Structure-Based Approaches for Protein–Protein Interaction Prediction Using Machine Learning and Deep Learning
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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Biomolecules |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2218-273X/15/1/141 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588929267662848 |
|---|---|
| author | Despoina P. Kiouri Georgios C. Batsis Christos T. Chasapis |
| author_facet | Despoina P. Kiouri Georgios C. Batsis Christos T. Chasapis |
| author_sort | Despoina P. Kiouri |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bbf027a2b3274b9f9a0028d1b93ceb69 |
| institution | Kabale University |
| issn | 2218-273X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomolecules |
| spelling | doaj-art-bbf027a2b3274b9f9a0028d1b93ceb692025-01-24T13:25:20ZengMDPI AGBiomolecules2218-273X2025-01-0115114110.3390/biom15010141Structure-Based Approaches for Protein–Protein Interaction Prediction Using Machine Learning and Deep LearningDespoina P. Kiouri0Georgios C. Batsis1Christos T. Chasapis2Institute of Chemical Biology, National Hellenic Research Foundation, 11635 Athens, GreeceInstitute of Chemical Biology, National Hellenic Research Foundation, 11635 Athens, GreeceInstitute of Chemical Biology, National Hellenic Research Foundation, 11635 Athens, GreeceProtein–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.https://www.mdpi.com/2218-273X/15/1/141Protein–Protein Interactionsmachine learningdeep learningproteomicsstructure representations |
| spellingShingle | Despoina P. Kiouri Georgios C. Batsis Christos T. Chasapis Structure-Based Approaches for Protein–Protein Interaction Prediction Using Machine Learning and Deep Learning Biomolecules Protein–Protein Interactions machine learning deep learning proteomics structure representations |
| title | Structure-Based Approaches for Protein–Protein Interaction Prediction Using Machine Learning and Deep Learning |
| title_full | Structure-Based Approaches for Protein–Protein Interaction Prediction Using Machine Learning and Deep Learning |
| title_fullStr | Structure-Based Approaches for Protein–Protein Interaction Prediction Using Machine Learning and Deep Learning |
| title_full_unstemmed | Structure-Based Approaches for Protein–Protein Interaction Prediction Using Machine Learning and Deep Learning |
| title_short | Structure-Based Approaches for Protein–Protein Interaction Prediction Using Machine Learning and Deep Learning |
| title_sort | structure based approaches for protein protein interaction prediction using machine learning and deep learning |
| topic | Protein–Protein Interactions machine learning deep learning proteomics structure representations |
| url | https://www.mdpi.com/2218-273X/15/1/141 |
| work_keys_str_mv | AT despoinapkiouri structurebasedapproachesforproteinproteininteractionpredictionusingmachinelearninganddeeplearning AT georgioscbatsis structurebasedapproachesforproteinproteininteractionpredictionusingmachinelearninganddeeplearning AT christostchasapis structurebasedapproachesforproteinproteininteractionpredictionusingmachinelearninganddeeplearning |