Intermolecular Non-Bonded Interactions from Machine Learning Datasets

A constant pH molecular dynamics and experimental study on the effect of different pH on the structure of urease from Sporosarcina pasteurii

CONTEXT

Urease is pivotal in microbial-induced calcium carbonate precipitation (MICP), where its catalytic efficiency directly governs calcium carbonate formation. However, practical MICP applications in extreme environments (e.g., acidic mine drainage, industrial waste sites) are hindered by limited understanding of urease behavior under extreme pH conditions. This study combines laboratory experiments and constant pH molecular dynamics (CpHMD) simulations to investigate how pH variations (3-11) affect the structural stability of Sporosarcina pasteurii urease, focusing on its α-subunit (PDB: 4CEU). Experimental validation identified pH 7-8 as optimal for urease activity, aligning with molecular dynamics results showing minimal structural deviations (RMSD) and stable protonation states under neutral to mildly alkaline conditions. Extreme pH (3, 4, 11) disrupted active-site geometry and induced charge fluctuations, impairing catalytic function. CpHMD simulations revealed that the α-subunit retains structural integrity at pH 7-8, suggesting potential reassembly post-environmental stress. This work bridges gaps in enzymatic stability under harsh conditions, offering insights for optimizing MICP in geotechnical and environmental remediation applications.

METHODS

The study combined experimental and computational approaches. Sporosarcina pasteurii urease activity was experimentally assessed across pH 3-11 by monitoring urea hydrolysis-induced conductivity changes. Computational analyses employed GROMACS constant pH with the CHARMM36 force field to perform pH-dependent molecular dynamics simulations. The urease structure was solvated, neutralized, energy-minimized, and subjected to constant pH simulations. Structural stability, active site dynamics, and protonation states of titratable residues were analyzed via RMSD, hydrogen bonds, solvent-accessible surface area (SASA), and Epock 1.0.5. Free energy landscapes and residue interactions were evaluated using principal component analysis (PCA) and λ-dynamics. Experimental data were processed with OriginPro 2024b and Python, linking pH-induced conformational shifts to enzymatic function.

Molecular dynamics in pharmaceutical nanotechnology: simulating interactions and advancing applications

Molecular Dynamics (MD) simulations are now widely utilized in pharmaceutical nanotechnology to gain deeper understanding of nanoscale processes imperative to drug design. This review has also detailed how MD simulation can be employed in the study of drug-nanocarrier interactions, controlling release of chemical compounds from drug delivery systems and increasing solubility and bioavailability of nanocarriers. Furthermore, MD contributes to examining the drug delivery systems, measuring the toxic effects, and determining biocompatibility of nanomedical systems. With the incorporation of artificial intelligence and the use of hybrid simulation systems, MD has gone a step ahead to model other niches of biology that make a tremendous opening to develop highly selective nanomedications. Nevertheless, with well-known issues such as computational constraints and the discrepancy between in silico and experiment results, MD remains a work in progress, with considerable promise for replacing or supplementing existing approaches to the development of precision medicine and nanomedicine, the continued progression of healthcare hopeful.

A Machine Learning Force Field for Bio-Macromolecular Modeling Based on Quantum Chemistry-Calculated Interaction Energy Datasets

Accurate energy data from noncovalent interactions are essential for constructing force fields for molecular dynamics simulations of bio-macromolecular systems. There are two important practical issues in the construction of a reliable force field with the hope of balancing the desired chemical accuracy and working efficiency. One is to determine a suitable quantum chemistry level of theory for calculating interaction energies. The other is to use a suitable continuous energy function to model the quantum chemical energy data. For the first issue, we have recently calculated the intermolecular interaction energies using the SAPT0 level of theory, and we have systematically organized these energies into the ab initio SOFG-31 (homodimer) and SOFG-31-heterodimer datasets. In this work, we re-calculate these interaction energies by using the more advanced SAPT2 level of theory with a wider series of basis sets. Our purpose is to determine the SAPT level of theory proper for interaction energies with respect to the CCSD(T)/CBS benchmark chemical accuracy. Next, to utilize these energy datasets, we employ one of the well-developed machine learning techniques, called the CLIFF scheme, to construct a general-purpose force field for biomolecular dynamics simulations. Here we use the SOFG-31 dataset and the SOFG-31-heterodimer dataset as the training and test sets, respectively. Our results demonstrate that using the CLIFF scheme can reproduce a diverse range of dimeric interaction energy patterns with only a small training set. The overall errors for each SAPT energy component, as well as the SAPT total energy, are all well below the desired chemical accuracy of ~1 kcal/mol.