Evidence map›Paper›PMID 42327279›Full record

ArticlebioRxiv : the preprint server for biology2026

Improving All-Atom Molecular Dynamics Models for Quantitative Prediction of Nanopore Blockade Current.

Jingqian Liu, Crystal Rodriguez, Min Chen, Aleksei Aksimentiev

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Jingqian LiuCenter for Biophysics and Computational Biology.
Crystal RodriguezDepartment of Chemistry, University of Massachusetts, Amherst, MA 01003, USA.
Min ChenCenter for Biophysics and Computational Biology.
Aleksei AksimentievCenter for Biophysics and Computational Biology.

Funding

Breakthrough Molecular Dynamics Research via an Anton2 SupercomputerR01GM116961 · NIGMS · CARNEGIE-MELLON UNIVERSITY · PI BLOOD, PHILIP D. · 2016 to 2023
$3.0M
Breakthrough Molecular Dynamics Research via an Anton 3 SupercomputerR24GM154042 · NIGMS · CARNEGIE-MELLON UNIVERSITY · PI Philip D. Blood · 2024 to 2026
$2.3M
Improving biological nanopores for precision nucleic acid sequencing using a computational microscopeR21HG011741 · NHGRI · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI AKSIMENTIEV, ALEKSEI, CHEN, MIN · 2021 to 2023
$625k
NHGRI NIH HHS R21 HG011741NIGMS NIH HHS R01 GM116961NIGMS NIH HHS R24 GM154042
6 · The paper itself

Abstract

All-atom molecular dynamics has become an indispensable tool in development of nanopore sensors of biological information. In a typical nanopore experiment, measurements of ionic current flowing through a nanopore report on the chemical structure of biomolecules that pass through the nanopore. Such experiments alone are often insufficient to relate the structure of the biomolecules to the ionic current modulations. The molecular dynamics method can establish such a relationship directly through a brute force simulation under applied electric field. Here, we examine the ability of molecular dynamics force fields to reproduce experimentally measured nanopore blockade currents produced by single-stranded DNA. Our simulations show that none of the "off the shelf" force fields (CHARMM36, AMBER Parmbsc1 and DES-AMBER) is capable of reproducing experimental data with the desired level of accuracy. To improve the accuracy, we examined and refined interactions between ions, protein nanopores and DNA, guided by experiments designed specifically for this purpose. Ultimately, the introduction of surgical corrections to non-bonded interactions within the CHARMM36 force field produced a favorable agreement between simulation and experiment. This refined parameterization, initially developed for nanopore sensing simulations, may have broader applications in computational studies of DNA-protein systems.

Identifiers

PMID42327279
PMCPMC13278064

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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.