Accurate Reproduction of Quantum Mechanical Many-Body Interactions in Peptide Main-Chain Hydrogen-Bonding Oligomers by the Polarizable Gaussian Multipole Model.

J Chem Theory Comput

Departments of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering, Materials Science and Engineering, and Biomedical Engineering, University of California, Irvine, Irvine, California92697, United States.

Published: October 2022

A key advantage of polarizable force fields is their ability to model the atomic polarization effects that play key roles in the atomic many-body interactions. In this work, we assessed the accuracy of the recently developed polarizable Gaussian Multipole (pGM) models in reproducing quantum mechanical (QM) interaction energies, many-body interaction energies, as well as the nonadditive and additive contributions to the many-body interactions for peptide main-chain hydrogen-bonding conformers, using glycine dipeptide oligomers as the model systems. Two types of pGM models were considered, including that with (pGM-perm) and without (pGM-ind) permanent atomic dipoles. The performances of the pGM models were compared with several widely used force fields, including two polarizable (Amoeba13 and ff12pol) and three additive (ff19SB, ff15ipq, and ff03) force fields. Encouragingly, the pGM models outperform all other force fields in terms of reproducing QM interaction energies, many-body interaction energies, as well as the nonadditive and additive contributions to the many-body interactions, as measured by the root-mean-square errors (RMSEs) and mean absolute errors (MAEs). Furthermore, we tested the robustness of the pGM models against polarizability parameterization errors by employing alternative polarizabilities that are either scaled or obtained from other force fields. The results show that the pGM models with alternative polarizabilities exhibit improved accuracy in reproducing QM many-body interaction energies as well as the nonadditive and additive contributions compared with other polarizable force fields, suggesting that the pGM models are robust against the errors in polarizability parameterizations. This work shows that the pGM models are capable of accurately modeling polarization effects and have the potential to serve as templates for developing next-generation polarizable force fields for modeling various biological systems.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10152986PMC
http://dx.doi.org/10.1021/acs.jctc.2c00710DOI Listing

Publication Analysis

Top Keywords

pgm models
32
force fields
28
interaction energies
20
many-body interactions
16
polarizable force
12
many-body interaction
12
energies well
12
well nonadditive
12
nonadditive additive
12
additive contributions
12

Similar Publications

Performance Tuning of Polarizable Gaussian Multipole Model in Molecular Dynamics Simulations.

J Chem Theory Comput

January 2025

Chemical and Materials Physics Graduate Program, Departments of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering, Materials Science and Engineering, and Biomedical Engineering, University of California, Irvine, Irvine, California 92697, United States.

Molecular dynamics (MD) simulations are essential for understanding molecular phenomena at the atomic level, with their accuracy largely dependent on both the employed force field and sampling. Polarizable force fields, which incorporate atomic polarization effects, represent a significant advancement in simulation technology. The polarizable Gaussian multipole (pGM) model has been noted for its accurate reproduction of ab initio electrostatic interactions.

View Article and Find Full Text PDF

Background Fetal growth restriction (FGR) is a leading risk factor for stillbirth, yet the diagnosis of FGR confers considerable prognostic uncertainty, as most infants with FGR do not experience any morbidity. Our objective was to use data from a large, deeply phenotyped observational obstetric cohort to develop a probabilistic graphical model (PGM), a type of "explainable artificial intelligence (AI)", as a potential framework to better understand how interrelated variables contribute to perinatal morbidity risk in FGR. Methods Using data from 9,558 pregnancies delivered at ≥ 20 weeks with available outcome data, we derived and validated a PGM using randomly selected sub-cohorts of 80% (n = 7645) and 20% (n = 1,912), respectively, to discriminate cases of FGR resulting in composite perinatal morbidity from those that did not.

View Article and Find Full Text PDF

Predictions to Increase Lasso Peptide Production in the Heterologous Host Streptomyces coelicolor M1152.

Biotechnol Bioeng

December 2024

Centre for Biotechnology and Bioengineering (CeBiB), Department of Chemical Engineering, Biotechnology and Materials, University of Chile, Santiago, Chile.

Production of specialized metabolites are restricted to the metabolic capabilities of the organisms. Genome-scale models (GEM)s are useful to study the whole metabolism and to find metabolic engineering targets to increase the yield of a target compound. In this work we use a modified model of Streptomyces coelicolor M145 to simulate the production of lagmysin A (LP4) and the novel lagmysin B (LP2) lasso peptide, in the heterologous host Streptomyces coelicolor M1152.

View Article and Find Full Text PDF

Phenotypic vs. Genetic Mismatch of BMI and Type 2 Diabetes: Evidence from Two Perspective Cohort Studies.

Diabetes

December 2024

Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.

Little is known about the population-based mismatch between phenotypic and genetic BMI (BMI-PGM) and its association with type 2 diabetes. We therefore used data from the China Kadoorie Biobank and UK Biobank and calculated BMI-PGM for each participant as the difference between the percentile for adjusted BMI at baseline and the percentile for adjusted polygenic risk score for BMI. Participants were categorized into discordantly low (BMI-PGM< the 1st quartile), concordant (the 1st quartile ≤BMI-PGM View Article and Find Full Text PDF

Breast cancer is the leading cancer among women, with a significant number experiencing recurrence and metastasis, thereby reducing survival rates. This study focuses on the role of long noncoding RNAs (lncRNAs) in breast cancer immunotherapy response. We conducted an analysis involving 1027 patients from Sun Yat-sen Memorial Hospital, Sun Yat-sen University, and The Cancer Genome Atlas, utilizing RNA sequencing and pathology whole-slide images.

View Article and Find Full Text PDF

Want 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!