Evidence map›Paper›PMID 37546728›Full record

ArticlebioRxiv : the preprint server for biology2023

Folding-upon-binding pathways of an intrinsically disordered protein from a deep Markov state model.

Thomas Sisk, Paul Robustelli

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. 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

5 · Who and what money

Authors and funding

2 authors.

Thomas SiskDartmouth College, Department of Chemistry, Hanover, NH, 03755.
Paul RobustelliDartmouth College, Department of Chemistry, Hanover, NH, 03755.

Funding

Characterizing the binding mechanisms of castration-resistant prostate cancer therapeutics to the intrinsically disordered N-terminal domain of the androgen receptorR35GM142750 · NIGMS · DARTMOUTH COLLEGE · PI Paul Robustelli · 2021 to 2026
$2.4M
NIGMS NIH HHS R35 GM142750
6 · The paper itself

Abstract

A central challenge in the study of intrinsically disordered proteins is the characterization of the mechanisms by which they bind their physiological interaction partners. Here, we utilize a deep learning based Markov state modeling approach to characterize the folding-upon-binding pathways observed in a long-time scale molecular dynamics simulation of a disordered region of the measles virus nucleoprotein N

Indexed as

Deep LearningIntrinsically Disordered ProteinsMarkov State ModelsMolecular DynamicsMolecular Recognition

Identifiers

PMID37546728
PMCPMC10401938

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.