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Researchers at Constructor University and Constructor Labs have developed BiteNetI, a deep-learning model that locates the binding sites of 14 biologically important ion types directly in three-dimensional protein structures. The model only needs several seconds per structure and reaches two- to three-fold higher accuracy than most existing predictors such as Google DeepMind's AlphaFold3. The study, authored by Igor Kozlovskii and Petr Popov, has been published in the peer-reviewed journal Communications Biology, a Nature Portfolio journal. The tool provides an open-access platform that could help accelerate drug discovery and the understanding of protein function.
Proteins are the molecular workhorses of human cells, but they rarely act alone. They rely on ions—electrically charged atoms or small charged groups like calcium, sodium, and potassium—to stabilize their structure, trigger chemical reactions, and transmit signals. When these ion interactions malfunction, it can lead to severe neurological, cardiovascular, or metabolic disorders.
Knowing where exactly an ion dock is located at atomic resolution is valuable for several reasons:
- It shows which conformation of a protein a bound ion stabilizes—information that cannot be read off of the amino acid sequence alone.
- It makes it possible to assess how the exchange of a single amino acid weakens or restores ion binding, one of the mechanisms by which mutations impair protein function.
- It helps to identify and prioritize pockets that are chemically suited to charged chemical groups—useful information in early-stage drug design.
Determining such sites experimentally, however, requires high-resolution X-ray crystallography or spectroscopy, which is laborious and expensive. Existing computational alternatives are typically limited to a single ion type, operate on the sequence rather than the structure, or do not scale to large numbers of proteins.
The physics-based AI model BiteNetI solves this bottleneck by bringing the intelligent image recognition technology found in modern smartphone cameras into molecular biology. The system translates a protein into a three-dimensional grid, scanning its complex 3D shape like an animated video across eleven different atom types. The model mastered its skills by studying a meticulously curated digital library of over 12,000 protein complexes containing more than 35,000 precisely mapped bound ions. Through this innovative multitask design, BiteNetI simultaneously predicts both the exact coordinates and the coordinating residues for 14 different ion types in just several seconds—making it roughly ten times faster than running individual, specialized models per ion type, without sacrificing accuracy.
Exceeding global benchmarks:
The generalized AI models like Google DeepMind’s AlphaFold3 have recently made massive leaps in predicting overall protein shapes, and they can also place ions during its predictions. The benchmark results showed that the specialized BiteNetI model achieved a two- to three-fold accuracy improvement over existing methods for vital physiological ions, delivering clearly superior scores for calcium, potassium, magnesium, phosphate, and sulfate, while AlphaFold3 held a slight edge for carbonate and sodium. BiteNetI’s unique sensitivity allows it to recognize intricate atomic patterns and evaluate how tiny genetic changes, like point mutations, might weaken or restore necessary ion bonds.
“AlphaFold3 and BiteNetI answer different questions, and the results should be read in that light,” said Petr Popov. “What our results show is that a compact, task-specific model can annotate ion binding sites in an existing structure very accurately and very fast.”
On the two reference benchmarks of the field, MIonSite and IonCom, BiteNetI outperformed competing methods for the majority of ion types. Remarkably, this all-in-one model even beat Metal3D, which was custom-built solely for zinc, proving that training on multiple ion types at once may boost overall intelligence.
The study also revealed that the AI's precision jumps significantly when surrounding water molecules are included, as they often help secure the ions in place. This high accuracy holds true even when analyzing proteins mapped through different imaging techniques like cryo-electron microscopy.
Looking ahead, the group is aiming for a development that mirrors the shift seen in generative AI. “Every binding site in a protein is a potential point of intervention for a drug,” explained Popov. “Today we predict them class by class— small molecules, peptides, ions— each with its own model. What would be a next step forward is one general model that recognizes all of them, which would give researchers a single tool for questions they currently have to approach from several directions."
To support scientific collaboration, the team has made BiteNetI available as a registration-free web application at bitenet-ion.imolecule.app.
Prof. Dr. Petr Popov
Associate Professor of Applied Mathematics and Computational Biology
ppopov@constructor.university
https://doi.org/10.1038/s42003-026-10659-1 - Kozlovskii, I., Popov, P. Multivalent ion binding site identification with structure-based deep learning. Commun Biol 9, 1039 (2026)
https://bitenet-ion.imolecule.app/ - BiteNetI available as a registration-free web application to support scientific collaboration.
Prof. Dr. Petr Popov, Associate Professor of Applied Mathematics and Computational Biology at Constr ...
Source: Constructor University
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