Evidence map›Paper›PMID 37104393›Full record

ArticlePloS one2023

Morphoscanner2.0: A new python module for analysis of molecular dynamics simulations.

Federico Fontana, Calogero Carlino, Ashish Malik, Fabrizio Gelain

Abstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Federico FontanaCenter for Nanomedicine and Tissue Engineering (CNTE), A.S.S.T. Grande Ospedale Metropolitano Niguarda, Milan, Italy.ORCID 0000-0001-9273-2556
Calogero CarlinoCenter for Nanomedicine and Tissue Engineering (CNTE), A.S.S.T. Grande Ospedale Metropolitano Niguarda, Milan, Italy.ORCID 0000-0003-0707-1007
Ashish MalikCenter for Nanomedicine and Tissue Engineering (CNTE), A.S.S.T. Grande Ospedale Metropolitano Niguarda, Milan, Italy.ORCID 0000-0002-7508-9519
Fabrizio GelainCenter for Nanomedicine and Tissue Engineering (CNTE), A.S.S.T. Grande Ospedale Metropolitano Niguarda, Milan, Italy.ORCID 0000-0002-2624-5853

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Molecular dynamics simulations, at different scales, have been exploited for investigating complex mechanisms ruling biologically inspired systems. Nonetheless, with recent advances and unprecedented achievements, the analysis of molecular dynamics simulations requires customized workflows. In 2018, we developed Morphoscanner to retrieve structural relations within self-assembling peptide systems. In particular, we conceived Morphoscanner for tracking the emergence of β-structured domains in self-assembling peptide systems. Here, we introduce Morphoscanner2.0. Morphoscanner2.0 is an object-oriented library for structural and temporal analysis of atomistic and coarse-grained molecular dynamics (CG-MD) simulations written in Python. The library leverages MDAnalysis, PyTorch and NetworkX to perform the pattern recognition of secondary structure patterns, and interfaces with Pandas, Numpy and Matplotlib to make the results accessible to the user. We used Morphoscanner2.0 on both simulation trajectories and protein structures. Because of its dependencies on the MDAnalysis package, Morphoscanner2.0 can read several file formats generated by widely-used molecular simulation packages such as NAMD, Gromacs, OpenMM. Morphoscanner2.0 also includes a routine for tracking the alpha-helix domain formation.

Indexed as

Molecular Dynamics SimulationSoftwarePeptidesProteinsProtein Structure, SecondaryPeptidesProteins

Identifiers

PMID37104393
PMCPMC10138828

What OpenQuestion holds

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LicenceCC BY
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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.