| Naslov: | Cheminformatic analysis of protein surfaces provides binding site insights andinforms identification strategies |
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| Avtorji: | ID Milisavljević, Andrej (Avtor)
ID Pražnikar, Jure (Avtor)
ID Bren, Urban (Avtor)
ID Jukič, Marko (Avtor) |
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| Datoteke: |  Cheminformatic_analysis_of_protein_surfaces_provides_binding_site_insights_and_informs_identification_strategies_(1).pdf (7,52 MB)
MD5: 8327D10A0650112C24E1B34BC8DB01F0
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| Jezik: | Angleški jezik |
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| Vrsta gradiva: | Članek v reviji |
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| Tipologija: | 1.01 - Izvirni znanstveni članek |
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| Organizacija: | FKKT - Fakulteta za kemijo in kemijsko tehnologijo
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| Opis: | Aims: Understanding protein–ligand binding site behavior is central to structure-based drug design. Weanalyzed amino acid composition and interactions in protein–ligand small-molecule binding sites anddeveloped a novel method for binding site prediction.Materials and methods: We analyzed the PDBBind+ database, which contains the largest protein–ligand binding site dataset known to us, using existing cheminformatics packages and in-house code.We used the resulting data to train a binding site prediction model.Results: Within solvent-accessible binding regions, tryptophan, phenylalanine, tyrosine, methionine,and glycine, were enriched. Interaction analysis revealed hydrophobic contacts as the most frequent,followed by hydrogen bonds, water-bridged hydrogen bonds, salt bridges, π–π, π–cation, and occa-sional halogen interactions. We introduced the amino acid binding site enrichment index (ABSE), tosupport small-molecule binding site detection, and developed a model that discriminates binding sitesequences from protein surface patches with 0.91 accuracy.Conclusions: This work offers interpretable composition–interaction relationships and practical tool forbinding site characterization. To facilitate application, we provide a free, open-source, fast, bindingsiteidentification tool (AABS), available at https://gitlab.com/Jukic/aabs. We anticipate that these findingsand tool will advance binding site prediction and accelerate computationally intensive drug discoverywithin medicinal chemistry. |
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| Ključne besede: | protein surface analysis, small-molecule binding site detection, machine learning, cheminformatics, amino acidindex, binding site, mall-molecule–protein interactions, in-silico drug design |
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| Status publikacije: | Objavljeno |
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| Verzija publikacije: | Objavljena publikacija |
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| Poslano v recenzijo: | 08.08.2025 |
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| Datum sprejetja članka: | 06.11.2025 |
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| Datum objave: | 02.12.2025 |
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| Založnik: | Taylor&Francis |
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| Leto izida: | 2025 |
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| Št. strani: | str. 2945-2958 |
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| Številčenje: | ǂissue ǂ24 , ǂVol. ǂ14 |
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| PID: | 20.500.12556/DKUM-96180  |
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| UDK: | 543 |
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| COBISS.SI-ID: | 259712771 |
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| DOI: | 10.1080/17568919.2025.2592531 |
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| ISSN pri članku: | 1756-8927 |
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| Avtorske pravice: | © 2025 The Author(s).
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| Datum objave v DKUM: | 08.12.2025 |
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| Število ogledov: | 147 |
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| Število prenosov: | 5 |
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| Metapodatki: |    |
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| Področja: | Ostalo
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