Evidence map›Paper›PMID 36795071›Full record

ArticleJournal of chemical theory and computation2023

NanoModeler CG: A Tool for Modeling and Engineering Functional Nanoparticles at a Coarse-Grained Resolution.

Sebastian Franco-Ulloa, Laura Riccardi, Federico Rimembrana, Edwin Grottin, Mattia Pini, Marco De Vivo

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Article in Journal of chemical theory and computation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Sebastian Franco-UlloaMolecular Modeling and Drug Discovery Lab, Istituto Italiano di Tecnologia, via Morego 30, Genova 16163, Italy.ORCID 0000-0001-6128-0630
Laura RiccardiMolecular Modeling and Drug Discovery Lab, Istituto Italiano di Tecnologia, via Morego 30, Genova 16163, Italy.ORCID 0000-0002-5315-5140
Federico RimembranaMolecular Modeling and Drug Discovery Lab, Istituto Italiano di Tecnologia, via Morego 30, Genova 16163, Italy.
Edwin GrottinMolecular Modeling and Drug Discovery Lab, Istituto Italiano di Tecnologia, via Morego 30, Genova 16163, Italy.
Mattia PiniMolecular Modeling and Drug Discovery Lab, Istituto Italiano di Tecnologia, via Morego 30, Genova 16163, Italy.
Marco De VivoMolecular Modeling and Drug Discovery Lab, Istituto Italiano di Tecnologia, via Morego 30, Genova 16163, Italy.ORCID 0000-0003-4022-5661

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Functionalized metal nanoparticles (NPs) are macromolecular assemblies with a tunable physicochemical profile that makes them interesting for biotechnology, materials science, and energy conversion. In this regard, molecular simulations offer a way to scrutinize the structural and dynamical features of monolayer-protected NPs and their interactions with relevant matrices. Previously, we developed NanoModeler, a webserver that automates the preparation of functionalized gold NPs for atomistic molecular dynamics (MD) simulations. Here, we present NanoModeler CG (www.nanomodeler.it), a new release of NanoModeler that now also allows the building and parametrizing of monolayer-protected metal NPs at a coarse-grained (CG) resolution. This new version extends our original methodology to NPs of eight different core shapes, conformed by up to 800,000 beads and coated by eight different monolayer morphologies. The resulting topologies are compatible with the Martini force field but are easily extendable to any other set of parameters parsed by the user. Finally, we demonstrate NanoModeler CG's capabilities by reproducing experimental structural features of alkylthiolated NPs and rationalizing the brush-to-mushroom phase transition of PEGylated anionic NPs. By automating the construction and parametrization of functionalized NPs, the NanoModeler series offers a standardized way to computationally model monolayer-protected nanosized systems.

Identifiers

PMID36795071
PMCPMC10018737

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.