Mutations of conserved residues in Zyxin or LPP reduce the extent of mechanoaccumulation at TCJs, supporting the docking prediction. Together, this study advances our understanding of Zyxin and LPP’s sensing of strained actin at TCJs under mechanical challenge. Significance Statement: The actomyosin cytoskeleton provides structural support for intercellular connections. This is particularly important at high force levels when actin must be reinforced by actin-binding proteins. Two of those proteins, Zyxin and LPP, stabilize strained actomyosin networks. Using frog embryos, we compare the force-dependent recruitment of these proteins at cell-cell junctions where three cells meet. Despite their similar sequences, Zyxin and LPP respond differently to increased tension. We find that the LIM-domain-containing region (LCR) confers this force-sensing behavior for strained actin at cell-cell junctions. The rest of the protein regulates the extent to which the proteins accumulate. Computational modeling suggests that the accumulation difference between Zyxin and LPP may be due to variations in the LCR and strained actin-binding interface.

The nucleotide state and rates of transitions between states regulate the dynamics of ATPases. Slow inorganic phosphate (P _i ) release following ATP hydrolysis is often rate-limiting and associated with key conformational changes. Actin filaments offer a unique opportunity to understand the fundamentals of phosphate release, because identical subunits at filament ends and the interior release P _i at markedly different rates. The molecular origin of this difference is debated, so we employed extensive all-atom molecular dynamics simulations to characterize P _i release from different subunits within an actin filament. The dissociation rates of P _i from ADP-Mg ²⁺ in the active site correlate with biochemically measured P _i release rates and scale inversely with the numbers of water molecules in the cavity surrounding the γ-phosphate. Simulations show that egress of P _i through protein channels, including through the N111-R177 backdoor, is not rate-limiting and, importantly, that subunits at the filament ends use alternative egress pathways.

The many-body decomposition of the lattice energies in seven ice polymorphs (ice Ih, II, VIII, IX, XIII, XIV, XV) reveals an intriguing correlation between the polymorph's local tetrahedral order, its density, and the inherent cooperativity of its hydrogen bond network. Low-pressure ice phases demonstrate significant cooperative effects, accounting for up to approximately 25% of the lattice energy and exhibit nearly perfect local tetrahedral order. In contrast, high-pressure ice phases exhibit smaller cooperative effects (around 6%) and local structures that are less tetrahedral. The distinct local tetrahedral order encompassing nearest neighbors in these ice polymorphs is attributed to the hydrogen bond cycles in the extended network (heterodromic, antidromic and homodromic), which represents a unifying feature that profoundly influences cooperativity based on cycle size and connectivity. These findings facilitate the establishment of a novel causality between structure and stability while they further identify cooperativity as the key descriptor of hydrogen bonding characteristics across the ice phase diagram.

The transformative impact of modern computational paradigms and technologies, such as high-performance computing (HPC), quantum computing, and cloud computing, has opened up profound new opportunities for scientific simulations. Scalable computational chemistry is one beneficiary of this technological progress. The main focus of this paper is on the performance of various quantum chemical formulations, ranging from low-order methods to high-accuracy approaches, implemented in different computational chemistry packages and libraries, such as NWChem, NWChemEx, Scalable Predictive Methods for Excitations and Correlated Phenomena, ExaChem, and Fermi–Löwdin orbital self-interaction correction on Azure Quantum Elements, Microsoft's cloud services platform for scientific discovery. We pay particular attention to the intricate workflows for performing complex chemistry simulations, associated data curation, and mechanisms for accuracy assessment, which is demonstrated with the Arrows automated workflow for high throughput simulations. Finally, we provide a perspective on the role of cloud computing in supporting the mission of leadership computational facilities.

Atomic structure prediction and associated property calculations are the bedrock of chemical physics. Since high-fidelity ab initio modeling techniques for computing the structure and properties can be prohibitively expensive, this motivates the development of machine-learning (ML) models that make these predictions more efficiently. Training graph neural networks over large atomistic databases introduces unique computational challenges, such as the need to process millions of small graphs with variable size and support communication patterns that are distinct from learning over large graphs, such as social networks. We demonstrate a novel hardware-software codesign approach to scale up the training of atomistic graph neural networks (GNN) for structure and property prediction. First, to eliminate redundant computation and memory associated with alternative padding techniques and to improve throughput via minimizing communication, we formulate the effective coalescing of the batches of variable-size atomistic graphs as the bin packing problem and introduce a hardware-agnostic algorithm to pack these batches. In addition, we propose hardware-specific optimizations, including a planner and vectorization for the gather-scatter operations targeted for Graphcore's Intelligence Processing Unit (IPU), as well as model-specific optimizations such as merged communication collectives and optimized softplus. Putting these all together, we demonstrate the effectiveness of the proposed codesign approach by providing an implementation of a well-established atomistic GNN on the Graphcore IPUs. We evaluate the training performance on multiple atomistic graph databases with varying degrees of graph counts, sizes, and sparsity. We demonstrate that such a codesign approach can reduce the training time of atomistic GNNs and can improve their performance by up to 1.5× compared to the baseline implementation of the model on the IPUs. Additionally, we compare our IPU implementation with a Nvidia GPU-based implementation and show that our atomistic GNN implementation on the IPUs can run 1.8× faster on average compared to the execution time on the GPUs.

We rely on a total of 23 (cluster size, 8 structural, and 14 connectivity) descriptors to investigate structural patterns and connectivity motifs associated with water cluster aggregation. In addition to the cluster size n (number of molecules), the 8 structural descriptors can be further categorized into (i) one-body (intramolecular): covalent OH bond length (rOH) and HOH bond angle (θHOH), (ii) two-body: OO distance (rOO), OHO angle (θOHO), and HOOX dihedral angle (ϕHOOX), where X lies on the bisector of the HOH angle, (iii) three-body: OOO angle (θOOO), and (iv) many-body: modified tetrahedral order parameter (q) to account for two-, three-, four-, five-coordinated molecules (qm, m = 2, 3, 4, 5) and radius of gyration (Rg). The 14 connectivity descriptors are all many-body in nature and consist of the AD, AAD, ADD, AADD, AAAD, AAADD adjacencies [number of hydrogen bonds accepted (A) and donated (D) by each water molecule], Wiener index, Average Shortest Path Length, hydrogen bond saturation (% HB), and number of non-short-circuited three-membered cycles, four-membered cycles, five-membered cycles, six-membered cycles, and seven-membered cycles. We mined a previously reported database of 4 948 959 water cluster minima for (H2O)n, n = 3–25 to analyze the evolution and correlation of these descriptors for the clusters within 5 kcal/mol of the putative minima. It was found that rOH and % HB correlated strongly with cluster size n, which was identified as the strongest predictor of energetic stability. Marked changes in the adjacencies and cycle count were observed, lending insight into changes in the hydrogen bond network upon aggregation. A Principal Component Analysis (PCA) was employed to identify descriptor dependencies and group clusters into specific structural patterns across different cluster sizes. The results of this study inform our understanding of how water clusters evolve in size and what appropriate descriptors of their structural and connectivity patterns are with respect to system size, stability, and similarity. The approach described in this study is general and can be easily extended to other hydrogen-bonded systems.

We incorporate geometry-dependent distributed multipole and polarizability surfaces into an induction model that is used to describe the 3- and 4-body terms of the interaction between water molecules. The moment expansion is carried out up to the hexadecapole with the multipoles distributed on the atom sites. Dipole-dipole, dipole-quadrupole, and quadrupole-quadrupole distributed polarizabilities are used to represent the response of the multipoles to an electric field. We compare the model against two large databases consisting of 43,844 3-body terms and 3,603 4-body terms obtained from high level ab initio calculations previously used to fit the MB-pol and q-AQUA classical interaction potentials for water. The classical induction model with no adjustable parameters reproduces the ab initio 3-/4-body terms contained in these two databases with a root-mean-square error (RMSE) of 0.104/0.058 and a mean-absolute error (MAE) of 0.054/0.026 kcal/mol, respectively. These results are on par with the ones obtained by fitting the same data using over 14,000 (for the 3-body) and 200 (for the 4-body) parameters via Permutationally Invariant Polynomials (PIPs). This demonstrates the accuracy of this physically motivated model in describing the 3- and 4-body terms in the interactions between water molecules with no adjustable parameters. The triple-dipole-dispersion energy, included in the calculation of the 3-body energy, was found to be small but not quite negligible. The model represents a practical, efficient, and transferable approach for obtaining accurate nonadditive interactions for multicomponent systems without the need to perform tens of thousands of high level electronic structure calculations and fitting them with PIPs.

We incorporate geometry-dependent distributed multipole and polarizability surfaces into an induction model that is used to describe the 3- and 4-body terms of the interaction between water molecules. The expansion is carried out up to hexadecapole with the multipoles distributed on the atom sites. Dipole-dipole, dipole-quadrupole, and quadrupole-quadrupole distributed polarizabilities are used to represent the response of the multipoles to an electric field. We compare the model against two large databases consisting of 43,844 3-body terms and 3,603 4-body terms obtained from high level ab initio calculations previously used to fit the MB-pol and q-AQUA interaction potentials. The classical induction model with no adjustable parameters reproduces the ab-initio 3- and 4-body terms contained in these two Databases with a Root-Mean-Square-Error (RMSE) of 0.104/0.058 and a Mean-Absolute-Error (MAE) of 0.054/0.026 kcal/mol, respectively, results that are on a par with those obtained by fitting the same data using tens of thousands of Permutationally Invariant Polynomials (PIPs). This demonstrates the accuracy of this physically motivated model in describing the 3- and 4-body terms in the interactions between water molecules with no adjustable parameters. The triple-dipole-dispersion energy was included in the 3-body energy and was found to be small but not quite negligible. The model represents a practical, efficient and transferable approach for obtaining accurate non-additive interactions for multi-component systems without the need of performing tens of thousands of high level electronic structure calculations and fitting them with tens of thousands of PIPs.

We assess the performance of 7 pairwise additive and 8 families of many-body potentials in reproducing high-level ab initio benchmark values for the binding energy and the many-body expansion (MBE) of water clusters n = 2–11, 16–17, 20, 25.