Prediction of Intrinsically Disordered Lipid Binding Residues with DisoLipPred.

Bi Zhao, Lukasz Kurgan

Abstract read

In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 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

2 authors.

Bi ZhaoGenomics Program, College of Public Health, University of South Florida, Tampa, FL, USA.

Lukasz KurganDepartment of Computer Science, College of Engineering, Virginia Commonwealth University, Virginia, VA, USA. lkurgan@vcu.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

DisoLipPred is a state-of-the-art predictor of intrinsically disordered lipid-binding residues in protein sequences. This method relies on a modern deep neural network model, produces accurate results, and is available as a convenient web server. We provide a practical and detailed introduction to the DisoLipPred's web server. We describe the underlying predictive process, which is fully automated and performed on the server side, and offer instructions for interactions with DisoLipPred's web interface. We also discuss how to obtain, read, and interpret results produced by this server using a case study that analyzes results generated for the vacuolar-sorting protein SNF8. The web server is freely available at http://biomine.cs.vcu.edu/servers/DisoLipPred/ .

Indexed as

Computational BiologyIntrinsically Disordered ProteinsLipidsSoftwareBinding SitesDatabases, ProteinInternetNeural Networks, ComputerProtein BindingIntrinsically Disordered ProteinsLipidsDeep learningIntrinsic disorderLipid interactionsMolecular recognitionPredictionProteinWeb server

Identifiers

PMID40728621

DOI10.1093/bioinformatics/btab509

What Socratic holds

Textmetadata

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 Socratic graph.