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Metalloproteins

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Towards peptide-based tunable multistate memristive materials.

Physical chemistry chemical physics : PCCP
Development of new memristive hardware is a technological requirement towards widespread neuromorphic computing. Molecular spintronics seems to be a fertile field for the design and preparation of this hardware. Within molecular spintronics, recent r...

Machine learning differentiates enzymatic and non-enzymatic metals in proteins.

Nature communications
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 b...

Machine Learning Approaches for Metalloproteins.

Molecules (Basel, Switzerland)
Metalloproteins are a family of proteins characterized by metal ion binding, whereby the presence of these ions confers key catalytic and ligand-binding properties. Due to their ubiquity among biological systems, researchers have made immense efforts...

Structural Bioinformatics and Deep Learning of Metalloproteins: Recent Advances and Applications.

International journal of molecular sciences
All living organisms require metal ions for their energy production and metabolic and biosynthetic processes. Within cells, the metal ions involved in the formation of adducts interact with metabolites and macromolecules (proteins and nucleic acids)....

Metal3D: a general deep learning framework for accurate metal ion location prediction in proteins.

Nature communications
Metal ions are essential cofactors for many proteins and play a crucial role in many applications such as enzyme design or design of protein-protein interactions because they are biologically abundant, tether to the protein using strong interactions,...

Machine Learning-Enhanced Quantum Chemistry-Assisted Refinement of the Active Site Structure of Metalloproteins.

Inorganic chemistry
Understanding the fine structural details of inhibitor binding at the active site of metalloenzymes can have a profound impact on the rational drug design targeted to this broad class of biomolecules. Structural techniques such as NMR, cryo-EM, and X...

DisDock: A Deep Learning Method for Metal Ion-Protein Redocking.

Proteins
The structures of metalloproteins are essential for comprehending their functions and interactions. The breakthrough of AlphaFold has made it possible to predict protein structures with experimental accuracy. However, the type of metal ion that a met...

Recent advances and future challenges in predictive modeling of metalloproteins by artificial intelligence.

Molecules and cells
Metal coordination is essential for structural/catalytic functions of metalloproteins that mediate a wide range of biological processes in living organisms. Advances in bioinformatics have significantly enhanced our understanding of metal-binding sit...

Join Persistent Homology (JPH)-Based Machine Learning for Metalloprotein-Ligand Binding Affinity Prediction.

Journal of chemical information and modeling
With the crucial role of metalloproteins in respiration, oxidative stress protection, photosynthesis, and drug metabolism, the design and discovery of drugs that can target metalloproteins are extremely important. Recently, enormous potential has bee...