Docking cyclic peptides formed by a disulfide bond through a hierarchical strategy

Tao, Huanyu; Zhao, Xuejun; Zhang, Keqiong; Lin, Ping; Huang, Sheng‐You

doi:10.1093/bioinformatics/btac486

Cited by 8 publications

(4 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, determining the structure of Peptide-MHC complexes is important for understanding the molecular mechanism of related biological processes and developing peptide-based immunotherapy. Typically, the RCSB PDB ( ) is used to obtain models of MHC molecules and then HEPEDOCK 2.0 is used to predict the possibility of MHC-peptide complexes and provide multiple docking models ( 36 ). EpiDOCK toolkit can be also used in MHC-II-peptide docking and predicting binding energy ( 37 ).…”

Section: Introductionmentioning

confidence: 99%

Integration: Gospel for immune bioinformatician on epitope-based therapy

Sun

Zhang

et al. 2023

Front. Immunol.

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Integration: Gospel for immune bioinformatician on epitope-based therapy

Sun

Zhang

et al. 2023

Front. Immunol.

View full text Add to dashboard Cite

“…We built the protein–cyclic peptide binding sites prediction test sets Testset_cyclic_holo and Testset_cyclic_holo_apo that stem from the works of ADCP [ 50 ] and HPEPDOCK2.0 [ 51 ]. The former only contains holo structures of protein receptors and the latter includes both holo and apo structures.…”

Section: Resultsmentioning

confidence: 99%

GAPS: a geometric attention-based network for peptide binding site identification by the transfer learning approach

Zhu,

Zhang,

Shang

et al. 2024

Briefings in Bioinformatics

View full text Add to dashboard Cite

Protein–peptide interactions (PPepIs) are vital to understanding cellular functions, which can facilitate the design of novel drugs. As an essential component in forming a PPepI, protein–peptide binding sites are the basis for understanding the mechanisms involved in PPepIs. Therefore, accurately identifying protein–peptide binding sites becomes a critical task. The traditional experimental methods for researching these binding sites are labor-intensive and time-consuming, and some computational tools have been invented to supplement it. However, these computational tools have limitations in generality or accuracy due to the need for ligand information, complex feature construction, or their reliance on modeling based on amino acid residues. To deal with the drawbacks of these computational algorithms, we describe a geometric attention-based network for peptide binding site identification (GAPS) in this work. The proposed model utilizes geometric feature engineering to construct atom representations and incorporates multiple attention mechanisms to update relevant biological features. In addition, the transfer learning strategy is implemented for leveraging the protein–protein binding sites information to enhance the protein–peptide binding sites recognition capability, taking into account the common structure and biological bias between proteins and peptides. Consequently, GAPS demonstrates the state-of-the-art performance and excellent robustness in this task. Moreover, our model exhibits exceptional performance across several extended experiments including predicting the apo protein–peptide, protein–cyclic peptide and the AlphaFold-predicted protein–peptide binding sites. These results confirm that the GAPS model is a powerful, versatile, stable method suitable for diverse binding site predictions.

show abstract

“…Firstly, we built a protein-cyclic peptide binding sites prediction test set Testset_cyclic based on the data of protein-cyclic peptide complexes used in ADCP 44 and HPEPDOCK2.0 45 (see method). Subsequently, we employed GAPS to make predictions on this test dataset.…”

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

View full text Add to dashboard Cite

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.

show abstract

Docking cyclic peptides formed by a disulfide bond through a hierarchical strategy

Cited by 8 publications

References 56 publications

Integration: Gospel for immune bioinformatician on epitope-based therapy

Integration: Gospel for immune bioinformatician on epitope-based therapy

GAPS: a geometric attention-based network for peptide binding site identification by the transfer learning approach

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

Contact Info

Product

Resources

About