Εξειδίκευση τύπου :	Άρθρο σε επιστημονικό περιοδικό
Τίτλος:	Structure-Based Approaches for Protein-Protein Interaction Prediction Using Machine Learning and Deep Learning
Δημιουργός/Συγγραφέας:	Kiouri, Despoina P Batsis, Georgios C [EL] Χασάπης, Χρήστος[EN] Chasapis, Christos
Ημερομηνία:	2025-01-17
Γλώσσα:	Αγγλικά
ISSN:	2218-273X
DOI:	10.3390/biom15010141
Άλλο:	39858535
Περίληψη:	Protein-Protein Interaction (PPI) prediction plays a pivotal role in understanding cellular processes and uncovering molecular mechanisms underlying health and disease. Structure-based PPI prediction has emerged as a robust alternative to sequence-based methods, offering greater biological accuracy by integrating three-dimensional spatial and biochemical features. This work summarizes the recent advances in computational approaches leveraging protein structure information for PPI prediction, focusing on machine learning (ML) and deep learning (DL) techniques. These methods not only improve predictive accuracy but also provide insights into functional sites, such as binding and catalytic residues. However, challenges such as limited high-resolution structural data and the need for effective negative sampling persist. Through the integration of experimental and computational tools, structure-based prediction paves the way for comprehensive proteomic network analysis, holding promise for advancements in drug discovery, biomarker identification, and personalized medicine. Future directions include enhancing scalability and dataset reliability to expand these approaches across diverse proteomes.
Τίτλος πηγής δημοσίευσης:	Biomolecules
Τόμος/Κεφάλαιο:	15
Τεύχος:	1
Θεματική Κατηγορία:	[EL] Βιοπληροφορική[EN] Bioinformatics [EL] Δομική Βιολογία[EN] Structural Biology [EL] Βιοχημεία[EN] Biochemistry
Λέξεις-Κλειδιά:	protein-protein interactions deep learning machine learning proteomics structure representations humans protein interaction mapping proteins proteomics computational biology deep learning machine learning
Κάτοχος πνευματικών δικαιωμάτων:	: © 2025 by the authors. Licensee MDPI, Basel, Switzerland.
Όροι και προϋποθέσεις δικαιωμάτων:	This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/).
Ηλεκτρονική διεύθυνση στον εκδότη (link):	https://doi.org/10.3390/biom15010141
Εμφανίζεται στις συλλογές:	Ινστιτούτο Χημικής Βιολογίας - Επιστημονικό έργο