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A computational and experimental framework for quantifying flow-enhanced nucleation (FEN) in polymers is presented and demonstrated for an industrial-grade linear low-density polyethylene (LLDPE). Experimentally, kinetic measurements of isothermal crystallization were performed by using fast-scanning calorimetry (FSC) for melts that were presheared at various strain rates. The effect of shear on the average conformation tensor of the melt was modeled with the discrete slip-link model (DSM). The conformation tensor was then related to the acceleration in nucleation kinetics by using an expression previously validated with nonequilibrium molecular dynamics (NEMD). The expression is based on the nematic order tensor of Kuhn segments, which can be obtained from the conformation tensor of entanglement strands. The single adjustable parameter of the model was determined by fitting to the experimental FSC data. This expression accurately describes FEN for the LLDPE, representing a significant advancement toward the development of a fully integrated processing model for crystallizable polymers.
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http://dx.doi.org/10.1021/acs.jpcb.2c03460 | DOI Listing |
J Am Chem Soc
September 2025
Department of Chemistry and Biochemistry, University of California, Los Angeles, California 90095, United States.
Enzymes that catalyze the same reaction yet bear no structural resemblance challenge the view that fold dictates function. Here, we probe whether intraprotein electrostatics are a unifying factor in such cases of enzyme catalysis. Focusing on chorismate mutase (CM), a textbook case of electrostatic catalysis found in two structurally unrelated families (AroH and AroQ), we ask (i) whether disparate scaffolds can converge on a common catalytic electric field, and (ii) whether a single reaction can be accelerated by distinct electrostatic fields.
View Article and Find Full Text PDFJ Chem Inf Model
July 2025
Department of Computer Science and Engineering, Korea University, Seoul 02841, Republic of Korea.
Accurate prediction of compound-protein interactions (CPI) remains a cornerstone challenge in computational drug discovery. While existing sequence-based approaches leverage molecular fingerprints or graph representations, they critically overlook the three-dimensional (3D) structural determinants of binding affinity. To bridge this gap, we present EquiCPI, an end-to-end geometric deep learning framework that synergizes first-principles structural modeling with SE(3)-equivariant neural networks.
View Article and Find Full Text PDFACS Macro Lett
July 2025
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States.
We present coarse-grained molecular dynamics simulations of salt-free polyelectrolyte chains in semidilute solutions under simple shear flow, with full hydrodynamic interactions and explicit dipolar solvent. At equilibrium, chain orientation statistics follow a pseudo-Voigt distribution, and the structural correlation length and chain end-to-end vector autocorrelation function exhibit scaling behavior consistent with theoretical predictions for polyelectrolytes. Under shear, chains transition from coiled to stretched states and the end-to-end vector autocorrelation function reveals oscillatory dynamics at high Weissenberg numbers.
View Article and Find Full Text PDFInterdiscip Sci
June 2025
School of Computer Science, Xiangtan University, Xiangtan, 411105, China.
Motivation: Predicting drug-target binding affinity (DTA) is essential for identifying potential therapeutic candidates in drug discovery. However, most existing models rely heavily on static protein structures, often overlooking the dynamic nature of proteins, which is crucial for capturing conformational flexibility that will be beneficial for protein binding interactions.
Methods: We introduce DynamicDTA, an innovative deep learning framework that incorporates static and dynamic protein features to enhance DTA prediction.
Magn Reson Chem
July 2025
Department of Physics and Nanotechnology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India.
Statistical analysis of backbone C and N chemical shielding tensors (CST) computed using the DFT-GIAO method is presented for 40 alanine residues located centrally in three-residue segments extracted from α-helical and β-sheet regions of 12 proteins with high-resolution crystal structures. Our results show that the projections of C shielding along the three covalent bond directions, C-C, C-H, and C-N, exhibit significantly higher sensitivity to secondary structure than the principal components. The increased sensitivity is due to the changes in the orientation of C CST in the molecular frame of the two secondary structures.
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