Evidence map›Paper›PMID 40799569›Full record

ArticlebioRxiv : the preprint server for biology2025

DRUMBEAT: Temporally resolved interpretable machine learning model for characterizing state transitions in protein dynamics.

Babgen Manookian, Elizaveta Mukhaleva, Grigoriy Gogoshin, Supriyo Bhattacharya, Nagarajan Vaidehi, Andrei S Rodin, Sergio Branciamore

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 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

5 · Who and what money

Authors and funding

7 authors.

Babgen ManookianDepartment of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, Duarte, CA.ORCID 0000-0002-6273-0995
Elizaveta MukhalevaDepartment of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, Duarte, CA.ORCID 0000-0002-9911-7625
Grigoriy GogoshinDepartment of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, Duarte, CA.
Supriyo BhattacharyaDepartment of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, Duarte, CA.
Nagarajan VaidehiDepartment of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, Duarte, CA.ORCID 0000-0001-8100-8132
Andrei S RodinDepartment of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, Duarte, CA.
Sergio BranciamoreDepartment of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, Duarte, CA.ORCID 0000-0002-2556-8765

Funding

Structural dynamics underlying GPCR-G protein selectivityR01GM117923 · NIGMS · BECKMAN RESEARCH INSTITUTE/CITY OF HOPE · PI VAIDEHI, NAGARAJAN · 2017 to 2024
$3.8M
An integrated toolkit combining computational systems biology techniques with molecular dynamics simulations to delineate functionality of GPCRsR01LM013876 · NLM · BECKMAN RESEARCH INSTITUTE/CITY OF HOPE · PI RODIN, ANDREI, VAIDEHI, NAGARAJAN · 2022 to 2025
$1.5M
Emergent role of allostery on function of GPCRs and Trimeric G proteinsR35GM156498 · NIGMS · BECKMAN RESEARCH INSTITUTE/CITY OF HOPE · PI Nagarajan Vaidehi · 2025 to 2026
$890k
Scalable Bayesian Network analysis of multimodal FACS and SUMOylation data, with generalization to other big mixed biological datasetsR01LM013138 · NLM · BECKMAN RESEARCH INSTITUTE/CITY OF HOPE · PI RODIN, ANDREI · 2020 to 2022
$776k
NIGMS NIH HHS R01 GM117923NIGMS NIH HHS R35 GM156498NLM NIH HHS R01 LM013138NLM NIH HHS R01 LM013876
6 · The paper itself

Abstract

Conformational transitions are central to protein function, yet their mechanistic analysis remains challenging due to the multi-dimensionality and timescales underlying the molecular motions. While interpretable network models such as Bayesian networks have advanced the identification of key residue interactions in molecular dynamics (MD) data, they lack temporal resolution and cannot capture the sequence of events during transitions. Here, we introduce

Identifiers

PMID40799569
PMCPMC12340804

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.