Aromatic and arginine content drives multiphasic condensation of protein–RNA mixtures DOI Open Access
Pin Yu Chew, Jerelle A. Joseph, Rosana Collepardo‐Guevara

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: April 6, 2023

Multiphasic architectures are found ubiquitously in biomolecular condensates and thought to have important implications for the organisation of multiple chemical reactions within same compartment. Many these multiphasic contain RNA addition proteins. Here, we investigate importance different interactions comprising two proteins using computer simulations with a residue-resolution coarse-grained model RNA. We find that multilayered containing both phases, protein–RNA dominate, aromatic residues arginine forming key stabilising interactions. The total content must be appreciably distinct phases form, show this difference increases as system is driven towards greater multiphasicity. Using trends observed interaction energies system, demonstrate can also construct preferentially concentrated one phase. ‘rules’ identified thus enable design synthetic facilitate further study their function.

Language: Английский

Deep eutectic solvents as green solvents for materials preparation DOI
Dongkun Yu, Depeng Jiang, Zhimin Xue

et al.

Green Chemistry, Journal Year: 2024, Volume and Issue: 26(13), P. 7478 - 7507

Published: Jan. 1, 2024

DESs play a Janus role (chemical or physical) in the preparation of materials. The physical aspect includes solvating, exfoliating, dispersing and confining, while chemical part reacting, composing, polymerizing modifying.

Language: Английский

Citations

25
Molecular determinants of condensate composition DOI Creative Commons
Alex S. Holehouse, Simon Alberti

Molecular Cell, Journal Year: 2025, Volume and Issue: 85(2), P. 290 - 308

Published: Jan. 1, 2025

Language: Английский

Citations

4
ænet-PyTorch: A GPU-supported implementation for machine learning atomic potentials training DOI Creative Commons
Jon López-Zorrilla, Xabier M. Aretxabaleta, In Won Yeu

et al.

The Journal of Chemical Physics, Journal Year: 2023, Volume and Issue: 158(16)

Published: April 25, 2023

In this work, we present ænet-PyTorch, a PyTorch-based implementation for training artificial neural network-based machine learning interatomic potentials. Developed as an extension of the atomic energy network (ænet), ænet-PyTorch provides access to all tools included in ænet application and usage The package has been designed alternative internal capabilities ænet, leveraging power graphic processing units facilitate direct on forces addition energies. This leads substantial reduction time by one two orders magnitude compared central unit implementation, enabling systems beyond small molecules. Here, demonstrate main features show its performance open databases. Our results that force information within dataset is not necessary, including between 10% 20% sufficient achieve optimally accurate potentials with least computational resources.

Language: Английский

Citations

17
Multiscale computational modeling techniques in study and design of 2D materials: recent advances, challenges, and opportunities DOI Creative Commons
Mohsen Asle Zaeem, Siby Thomas, Sepideh Kavousi

et al.

2D Materials, Journal Year: 2024, Volume and Issue: 11(4), P. 042004 - 042004

Published: Sept. 9, 2024

Abstract This article provides an overview of recent advances, challenges, and opportunities in multiscale computational modeling techniques for study design two-dimensional (2D) materials. We discuss the role understanding structures properties 2D materials, followed by a review various length-scale models aiding their synthesis. present integration including density functional theory, molecular dynamics, phase-field modeling, continuum-based mechanics, machine learning. The focuses on advancements, future prospects tailored emerging Key challenges include accurately capturing intricate behaviors across scales environments. Conversely, lie enhancing predictive capabilities to accelerate materials discovery applications spanning from electronics, photonics, energy storage, catalysis, nanomechanical devices. Through this comprehensive review, our aim is provide roadmap research simulation

Language: Английский

Citations

7
Aromatic and arginine content drives multiphasic condensation of protein-RNA mixtures DOI Creative Commons
Pin Yu Chew, Jerelle A. Joseph, Rosana Collepardo‐Guevara

et al.

Biophysical Journal, Journal Year: 2023, Volume and Issue: unknown

Published: July 1, 2023

Multiphasic architectures are found ubiquitously in biomolecular condensates and thought to have important implications for the organization of multiple chemical reactions within same compartment. Many these multiphasic contain RNA addition proteins. Here, we investigate importance different interactions comprising two proteins using computer simulations with a residue-resolution coarse-grained model RNA. We find that multilayered containing both phases, protein-RNA dominate, aromatic residues arginine forming key stabilizing interactions. The total content must be appreciably distinct phases form, show this difference increases as system is driven toward greater multiphasicity. Using trends observed interaction energies system, demonstrate can also construct preferentially concentrated one phase. “rules” identified thus enable design synthetic facilitate further study their function.

Language: Английский

Citations

13
A simple and accurate method to determine fluid–crystal phase boundaries from direct coexistence simulations DOI
Frank Smallenburg, Giovanni Del Monte, Marjolein de Jager

et al.

The Journal of Chemical Physics, Journal Year: 2024, Volume and Issue: 160(22)

Published: June 13, 2024

One method for computationally determining phase boundaries is to explicitly simulate a direct coexistence between the two phases of interest. Although this approach works very well fluid–fluid coexistences, it often considered be less useful fluid–crystal transitions, as additional care must taken prevent simulation from imposing unwanted strains on crystal phase. Here, we present simple adaptation that nonetheless allows us obtain highly accurate predictions conditions, assuming interface can readily simulated. We test our hard spheres, screened Coulomb potential, and 2D patchy-particle model. In all cases, find excellent agreement (much more cumbersome) free-energy calculation methods. Moreover, sufficiently resolve (tiny) difference face-centered cubic hexagonally close-packed spheres in thermodynamic limit. The simplicity also ensures trivially implemented essentially any or package. Hence, provides an alternative based methods precise determination boundaries.

Language: Английский

Citations

4
From electrons to phase diagrams with machine learning potentials using pyiron based automated workflows DOI Creative Commons
Sarath Menon, Yury Lysogorskiy, A. Knoll

et al.

npj Computational Materials, Journal Year: 2024, Volume and Issue: 10(1)

Published: Nov. 17, 2024

Abstract We present a comprehensive and user-friendly framework built upon the integrated development environment (IDE), enabling researchers to perform entire Machine Learning Potential (MLP) cycle consisting of (i) creating systematic DFT databases, (ii) fitting Density Functional Theory (DFT) data empirical potentials or MLPs, (iii) validating in largely automatic approach. The power performance this are demonstrated for three conceptually very different classes interatomic potentials: an potential (embedded atom method - EAM), neural networks (high-dimensional network HDNNP) expansions basis sets (atomic cluster expansion ACE). As advanced example validation application, we show computation binary composition-temperature phase diagram Al-Li, technologically important lightweight alloy system with applications aerospace industry.

Language: Английский

Citations

4
ChemBERTa Embeddings and Ensemble Learning for Prediction of Density and Melting Point of Deep Eutectic Solvents with Hybrid Features DOI
Ting Wu, Peng Zhan, Weiqiu Chen

et al.

Computers & Chemical Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 109065 - 109065

Published: Feb. 1, 2025

Language: Английский

Citations

0
Machine Learning-Driven Prediction of Composite Materials Properties Based on Experimental Testing Data DOI Open Access
Kristina Berladir, Katarzyna Antosz, Vitalii Ivanov

et al.

Polymers, Journal Year: 2025, Volume and Issue: 17(5), P. 694 - 694

Published: March 5, 2025

The growing demand for high-performance and cost-effective composite materials necessitates advanced computational approaches optimizing their composition properties. This study aimed at the application of machine learning prediction optimization functional properties composites based on a thermoplastic matrix with various fillers (two types fibrous, four dispersed, two nano-dispersed fillers). experimental methods involved material production through powder metallurgy, further microstructural analysis, mechanical tribological testing. analysis revealed distinct structural modifications interfacial interactions influencing key findings indicate that optimal filler selection can significantly enhance wear resistance while maintaining adequate strength. Carbon fibers 20 wt. % improved (by 17–25 times) reducing tensile strength elongation. Basalt 10 provided an effective balance between reinforcement 11–16 times). Kaolin 2 greatly enhanced 45–57 moderate reduction. Coke maximized 9−15 acceptable Graphite ensured wear, as higher concentrations drastically decreased Sodium chloride 5 offered improvement 3–4 minimal impact Titanium dioxide 3 11–12.5 slightly Ultra-dispersed PTFE 1 optimized both work analyzed in detail effect content learning-driven prediction. Regression models demonstrated high R-squared values (0.74 density, 0.67 strength, 0.80 relative elongation, 0.79 intensity), explaining up to 80% variability Despite its efficiency, limitations include potential multicollinearity, lack consideration external factors, need validation under real-world conditions. Thus, approach reduces extensive testing, minimizing waste costs, contributing SDG 9. highlights use polymer design, offering data-driven framework rational choice fillers, thereby sustainable industrial practices.

Language: Английский

Citations

0
Artificial Intelligence and Multiscale Modeling for Sustainable Biopolymers and Bioinspired Materials DOI Creative Commons
Xing Quan Wang, Zeqing Jin, Dharneedar Ravichandran

et al.

Advanced Materials, Journal Year: 2025, Volume and Issue: unknown

Published: March 10, 2025

Biopolymers and bioinspired materials contribute to the construction of intricate hierarchical structures that exhibit advanced properties. The remarkable toughness damage tolerance such multilevel are conferred through assembly their multiscale (i.e., atomistic macroscale) components architectures. Here, functionality mechanisms biopolymers bio-inspired at multilength scales explored summarized, focusing on biopolymer nanofibril configurations, biocompatible synthetic biopolymers, composites. Their modeling methods with theoretical basis multiple lengths time reviewed for applications. Additionally, exploration artificial intelligence-powered methodologies is emphasized realize improvements in these from functionality, biodegradability, sustainability characterization, fabrication process, superior designs. Ultimately, a promising future versatile manufacturing across wider applications greater lifecycle impacts foreseen.

Language: Английский

Citations

0