ArticleProteins2011
FINDSITE-metal: integrating evolutionary information and machine learning for structure-based metal-binding site prediction at the proteome level.
Article in Proteins, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed, 100 citations in OpenAlex.
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- Drug Repurposing: Insights into Current Advances and Future Applications.Current medicinal chemistry · 2025Review
- A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond.Briefings in bioinformatics · 2024Review
- Trace Element Distribution and Arsenic Speciation in Toenails as Affected by External Contamination and Evaluation of a Cleaning Protocol.Analytical chemistry · 2024Article
- Exonic splicing code and coordination of divalent metals in proteins.Nucleic acids research · 2024Article
- Review
- Identifying Metal Binding Sites in Proteins Using Homologous Structures, the MADE Approach.Journal of chemical information and modeling · 2023Article
- Metal3D: a general deep learning framework for accurate metal ion location prediction in proteins.Nature communications · 2023Article
- In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets.Molecules (Basel, Switzerland) · 2022Review
- Implications of the Essential Role of Small Molecule Ligand Binding Pockets in Protein-Protein Interactions.The journal of physical chemistry. B · 2022Article
- Structure-based drug repurposing: Traditional and advanced AI/ML-aided methods.Drug discovery today · 2022Review
- Exonic splicing code and protein binding sites for calcium.Nucleic acids research · 2022Article
- Machine Learning Approaches for Metalloproteins.Molecules (Basel, Switzerland) · 2022Review
- A Comprehensive Review of Computation-Based Metal-Binding Prediction Approaches at the Residue Level.BioMed research international · 2022Review
- Cobalt: An Essential Micronutrient for Plant Growth?Frontiers in plant science · 2021Review
- Identification of Disease-Related 2-Oxoglutarate/Fe (II)-Dependent Oxygenase Based on Reduced Amino Acid Cluster Strategy.Frontiers in cell and developmental biology · 2021Article
- Protein structure, amino acid composition and sequence determine proteome vulnerability to oxidation-induced damage.The EMBO journal · 2020Article
- Bioinformatics of Metalloproteins and Metalloproteomes.Molecules (Basel, Switzerland) · 2020Review
- A Generic Procedure for the Isolation of pH- and Magnesium-Responsive Chicken scFvs for Downstream Purification of Human Antibodies.Frontiers in bioengineering and biotechnology · 2020Article
- Simple Coordination Geometry Descriptors Allow to Accurately Predict Metal-Binding Sites in Proteins.ACS omega · 2019Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
The rapid accumulation of gene sequences, many of which are hypothetical proteins with unknown function, has stimulated the development of accurate computational tools for protein function prediction with evolution/structure-based approaches showing considerable promise. In this article, we present FINDSITE-metal, a new threading-based method designed specifically to detect metal-binding sites in modeled protein structures. Comprehensive benchmarks using different quality protein structures show that weakly homologous protein models provide sufficient structural information for quite accurate annotation by FINDSITE-metal. Combining structure/evolutionary information with machine learning results in highly accurate metal-binding annotations; for protein models constructed by TASSER, whose average Cα RMSD from the native structure is 8.9 Å, 59.5% (71.9%) of the best of top five predicted metal locations are within 4 Å (8 Å) from a bound metal in the crystal structure. For most of the targets, multiple metal-binding sites are detected with the best predicted binding site at rank 1 and within the top two ranks in 65.6% and 83.1% of the cases, respectively. Furthermore, for iron, copper, zinc, calcium, and magnesium ions, the binding metal can be predicted with high, typically 70% to 90%, accuracy. FINDSITE-metal also provides a set of confidence indexes that help assess the reliability of predictions. Finally, we describe the proteome-wide application of FINDSITE-metal that quantifies the metal-binding complement of the human proteome. FINDSITE-metal is freely available to the academic community at http://cssb.biology.gatech.edu/findsite-metal/.
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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.