Research Project: İnsan interaktom projesi : Genom ölçeğinde yapısal protein-protein etkileşim veri kaynağı
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Contributors
Funders
ID
TB.00577
Authors
Keskin, Özlem
Faculty Member
Publications
DeepAllo: allosteric site prediction using protein language model (pLM) with multitask learning
(Oxford Univ Press, 2025) Keskin, Özlem; Gürsoy, Attila; Khokhar, Moaaz Ur-Rehman; Graduate School of Sciences and Engineering; KUIS AI (Koç University & İş Bank Artificial Intelligence Center); Department of Computer Engineering; Department of Chemical and Biological Engineering; Yes; Khokhar, Moaaz Ur-Rehman; GRADUATE SCHOOL OF SCIENCES AND ENGINEERING; Research Center; College of Engineering
Motivation Allostery, the process by which binding at one site perturbs a distant site, is being rendered as a key focus in the field of drug development with its substantial impact on protein function. The identification of allosteric pockets (sites) is a challenging task and several techniques have been developed, including Machine Learning to predict allosteric pockets that utilize both static and pocket features.Results Our work, DeepAllo, is the first study that combines fine-tuned protein language model (pLM) with FPocket features and shows an increase in prediction performance of allosteric sites over previous studies. The pLM model was fine-tuned on AlloSteric Database (ASD) in Multitask Learning setting and was further used as a feature extractor to train XGBoost and AutoML models. The best model predicts allosteric pockets with 89.66% F1 score and 90.5% of allosteric pockets in the top 3 positions, outperforming previous results. A case study has been performed on proteins with known allosteric pockets, which shows the proof of our approach. Moreover, an effort was made to explain the pLM by visualizing its attention mechanism among allosteric and non-allosteric residues.Availability and implementation The source code is available on GitHub (https://github.com/MoaazK/deepallo) and archived on Zenodo (DOI: 10.5281/zenodo.15255379). The trained model is hosted on Hugging Face (DOI: 10.57967/hf/5198). The dataset used for training and evaluation is archived on Zenodo (DOI: 10.5281/zenodo.15255437).
HMI-PRED 2.0: a biologist-oriented web application for prediction of host-microbe protein-protein interaction by interface mimicry
(Oxford University Press (OUP), 2022) Gürsoy, Attila; Keskin, Özlem; Lim, H., Tsai, C.J.; Nussinov, R.; Department of Computer Engineering; Department of Chemical and Biological Engineering; Yes; College of Engineering
HMI-PRED 2.0 is a publicly available web service for the prediction of host-microbe protein-protein interaction by interface mimicry that is intended to be used without extensive computational experience. A microbial protein structure is screened against a database covering the entire available structural space of complexes of known human proteins.
PPInterface: a comprehensive dataset of 3D protein-protein interface structures
(Academic Press, 2024) Abalı, Zeynep; Aydın, Zeynep; Gürsoy, Attila; Keskin, Özlem; Khokhar, Moaaz Ur-Rehman; Yiğit, Ateş Can; Department of Computer Engineering; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; Yes; College of Engineering; GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
The PPInterface dataset contains 815,082 interface structures, providing the most comprehensive structural information on protein–protein interfaces. This resource is extracted from over 215,000 three-dimensional protein structures stored in the Protein Data Bank (PDB). The dataset contains a wide range of protein complexes, providing a wealth of information for researchers investigating the structural properties of protein–protein interactions. The accompanying web server has a user-friendly interface that allows for efficient search and download functions. Researchers can access detailed information on protein interface structures, visualize them, and explore a variety of features, increasing the dataset's utility and accessibility.
DiPPI: a curated data set for drug-like molecules in protein-protein interfaces
(Amer Chemical Soc, 2024) Cankara, Fatma; Gürsoy, Attila; Keskin, Özlem; Sayın, Ahenk Zeynep; Şenyüz, Simge; Department of Computer Engineering; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; Yes; College of Engineering; GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
Proteins interact through their interfaces, and dysfunction of protein-protein interactions (PPIs) has been associated with various diseases. Therefore, investigating the properties of the drug-modulated PPIs and interface-targeting drugs is critical. Here, we present a curated large data set for drug-like molecules in protein interfaces. We further introduce DiPPI (Drugs in Protein-Protein Interfaces), a two-module web site to facilitate the search for such molecules and their properties by exploiting our data set in drug repurposing studies. In the interface module of the web site, we present several properties, of interfaces, such as amino acid properties, hotspots, evolutionary conservation of drug-binding amino acids, and post-translational modifications of these residues. On the drug-like molecule side, we list drug-like small molecules and FDA-approved drugs from various databases and highlight those that bind to the interfaces. We further clustered the drugs based on their molecular fingerprints to confine the search for an alternative drug to a smaller space. Drug properties, including Lipinski's rules and various molecular descriptors, are also calculated and made available on the web site to guide the selection of drug molecules. Our data set contains 534,203 interfaces for 98,632 protein structures, of which 55,135 are detected to bind to a drug-like molecule. 2214 drug-like molecules are deposited on our web site, among which 335 are FDA-approved. DiPPI provides users with an easy-to-follow scheme for drug repurposing studies through its well-curated and clustered interface and drug data and is freely available at http://interactome.ku.edu.tr:8501.
Rewiring enzyme regulation: allosteric drugs and predictive tools
(Elsevier, 2025) Keskin, Özlem; Gürsoy, Attila; Fidan, Vahap Gazi; Konuralp, İlim; Fidan, Vahap Gazi; Konuralp, İlim; Özkan, S. Banu; Department of Chemical and Biological Engineering; Department of Computer Engineering; Graduate School of Sciences and Engineering; Yes; College of Engineering; GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
Allosteric modulation offers an increasingly attractive route for precise intervention in enzymatic pathways. This review outlines emerging strategies for the identification and exploitation of allosteric sites, emphasizing computational frameworks that integrate evolutionary, structural, and dynamic features with machine learning models. We discuss how perturbation-based simulations, network analyses, and deep mutational data are reshaping our understanding of allosteric regulation. In parallel, advances in experimental techniques have enabled validation of cryptic and functionally relevant pockets across diverse enzyme families. We further catalog FDA-approved allosteric modulators of enzymes, highlighting therapeutic designs that leverage distal regulation to enhance specificity and overcome resistance. Taken together, these developments reveal the growing utility of allostery in drug design and underscore its potential to expand the therapeutic target space beyond conventional binding sites.
