Evidence map›Paper›PMID 36817955›Full record

ArticleComputational and structural biotechnology journal2023

HIT-2: Implementing machine learning algorithms to treat bound ions in biomolecules.

Shengjie Sun, Honglun Xu, Yixin Xie, Jason E Sanchez, Wenhan Guo, Dongfang Liu, Lin Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 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

7 authors.

Shengjie SunComputational Science Program, The University of Texas at El Paso, 500 W University Ave, TX 79968, USA.
Honglun XuComputational Science Program, The University of Texas at El Paso, 500 W University Ave, TX 79968, USA.
Yixin XieDepartment of Information Technology, College of Computing and Software Engineering, Kennesaw State University, 1000 Chastain Rd NW, Kennesaw, GA 30144, USA.
Jason E SanchezComputational Science Program, The University of Texas at El Paso, 500 W University Ave, TX 79968, USA.
Wenhan GuoComputational Science Program, The University of Texas at El Paso, 500 W University Ave, TX 79968, USA.
Dongfang LiuDepartment of Computer Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA.
Lin LiComputational Science Program, The University of Texas at El Paso, 500 W University Ave, TX 79968, USA.

Funding

Developing and applying large-scale simulation approach to understand the mechanisms of kinesins' motilities along microtubulesSC1GM132043 · NIGMS · UNIVERSITY OF TEXAS EL PASO · PI LI, LIN · 2019 to 2022
$1.5M
NIGMS NIH HHS SC1 GM132043
6 · The paper itself

Abstract

Electrostatic features are fundamental to protein functions and protein-protein interactions. Studying highly charged biomolecules is challenging given the heterogeneous distribution of the ionic cloud around such biomolecules. Here we report a new computational method, Hybridizing Ions Treatment-2 (HIT-2), which is used to model biomolecule-bound ions using the implicit solvation model. By modeling ions, HIT-2 allows the user to calculate important electrostatic features of the biomolecules. HIT-2 applies an efficient algorithm to calculate the position of bound ions from molecular dynamics simulations. Modeling parameters were optimized by machine learning methods from thousands of datasets. The optimized parameters produced results with errors lower than 0.2 Å. The testing results on bound Ca

Indexed as

Bound ionsDelphiElectrostatic calculationExplicit solvent modelImplicit solvent model

Identifiers

PMID36817955
PMCPMC9929202

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.