ArticleNature communications2021
Machine learning differentiates enzymatic and non-enzymatic metals in proteins.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed.
- The invasion of Cassytha filiformis accelerated the litter decomposition of native plant communities in small tropical coral islands.BMC plant biology · 2025Article
- Recent advances and future challenges in predictive modeling of metalloproteins by artificial intelligence.Molecules and cells · 2025Review
- TopEC: prediction of Enzyme Commission classes by 3D graph neural networks and localized 3D protein descriptor.Nature communications · 2025Article
- Bacterial Metallostasis: Metal Sensing, Metalloproteome Remodeling, and Metal Trafficking.Chemical reviews · 2024Review
- MetalHawk: Enhanced Classification of Metal Coordination Geometries by Artificial Neural Networks.Journal of chemical information and modeling · 2024Article
- Strategies for designing biocatalysts with new functions.Chemical Society reviews · 2024Review
- Machine Learning to Predict Enzyme-Substrate Interactions in Elucidation of Synthesis Pathways: A Review.Metabolites · 2024Review
- Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering.ACS central science · 2024Review
- Enzyme function and evolution through the lens of bioinformatics.The Biochemical journal · 2023Review
- Hunting down zinc(II)-binding sites in proteins with distance matrices.Bioinformatics (Oxford, England) · 2023Article
- Prediction and design of protease enzyme specificity using a structure-aware graph convolutional network.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Metal3D: a general deep learning framework for accurate metal ion location prediction in proteins.Nature communications · 2023Article
- MAHOMES II: A webserver for predicting if a metal binding site is enzymatic.Protein science : a publication of the Protein Society · 2023Article
- MAHOMES II: A webserver for predicting if a metal binding site is enzymatic.bioRxiv : the preprint server for biology · 2023Article
- Prediction and Design of Protease Enzyme Specificity Using a Structure-Aware Graph Convolutional Network.bioRxiv : the preprint server for biology · 2023Article
- Structural Bioinformatics and Deep Learning of Metalloproteins: Recent Advances and Applications.International journal of molecular sciences · 2022Review
- Learning to Identify Physiological and Adventitious Metal-Binding Sites in the Three-Dimensional Structures of Proteins by Following the Hints of a Deep Neural Network.Journal of chemical information and modeling · 2022Review
- Machine Learning Approaches for Metalloproteins.Molecules (Basel, Switzerland) · 2022Review
- Identification of Iron-Sulfur (Fe-S) Cluster and Zinc (Zn) Binding Sites Within Proteomes Predicted by DeepMind's AlphaFold2 Program Dramatically Expands the Metalloproteome.Journal of molecular biology · 2022Article
- MAResNet: predicting transcription factor binding sites by combining multi-scale bottom-up and top-down attention and residual network.Briefings in bioinformatics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Metalloenzymes are 40% of all enzymes and can perform all seven classes of enzyme reactions. Because of the physicochemical similarities between the active sites of metalloenzymes and inactive metal binding sites, it is challenging to differentiate between them. Yet distinguishing these two classes is critical for the identification of both native and designed enzymes. Because of similarities between catalytic and non-catalytic metal binding sites, finding physicochemical features that distinguish these two types of metal sites can indicate aspects that are critical to enzyme function. In this work, we develop the largest structural dataset of enzymatic and non-enzymatic metalloprotein sites to date. We then use a decision-tree ensemble machine learning model to classify metals bound to proteins as enzymatic or non-enzymatic with 92.2% precision and 90.1% recall. Our model scores electrostatic and pocket lining features as more important than pocket volume, despite the fact that volume is the most quantitatively different feature between enzyme and non-enzymatic sites. Finally, we find our model has overall better performance in a side-to-side comparison against other methods that differentiate enzymatic from non-enzymatic sequences. We anticipate that our model's ability to correctly identify which metal sites are responsible for enzymatic activity could enable identification of new enzymatic mechanisms and de novo enzyme design.
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.