1
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Tran-Nguyen VK, Bret G, Rognan D. True Accuracy of Fast Scoring Functions to Predict High-Throughput Screening Data from Docking Poses: The Simpler the Better. J Chem Inf Model 2021; 61:2788-2797. [PMID: 34109796 DOI: 10.1021/acs.jcim.1c00292] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Hundreds of fast scoring functions have been developed over the last 20 years to predict binding free energies from three-dimensional structures of protein-ligand complexes. Despite numerous statistical promises, we believe that none of them has been properly validated for daily prospective high-throughput virtual screening studies, mostly because in silico screening challenges usually employ artificially built and biased datasets. We here carry out a fully unbiased evaluation of four scoring functions (Pafnucy, ΔvinaRF20, IFP, and GRIM) on an in-house developed data collection of experimental high-confidence screening data (LIT-PCBA) covering about 3 million data points on 15 diverse pharmaceutical targets. All four scoring functions were applied to rescore the docking poses of LIT-PCBA compounds in conditions mimicking exactly standard drug discovery scenarios and were compared in terms of propensity to enrich true binders in the top 1%-ranked hit lists. Interestingly, rescoring based on simple interaction fingerprints or interaction graphs outperforms state-of-the-art machine learning and deep learning scoring functions in most of the cases. The current study notably highlights the strong tendency of deep learning methods to predict affinity values within a very narrow range centered on the mean value of samples used for training. Moreover, it suggests that knowledge of pre-existing binding modes is the key to detecting the most potent binders.
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Affiliation(s)
- Viet-Khoa Tran-Nguyen
- Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS-Université de Strasbourg, 67400 Illkirch, France
| | - Guillaume Bret
- Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS-Université de Strasbourg, 67400 Illkirch, France
| | - Didier Rognan
- Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS-Université de Strasbourg, 67400 Illkirch, France
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2
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Mandal SK, Munshi P. Predicting Accurate Lead Structures for Screening Molecular Libraries: A Quantum Crystallographic Approach. Molecules 2021; 26:molecules26092605. [PMID: 33946965 PMCID: PMC8124947 DOI: 10.3390/molecules26092605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 04/22/2021] [Accepted: 04/24/2021] [Indexed: 11/17/2022] Open
Abstract
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 IC50 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.
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3
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Berenger F, Kumar A, Zhang KYJ, Yamanishi Y. Lean-Docking: Exploiting Ligands' Predicted Docking Scores to Accelerate Molecular Docking. J Chem Inf Model 2021; 61:2341-2352. [PMID: 33861591 DOI: 10.1021/acs.jcim.0c01452] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
In structure-based virtual screening (SBVS), a binding site on a protein structure is used to search for ligands with favorable nonbonded interactions. Because it is computationally difficult, docking is time-consuming and any docking user will eventually encounter a chemical library that is too big to dock. This problem might arise because there is not enough computing power or because preparing and storing so many three-dimensional (3D) ligands requires too much space. In this study, however, we show that quality regressors can be trained to predict docking scores from molecular fingerprints. Although typical docking has a screening rate of less than one ligand per second on one CPU core, our regressors can predict about 5800 docking scores per second. This approach allows us to focus docking on the portion of a database that is predicted to have docking scores below a user-chosen threshold. Herein, usage examples are shown, where only 25% of a ligand database is docked, without any significant virtual screening performance loss. We call this method "lean-docking". To validate lean-docking, a massive docking campaign using several state-of-the-art docking software packages was undertaken on an unbiased data set, with only wet-lab tested active and inactive molecules. Although regressors allow the screening of a larger chemical space, even at a constant docking power, it is also clear that significant progress in the virtual screening power of docking scores is desirable.
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Affiliation(s)
- Francois Berenger
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka 820-8502, Japan
| | - Ashutosh Kumar
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Kam Y J Zhang
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka 820-8502, Japan
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4
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Diallo BN, Swart T, Hoppe HC, Tastan Bishop Ö, Lobb K. Potential repurposing of four FDA approved compounds with antiplasmodial activity identified through proteome scale computational drug discovery and in vitro assay. Sci Rep 2021; 11:1413. [PMID: 33446838 PMCID: PMC7809352 DOI: 10.1038/s41598-020-80722-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 12/01/2020] [Indexed: 12/14/2022] Open
Abstract
Malaria elimination can benefit from time and cost-efficient approaches for antimalarials such as drug repurposing. In this work, 796 DrugBank compounds were screened against 36 Plasmodium falciparum targets using QuickVina-W. Hits were selected after rescoring using GRaph Interaction Matching (GRIM) and ligand efficiency metrics: surface efficiency index (SEI), binding efficiency index (BEI) and lipophilic efficiency (LipE). They were further evaluated in Molecular dynamics (MD). Twenty-five protein-ligand complexes were finally retained from the 28,656 (36 × 796) dockings. Hit GRIM scores (0.58 to 0.78) showed their molecular interaction similarity to co-crystallized ligands. Minimum LipE (3), SEI (23) and BEI (7) were in at least acceptable thresholds for hits. Binding energies ranged from -6 to -11 kcal/mol. Ligands showed stability in MD simulation with good hydrogen bonding and favorable protein-ligand interactions energy (the poorest being -140.12 kcal/mol). In vitro testing showed 4 active compounds with two having IC50 values in the single-digit μM range.
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Affiliation(s)
- Bakary N'tji Diallo
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown, 6140, South Africa
| | - Tarryn Swart
- Department of Biochemistry and Microbiology, Rhodes University, Grahamstown, 6140, South Africa
| | - Heinrich C Hoppe
- Department of Biochemistry and Microbiology, Rhodes University, Grahamstown, 6140, South Africa
| | - Özlem Tastan Bishop
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown, 6140, South Africa
| | - Kevin Lobb
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown, 6140, South Africa.
- Department of Chemistry, Rhodes University, Grahamstown, 6140, South Africa.
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5
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Wang Z, Wang X, Li Y, Lei T, Wang E, Li D, Kang Y, Zhu F, Hou T. farPPI: a webserver for accurate prediction of protein-ligand binding structures for small-molecule PPI inhibitors by MM/PB(GB)SA methods. Bioinformatics 2020; 35:1777-1779. [PMID: 30329012 DOI: 10.1093/bioinformatics/bty879] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Revised: 09/20/2018] [Accepted: 10/15/2018] [Indexed: 12/31/2022] Open
Abstract
SUMMARY Protein-protein interactions (PPIs) have been regarded as an attractive emerging class of therapeutic targets for the development of new treatments. Computational approaches, especially molecular docking, have been extensively employed to predict the binding structures of PPI-inhibitors or discover novel small molecule PPI inhibitors. However, due to the relatively 'undruggable' features of PPI interfaces, accurate predictions of the binding structures for ligands towards PPI targets are quite challenging for most docking algorithms. Here, we constructed a non-redundant pose ranking benchmark dataset for small-molecule PPI inhibitors, which contains 900 binding poses for 184 protein-ligand complexes. Then, we evaluated the performance of MM/PB(GB)SA approaches to identify the correct binding poses for PPI inhibitors, including two Prime MM/GBSA procedures from the Schrödinger suite and seven different MM/PB(GB)SA procedures from the Amber package. Our results showed that MM/PBSA outperformed the Glide SP scoring function (success rate of 58.6%) and MM/GBSA in most cases, especially the PB3 procedure which could achieve an overall success rate of ∼74%. Moreover, the GB6 procedure (success rate of 68.9%) performed much better than the other MM/GBSA procedures, highlighting the excellent potential of the GBNSR6 implicit solvation model for pose ranking. Finally, we developed the webserver of Fast Amber Rescoring for PPI Inhibitors (farPPI), which offers a freely available service to rescore the docking poses for PPI inhibitors by using the MM/PB(GB)SA methods. AVAILABILITY AND IMPLEMENTATION farPPI web server is freely available at http://cadd.zju.edu.cn/farppi/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zhe Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xuwen Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Youyong Li
- Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu, China
| | - Tailong Lei
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Ercheng Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Dan Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yu Kang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China.,State Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang, China
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6
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Vazquez J, Deplano A, Herrero A, Gibert E, Herrero E, Luque FJ. Assessing the Performance of Mixed Strategies To Combine Lipophilic Molecular Similarity and Docking in Virtual Screening. J Chem Inf Model 2020; 60:4231-4245. [PMID: 32364713 DOI: 10.1021/acs.jcim.9b01191] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The accuracy of structure-based (SB) virtual screening (VS) is heavily affected by the scoring function used to rank a library of screened compounds. Even in cases where the docked pose agrees with the experimental binding mode of the ligand, the limitations of current scoring functions may lead to sensible inaccuracies in the ability to discriminate between actives and inactives. In this context, the combination of SB and ligand-based (LB) molecular similarity may be a promising strategy to increase the hit rates in VS. This study explores different strategies that aim to exploit the synergy between LB and SB methods in order to mitigate the limitations of these techniques, and to enhance the performance of VS studies by means of a balanced combination between docking scores and three-dimensional (3D) similarity. Particularly, attention is focused to the use of measurements of molecular similarity with PharmScreen, which exploits the 3D distribution of atomic lipophilicity determined from quantum mechanical-based continuum solvation calculations performed with the MST model, in conjunction with three docking programs: Glide, rDock, and GOLD. Different strategies have been explored to combine the information provided by docking and similarity measurements for re-ranking the screened ligands. For a benchmarking of 44 datasets, including 41 targets, the hybrid methods increase the identification of active compounds, according to the early (ROCe%) and total (AUC) enrichment metrics of VS, compared to pure LB and SB methods. Finally, the hybrid approaches are also more effective in enhancing the chemical diversity of active compounds. The datasets employed in this work are available in https://github.com/Pharmacelera/Molecular-Similarity-and-Docking.
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Affiliation(s)
- Javier Vazquez
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, Barcelona 08039, Spain.,Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Institute of Biomedicine (IBUB), and Institute of Theoretical and Computational Chemistry (IQTC-UB), University of Barcelona, Av. Prat de la Riba 171, Santa Coloma de Gramanet E-08921, Spain
| | - Alessandro Deplano
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, Barcelona 08039, Spain
| | - Albert Herrero
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, Barcelona 08039, Spain
| | - Enric Gibert
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, Barcelona 08039, Spain
| | - Enric Herrero
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, Barcelona 08039, Spain
| | - F Javier Luque
- Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Institute of Biomedicine (IBUB), and Institute of Theoretical and Computational Chemistry (IQTC-UB), University of Barcelona, Av. Prat de la Riba 171, Santa Coloma de Gramanet E-08921, Spain
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7
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Parks CD, Gaieb Z, Chiu M, Yang H, Shao C, Walters WP, Jansen JM, McGaughey G, Lewis RA, Bembenek SD, Ameriks MK, Mirzadegan T, Burley SK, Amaro RE, Gilson MK. D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies. J Comput Aided Mol Des 2020; 34:99-119. [PMID: 31974851 PMCID: PMC7261493 DOI: 10.1007/s10822-020-00289-y] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 01/13/2020] [Indexed: 12/11/2022]
Abstract
The Drug Design Data Resource (D3R) aims to identify best practice methods for computer aided drug design through blinded ligand pose prediction and affinity challenges. Herein, we report on the results of Grand Challenge 4 (GC4). GC4 focused on proteins beta secretase 1 and Cathepsin S, and was run in an analogous manner to prior challenges. In Stage 1, participant ability to predict the pose and affinity of BACE1 ligands were assessed. Following the completion of Stage 1, all BACE1 co-crystal structures were released, and Stage 2 tested affinity rankings with co-crystal structures. We provide an analysis of the results and discuss insights into determined best practice methods.
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Affiliation(s)
- Conor D Parks
- Drug Design Data Resource, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Zied Gaieb
- Drug Design Data Resource, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Michael Chiu
- Drug Design Data Resource, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Huanwang Yang
- RCSB Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, New Brunswick, NJ, 08903, USA
- San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Chenghua Shao
- RCSB Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, New Brunswick, NJ, 08903, USA
- San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA, 92093, USA
| | | | - Johanna M Jansen
- Novartis Institutes for BioMedical Research, Emeryville, CA, 94608, USA
| | | | - Richard A Lewis
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, 4002, Basel, Switzerland
| | | | | | | | - Stephen K Burley
- RCSB Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, New Brunswick, NJ, 08903, USA
- San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Rommie E Amaro
- Drug Design Data Resource, University of California, San Diego, La Jolla, CA, 92093, USA.
- Department of Chemistry and Biochemistry, UC San Diego, La Jolla, CA, 92093-0340, USA.
| | - Michael K Gilson
- Drug Design Data Resource, University of California, San Diego, La Jolla, CA, 92093, USA.
- Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego, 9500 Gilman Drive, MC0751, La Jolla, CA, 92093, USA.
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8
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Kotelnikov S, Alekseenko A, Liu C, Ignatov M, Padhorny D, Brini E, Lukin M, Coutsias E, Dill KA, Kozakov D. Sampling and refinement protocols for template-based macrocycle docking: 2018 D3R Grand Challenge 4. J Comput Aided Mol Des 2019; 34:179-189. [PMID: 31879831 DOI: 10.1007/s10822-019-00257-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 11/19/2019] [Indexed: 12/25/2022]
Abstract
We describe a new template-based method for docking flexible ligands such as macrocycles to proteins. It combines Monte-Carlo energy minimization on the manifold, a fast manifold search method, with BRIKARD for complex flexible ligand searching, and with the MELD accelerator of Replica-Exchange Molecular Dynamics simulations for atomistic degrees of freedom. Here we test the method in the Drug Design Data Resource blind Grand Challenge competition. This method was among the best performers in the competition, giving sub-angstrom prediction quality for the majority of the targets.
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Affiliation(s)
- Sergei Kotelnikov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.,Innopolis University, Innopolis, Russia
| | - Andrey Alekseenko
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Cong Liu
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Chemistry, Stony Brook University, Stony Brook, NY, USA
| | - Mikhail Ignatov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.,Institute for Advanced Computational Sciences, Stony Brook University, Stony Brook, NY, USA
| | - Dzmitry Padhorny
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Emiliano Brini
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Mark Lukin
- Department of Pharmacological Sciences, Stony Brook University, Stony Brook, NY, USA
| | - Evangelos Coutsias
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Ken A Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Chemistry, Stony Brook University, Stony Brook, NY, USA.,Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY, USA
| | - Dima Kozakov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA. .,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA. .,Institute for Advanced Computational Sciences, Stony Brook University, Stony Brook, NY, USA.
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9
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Jacquemard C, Tran-Nguyen VK, Drwal MN, Rognan D, Kellenberger E. Local Interaction Density (LID), a Fast and Efficient Tool to Prioritize Docking Poses. Molecules 2019; 24:molecules24142610. [PMID: 31323745 PMCID: PMC6681060 DOI: 10.3390/molecules24142610] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Revised: 07/11/2019] [Accepted: 07/16/2019] [Indexed: 12/18/2022] Open
Abstract
Ligand docking at a protein site can be improved by prioritizing poses by similarity to validated binding modes found in the crystal structures of ligand/protein complexes. The interactions formed in the predicted model are searched in each of the reference 3D structures, taken individually. We propose to merge the information provided by all references, creating a single representation of all known binding modes. The method is called LID, an acronym for Local Interaction Density. LID was benchmarked in a pose prediction exercise on 19 proteins and 1382 ligands using PLANTS as docking software. It was also tested in a virtual screening challenge on eight proteins, with a dataset of 140,000 compounds from DUD-E and PubChem. LID significantly improved the performance of the docking program in both pose prediction and virtual screening. The gain is comparable to that obtained with a rescoring approach based on the individual comparison of reference binding modes (the GRIM method). Importantly, LID is effective with a small number of references. LID calculation time is negligible compared to the docking time.
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Affiliation(s)
- Célien Jacquemard
- Laboratoire D'innovation Thérapeutique, UMR7200, CNRS, Université de Strasbourg, 67400 Illkirch, France
| | - Viet-Khoa Tran-Nguyen
- Laboratoire D'innovation Thérapeutique, UMR7200, CNRS, Université de Strasbourg, 67400 Illkirch, France
| | - Malgorzata N Drwal
- Laboratoire D'innovation Thérapeutique, UMR7200, CNRS, Université de Strasbourg, 67400 Illkirch, France
| | - Didier Rognan
- Laboratoire D'innovation Thérapeutique, UMR7200, CNRS, Université de Strasbourg, 67400 Illkirch, France
| | - Esther Kellenberger
- Laboratoire D'innovation Thérapeutique, UMR7200, CNRS, Université de Strasbourg, 67400 Illkirch, France.
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10
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Pollinger J, Schierle S, Neumann S, Ohrndorf J, Kaiser A, Merk D. Computer-Assisted Selective Optimization of Side-Activities-from Cinalukast to a PPARα Modulator. ChemMedChem 2019; 14:1343-1348. [PMID: 31141287 DOI: 10.1002/cmdc.201900286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Indexed: 11/10/2022]
Abstract
Automated computational analogue design and scoring can speed up hit-to-lead optimization and appears particularly promising in selective optimization of side-activities (SOSA) where possible analogue diversity is confined. Probing this concept, we employed the cysteinyl leukotriene receptor 1 (CysLT1 R) antagonist cinalukast as lead for which we discovered peroxisome proliferator-activated receptor α (PPARα) modulatory activity. We automatically generated a virtual library of close analogues and classified these roughly 8000 compounds for PPARα agonism and CysLT1 R antagonism using automated affinity scoring and machine learning. A computationally preferred analogue for SOSA was synthesized, and in vitro characterization indeed revealed a marked activity shift toward enhanced PPARα activation and diminished CysLT1 R antagonism. Thereby, this prospective application study highlights the potential of automating SOSA.
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Affiliation(s)
- Julius Pollinger
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Max-von-Laue-Str. 9, 60438, Frankfurt, Germany
| | - Simone Schierle
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Max-von-Laue-Str. 9, 60438, Frankfurt, Germany
| | - Sebastian Neumann
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Max-von-Laue-Str. 9, 60438, Frankfurt, Germany
| | - Julia Ohrndorf
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Max-von-Laue-Str. 9, 60438, Frankfurt, Germany
| | - Astrid Kaiser
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Max-von-Laue-Str. 9, 60438, Frankfurt, Germany
| | - Daniel Merk
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Max-von-Laue-Str. 9, 60438, Frankfurt, Germany
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11
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Da Silva F, Desaphy J, Rognan D. IChem: A Versatile Toolkit for Detecting, Comparing, and Predicting Protein-Ligand Interactions. ChemMedChem 2017; 13:507-510. [PMID: 29024463 PMCID: PMC5901026 DOI: 10.1002/cmdc.201700505] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Revised: 10/06/2017] [Indexed: 12/21/2022]
Abstract
Structure-based ligand design requires an exact description of the topology of molecular entities under scrutiny. IChem is a software package that reflects the many contributions of our research group in this area over the last decade. It facilitates and automates many tasks (e.g., ligand/cofactor atom typing, identification of key water molecules) usually left to the modeler's choice. It therefore permits the detection of molecular interactions between two molecules in a very precise and flexible manner. Moreover, IChem enables the conversion of intricate three-dimensional (3D) molecular objects into simple representations (fingerprints, graphs) that facilitate knowledge acquisition at very high throughput. The toolkit is an ideal companion for setting up and performing many structure-based design computations.
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Affiliation(s)
- Franck Da Silva
- Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS-Université de Strasbourg, 74 route du Rhin, 67400, Illkirch, France
| | - Jeremy Desaphy
- Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS-Université de Strasbourg, 74 route du Rhin, 67400, Illkirch, France.,Discovery Chemistry Research and Technologies, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, 46285, USA
| | - Didier Rognan
- Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS-Université de Strasbourg, 74 route du Rhin, 67400, Illkirch, France
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