Protein-ligand binding affinity prediction based on profiles of intermolecular contacts
As a key element in structure-based drug design, binding affinity prediction (BAP) for putative protein-ligand complexes can be efficiently achieved by the incorporation of structural descriptors and machine-learning models. However, developing concise descriptors that will lead to accurate and inte...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-01-01
|
| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037022000411 |
| _version_ | 1797978229413249024 |
|---|---|
| author | Debby D. Wang Moon-Tong Chan |
| author_facet | Debby D. Wang Moon-Tong Chan |
| author_sort | Debby D. Wang |
| collection | DOAJ |
| description | As a key element in structure-based drug design, binding affinity prediction (BAP) for putative protein-ligand complexes can be efficiently achieved by the incorporation of structural descriptors and machine-learning models. However, developing concise descriptors that will lead to accurate and interpretable BAP remains a difficult problem in this field. Herein, we introduce the profiles of intermolecular contacts (IMCPs) as descriptors for machine-learning-based BAP. IMCPs describe each group of protein-ligand contacts by the count and average distance of the group members, and collaborate closely with classical machine-learning models. Performed on multiple validation sets, IMCP-based models often result in better BAP accuracy than those originating from other similar descriptors. Additionally, IMCPs are simple and concise, and easy to interpret in model training. These descriptors highly conclude the structural information of protein-ligand complexes and can be easily updated with personalized profile features. IMCPs have been implemented in the BAP Toolkit on github ( https://github.com/debbydanwang/BAP). |
| first_indexed | 2024-04-11T05:20:30Z |
| format | Article |
| id | doaj.art-8e56df6a5fa8406dbdd247deb9488122 |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-04-11T05:20:30Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-8e56df6a5fa8406dbdd247deb94881222022-12-24T04:51:26ZengElsevierComputational and Structural Biotechnology Journal2001-03702022-01-012010881096Protein-ligand binding affinity prediction based on profiles of intermolecular contactsDebby D. Wang0Moon-Tong Chan1School of Health Science and Engineering, University of Shanghai for Science and Technology, 516 Jungong Rd, Shanghai 200093, China; Corresponding author.School of Science and Technology, Hong Kong Metropolitan University, 30 Good Shepherd St, Ho Man Tin, Hong KongAs a key element in structure-based drug design, binding affinity prediction (BAP) for putative protein-ligand complexes can be efficiently achieved by the incorporation of structural descriptors and machine-learning models. However, developing concise descriptors that will lead to accurate and interpretable BAP remains a difficult problem in this field. Herein, we introduce the profiles of intermolecular contacts (IMCPs) as descriptors for machine-learning-based BAP. IMCPs describe each group of protein-ligand contacts by the count and average distance of the group members, and collaborate closely with classical machine-learning models. Performed on multiple validation sets, IMCP-based models often result in better BAP accuracy than those originating from other similar descriptors. Additionally, IMCPs are simple and concise, and easy to interpret in model training. These descriptors highly conclude the structural information of protein-ligand complexes and can be easily updated with personalized profile features. IMCPs have been implemented in the BAP Toolkit on github ( https://github.com/debbydanwang/BAP).http://www.sciencedirect.com/science/article/pii/S2001037022000411Intermolecular contact profilesProtein-ligand binding affinityScoring functionMachine learningComputer-aided drug design |
| spellingShingle | Debby D. Wang Moon-Tong Chan Protein-ligand binding affinity prediction based on profiles of intermolecular contacts Computational and Structural Biotechnology Journal Intermolecular contact profiles Protein-ligand binding affinity Scoring function Machine learning Computer-aided drug design |
| title | Protein-ligand binding affinity prediction based on profiles of intermolecular contacts |
| title_full | Protein-ligand binding affinity prediction based on profiles of intermolecular contacts |
| title_fullStr | Protein-ligand binding affinity prediction based on profiles of intermolecular contacts |
| title_full_unstemmed | Protein-ligand binding affinity prediction based on profiles of intermolecular contacts |
| title_short | Protein-ligand binding affinity prediction based on profiles of intermolecular contacts |
| title_sort | protein ligand binding affinity prediction based on profiles of intermolecular contacts |
| topic | Intermolecular contact profiles Protein-ligand binding affinity Scoring function Machine learning Computer-aided drug design |
| url | http://www.sciencedirect.com/science/article/pii/S2001037022000411 |
| work_keys_str_mv | AT debbydwang proteinligandbindingaffinitypredictionbasedonprofilesofintermolecularcontacts AT moontongchan proteinligandbindingaffinitypredictionbasedonprofilesofintermolecularcontacts |