A comprehensive survey of scoring functions for protein docking models
Abstract Background While protein-protein docking is fundamental to our understanding of how proteins interact, scoring protein-protein complex conformations is a critical component of successful docking programs. Without accurate and efficient scoring functions to differentiate between native and n...
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| Format: | Article |
| Language: | English |
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BMC
2025-01-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-024-05991-4 |
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| author | Azam Shirali Vitalii Stebliankin Ukesh Karki Jimeng Shi Prem Chapagain Giri Narasimhan |
| author_facet | Azam Shirali Vitalii Stebliankin Ukesh Karki Jimeng Shi Prem Chapagain Giri Narasimhan |
| author_sort | Azam Shirali |
| collection | DOAJ |
| description | Abstract Background While protein-protein docking is fundamental to our understanding of how proteins interact, scoring protein-protein complex conformations is a critical component of successful docking programs. Without accurate and efficient scoring functions to differentiate between native and non-native binding complexes, the accuracy of current docking tools cannot be guaranteed. Although many innovative scoring functions have been proposed, a good scoring function for docking remains elusive. Deep learning models offer alternatives to using explicit empirical or mathematical functions for scoring protein-protein complexes. Results In this study, we perform a comprehensive survey of the state-of-the-art scoring functions by considering the most popular and highly performant approaches, both classical and deep learning-based, for scoring protein-protein complexes. The methods were also compared based on their runtime as it directly impacts their use in large-scale docking applications. Conclusions We evaluate the strengths and weaknesses of classical and deep learning-based approaches across seven public and popular datasets to aid researchers in understanding the progress made in this field. |
| format | Article |
| id | doaj-art-1a094963d4854819a720c51d50e7f240 |
| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-1a094963d4854819a720c51d50e7f2402025-01-26T12:54:54ZengBMCBMC Bioinformatics1471-21052025-01-0126112510.1186/s12859-024-05991-4A comprehensive survey of scoring functions for protein docking modelsAzam Shirali0Vitalii Stebliankin1Ukesh Karki2Jimeng Shi3Prem Chapagain4Giri Narasimhan5Bioinformatics Research Group (BioRG), Knight Foundation School of Computing and Information Sciences, Florida International UniversityBioinformatics Research Group (BioRG), Knight Foundation School of Computing and Information Sciences, Florida International UniversityDepartment of Physics, Florida International UniversityBioinformatics Research Group (BioRG), Knight Foundation School of Computing and Information Sciences, Florida International UniversityDepartment of Physics, Florida International UniversityBioinformatics Research Group (BioRG), Knight Foundation School of Computing and Information Sciences, Florida International UniversityAbstract Background While protein-protein docking is fundamental to our understanding of how proteins interact, scoring protein-protein complex conformations is a critical component of successful docking programs. Without accurate and efficient scoring functions to differentiate between native and non-native binding complexes, the accuracy of current docking tools cannot be guaranteed. Although many innovative scoring functions have been proposed, a good scoring function for docking remains elusive. Deep learning models offer alternatives to using explicit empirical or mathematical functions for scoring protein-protein complexes. Results In this study, we perform a comprehensive survey of the state-of-the-art scoring functions by considering the most popular and highly performant approaches, both classical and deep learning-based, for scoring protein-protein complexes. The methods were also compared based on their runtime as it directly impacts their use in large-scale docking applications. Conclusions We evaluate the strengths and weaknesses of classical and deep learning-based approaches across seven public and popular datasets to aid researchers in understanding the progress made in this field.https://doi.org/10.1186/s12859-024-05991-4Computational structural biologyProtein-protein interactionsScoring functionsMolecular dockingDeep learningProtein surface properties |
| spellingShingle | Azam Shirali Vitalii Stebliankin Ukesh Karki Jimeng Shi Prem Chapagain Giri Narasimhan A comprehensive survey of scoring functions for protein docking models BMC Bioinformatics Computational structural biology Protein-protein interactions Scoring functions Molecular docking Deep learning Protein surface properties |
| title | A comprehensive survey of scoring functions for protein docking models |
| title_full | A comprehensive survey of scoring functions for protein docking models |
| title_fullStr | A comprehensive survey of scoring functions for protein docking models |
| title_full_unstemmed | A comprehensive survey of scoring functions for protein docking models |
| title_short | A comprehensive survey of scoring functions for protein docking models |
| title_sort | comprehensive survey of scoring functions for protein docking models |
| topic | Computational structural biology Protein-protein interactions Scoring functions Molecular docking Deep learning Protein surface properties |
| url | https://doi.org/10.1186/s12859-024-05991-4 |
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