Predicting Accurate Lead Structures for Screening Molecular Libraries: A Quantum Crystallographic Approach
Optimization of lead structures is crucial for drug discovery. However, the accuracy of such a prediction using the traditional molecular docking approach remains a major concern. Our study demonstrates that the employment of quantum crystallographic approach-counterpoise corrected kernel energy met...
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| Format: | Article |
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MDPI AG
2021-04-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/26/9/2605 |
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| author | Suman Kumar Mandal Parthapratim Munshi |
| author_facet | Suman Kumar Mandal Parthapratim Munshi |
| author_sort | Suman Kumar Mandal |
| collection | DOAJ |
| description | Optimization of lead structures is crucial for drug discovery. However, the accuracy of such a prediction using the traditional molecular docking approach remains a major concern. Our study demonstrates that the employment of quantum crystallographic approach-counterpoise corrected kernel energy method (KEM-CP) can improve the accuracy by and large. We select human aldose reductase at 0.66 Å, cyclin dependent kinase 2 at 2.0 Å and estrogen receptor β at 2.7 Å resolutions with active site environment ranging from highly hydrophilic to moderate to highly hydrophobic and several of their known ligands. Overall, the use of KEM-CP alongside the GoldScore resulted superior prediction than the GoldScore alone. Unlike GoldScore, the KEM-CP approach is neither environment-specific nor structural resolution dependent, which highlights its versatility. Further, the ranking of the ligands based on the KEM-CP results correlated well with that of the experimental IC<sub>50</sub> values. This computationally inexpensive yet simple approach is expected to ease the process of virtual screening of potent ligands, and it would advance the drug discovery research. |
| first_indexed | 2024-03-10T11:50:12Z |
| format | Article |
| id | doaj.art-76bf1e1888624557baaa71ab3f1389cc |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-10T11:50:12Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-76bf1e1888624557baaa71ab3f1389cc2023-11-21T17:47:55ZengMDPI AGMolecules1420-30492021-04-01269260510.3390/molecules26092605Predicting Accurate Lead Structures for Screening Molecular Libraries: A Quantum Crystallographic ApproachSuman Kumar Mandal0Parthapratim Munshi1Chemical and Biological Crystallography, Department of Chemistry, School of Natural Sciences, Shiv Nadar University, Dadri 201314, Uttar Pradesh, IndiaChemical and Biological Crystallography, Department of Chemistry, School of Natural Sciences, Shiv Nadar University, Dadri 201314, Uttar Pradesh, IndiaOptimization of lead structures is crucial for drug discovery. However, the accuracy of such a prediction using the traditional molecular docking approach remains a major concern. Our study demonstrates that the employment of quantum crystallographic approach-counterpoise corrected kernel energy method (KEM-CP) can improve the accuracy by and large. We select human aldose reductase at 0.66 Å, cyclin dependent kinase 2 at 2.0 Å and estrogen receptor β at 2.7 Å resolutions with active site environment ranging from highly hydrophilic to moderate to highly hydrophobic and several of their known ligands. Overall, the use of KEM-CP alongside the GoldScore resulted superior prediction than the GoldScore alone. Unlike GoldScore, the KEM-CP approach is neither environment-specific nor structural resolution dependent, which highlights its versatility. Further, the ranking of the ligands based on the KEM-CP results correlated well with that of the experimental IC<sub>50</sub> values. This computationally inexpensive yet simple approach is expected to ease the process of virtual screening of potent ligands, and it would advance the drug discovery research.https://www.mdpi.com/1420-3049/26/9/2605lead structuremolecular dockingscoring functionkernel energy methodquantum crystallographyprotein-ligand interaction |
| spellingShingle | Suman Kumar Mandal Parthapratim Munshi Predicting Accurate Lead Structures for Screening Molecular Libraries: A Quantum Crystallographic Approach Molecules lead structure molecular docking scoring function kernel energy method quantum crystallography protein-ligand interaction |
| title | Predicting Accurate Lead Structures for Screening Molecular Libraries: A Quantum Crystallographic Approach |
| title_full | Predicting Accurate Lead Structures for Screening Molecular Libraries: A Quantum Crystallographic Approach |
| title_fullStr | Predicting Accurate Lead Structures for Screening Molecular Libraries: A Quantum Crystallographic Approach |
| title_full_unstemmed | Predicting Accurate Lead Structures for Screening Molecular Libraries: A Quantum Crystallographic Approach |
| title_short | Predicting Accurate Lead Structures for Screening Molecular Libraries: A Quantum Crystallographic Approach |
| title_sort | predicting accurate lead structures for screening molecular libraries a quantum crystallographic approach |
| topic | lead structure molecular docking scoring function kernel energy method quantum crystallography protein-ligand interaction |
| url | https://www.mdpi.com/1420-3049/26/9/2605 |
| work_keys_str_mv | AT sumankumarmandal predictingaccurateleadstructuresforscreeningmolecularlibrariesaquantumcrystallographicapproach AT parthapratimmunshi predictingaccurateleadstructuresforscreeningmolecularlibrariesaquantumcrystallographicapproach |