ArticleBioinformatics (Oxford, England)2024
M-Ionic: prediction of metal-ion-binding sites from sequence using residue embeddings.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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.
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.
Who cites it
11 citing papers in PubMed, 23 citations in OpenAlex.
- PrOMet: Accurate and Robust Metal-Binding Site and Type Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- MBPBERT: A Large Language Model for Metal-Binding Peptide Discovery.Interdisciplinary sciences, computational life sciences · 2026Article
- Multivalent ion binding site identification with structure-based deep learning.Communications biology · 2026Article
- Probe-based identification of metal-binding sites using deep learning representations.Nature communications · 2026Article
- A protein surface-aware multimodal framework for residue-level metal-binding site recognition.Cell reports methods · 2026Article
- IST: an ontology-guided attention-based autoencoder for interpretable analysis of single-cell transcriptomic data.Scientific reports · 2026Article
- Scalable embedding fusion with protein language models: insights from benchmarking text-integrated representations.Briefings in bioinformatics · 2026Article
- Scalable embedding fusion with protein language models: insights from benchmarking text-integrated representations.bioRxiv : the preprint server for biology · 2025Article
- Interpretable prediction of zinc ion location in proteins with ZincSight.Protein science : a publication of the Protein Society · 2025Article
- SuperMetal: a generative AI framework for rapid and precise metal ion location prediction in proteins.Journal of cheminformatics · 2025Article
- bindNode24: Competitive binding residue prediction with 60 % smaller model.Computational and structural biotechnology journal · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
motivationUnderstanding metal-protein interaction can provide structural and functional insights into cellular processes. As the number of protein sequences increases, developing fast yet precise computational approaches to predict and annotate metal-binding sites becomes imperative. Quick and resource-efficient pre-trained protein language model (pLM) embeddings have successfully predicted binding sites from protein sequences despite not using structural or evolutionary features (multiple sequence alignments). Using residue-level embeddings from the pLMs, we have developed a sequence-based method (M-Ionic) to identify metal-binding proteins and predict residues involved in metal binding.
resultsOn independent validation of recent proteins, M-Ionic reports an area under the curve (AUROC) of 0.83 (recall = 84.6%) in distinguishing metal binding from non-binding proteins compared to AUROC of 0.74 (recall = 61.8%) of the next best method. In addition to comparable performance to the state-of-the-art method for identifying metal-binding residues (Ca2+, Mg2+, Mn2+, Zn2+), M-Ionic provides binding probabilities for six additional ions (i.e. Cu2+, Po43-, So42-, Fe2+, Fe3+, Co2+). We show that the pLM embedding of a single residue contains sufficient information about its neighbours to predict its binding properties. AVAILABILITY AND IMPLEMENTATION: M-Ionic can be used on your protein of interest using a Google Colab Notebook (https://bit.ly/40FrRbK). The GitHub repository (https://github.com/TeamSundar/m-ionic) contains all code and data.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.