The accuracy of classical force fields (FFs) has been shown to be limited for the simulation of cation-protein systems despite their importance in understanding the processes of life. Improvements can result from optimizing the parameters of classical FFs or by extending the FF formulation by terms describing charge transfer (CT) and polarization (POL) effects. In this work, we introduce our implementation of the CTPOL model in OpenMM, which extends the classical additive FF formula by adding CT and POL. Furthermore, we present an open-source parametrization tool, called FFAFFURR, that enables the (system-specific) parametrization of OPLS-AA and CTPOL models. The performance of our workflow was evaluated by its ability to reproduce quantum chemistry energies and by molecular dynamics simulations of a zinc-finger protein.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10867808 | PMC |
| http://dx.doi.org/10.1021/acs.jctc.3c01141 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Investigation the CMP process of 6 H-SiC in HO solution with ReaxFF molecular dynamics simulation.
Sci Rep
January 2025
College of Mechanical Engineering, Zhejiang University of Technology, No.18, Chaowang Road, Hangzhou, 310014, Zhejiang Province, China.
To observe the chemical mechanical polishing (CMP) process at the atomic scale, reactive force field molecular dynamics (ReaxFF-MD) was employed to simulate the polishing of 6 H-SiC under three conditions: dry, pure water, and HO solution. This study examined the reactants on the surface of 6 H-SiC during the reaction in the HO solution, along with the dissociation and adsorption processes of HO and water molecules. The mechanisms for atom removal during the CMP process were elucidated.
View Article and Find Full Text PDFN2GNet tracks gait performance from subthalamic neural signals in Parkinson's disease.
NPJ Digit Med
January 2025
Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
Adaptive deep brain stimulation (DBS) provides individualized therapy for people with Parkinson's disease (PWP) by adjusting the stimulation in real-time using neural signals that reflect their motor state. Current algorithms, however, utilize condensed and manually selected neural features which may result in a less robust and biased therapy. In this study, we propose Neural-to-Gait Neural network (N2GNet), a novel deep learning-based regression model capable of tracking real-time gait performance from subthalamic nucleus local field potentials (STN LFPs).
View Article and Find Full Text PDFEnd-Point Affinity Estimation of Galectin Ligands by Classical and Semiempirical Quantum Mechanical Potentials.
J Chem Inf Model
January 2025
Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Gilead Sciences & IOCB Research Centre, Flemingovo nám. 2, 166 10 Prague, Czech Republic.
The use of quantum mechanical potentials in protein-ligand affinity prediction is becoming increasingly feasible with growing computational power. To move forward, validation of such potentials on real-world challenges is necessary. To this end, we have collated an extensive set of over a thousand galectin inhibitors with known affinities and docked them into galectin-3.
View Article and Find Full Text PDFDesign Principles for Gradient Porous Carbon on Aqueous Zinc-Ion Hybrid Capacitors: A Combined Molecular Dynamic and Machine Learning Study.
ACS Appl Mater Interfaces
January 2025
School of Physics, Dalian University of Technology, Dalian 116024, P. R. China.
Gradient porous carbon has become a potential electrode material for energy storage devices, including the aqueous zinc-ion hybrid capacitor (ZIHC). Compared with the sufficient studies on the fabrication of ZIHCs with high electrochemical performance, there is still lack of in-depth understanding of the underlying mechanisms of gradient porous structure for energy storage, especially the synergistic effect of ultramicropores (<1 nm) and micropores (1-2 nm). Here, we report a design principle for the gradient porous carbon structure used for ZIHC based on the data-mining machine learning (ML) method.
View Article and Find Full Text PDFInvestigating the Current-Carrying Friction Mechanism of n-Type and p-Type Two-Dimensional TMD under a Positive Electric Field.
ACS Appl Mater Interfaces
January 2025
State Key Laboratory of Solid Lubrication, Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences, Lanzhou 730000, China.
Nanofriction plays an important role in the performance and lifetime of n-type or p-type TMD-based semiconductor nanodevices. However, the mechanism of nanofriction in n-type and p-type TMD semiconductors under an electric field is still blurry. In this paper, monolayers of n-MoSe and p-WSe materials were prepared by chemical vapor deposition (CVD), and their nanofriction behavior under positive electric field was investigated.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!