Evidence map›Paper›PMID 39633021›Full record

ArticleCommunications chemistry2024

BioDolphin as a comprehensive database of lipid-protein binding interactions.

Li-Yen Yang, Kaike Ping, Yunan Luo, Andrew C McShan

Abstract read
In one paragraph

Article in Communications chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
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  3. Review
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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

4 authors.

Li-Yen YangSchool of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Kaike PingDepartment of Computer Science, Virginia Tech, Blacksburg, VA, 24061, USA.ORCID http://orcid.org/0000-0002-7520-7503
Yunan LuoSchool of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.ORCID http://orcid.org/0000-0001-7728-6412
Andrew C McShanSchool of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, 30332, USA. andrew.mcshan@chemistry.gatech.edu.ORCID http://orcid.org/0000-0002-3212-9867

Funding

Integrative deep learning algorithms for understanding protein sequence-structure-function relationships: representation, prediction, and discoveryR35GM150890 · NIGMS · GEORGIA INSTITUTE OF TECHNOLOGY · PI Yunan Luo · 2023 to 2026
$1.6M
NIGMS NIH HHS R35 GM150890
6 · The paper itself

Abstract

Lipid-protein interactions are crucial for virtually all biological processes in living cells. However, existing structural databases focusing on these interactions are limited to integral membrane proteins. A systematic understanding of diverse lipid-protein interactions also encompassing lipid-anchored, peripheral membrane and soluble lipid binding proteins remains to be elucidated. To address this gap and facilitate the research of universal lipid-protein assemblies, we developed BioDolphin - a curated database with over 127,000 lipid-protein interactions. BioDolphin provides comprehensive annotations, including protein functions, protein families, lipid classifications, lipid-protein binding affinities, membrane association type, and atomic structures. Accessible via a publicly available web server ( www.biodolphin.chemistry.gatech.edu ), users can efficiently search for lipid-protein interactions using a wide range of options and download datasets of interest. Additionally, BioDolphin features interactive 3D visualization of each lipid-protein complex, facilitating the exploration of structure-function relationships. BioDolphin also includes detailed information on atomic-level intermolecular interactions between lipids and proteins that enable large scale analysis of both paired complexes and larger assemblies. As an open-source resource, BioDolphin enables global analysis of lipid-protein interactions and supports data-driven approaches for developing predictive machine learning algorithms for lipid-protein binding affinity and structures.

Identifiers

PMID39633021
PMCPMC11618342

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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.