| Title: | Cheminformatic analysis of protein surfaces provides binding site insights andinforms identification strategies |
|---|
| Authors: | ID Milisavljević, Andrej (Author)
ID Pražnikar, Jure (Author)
ID Bren, Urban (Author)
ID Jukič, Marko (Author) |
|---|
| Files: |  Cheminformatic_analysis_of_protein_surfaces_provides_binding_site_insights_and_informs_identification_strategies_(1).pdf (7,52 MB)
MD5: 8327D10A0650112C24E1B34BC8DB01F0
|
|---|
| Language: | English |
|---|
| Work type: | Article |
|---|
| Typology: | 1.01 - Original Scientific Article |
|---|
| Organization: | FKKT - Faculty of Chemistry and Chemical Engineering
|
|---|
| Abstract: | 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. |
|---|
| Keywords: | protein surface analysis, small-molecule binding site detection, machine learning, cheminformatics, amino acidindex, binding site, mall-molecule–protein interactions, in-silico drug design |
|---|
| Publication status: | Published |
|---|
| Publication version: | Version of Record |
|---|
| Submitted for review: | 08.08.2025 |
|---|
| Article acceptance date: | 06.11.2025 |
|---|
| Publication date: | 02.12.2025 |
|---|
| Publisher: | Taylor&Francis |
|---|
| Year of publishing: | 2025 |
|---|
| Number of pages: | str. 2945-2958 |
|---|
| Numbering: | ǂissue ǂ24 , ǂVol. ǂ14 |
|---|
| PID: | 20.500.12556/DKUM-96180  |
|---|
| UDC: | 543 |
|---|
| ISSN on article: | 1756-8927 |
|---|
| COBISS.SI-ID: | 259712771 |
|---|
| DOI: | 10.1080/17568919.2025.2592531 |
|---|
| Copyright: | © 2025 The Author(s).
|
|---|
| Publication date in DKUM: | 08.12.2025 |
|---|
| Views: | 150 |
|---|
| Downloads: | 5 |
|---|
| Metadata: |    |
|---|
| Categories: | Misc.
|
|---|
|
:
|
Copy citation |
|---|
| | | | Average score: | (0 votes) |
|---|
| Your score: | Voting is allowed only for logged in users. |
|---|
| Share: |  |
|---|
Hover the mouse pointer over a document title to show the abstract or click
on the title to get all document metadata. |
