Advancements in one-dimensional protein structure prediction using machine learning and deep learning
The accurate prediction of protein structures remains a cornerstone challenge in structural bioinformatics, essential for understanding the intricate relationship between protein sequence, structure, and function. Recent advancements in Machine Learning (ML) and Deep Learning (DL) have revolutionize...
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| Language: | English |
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Elsevier
2025-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037025001254 |
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| author | Wafa Alanazi Di Meng Gianluca Pollastri |
| author_facet | Wafa Alanazi Di Meng Gianluca Pollastri |
| author_sort | Wafa Alanazi |
| collection | DOAJ |
| description | The accurate prediction of protein structures remains a cornerstone challenge in structural bioinformatics, essential for understanding the intricate relationship between protein sequence, structure, and function. Recent advancements in Machine Learning (ML) and Deep Learning (DL) have revolutionized this field, offering innovative approaches to tackle one- dimensional (1D) protein structure annotations, including secondary structure, solvent accessibility, and intrinsic disorder. This review highlights the evolution of predictive methodologies, from early machine learning models to sophisticated deep learning frameworks that integrate sequence embeddings and pretrained language models. Key advancements, such as AlphaFold’s transformative impact on structure prediction and the rise of protein language models (PLMs), have enabled unprecedented accuracy in capturing sequence-structure relationships. Furthermore, we explore the role of specialized datasets, benchmarking competitions, and multimodal integration in shaping state-of-the-art prediction models. By addressing challenges in data quality, scalability, interpretability, and task-specific optimization, this review underscores the transformative impact of ML, DL, and PLMs on 1D protein prediction while providing insights into emerging trends and future directions in this rapidly evolving field. |
| format | Article |
| id | doaj-art-bc94e5a21d9543f9a435959e5d7c01df |
| institution | DOAJ |
| issn | 2001-0370 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-bc94e5a21d9543f9a435959e5d7c01df2025-08-20T03:17:51ZengElsevierComputational and Structural Biotechnology Journal2001-03702025-01-01271416143010.1016/j.csbj.2025.04.005Advancements in one-dimensional protein structure prediction using machine learning and deep learningWafa Alanazi0Di Meng1Gianluca Pollastri2School of Computer Science, University College Dublin, Belfield, Dublin D04 C1P1, Ireland; Department of Computer Science, College of Science, Northern Border University, Arar, Saudi Arabia; Corresponding author at: School of Computer Science, University College Dublin, Belfield, Dublin D04 C1P1, Ireland.School of Computer Science, University College Dublin, Belfield, Dublin D04 C1P1, IrelandSchool of Computer Science, University College Dublin, Belfield, Dublin D04 C1P1, IrelandThe accurate prediction of protein structures remains a cornerstone challenge in structural bioinformatics, essential for understanding the intricate relationship between protein sequence, structure, and function. Recent advancements in Machine Learning (ML) and Deep Learning (DL) have revolutionized this field, offering innovative approaches to tackle one- dimensional (1D) protein structure annotations, including secondary structure, solvent accessibility, and intrinsic disorder. This review highlights the evolution of predictive methodologies, from early machine learning models to sophisticated deep learning frameworks that integrate sequence embeddings and pretrained language models. Key advancements, such as AlphaFold’s transformative impact on structure prediction and the rise of protein language models (PLMs), have enabled unprecedented accuracy in capturing sequence-structure relationships. Furthermore, we explore the role of specialized datasets, benchmarking competitions, and multimodal integration in shaping state-of-the-art prediction models. By addressing challenges in data quality, scalability, interpretability, and task-specific optimization, this review underscores the transformative impact of ML, DL, and PLMs on 1D protein prediction while providing insights into emerging trends and future directions in this rapidly evolving field.http://www.sciencedirect.com/science/article/pii/S2001037025001254Deep learning1D protein predictionProtein databasesSecondary structureIntrinsic disorderSolvent accessibility |
| spellingShingle | Wafa Alanazi Di Meng Gianluca Pollastri Advancements in one-dimensional protein structure prediction using machine learning and deep learning Computational and Structural Biotechnology Journal Deep learning 1D protein prediction Protein databases Secondary structure Intrinsic disorder Solvent accessibility |
| title | Advancements in one-dimensional protein structure prediction using machine learning and deep learning |
| title_full | Advancements in one-dimensional protein structure prediction using machine learning and deep learning |
| title_fullStr | Advancements in one-dimensional protein structure prediction using machine learning and deep learning |
| title_full_unstemmed | Advancements in one-dimensional protein structure prediction using machine learning and deep learning |
| title_short | Advancements in one-dimensional protein structure prediction using machine learning and deep learning |
| title_sort | advancements in one dimensional protein structure prediction using machine learning and deep learning |
| topic | Deep learning 1D protein prediction Protein databases Secondary structure Intrinsic disorder Solvent accessibility |
| url | http://www.sciencedirect.com/science/article/pii/S2001037025001254 |
| work_keys_str_mv | AT wafaalanazi advancementsinonedimensionalproteinstructurepredictionusingmachinelearninganddeeplearning AT dimeng advancementsinonedimensionalproteinstructurepredictionusingmachinelearninganddeeplearning AT gianlucapollastri advancementsinonedimensionalproteinstructurepredictionusingmachinelearninganddeeplearning |