GeneoNet

The identification of binding pockets in proteins is crucial for understanding protein function, drug discovery, annotating protein function, predicting protein-protein interactions, and facilitating structural studies. ​ GENEOnet provides a foundation for advancing the knowledge of biological systems and developing new therapeutic strategies.​

USE IT HOW TO

How it works

The GENEOnet approach features a small number of parameters, a higher transparency (explainability) of the results, and the possibility of knowledge injection. Within our method, the pocket detection step is also composed by a voxellization and scoring phase of the​ empty volume (non-protein), where all the detected protein pockets are ranked according to the score value and filtered out based on a threshold.​

AAPK1_HUMAN

5'-AMP-activated protein kinase catalytic subunit alpha-1

Results

GENEOnet incorporates knowledge such as lipophilicity, hydrophilicity, electrostatic, distances and polar information, necessary to identify a binding site, so we define a GENEO (Bergomi et al.,2019) for each chemical-physical parameter, which can identify areas with the best values for these values. ​ The software compute information, such as pocket’s location, volume, and their druggability scores (goes from 0 to 1.0, where 1.0 is a high druggable site), unabling molecular modelling studies.​

Binding pockets identification on two different kinase proteins. The orthosteric and the allosteric sites are reported in green and blue respectively.

Aurora kinase A

Aurora kinase A

Q13131

5'-AMP-activated protein kinase catalytic subunit alpha-1

Please insert here the pdb Structure

 

References

REF. PDB:
RCSB PDB should be referenced with the URL RCSB.org and the citation: H.M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T.N. Bhat, H. Weissig, I.N. Shindyalov, P.E. Bourne, The Protein Data Bank (2000) Nucleic Acids Research 28: 235-242 https://doi.org/10.1093/nar/28.1.235.

REF. MOL*
Images created using Mol* D. Sehnal, S. Bittrich, M. Deshpande, R. Svobodová, K. Berka, V. Bazgier, S. Velankar, S.K. Burley, J. Koča, A.S. Rose (2021) Mol* Viewer: modern web app for 3D visualization and analysis of large biomolecular structures (2021) Nucleic Acids Research 49:W431-W437 https://doi.org/10.1093/nar/gkab314, and RCSB PDB.

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