Peptide-Binding Site Prediction From Protein Structure via points on the Solvent Accessible Surface

Krivák, Radoslav; Jendele, Lukáš; Hoksza, David

doi:10.1145/3233547.3233708

Cited by 10 publications

(7 citation statements)

References 29 publications

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“…Such tasks would be better served by models trained on specialized datasets. We plan to build on our current work by including such models into PrankWeb in the future (38).…”

Section: Discussionmentioning

confidence: 99%

PrankWeb: a web server for ligand binding site prediction and visualization

Jendele

Krivák

Škoda

et al. 2019

Nucleic Acids Research

Self Cite

282

177

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PrankWeb is an online resource providing an interface to P2Rank, a state-of-the-art method for ligand binding site prediction. P2Rank is a template-free machine learning method based on the prediction of local chemical neighborhood ligandability centered on points placed on a solvent-accessible protein surface. Points with a high ligandability score are then clustered to form the resulting ligand binding sites. In addition, PrankWeb provides a web interface enabling users to easily carry out the prediction and visually inspect the predicted binding sites via an integrated sequence-structure view. Moreover, PrankWeb can determine sequence conservation for the input molecule and use this in both the prediction and result visualization steps. Alongside its online visualization options, PrankWeb also offers the possibility of exporting the results as a PyMOL script for offline visualization. The web frontend communicates with the server side via a REST API. In high-throughput scenarios, therefore, users can utilize the server API directly, bypassing the need for a web-based frontend or installation of the P2Rank application. PrankWeb is available at http://prankweb.cz/, while the web application source code and the P2Rank method can be accessed at https://github.com/jendelel/PrankWebApp and https://github.com/rdk/p2rank, respectively.

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“…Such tasks would be better served by models trained on specialized datasets. We plan to build on our current work by including such models into PrankWeb in the future (38).…”

Section: Discussionmentioning

confidence: 99%

PrankWeb: a web server for ligand binding site prediction and visualization

Jendele

Krivák

Škoda

et al. 2019

Nucleic Acids Research

Self Cite

282

177

View full text Add to dashboard Cite

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“…To further assess GAPS’ performance in predicting protein-peptide binding sites, we also fine-tuned the pre-trained GAPS using the Data_finetuning_BN and then compared it on the test set TS125 with other models including BiteNet Pp 28 , PepNN-Struct, PepNN-Seq, MTDsite 29 , Visual 30 , P2Rank-Pept 31 , PepBind 32 , Multi-VORFFIP 33 , SPRINT-Str 12 , SPRINT 14 , PeptiMap 34 , and PepSite 35 (see methods). The results indicate that GAPS outperforms all other methods (Fig.…”

Section: Resultsmentioning

confidence: 99%

GAPS: Geometric Attention-based Networks for Peptide Binding Sites Identification by the Transfer Learning Approach

Zhu,

Zhang,

Shang

et al. 2023

Preprint

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The identification of protein-peptide binding sites significantly advances our understanding of their interaction. Recent advancements in deep learning have profoundly transformed the prediction of protein-peptide binding sites. In this work, we describe the Geometric Attention-based networks for Peptide binding Sites identification (GAPS). The GAPS constructs atom representations using geometric feature engineering and employs various attention mechanisms to update pertinent biological features. In addition, the transfer learning strategy is implemented for leveraging the pre-trained protein-protein binding sites information to enhance training of the protein-peptide binding sites recognition, taking into account the similarity of proteins and peptides. Consequently, GAPS demonstrates state-of-the-art (SOTA) performance in this task. Our model also exhibits exceptional performance across several expanded experiments including predicting the apo protein-peptide, the protein-cyclic peptide, and the predicted protein-peptide binding sites. Overall, the GAPS is a powerful, versatile, stable method suitable for diverse binding site predictions.

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“…The docked complexes were examined using Discovery Studio software to see what surfaces were near the ligand [ 47 ]. Several data of the molecular docking investigation of [(TZD)(DCQ)]-serotonin or (TZD)-serotonin are shown in Figure 12 and Figure 13 , respectively.…”

Section: Resultsmentioning

confidence: 99%

Attempting to Increase the Effectiveness of the Antidepressant Trazodone Hydrochloride Drug Using π-Acceptors

Alsanie

Alhomrani

Alamri

et al. 2022

IJERPH

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Major depressive disorder is a prevalent mood illness that is mildly heritable. Cases with the highest familial risk had recurrence and onset at a young age. Trazodone hydrochloride is an antidepressant medicine that affects the chemical messengers in the brain known as neurotransmitters, which include acetylcholine, norepinephrine, dopamine, and serotonin. In the present research, in solid and liquid phases, the 1:1 charge-transfer complexes between trazodone hydrochloride (TZD) and six different π-acceptors were synthesized and investigated using different microscopic techniques. The relation of dative ion pairs [TZD+, A−], where A is the acceptor, was inferred via intermolecular charge-transfer complexes. Additionally, a molecular docking examination was utilized to compare the interactions of protein receptors (serotonin-6BQH) with the TZD alone or in combination with the six distinct acceptor charge-transfer complexes. To refine the docking results acquired from AutoDock Vina and to better examine the molecular mechanisms of receptor-ligand interactions, a 100 ns run of molecular dynamics simulation was used. All the results obtained in this study prove that the 2,6-dichloroquinone-4-chloroimide (DCQ)/TZD complex interacts with serotonin receptors more efficiently than reactant donor TZD only and that [(TZD)(DCQ)]-serotonin has the highest binding energy value of all π-acceptor complexes.

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Peptide-Binding Site Prediction From Protein Structure via points on the Solvent Accessible Surface

Cited by 10 publications

References 29 publications

PrankWeb: a web server for ligand binding site prediction and visualization

PrankWeb: a web server for ligand binding site prediction and visualization

GAPS: Geometric Attention-based Networks for Peptide Binding Sites Identification by the Transfer Learning Approach

Attempting to Increase the Effectiveness of the Antidepressant Trazodone Hydrochloride Drug Using π-Acceptors

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