Evidence map›Paper›PMID 39999471›Full record

ArticleThe journal of physical chemistry. B2025

Residue Interactions Guide Translational Diffusion of Proteins.

Elham Fazelpour, Jennifer M Haseleu, Christopher J Fennell

Abstract read
In one paragraph

Article in The journal of physical chemistry. B, 2025. 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

3 authors.

Elham FazelpourDepartment of Chemistry, Oklahoma State University, Stillwater, Oklahoma 74078, United States.ORCID 0009-0009-0307-1609
Jennifer M HaseleuDepartment of Chemistry, Oklahoma State University, Stillwater, Oklahoma 74078, United States.
Christopher J FennellDepartment of Chemistry, Oklahoma State University, Stillwater, Oklahoma 74078, United States.ORCID 0000-0001-8963-4103

Funding

Solvation modeling for next-gen biomolecule simulationsRM1GM135136 · NIGMS · STATE UNIVERSITY NEW YORK STONY BROOK · PI COUTSIAS, EVANGELOS A., DILL, KEN A · 2020 to 2024
$6.0M
NIGMS NIH HHS RM1 GM135136
6 · The paper itself

Abstract

Diffusion at the molecular level involves random collisions between particles, the structure of local microscopic environments, and interactions among the molecules involved. Sampling all of these aspects, along with correcting for finite-size effects, can make the calculation of infinitely dilute diffusion coefficients computationally difficult. We present a new approach for estimating the translational diffusion coefficient of biomolecular structures by encapsulating these driving forces of diffusion through piecewise assembly of the component residues of the protein structure. By linking the local chemistry of a solvent-exposed patch of a molecule to its contribution to the overall hydrodynamic radius, an accurate prediction of the computationally and experimentally comparable diffusion coefficients can be constructed following a solvent-excluded surface area calculation. We demonstrate that the resulting predictions for diffusion coefficients from peptides through to protein structures are comparable to explicit molecular simulations and improve on statistical mass-based predictions, which tend to rely on limited training data. As this approach uses the chemical identity of molecular structures, we find that it is able to predict and identify differences in diffusivity for structures that would be indistinguishable by mass information alone.

Indexed as

ProteinsDiffusionMolecular Dynamics SimulationPeptidesSolventsPeptidesProteinsSolvents

Identifiers

PMID39999471
PMCPMC11891898

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.