bindNode24: Competitive binding residue prediction with 60 % smaller model.

Kyra Erckert, Franz Birkeneder, Burkhard Rost

Abstract read

In one paragraph

Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it

0cells of the map it votes in

4citing 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

4 citing papers in PubMed.

Symphony-Bind: Prediction of Protein Binding Sites for 11 Representative Small Molecules and Ions via Fine-Tuning Protein Language Models and Grouped Multi-Task Learning.ACS omega · 2026
Article
UNKAI: A Protein Functional Identity Prediction Model Based on ESM-C Latent Representations and the Attention Mechanism.Computational and structural biotechnology journal · 2026
Article
AlphaFold in protein structure and function prediction.Computational and structural biotechnology journal · 2025
Article
Multimodal deep learning for predicting protein ubiquitination sites.Bioinformatics advances · 2025
Article

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.

Kyra ErckertTUM School of Computation, Information and Technology, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, Garching, Munich 85748, Germany.

Franz BirkenederTUM School of Computation, Information and Technology, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, Garching, Munich 85748, Germany.

Burkhard RostTUM School of Computation, Information and Technology, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, Garching, Munich 85748, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many proteins function through ligand binding. Yet, reliable experimental binding data remains limited. Recent advances predict binding residues from sequences using protein Language Model embeddings. The AlphaFold Protein Structure Database, which has reliable 3D structure predictions from AlphaFold2, opens the way for graph neural networks that predict binding residues. Here, we introduce

Indexed as

Binding residue predictionBinding residuesEmbeddingsGraph neural networksMachine learningProtein bindingProtein language model

Identifiers

PMID40165821

DOI10.1016/j.csbj.2025.02.042

PMCPMC11957672

What Socratic holds

Textmetadata

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 Socratic graph.