C-P.16: ProInterVal-BioXtal: A Web Server to Distinguish Biological Interfaces from Crystal Contacts Using Graph Deep Learning
Damla Ovek Baydar
NCMBM University of Oslo
Ozlem Keskin
Koc University
Attila Gursoy
Koc University
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Accurate discrimination between biologically relevant protein-protein interfaces (PPIs) and crystallographic contacts is essential for reliable interpretation of macromolecular assemblies and their cellular functions. While X-ray crystallography remains a primary method for determining protein complex structures, crystallographic interfaces may be formed as a byproduct of the crystal packing. Therefore, robust computational approaches for interface annotation are needed.
This study introduces ProInterVal-BioXtal, a web server that predicts class scores for PPIs, differentiating between biologically relevant and crystallographic interfaces. The method leverages protein representation learning and graph-based deep learning to capture structural and physicochemical features. Interface graphs derived from input complexes are processed by our novel graph-based contrastive model to learn interface representations which are used by a graph neural network for classification.
The model is trained and validated on the MANY benchmark dataset comprising 5739 dimers with a balanced distribution of biological and crystal interfaces and evaluated on the DC benchmark dataset with curated interfaces of similar interface areas.
On the DC test set, ProInterVal-BioXtal achieves 88% accuracy, 88% precision, and 85% F1 score, outperforming state-of-the-art methods including DeepRank-GNN, PRODIGY-CRYSTAL, EPPIC 3, PISA, and QSAlign. On an independent benchmark, the method achieves 83% accuracy and 0.91 AUC, surpassing DeepRank-GNN (AUC = 0.85).
Through a user-friendly web interface, ProInterVal-BioXtal enables rapid predictions from PDB files or IDs, returning probabilistic classification scores. Our tool addresses a fundamental challenge in structural biology by utilizing graph-based protein representation which can detect complex interactions and dependencies. ProInterVal-BioXtal server is freely available at https://3dpath.ku.edu.tr/prointerval-bioxtal/
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