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| http://dx.doi.org/10.1021/bi300812f | DOI Listing |
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Loss of structural specificity in 3D genome organization upon viral infection is predicted by polymer physics.
J Chem Phys
January 2025
Dipartimento di Fisica, Università degli Studi di Napoli Federico II, and INFN Napoli, Complesso Universitario di Monte Sant'Angelo, 80126 Naples, Italy.
In the last years, it has been proved that some viruses are able to re-structure chromatin organization and alter the epigenomic landscape of the host genome. In addition, they are able to affect the physical mechanisms shaping chromatin 3D structure, with a consequent impact on gene activity. Here, we investigate with polymer physics genome re-organization of the host genome upon SARS-CoV-2 viral infection and how it can impact structural variability within the population of single-cell chromatin configurations.
View Article and Find Full Text PDFOn machine learnability of local contributions to interatomic potentials from density functional theory calculations.
Sci Rep
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Department of Physics, Shahid Beheshti University, Tehran, 1983969411, Iran.
Machine learning interatomic potentials, as a modern generation of classical force fields, take atomic environments as input and predict the corresponding atomic energies and forces. We challenge the commonly accepted assumption that the contribution of an atom can be learned from the short-range local environment of that atom. We employ density functional theory calculations to quantify the decay of the induced electron density and electrostatic potential in response to local perturbations throughout insulating, semiconducting and metallic samples of different dimensionalities.
View Article and Find Full Text PDFEvaluations of the Perturbation Resistance of the Deep-Learning-Based Ligand Conformation Optimization Algorithm.
J Chem Inf Model
December 2024
School of Physics, Shandong University, Jinan 250100, China.
In recent years, the deep learning (DL) technique has rapidly developed and shown great success in scoring the protein-ligand binding affinities. The protein-ligand conformation optimization based on DL-derived scoring functions holds broad application prospects, for instance, drug design and enzyme engineering. In this study, we evaluated the robustness of a DL-based ligand conformation optimization protocol (DeepRMSD+Vina) for optimizing structures with input perturbations by examining the predicted ligand binding poses and scoring.
View Article and Find Full Text PDFPredicting RNA Structure and Dynamics with Deep Learning and Solution Scattering.
Biophys J
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School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel. Electronic address:
Advanced deep learning and statistical methods can predict structural models for RNA molecules. However, RNAs are flexible, and it remains difficult to describe their macromolecular conformations in solutions where varying conditions can induce conformational changes. Small-angle X-ray scattering (SAXS) in solution is an efficient technique to validate structural predictions by comparing the experimental SAXS profile with those calculated from predicted structures.
View Article and Find Full Text PDFTrue Dynamics of Pillararene Host-Guest Binding.
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December 2024
Changping Laboratory, No. 28 Life Science Park Rd., Beijing 102206, China.
Accurate modeling of host-guest systems is challenging in modern computational chemistry. It requires intermolecular interaction patterns to be correctly described and, more importantly, the dynamic behaviors of macrocyclic hosts to be accurately modeled. Pillar[]arenes as a crucial family of macrocycles play a critical role in host-guest chemistry and biomedical applications.
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