Elucidating governing factors of PFAS removal by polyamide membranes using machine learning and molecular simulations.

Per- and polyfluoroalkyl substances (PFASs) have recently garnered considerable concerns regarding their impacts on human and ecological health. Despite the important roles of polyamide membranes in remediating PFASs-contaminated water, the governing factors influencing PFAS transport across these membranes remain elusive. In this study, we investigate PFAS rejection by polyamide membranes using two machine learning (ML) models, namely XGBoost and multimodal transformer models.

Automated Parameterization of Coarse-Grained Polyethylenimine under a Martini Framework.

As a versatile polymer in many applications, synthesized polyethylenimine (PEI) is polydisperse with diverse branched structures that attain pH-dependent protonation states. Understanding the structure-function relationship of PEI is necessary for enhancing its efficacy in various applications. Coarse-grained (CG) simulations can be performed at length and time scales directly comparable with experimental data while maintaining the molecular perspective.

Polyethylenimine-DNA Nanoparticles under Endosomal Acidification and Implication to Gene Delivery.

Non-viral gene delivery using polyethylenimine (PEI) has shown tremendous promise as a therapeutic technique. Through the formation of nanoparticles (NPs), PEIs protect genetic material such as DNA from degradation. Escape of the NPs from endosomes and lysosomes is facilitated by PEI's buffering capacity over a wide range of pH.

Comment on "Martini force field for protonated polyethyleneimine".

We comment on the recently published Martini forcefield for linear polyethylenimine (Beu et al., J. Comput.

Erratum: "Martini Coarse-Grained Model for Polyethylenimine" [J. Comput. Chem. 2019, 40, 607-618, DOI:10.1002/jcc.25747].

Correction of calculation errors in the original article led to the change of bead type for the unprotonated beads in the coarse-grained polyethylenimine model. The original model was still of good quality while the updated model showed better performance in describing the interaction between polyethylenimine and DNA.

Polyethylenimine-DNA Ratio Strongly Affects Their Nanoparticle Formation: A Large-Scale Coarse-Grained Molecular Dynamics Study.

Polyethylenimine (PEI)-DNA nanoparticles (NPs) have shown a lot of potential in gene delivery. The N/P ratio, the ratio between the total number of amines in PEIs and total number of phosphates in DNAs, is an essential factor determining the efficacy of delivery. In this work, the aggregation of PEIs and DNAs under different N/P ratios is studied using large-scale coarse-grained simulations under the Martini framework.

Martini coarse-grained model for polyethylenimine.

As a polycation with diverse applications in biomedical and environmental engineering, polyethylenimine (PEI) can be synthesized with varying degrees of branching, polymerization, and can exist in different protonation states. There have been some interests in molecular modeling of PEI at all-atom or coarse-grained (CG) levels, but present CG models are limited to linear PEIs. Here we present the methodology to systematically categorize bond lengths, bond angles and dihedral angles, which allows us to model branched PEIs.