1
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Sanbonmatsu K. Supercomputing in the biological sciences: Toward Zettascale and Yottascale simulations. Curr Opin Struct Biol 2024; 88:102889. [PMID: 39163795 DOI: 10.1016/j.sbi.2024.102889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2024] [Revised: 07/03/2024] [Accepted: 07/03/2024] [Indexed: 08/22/2024]
Abstract
Molecular simulations of biological systems tend to be significantly more compute-intensive than those in materials science and astrophysics, due to important contributions of long-range electrostatic forces and large numbers of time steps (>1E9) required. Simulations of biomolecular complexes of microseconds to milliseconds are considered state-of-the-art today. However, these time scales are miniscule in comparison to physiological time scales relevant to molecular machine activity, drug action, and elongation cycles for protein synthesis, RNA synthesis, and DNA synthesis (seconds to days). While an exascale supercomputer has simulated an entire virus for nanoseconds, this supercomputer would need to be 10 billion times faster to simulate that virus for 3 hours of physiological time, demonstrating the insatiable need for computing power. With growing interest in computational drug design from the pharmaceutical sector, the biological sciences are positioned to be an industry driver in computing.
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Affiliation(s)
- Karissa Sanbonmatsu
- Los Alamos National Laboratory, United States; New Mexico Consortium, New Mexico.
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2
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Zhang Z, Li Y, Yang J, Li J, Lin X, Liu T, Yang S, Lin J, Xue S, Yu J, Tang C, Li Z, Liu L, Ye Z, Deng Y, Li Z, Chen K, Ding H, Luo C, Lin H. Dual-site molecular glues for enhancing protein-protein interactions of the CDK12-DDB1 complex. Nat Commun 2024; 15:6477. [PMID: 39090085 PMCID: PMC11294606 DOI: 10.1038/s41467-024-50642-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Accepted: 07/18/2024] [Indexed: 08/04/2024] Open
Abstract
Protein-protein interactions (PPIs) stabilization with molecular glues plays a crucial role in drug discovery, albeit with significant challenges. In this study, we propose a dual-site approach, targeting the PPI region and its dynamic surroundings. We conduct molecular dynamics simulations to identify critical sites on the PPI that stabilize the cyclin-dependent kinase 12 - DNA damage-binding protein 1 (CDK12-DDB1) complex, resulting in further cyclin K degradation. This exploration leads to the creation of LL-K12-18, a dual-site molecular glue, which enhances the glue properties to augment degradation kinetics and efficiency. Notably, LL-K12-18 demonstrates strong inhibition of gene transcription and anti-proliferative effects in tumor cells, showing significant potency improvements in MDA-MB-231 (88-fold) and MDA-MB-468 cells (307-fold) when compared to its precursor compound SR-4835. These findings underscore the potential of dual-site approaches in disrupting CDK12 function and offer a structural insight-based framework for the design of cyclin K molecular glues.
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Affiliation(s)
- Zemin Zhang
- The School of Pharmacy, Fujian Medical University, Fuzhou, China
| | - Yuanqing Li
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, China
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Jie Yang
- Key Laboratory of Microbial Pathogenesis and Interventions of Fujian Province University, the Key Laboratory of Innate Immune Biology of Fujian Province, Biomedical Research Center of South China, College of Life Sciences, Fujian Normal University, Fuzhou, China
| | - Jiacheng Li
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Xiongqiang Lin
- The School of Pharmacy, Fujian Medical University, Fuzhou, China
| | - Ting Liu
- The School of Pharmacy, Fujian Medical University, Fuzhou, China
| | - Shiling Yang
- Key Laboratory of Microbial Pathogenesis and Interventions of Fujian Province University, the Key Laboratory of Innate Immune Biology of Fujian Province, Biomedical Research Center of South China, College of Life Sciences, Fujian Normal University, Fuzhou, China
| | - Jin Lin
- The School of Pharmacy, Fujian Medical University, Fuzhou, China
| | - Shengyu Xue
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, China
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Jiamin Yu
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, China
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Cailing Tang
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, China
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Ziteng Li
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Liping Liu
- Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan, China
| | - Zhengzheng Ye
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China
| | - Yanan Deng
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China
| | - Zhihai Li
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China
| | - Kaixian Chen
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, China
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Hong Ding
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.
- State Key Laboratory of Functions and Applications of Medicinal Plants, School of Pharmacy, Guizhou Medical University, Guiyang, China.
| | - Cheng Luo
- The School of Pharmacy, Fujian Medical University, Fuzhou, China.
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.
- Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan, China.
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China.
- State Key Laboratory of Functions and Applications of Medicinal Plants, School of Pharmacy, Guizhou Medical University, Guiyang, China.
| | - Hua Lin
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.
- Key Laboratory of Microbial Pathogenesis and Interventions of Fujian Province University, the Key Laboratory of Innate Immune Biology of Fujian Province, Biomedical Research Center of South China, College of Life Sciences, Fujian Normal University, Fuzhou, China.
- Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan, China.
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3
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Zia SR, Coricello A, Bottegoni G. Increased throughput in methods for simulating protein ligand binding and unbinding. Curr Opin Struct Biol 2024; 87:102871. [PMID: 38924980 DOI: 10.1016/j.sbi.2024.102871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 06/03/2024] [Accepted: 06/04/2024] [Indexed: 06/28/2024]
Abstract
By incorporating full flexibility and enabling the quantification of crucial parameters such as binding free energies and residence times, methods for investigating protein-ligand binding and unbinding via molecular dynamics provide details on the involved mechanisms at the molecular level. While these advancements hold promise for impacting drug discovery, a notable drawback persists: their relatively time-consuming nature limits throughput. Herein, we survey recent implementations which, employing a blend of enhanced sampling techniques, a clever choice of collective variables, and often machine learning, strive to enhance the efficiency of new and previously reported methods without compromising accuracy. Particularly noteworthy is the validation of these methods that was often performed on systems mirroring real-world drug discovery scenarios.
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Affiliation(s)
- Syeda Rehana Zia
- Department of Paediatrics and Child Health, Faculty of Health Sciences, Medical College, The Aga Khan University, Karachi, 74800, Pakistan
| | - Adriana Coricello
- Department of Biomolecular Sciences, University of Urbino Carlo Bo, Urbino, 61029, Italy.
| | - Giovanni Bottegoni
- Department of Biomolecular Sciences, University of Urbino Carlo Bo, Urbino, 61029, Italy; Institute of Clinical Sciences, College of Medical and Dental Sciences, University of Birmingham, B15 2TT, United Kingdom.
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4
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Menchon G, Maveyraud L, Czaplicki G. Molecular Dynamics as a Tool for Virtual Ligand Screening. Methods Mol Biol 2024; 2714:33-83. [PMID: 37676592 DOI: 10.1007/978-1-0716-3441-7_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Rational drug design is essential for new drugs to emerge, especially when the structure of a target protein or nucleic acid is known. To that purpose, high-throughput virtual ligand screening campaigns aim at discovering computationally new binding molecules or fragments to modulate particular biomolecular interactions or biological activities, related to a disease process. The structure-based virtual ligand screening process primarily relies on docking methods which allow predicting the binding of a molecule to a biological target structure with a correct conformation and the best possible affinity. The docking method itself is not sufficient as it suffers from several and crucial limitations (lack of full protein flexibility information, no solvation and ion effects, poor scoring functions, and unreliable molecular affinity estimation).At the interface of computer techniques and drug discovery, molecular dynamics (MD) allows introducing protein flexibility before or after a docking protocol, refining the structure of protein-drug complexes in the presence of water, ions, and even in membrane-like environments, describing more precisely the temporal evolution of the biological complex and ranking these complexes with more accurate binding energy calculations. In this chapter, we describe the up-to-date MD, which plays the role of supporting tools in the virtual ligand screening (VS) process.Without a doubt, using docking in combination with MD is an attractive approach in structure-based drug discovery protocols nowadays. It has proved its efficiency through many examples in the literature and is a powerful method to significantly reduce the amount of required wet experimentations (Tarcsay et al, J Chem Inf Model 53:2990-2999, 2013; Barakat et al, PLoS One 7:e51329, 2012; De Vivo et al, J Med Chem 59:4035-4061, 2016; Durrant, McCammon, BMC Biol 9:71-79, 2011; Galeazzi, Curr Comput Aided Drug Des 5:225-240, 2009; Hospital et al, Adv Appl Bioinforma Chem 8:37-47, 2015; Jiang et al, Molecules 20:12769-12786, 2015; Kundu et al, J Mol Graph Model 61:160-174, 2015; Mirza et al, J Mol Graph Model 66:99-107, 2016; Moroy et al, Future Med Chem 7:2317-2331, 2015; Naresh et al, J Mol Graph Model 61:272-280, 2015; Nichols et al, J Chem Inf Model 51:1439-1446, 2011; Nichols et al, Methods Mol Biol 819:93-103, 2012; Okimoto et al, PLoS Comput Biol 5:e1000528, 2009; Rodriguez-Bussey et al, Biopolymers 105:35-42, 2016; Sliwoski et al, Pharmacol Rev 66:334-395, 2014).
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Affiliation(s)
- Grégory Menchon
- Inserm U1242, Oncogenesis, Stress and Signaling (OSS), Université de Rennes 1, Rennes, France
| | - Laurent Maveyraud
- Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, Université Toulouse III - Paul Sabatier (UT3), Toulouse, France
| | - Georges Czaplicki
- Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, Université Toulouse III - Paul Sabatier (UT3), Toulouse, France.
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5
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Mteremko D, Chilongola J, Paluch AS, Chacha M. Targeting human thymidylate synthase: Ensemble-based virtual screening for drug repositioning and the role of water. J Mol Graph Model 2023; 118:108348. [PMID: 36257147 DOI: 10.1016/j.jmgm.2022.108348] [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: 06/07/2022] [Revised: 09/21/2022] [Accepted: 09/23/2022] [Indexed: 11/29/2022]
Abstract
A drug repositioning computational approach was carried to search inhibitors for human thymidylate synthase. An ensemble-based virtual screening of FDA-approved drugs showed the drugs Imatinib, Lumacaftor and Naldemedine to be likely candidates for repurposing. The role of water in the drug-receptor interactions was revealed by the application of an extended AutoDock scoring function that included the water forcefield. The binding affinity scores when hydrated ligands were docked were improved in the drugs considered. Further binding free energy calculations based on the Molecular Mechanics Poisson-Boltzmann Surface Area method revealed that Imatinib, Lumacaftor and Naldemedine scored -130.7 ± 28.1, -210.6 ± 29.9 and -238.0 ± 25.4 kJ/mol, respectively, showing good binding affinity for the candidates considered. Overall, the analysis of the molecular dynamics trajectory of the receptor-drug complexes revealed stable structures for Imatinib, Lumacaftor and Naldemedine, for the entire simulation time.
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Affiliation(s)
- Denis Mteremko
- The Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania.
| | - Jaffu Chilongola
- Kilimanjaro Christian Medical University College, Moshi, Tanzania
| | - Andrew S Paluch
- Department of Chemical, Paper, and Biomedical Engineering, Miami University, Oxford, OH, 45056, USA
| | - Musa Chacha
- The Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania; Arusha Technical College, Arusha, Tanzania
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6
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Fukunishi Y, Higo J, Kasahara K. Computer simulation of molecular recognition in biomolecular system: from in silico screening to generalized ensembles. Biophys Rev 2022; 14:1423-1447. [PMID: 36465086 PMCID: PMC9703445 DOI: 10.1007/s12551-022-01015-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 11/06/2022] [Indexed: 11/29/2022] Open
Abstract
Prediction of ligand-receptor complex structure is important in both the basic science and the industry such as drug discovery. We report various computation molecular docking methods: fundamental in silico (virtual) screening, ensemble docking, enhanced sampling (generalized ensemble) methods, and other methods to improve the accuracy of the complex structure. We explain not only the merits of these methods but also their limits of application and discuss some interaction terms which are not considered in the in silico methods. In silico screening and ensemble docking are useful when one focuses on obtaining the native complex structure (the most thermodynamically stable complex). Generalized ensemble method provides a free-energy landscape, which shows the distribution of the most stable complex structure and semi-stable ones in a conformational space. Also, barriers separating those stable structures are identified. A researcher should select one of the methods according to the research aim and depending on complexity of the molecular system to be studied.
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Affiliation(s)
- Yoshifumi Fukunishi
- Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), 2-3-26, Aomi, Koto-Ku, Tokyo, 135-0064 Japan
| | - Junichi Higo
- Graduate School of Information Science, University of Hyogo, 7-1-28 Minatojima Minamimachi, Chuo-Ku, Kobe, Hyogo 650-0047 Japan ,Research Organization of Science and Technology, Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga 525-8577 Japan
| | - Kota Kasahara
- College of Life Sciences, Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga 525-8577 Japan
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7
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Bhardwaj VK, Purohit R. A lesson for the maestro of the replication fork: Targeting the protein-binding interface of proliferating cell nuclear antigen for anticancer therapy. J Cell Biochem 2022; 123:1091-1102. [PMID: 35486518 DOI: 10.1002/jcb.30265] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 03/31/2022] [Accepted: 04/18/2022] [Indexed: 12/14/2022]
Abstract
The proliferating cell nuclear antigen (PCNA) has emerged as a promising candidate for the development of novel cancer therapeutics. PCNA is a nononcogenic mediator of DNA replication that regulates a diverse range of cellular functions and pathways through a comprehensive list of protein-protein interactions. The hydrophobic binding pocket on PCNA offers an opportunity for the development of inhibitors to target various types of cancers and modulate protein-protein interactions. In the present study, we explored the binding modes and affinity of molecule I1 (standard molecule) with the previously suggested dimer interface pocket and the hydrophobic pocket present on the frontal side of the PCNA monomer. We also identified potential lead molecules from the library of in-house synthesized 3-methylenisoindolin-1-one based molecules to inhibit the protein-protein interactions of PCNA. Our results were based on robust computational methods, including molecular docking, conventional, steered, and umbrella sampling molecular dynamics simulations. Our results suggested that the standard inhibitor I1 interacts with the hydrophobic pocket of PCNA with a higher affinity than the previously suggested binding site. Also, the proposed molecules showed better or comparable binding free energies as calculated by the Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) approach and further validated by enhanced umbrella sampling simulations. In vitro and in vivo methods could test the computationally suggested molecules for advancement in the drug discovery pipeline.
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Affiliation(s)
- Vijay Kumar Bhardwaj
- Structural Bioinformatics Lab, CSIR-Institute of Himalayan Bioresource Technology (CSIR-IHBT), Palampur, Himachal Pradesh, India.,Division of Biotechnology, CSIR-IHBT, Palampur, Himachal Pradesh, India.,Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India
| | - Rituraj Purohit
- Structural Bioinformatics Lab, CSIR-Institute of Himalayan Bioresource Technology (CSIR-IHBT), Palampur, Himachal Pradesh, India.,Division of Biotechnology, CSIR-IHBT, Palampur, Himachal Pradesh, India.,Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India
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8
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Xie L, Xu L, Chang S, Xu X, Meng L. Multitask deep networks with grid featurization achieve improved scoring performance for protein-ligand binding. Chem Biol Drug Des 2021; 96:973-983. [PMID: 33058459 DOI: 10.1111/cbdd.13648] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 10/11/2019] [Accepted: 10/27/2019] [Indexed: 01/10/2023]
Abstract
Deep learning-based methods have been extensively developed to improve scoring performance in structure-based drug discovery. Extending multitask deep networks in addressing pharmaceutical problems shows remarkable improvements over single task network. Recently, grid featurization has been introduced to convert protein-ligand complex co-ordinates into fingerprints with the advantage of incorporating inter- and intra-molecular information. The combination of grid featurization with multitask deep networks would hold great potential to boost the scoring performance. We examined the performance of three novel multitask deep networks (standard multitask, bypass, and progressive network) in reproducing the binding affinities of protein-ligand complexes in comparison with AutoDock Vina docking and MM/GBSA method. Among five evaluated methods, progressive network combined with grid featurization provided the best Pearson correlation coefficient (0.74) and least mean absolute average error (0.98) for the overall scoring performance. Moreover, all networks increased screening ability for the re-docking pose and progressive network even achieved AUC of 0.87 over 0.52 of AutoDock Vina. Our results demonstrated that progressive network combined with grid featurization would be one powerful rescoring approach to strengthen screening results after obtaining protein-ligand complex in the conventional docking software.
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Affiliation(s)
- Liangxu Xie
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Lei Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Shan Chang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Xiaojun Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Li Meng
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
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9
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Kim SS, Alves MJ, Gygli P, Otero J, Lindert S. Identification of Novel Cyclin A2 Binding Site and Nanomolar Inhibitors of Cyclin A2-CDK2 Complex. Curr Comput Aided Drug Des 2021; 17:57-68. [PMID: 31889491 DOI: 10.2174/1573409916666191231113055] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 11/25/2019] [Accepted: 12/09/2019] [Indexed: 01/10/2023]
Abstract
BACKGROUND Given the diverse roles of cyclin A2 both in cell cycle regulation and in DNA damage response, identifying small molecule regulators of cyclin A2 activity carries significant potential to regulate diverse cellular processes in both ageing/neurodegeneration and in cancer. OBJECTIVE Based on cyclin A2's recently discovered role in DNA repair, we hypothesized that small molecule inhibitors that were predicted to bind to both cyclin A2 and CDK2 will be useful as a radiosensitizer of cancer cells. In this study, we used structure-based drug discovery to find inhibitors that target both cyclin A2 and CDK2. METHODS Molecular dynamics simulations were used to generate diverse binding pocket conformations for application of the relaxed complex scheme. We then used structure-based virtual screening to find potential dual cyclin A2 and CDK2 inhibitors. Based on a consensus score of docked poses from Glide and AutoDock Vina, we identified about 40 promising hit compounds, where all PAINS scaffolds were removed from consideration. A biochemical luminescence assay of cyclin A2-CDK2 function was used for experimental verification. RESULTS Four lead inhibitors of cyclin A2-CDK2 complex have been identified using a relaxed complex scheme virtual screen have been verified in a biochemical luminescence assay of cyclin A2- CDK2 function. Two of the four lead inhibitors had inhibitory concentrations in the nanomolar range. CONCLUSION The four cyclin A2-CDK2 complex inhibitors are the first reported inhibitors that were specifically designed not to target the cyclin A2-CDK2 protein-protein interface. Overall, our results highlight the potential of combined advanced computational tools and biochemical verification to discover novel binding scaffolds.
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Affiliation(s)
- Stephanie S Kim
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, 43210, United States
| | - Michele J Alves
- Departments of Neuroscience, Pathology and Neuropathology, Ohio State University, Columbus, OH, 43210, United States
| | - Patrick Gygli
- Departments of Neuroscience, Pathology and Neuropathology, Ohio State University, Columbus, OH, 43210, United States
| | - Jose Otero
- Departments of Neuroscience, Pathology and Neuropathology, Ohio State University, Columbus, OH, 43210, United States
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, 43210, United States
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10
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Wang A, Zhang Y, Chu H, Liao C, Zhang Z, Li G. Higher Accuracy Achieved for Protein-Ligand Binding Pose Prediction by Elastic Network Model-Based Ensemble Docking. J Chem Inf Model 2020; 60:2939-2950. [PMID: 32383873 DOI: 10.1021/acs.jcim.9b01168] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Molecular docking plays an indispensable role in predicting the receptor-ligand interactions in which the protein receptor is usually kept rigid, whereas the ligand is treated as being flexible. Because of the inherent flexibility of proteins, the binding pocket of apo receptors might undergo significant conformational rearrangement upon ligand binding, which limits the prediction accuracy of docking. Here, we present an iterative anisotropic network model (iterANM)-based ensemble docking approach, which generates multiple holo-like receptor structures starting from the apo receptor and incorporates protein flexibility into docking. In a validation data set consisting of 233 chemically diverse cyclin-dependent kinase 2 (CDK2) inhibitors, the iterANM-based ensemble docking achieves higher capacity to reproduce native-like binding poses compared with those using single apo receptor conformation or conformational ensemble from molecular dynamics simulations. The prediction success rate within the top5-ranked binding poses produced by the iterANM can further be improved through reranking with the molecular mechanics-Poisson-Boltzmann surface area method. In a smaller data set with 58 CDK2 inhibitors, the iterANM-based ensemble shows a higher success rate compared with the flexible receptor-based docking procedure AutoDockFR and other receptor conformation generation approaches. Further, an additional docking test consisting of 10 diverse receptor-ligand combinations shows that the iterANM is robustly applicable for different receptor structures. These results suggest the iterANM-based ensemble docking as an accurate, efficient, and practical framework to predict the binding mode of a ligand for receptors with flexibility.
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Affiliation(s)
- Anhui Wang
- State Key Laboratory of Fine Chemicals, School of Chemistry, Dalian University of Technology, Dalian 116024, China.,Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Yuebin Zhang
- Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Huiying Chu
- Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Chenyi Liao
- Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Zhichao Zhang
- State Key Laboratory of Fine Chemicals, School of Chemistry, Dalian University of Technology, Dalian 116024, China
| | - Guohui Li
- Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
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11
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Nirwan S, Chahal V, Kakkar R. Structure-based virtual screening, free energy of binding and molecular dynamics simulations to propose novel inhibitors of Mtb-MurB oxidoreductase enzyme. J Biomol Struct Dyn 2020; 39:656-671. [DOI: 10.1080/07391102.2020.1712258] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Sonam Nirwan
- Computational Chemistry Laboratory, Department of Chemistry, University of Delhi, Delhi, India
| | - Varun Chahal
- Computational Chemistry Laboratory, Department of Chemistry, University of Delhi, Delhi, India
| | - Rita Kakkar
- Computational Chemistry Laboratory, Department of Chemistry, University of Delhi, Delhi, India
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12
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Devaurs D, Antunes DA, Hall-Swan S, Mitchell N, Moll M, Lizée G, Kavraki LE. Using parallelized incremental meta-docking can solve the conformational sampling issue when docking large ligands to proteins. BMC Mol Cell Biol 2019; 20:42. [PMID: 31488048 PMCID: PMC6729087 DOI: 10.1186/s12860-019-0218-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 08/08/2019] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Docking large ligands, and especially peptides, to protein receptors is still considered a challenge in computational structural biology. Besides the issue of accurately scoring the binding modes of a protein-ligand complex produced by a molecular docking tool, the conformational sampling of a large ligand is also often considered a challenge because of its underlying combinatorial complexity. In this study, we evaluate the impact of using parallelized and incremental paradigms on the accuracy and performance of conformational sampling when docking large ligands. We use five datasets of protein-ligand complexes involving ligands that could not be accurately docked by classical protein-ligand docking tools in previous similar studies. RESULTS Our computational evaluation shows that simply increasing the amount of conformational sampling performed by a protein-ligand docking tool, such as Vina, by running it for longer is rarely beneficial. Instead, it is more efficient and advantageous to run several short instances of this docking tool in parallel and group their results together, in a straightforward parallelized docking protocol. Even greater accuracy and efficiency are achieved by our parallelized incremental meta-docking tool, DINC, showing the additional benefits of its incremental paradigm. Using DINC, we could accurately reproduce the vast majority of the protein-ligand complexes we considered. CONCLUSIONS Our study suggests that, even when trying to dock large ligands to proteins, the conformational sampling of the ligand should no longer be considered an issue, as simple docking protocols using existing tools can solve it. Therefore, scoring should currently be regarded as the biggest unmet challenge in molecular docking.
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Affiliation(s)
- Didier Devaurs
- Department of Computer Science, Rice University, 6100 Main St, Houston, TX 77005 USA
| | - Dinler A Antunes
- Department of Computer Science, Rice University, 6100 Main St, Houston, TX 77005 USA
| | - Sarah Hall-Swan
- Department of Computer Science, Rice University, 6100 Main St, Houston, TX 77005 USA
| | - Nicole Mitchell
- Department of Computer Science, Rice University, 6100 Main St, Houston, TX 77005 USA
| | - Mark Moll
- Department of Computer Science, Rice University, 6100 Main St, Houston, TX 77005 USA
| | - Gregory Lizée
- Department of Melanoma Medical Oncology - Research, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030 USA
| | - Lydia E Kavraki
- Department of Computer Science, Rice University, 6100 Main St, Houston, TX 77005 USA
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13
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Molecular basis for the different interactions of congeneric substrates with the polyspecific transporter AcrB. BIOCHIMICA ET BIOPHYSICA ACTA-BIOMEMBRANES 2019; 1861:1397-1408. [DOI: 10.1016/j.bbamem.2019.05.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2018] [Revised: 12/20/2018] [Accepted: 01/06/2019] [Indexed: 12/20/2022]
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14
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Wagner JR, Churas CP, Liu S, Swift RV, Chiu M, Shao C, Feher VA, Burley SK, Gilson MK, Amaro RE. Continuous Evaluation of Ligand Protein Predictions: A Weekly Community Challenge for Drug Docking. Structure 2019; 27:1326-1335.e4. [PMID: 31257108 DOI: 10.1016/j.str.2019.05.012] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 03/14/2019] [Accepted: 05/30/2019] [Indexed: 12/19/2022]
Abstract
Docking calculations can accelerate drug discovery by predicting the bound poses of ligands for a targeted protein. However, it is not clear which docking methods work best. Furthermore, predicting poses requires steps outside the docking algorithm itself, such as preparation of the protein and ligand, and it is not known which components are most in need of improvement. The Continuous Evaluation of Ligand Protein Predictions (CELPP) is a blinded prediction challenge designed to address these issues. Participants create a workflow to predict protein-ligand binding poses, which is then tasked with predicting 10-100 new protein-ligand crystal structures each week. CELPP evaluates the accuracy of each workflow's predictions and posts the scores online. The results can be used to identify the strengths and weaknesses of current approaches, help map docking problems to the algorithms most likely to overcome them, and illuminate areas of unmet need in structure-guided drug design.
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Affiliation(s)
- Jeffrey R Wagner
- Drug Design Data Resource, University of California San Diego, La Jolla, CA 92093, USA
| | - Christopher P Churas
- Drug Design Data Resource, University of California San Diego, La Jolla, CA 92093, USA
| | - Shuai Liu
- Drug Design Data Resource, University of California San Diego, La Jolla, CA 92093, USA
| | - Robert V Swift
- 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
| | - Chenghua Shao
- RCSB Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Victoria A Feher
- Drug Design Data Resource, University of California San Diego, La Jolla, CA 92093, USA
| | - Stephen K Burley
- RCSB Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, 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, 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, University of California San Diego, La Jolla, CA 92093, USA.
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15
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Matthews N, Kitao A, Laycock S, Hayward S. Haptic-Assisted Interactive Molecular Docking Incorporating Receptor Flexibility. J Chem Inf Model 2019; 59:2900-2912. [DOI: 10.1021/acs.jcim.9b00112] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Nick Matthews
- School of Computing Sciences, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, United Kingdom
| | - Akio Kitao
- School of Life Science and Technology, Tokyo Institute of Technology, 2-12-1 Ookayama, M6-13, Meguro, Tokyo 152-8550, Japan
| | - Stephen Laycock
- School of Computing Sciences, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, United Kingdom
| | - Steven Hayward
- School of Computing Sciences, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, United Kingdom
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16
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Chen JJ, Schmucker LN, Visco DP. Identifying de-NEDDylation inhibitors: Virtual high-throughput screens targeting SENP8. Chem Biol Drug Des 2019; 93:590-604. [PMID: 30560590 DOI: 10.1111/cbdd.13457] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 11/21/2018] [Accepted: 11/24/2018] [Indexed: 12/16/2022]
Abstract
Protein modification can have far-reaching effects. NEDDylation, a protein modification process with the protein NEDD8, stabilizes and modifies how the targeted protein interacts with other proteins. Its role in system regulation makes it a prime therapeutic target, and virtual high-throughput screening has already identified new NEDD8 inhibitors. SENP8 matures the NEDD8 proenzyme into the active form and regulates NEDDylation by removing NEDD8 from over-NEDDylated proteins. In this work, SENP8 inhibitor candidates were identified in two rounds of virtual high-throughput screening. Of the ten candidates identified in the first round of screening, four were active in validation experiments to yield an experimental hit rate of 40%. Of the five candidates identified in the second round of screening, one was active in validation experiments to yield an experimental hit rate of 20%. Results indicate virtual high-throughput screening improved hit rates over traditional high-throughput screening. The SENP8 inhibitor candidates can be used to interrogate the NEDDylation regulation mechanism.
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Affiliation(s)
| | - Lyndsey N Schmucker
- Department of Chemical and Biomolecular Engineering, University of Akron, Akron, OH
| | - Donald P Visco
- Department of Chemical and Biomolecular Engineering, University of Akron, Akron, OH
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17
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Chen JJ, Schmucker LN, Visco DP. Virtual high-throughput screens identifying hPK-M2 inhibitors: Exploration of model extrapolation. Comput Biol Chem 2019; 78:317-329. [PMID: 30623877 DOI: 10.1016/j.compbiolchem.2018.12.006] [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: 10/16/2018] [Revised: 12/11/2018] [Accepted: 12/13/2018] [Indexed: 10/27/2022]
Abstract
Glycolysis with PK-M2 occurs typically in anaerobic conditions and atypically in aerobic conditions, which is known as the Warburg effect. The Warburg effect is found in many oncogenic situations and is believed to provide energy and biomass for oncogenesis to persist. The work presented targets human PK-M2 (hPK-M2) in a virtual high-throughput screen to identify new inhibitors and leads for further study. In the initial screen, one of the 12 candidates selected for experimental validation showed biological activity (hit-rate = 8.13%). In the second screen with retrained models, six of 11 candidates selected for experimental validation showed biological activity (hit-rate: 54.5%). Additionally, four different scaffolds were identified for further analysis when examining the tested candidates and compounds in the training data. Finally, extrapolation was necessary to identify a sufficient number of candidates to test in the second screen. Examination of the results suggested stepwise extrapolation to maximize efficiency.
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Affiliation(s)
- Jonathan J Chen
- Department of Biology, The University of Akron, 302 Buchtel Common, Akron, OH 44325, USA.
| | - Lyndsey N Schmucker
- Department of Chemical and Biomolecular Engineering, The University of Akron, 302 Buchtel Common, Akron, OH 44325, USA.
| | - Donald P Visco
- Department of Chemical and Biomolecular Engineering, The University of Akron, 302 Buchtel Common, Akron, OH 44325, USA.
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18
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Kim SS, Aprahamian ML, Lindert S. Improving inverse docking target identification with Z-score selection. Chem Biol Drug Des 2019; 93:1105-1116. [PMID: 30604454 DOI: 10.1111/cbdd.13453] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 10/22/2018] [Accepted: 11/17/2018] [Indexed: 12/12/2022]
Abstract
The utilization of inverse docking methods for target identification has been driven by an increasing demand for efficient tools for detecting potential drug side-effects. Despite impressive achievements in the field of inverse docking, identifying true positives from a pool of potential targets still remains challenging. Notably, most of the developed techniques have low accuracies, limit the pool of possible targets that can be investigated or are not easy to use for non-experts due to a lack of available scripts or webserver. Guided by our finding that the absolute docking score was a poor indication of a ligand's protein target, we developed a novel "combined Z-score" method that used a weighted fraction of ligand and receptor-based Z-scores to identify the most likely binding target of a ligand. With our combined Z-score method, an additional 14%, 3.6%, and 6.3% of all ligand-protein pairs of the Astex, DUD, and DUD-E databases, respectively, were correctly predicted compared to a docking score-based selection. The combined Z-score had the highest area under the curve in a ROC curve analysis of all three datasets and the enrichment factor for the top 1% predictions using the combined Z-score analysis was the highest for the Astex and DUD-E datasets. Additionally, we developed a user-friendly python script (compatible with both Python2 and Python3) that enables users to employ the combined Z-score analysis for target identification using a user-defined list of ligands and targets. We are providing this python script and a user tutorial as part of the supplemental information.
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Affiliation(s)
- Stephanie S Kim
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio
| | | | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio
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19
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Abstract
Computational methods, applied at the early stages of the drug design process, use current technology to provide valuable insights into the understanding of chemical systems in a virtual manner, complementing experimental analysis. Molecular docking is an in silico method employed to foresee binding modes of small compounds or macromolecules in contact with a receptor and to predict their molecular interactions. Moreover, the methodology opens up the possibility of ranking these compounds according to a hierarchy determined using particular scoring functions. Docking protocols assign many approximations, and most of them lack receptor flexibility. Therefore, the reliability of the resulting protein-ligand complexes is uncertain. The association with the costly but more accurate MD techniques provides significant complementary with docking. MD simulations can be used before docking since a series of "new" and broader protein conformations can be extracted from the processing of the resulting trajectory and employed as targets for docking. They also can be utilized a posteriori to optimize the structures of the final complexes from docking, calculate more detailed interaction energies, and provide information about the ligand binding mechanism. Here, we focus on protocols that offer the docking-MD combination as a logical approach to improving the drug discovery process.
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20
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Seidel T, Schuetz DA, Garon A, Langer T. The Pharmacophore Concept and Its Applications in Computer-Aided Drug Design. PROGRESS IN THE CHEMISTRY OF ORGANIC NATURAL PRODUCTS 2019; 110:99-141. [PMID: 31621012 DOI: 10.1007/978-3-030-14632-0_4] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Pharmacophore-based techniques currently are an integral part of many computer-aided drug design workflows and have been successfully and extensively applied for tasks such as virtual screening, de novo design, and lead optimization. Pharmacophore models can be derived both in a receptor-based and in a ligand-based manner, and provide an abstract description of essential non-bonded interactions that typically occur between small-molecule ligands and macromolecular targets. Due to their simplistic and abstract nature, pharmacophores are both perfectly suited for efficient computer processing and easy to comprehend by life and physical scientists. As a consequence, they have also proven to be a valuable tool for communicating between computational and medicinal chemists.This chapter aims to provide a short overview of the pharmacophore concept and its applications in modern computer-aided drug design. The chapter is divided into three distinct parts. The first section contains a brief introduction to the pharmacophore concept. The second section provides a description of the most common nonbonded interaction types and their representation as pharmacophoric features. Furthermore, it gives an overview of the various methods for pharmacophore generation and important pharmacophore-based techniques in drug design. This part concludes with examples for recent pharmacophore concept-related research and development. The last section is dedicated to a review of research in the field of natural product chemistry as carried out by employing pharmacophore-based drug design methods.
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Affiliation(s)
- Thomas Seidel
- Department of Pharmaceutical Chemistry, University of Vienna, Vienna, Austria.
| | - Doris A Schuetz
- InteLigand GmbH, IRIC-Institut de Recherche en Immunologie et en Cancérologie, Université de Montréal, Montréal, QC, Canada
| | - Arthur Garon
- Department of Pharmaceutical Chemistry, University of Vienna, Vienna, Austria
| | - Thierry Langer
- Department of Pharmaceutical Chemistry, University of Vienna, Vienna, Austria
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21
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Chen JJ, Schmucker LN, Visco DP. Identifying new clotting factor XIa inhibitors in virtual high-throughput screens using PCA-GA-SVM models and signature. Biotechnol Prog 2018; 34:1553-1565. [PMID: 30009405 DOI: 10.1002/btpr.2693] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2017] [Revised: 05/08/2018] [Accepted: 06/28/2018] [Indexed: 12/17/2022]
Abstract
Blood Clotting Factor XI is an important actor in the clotting mechanism: it activates downstream zymogen involved in the clotting process. It can be targeted for activation or inhibition depending on treatment goals to enhance or inhibit clotting. In terms of antithrombosis treatment, Factor XI has emerged as a promising target to focus on. In this work, an iterative virtual high-throughput screening pipeline was proposed that can supplement current efforts to find inhibitors. The first iteration identified 11 compounds to test with 3 active for a hit-rate of 27.3%. The second iteration of the pipeline identified another 11 compounds to test with 7 active for a hit-rate of 63.6%. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:1553-1565, 2018.
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Affiliation(s)
- Jonathan J Chen
- Dept. of Biology, The University of Akron, 302 Buchtel Common, Akron, OH, 44325
| | - Lyndsey N Schmucker
- Dept. of Chemical and Biomolecular Engineering, The University of Akron, 302 Buchtel Common, Akron, OH, 44325
| | - Donald P Visco
- Dept. of Chemical and Biomolecular Engineering, The University of Akron, 302 Buchtel Common, Akron, OH, 44325
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22
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Chen JJ, Schmucker LN, Visco DP. Pharmaceutical Machine Learning: Virtual High-Throughput Screens Identifying Promising and Economical Small Molecule Inhibitors of Complement Factor C1s. Biomolecules 2018; 8:E24. [PMID: 29735903 PMCID: PMC6023033 DOI: 10.3390/biom8020024] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Revised: 04/26/2018] [Accepted: 04/27/2018] [Indexed: 12/17/2022] Open
Abstract
When excessively activated, C1 is insufficiently regulated, which results in tissue damage. Such tissue damage causes the complement system to become further activated to remove the resulting tissue damage, and a vicious cycle of activation/tissue damage occurs. Current Food and Drug Administration approved treatments include supplemental recombinant C1 inhibitor, but these are extremely costly and a more economical solution is desired. In our work, we have utilized an existing data set of 136 compounds that have been previously tested for activity against C1. Using these compounds and the activity data, we have created models using principal component analysis, genetic algorithm, and support vector machine approaches to characterize activity. The models were then utilized to virtually screen the 72 million compound PubChem repository. This first round of virtual high-throughput screening identified many economical and promising inhibitor candidates, a subset of which was tested to validate their biological activity. These results were used to retrain the models and rescreen PubChem in a second round vHTS. Hit rates for the first round vHTS were 57%, while hit rates for the second round vHTS were 50%. Additional structure⁻property analysis was performed on the active and inactive compounds to identify interesting scaffolds for further investigation.
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Affiliation(s)
- Jonathan J Chen
- Department of Biology, The University of Akron, 302 Buchtel Common, Akron, OH 44325, USA.
| | - Lyndsey N Schmucker
- Department of Chemical and Biomolecular Engineering, The University of Akron, 302 Buchtel Common, Akron, OH 44325, USA.
| | - Donald P Visco
- Department of Chemical and Biomolecular Engineering, The University of Akron, 302 Buchtel Common, Akron, OH 44325, USA.
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23
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Fu D, Meiler J. RosettaLigandEnsemble: A Small-Molecule Ensemble-Driven Docking Approach. ACS OMEGA 2018; 3:3655-3664. [PMID: 29732444 PMCID: PMC5928483 DOI: 10.1021/acsomega.7b02059] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2017] [Accepted: 03/20/2018] [Indexed: 05/27/2023]
Abstract
RosettaLigand is a protein-small-molecule (ligand) docking software capable of predicting binding poses and is used for virtual screening of medium-sized ligand libraries. Structurally similar small molecules are generally found to bind in the same pose to one binding pocket, despite some prominent exceptions. To make use of this information, we have developed RosettaLigandEnsemble (RLE). RLE docks a superimposed ensemble of congeneric ligands simultaneously. The program determines a well-scoring overall pose for this superimposed ensemble before independently optimizing individual protein-small-molecule interfaces. In a cross-docking benchmark of 89 protein-small-molecule co-crystal structures across 20 biological systems, we found that RLE improved sampling efficiency in 62 cases, with an average change of 18%. In addition, RLE generated more consistent docking results within a congeneric series and was capable of rescuing the unsuccessful docking of individual ligands, identifying a nativelike top-scoring model in 10 additional cases. The improvement in RLE is driven by a balance between having a sizable common chemical scaffold and meaningful modifications to distal groups. The new ensemble docking algorithm will work well in conjunction with medicinal chemistry structure-activity relationship (SAR) studies to more accurately recapitulate protein-ligand interfaces. We also tested whether optimizing the rank correlation of RLE-binding scores to SAR data in the refinement step helps the high-resolution positioning of the ligand. However, no significant improvement was observed.
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24
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Fu DY, Meiler J. Predictive Power of Different Types of Experimental Restraints in Small Molecule Docking: A Review. J Chem Inf Model 2018; 58:225-233. [PMID: 29286651 DOI: 10.1021/acs.jcim.7b00418] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Incorporating experimental restraints is a powerful method of increasing accuracy in computational protein small molecule docking simulations. Different algorithms integrate distinct forms of biochemical data during the docking and/or scoring stages. These so-called hybrid methods make use of receptor-based information such as nuclear magnetic resonance (NMR) restraints or small molecule-based information such as structure-activity relationships (SARs). A third class of methods directly interrogates contacts between the protein receptor and the small molecule. This work reviews the current state of using such restraints in docking simulations, evaluates their feasibility across broad systems, and identifies potential areas of algorithm development.
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Affiliation(s)
- Darwin Y Fu
- Department of Chemistry Vanderbilt University Nashville, Tennessee 37235, United States
| | - Jens Meiler
- Department of Chemistry Vanderbilt University Nashville, Tennessee 37235, United States
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25
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Callegari D, Ranaghan KE, Woods CJ, Minari R, Tiseo M, Mor M, Mulholland AJ, Lodola A. L718Q mutant EGFR escapes covalent inhibition by stabilizing a non-reactive conformation of the lung cancer drug osimertinib. Chem Sci 2018; 9:2740-2749. [PMID: 29732058 PMCID: PMC5911825 DOI: 10.1039/c7sc04761d] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Accepted: 02/03/2018] [Indexed: 12/15/2022] Open
Abstract
Impact of L718Q mutation on the inhibitory activity of osimertinib on EGFR revealed by free-energy simulations.
Osimertinib is a third-generation inhibitor approved for the treatment of non-small cell lung cancer. It overcomes resistance to first-generation inhibitors by incorporating an acrylamide group which alkylates Cys797 of EGFR T790M. The mutation of a residue in the P-loop (L718Q) was shown to cause resistance to osimertinib, but the molecular mechanism of this process is unknown. Here, we investigated the inhibitory process for EGFR T790M (susceptible to osimertinib) and EGFR T790M/L718Q (resistant to osimertinib), by modelling the chemical step (i.e., alkylation of Cys797) using QM/MM simulations and the recognition step by MD simulations coupled with free-energy calculations. The calculations indicate that L718Q has a negligible impact on both the activation energy for Cys797 alkylation and the free-energy of binding for the formation of the non-covalent complex. The results show that Gln718 affects the conformational space of the EGFR–osimertinib complex, stabilizing a conformation of acrylamide which prevents reaction with Cys797.
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Affiliation(s)
- D Callegari
- Department of Food and Drug , University of Parma , Parma , Italy .
| | - K E Ranaghan
- School of Chemistry , University of Bristol , Bristol , UK
| | - C J Woods
- School of Chemistry , University of Bristol , Bristol , UK
| | - R Minari
- Medical Oncology Unit , University Hospital of Parma , Parma , Italy
| | - M Tiseo
- Medical Oncology Unit , University Hospital of Parma , Parma , Italy
| | - M Mor
- Department of Food and Drug , University of Parma , Parma , Italy .
| | - A J Mulholland
- School of Chemistry , University of Bristol , Bristol , UK
| | - A Lodola
- Department of Food and Drug , University of Parma , Parma , Italy .
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26
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Popov P, Grudinin S. Eurecon: Equidistant uniform rigid-body ensemble constructor. J Mol Graph Model 2018; 80:313-319. [PMID: 29427936 DOI: 10.1016/j.jmgm.2018.01.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Revised: 01/07/2018] [Accepted: 01/23/2018] [Indexed: 12/14/2022]
Abstract
Conformational ensembles comprise one of the fundamental concepts in statistical bioinformatics and appear in a variety of applications, e.g. molecular docking, virtual screening, searching for pharmacophores, etc. High-throughput applications require billions of conformations to be considered, thus, one often uses the rigid-body representation of molecules or its fragments to cope with the computational cost. Of particular interest is generation of the near-native conformational ensembles, which consist of conformations structurally close to the biologically relevant ones. One possible way to compose such ensembles is to control the root mean square deviation (RMSD) between the original and the generated conformations. To the best of our knowledge there is no computational approach that guarantees that all the generated conformations have the desired RMSD with respect to the reference structure. In this study we presented a fast algorithm for the construction of rigid-body conformational ensembles, which possess two main properties: (i) each generated conformation has a fixed RMSD with respect to the original conformation, (ii) generated conformations are distributed uniformly over the sphere of axes corresponding to the rigid-body motions. The algorithm is very efficient, it does not require any standard RMSD computation between the conformations and has the O(N + M) complexity to generate the required rigid-body transforms, where N is the number of atoms in the system, and M is the size of the conformational ensemble. Eurecon is applicable to an arbitrary atomic system, thus, it could be used for molecular systems of various size and type. We demonstrated Eurecon application by generating near-native conformational ensembles for a ligand placed inside a binding site, a protein dimer embedded into a membrane, and a ribosomal complex. We implemented the developed algorithm in C++ and called it Eurecon, which stands for Equidistant Uniform Rigid-body Ensemble CONstructor. A user-friendly interface allows to define the desired RMSD value, the relative amplitudes for rotation and translation motions by means of the partition parameter, and the set of axes corresponding to the rigid-body motions. Eurecon is available as the SAMSON Element (https://samson-connect.net).
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Affiliation(s)
- P Popov
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia.
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27
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Zheng M, Zhao J, Cui C, Fu Z, Li X, Liu X, Ding X, Tan X, Li F, Luo X, Chen K, Jiang H. Computational chemical biology and drug design: Facilitating protein structure, function, and modulation studies. Med Res Rev 2018; 38:914-950. [DOI: 10.1002/med.21483] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Revised: 12/13/2017] [Accepted: 12/15/2017] [Indexed: 12/12/2022]
Affiliation(s)
- Mingyue Zheng
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Jihui Zhao
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Chen Cui
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Zunyun Fu
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Xutong Li
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Xiaohong Liu
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
- School of Life Science and Technology; ShanghaiTech University; Shanghai China
| | - Xiaoyu Ding
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Xiaoqin Tan
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Fei Li
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
- Department of Chemistry, College of Sciences; Shanghai University; Shanghai China
| | - Xiaomin Luo
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Kaixian Chen
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
- School of Life Science and Technology; ShanghaiTech University; Shanghai China
| | - Hualiang Jiang
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
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28
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Menchon G, Maveyraud L, Czaplicki G. Molecular Dynamics as a Tool for Virtual Ligand Screening. Methods Mol Biol 2018; 1762:145-178. [PMID: 29594772 DOI: 10.1007/978-1-4939-7756-7_9] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Rational drug design is essential for new drugs to emerge, especially when the structure of a target protein or catalytic enzyme is known experimentally. To that purpose, high-throughput virtual ligand screening campaigns aim at discovering computationally new binding molecules or fragments to inhibit a particular protein interaction or biological activity. The virtual ligand screening process often relies on docking methods which allow predicting the binding of a molecule into a biological target structure with a correct conformation and the best possible affinity. The docking method itself is not sufficient as it suffers from several and crucial limitations (lack of protein flexibility information, no solvation effects, poor scoring functions, and unreliable molecular affinity estimation).At the interface of computer techniques and drug discovery, molecular dynamics (MD) allows introducing protein flexibility before or after a docking protocol, refining the structure of protein-drug complexes in the presence of water, ions and even in membrane-like environments, and ranking complexes with more accurate binding energy calculations. In this chapter we describe the up-to-date MD protocols that are mandatory supporting tools in the virtual ligand screening (VS) process. Using docking in combination with MD is one of the best computer-aided drug design protocols nowadays. It has proved its efficiency through many examples, described below.
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Affiliation(s)
- Grégory Menchon
- Laboratory of Biomolecular Research, Paul Scherrer Institute, Villigen PSI, Switzerland
| | - Laurent Maveyraud
- Institute of Pharmacology and Structural Biology, UMR 5089, University of Toulouse III, Toulouse, France
| | - Georges Czaplicki
- Institute of Pharmacology and Structural Biology, UMR 5089, University of Toulouse III, Toulouse, France.
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29
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Evaluation of heteroatom-rich derivatives as antitubercular agents with InhA inhibition properties. Med Chem Res 2018. [DOI: 10.1007/s00044-017-2064-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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30
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Kumar A, Sharma A. Computational Modeling of Multi-target-Directed Inhibitors Against Alzheimer’s Disease. NEUROMETHODS 2018. [DOI: 10.1007/978-1-4939-7404-7_19] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
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31
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Aprahamian ML, Tikunova SB, Price MV, Cuesta AF, Davis JP, Lindert S. Successful Identification of Cardiac Troponin Calcium Sensitizers Using a Combination of Virtual Screening and ROC Analysis of Known Troponin C Binders. J Chem Inf Model 2017; 57:3056-3069. [PMID: 29144742 DOI: 10.1021/acs.jcim.7b00536] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Calcium-dependent cardiac muscle contraction is regulated by the protein complex troponin. Calcium binds to the N-terminal domain of troponin C (cNTnC) which initiates the process of contraction. Heart failure is a consequence of a disruption of this process. With the prevalence of this condition, a strong need exists to find novel compounds to increase the calcium sensitivity of cNTnC. Desirable are small chemical molecules that bind to the interface between cTnC and the cTnI switch peptide and exhibit calcium sensitizing properties by possibly stabilizing cTnC in an open conformation. To identify novel drug candidates, we employed a structure-based drug discovery protocol that incorporated the use of a relaxed complex scheme (RCS). In preparation for the virtual screening, cNTnC conformations were identified based on their ability to correctly predict known cNTnC binders using a receiver operating characteristics analysis. Following a virtual screen of the National Cancer Institute's Developmental Therapeutic Program database, a small number of molecules were experimentally tested using stopped-flow kinetics and steady-state fluorescence titrations. We identified two novel compounds, 3-(4-methoxyphenyl)-6,7-chromanediol (NSC600285) and 3-(4-methylphenyl)-7,8-chromanediol (NSC611817), that show increased calcium sensitivity of cTnC in the presence of the regulatory domain of cTnI. The effects of NSC600285 and NSC611817 on the calcium dissociation rate was stronger than that of the known calcium sensitizer bepridil. Thus, we identified a 3-phenylchromane group as a possible key pharmacophore in the sensitization of cardiac muscle contraction. Building on this finding is of interest to researchers working on development of drugs for calcium sensitization.
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Affiliation(s)
- Melanie L Aprahamian
- Department of Chemistry and Biochemistry, Ohio State University , Columbus, Ohio 43210, United States
| | - Svetlana B Tikunova
- Davis Heart and Lung Research Institute and Department of Physiology and Cell Biology, Ohio State University , Columbus, Ohio 43210, United States
| | - Morgan V Price
- Davis Heart and Lung Research Institute and Department of Physiology and Cell Biology, Ohio State University , Columbus, Ohio 43210, United States
| | - Andres F Cuesta
- Davis Heart and Lung Research Institute and Department of Physiology and Cell Biology, Ohio State University , Columbus, Ohio 43210, United States
| | - Jonathan P Davis
- Davis Heart and Lung Research Institute and Department of Physiology and Cell Biology, Ohio State University , Columbus, Ohio 43210, United States
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University , Columbus, Ohio 43210, United States
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32
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Identifying novel factor XIIa inhibitors with PCA-GA-SVM developed vHTS models. Eur J Med Chem 2017; 140:31-41. [DOI: 10.1016/j.ejmech.2017.08.056] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2017] [Revised: 08/21/2017] [Accepted: 08/23/2017] [Indexed: 01/18/2023]
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33
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Exploring Binding Mechanisms in Nuclear Hormone Receptors by Monte Carlo and X-ray-derived Motions. Biophys J 2017; 112:1147-1156. [PMID: 28355542 DOI: 10.1016/j.bpj.2017.02.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Revised: 01/25/2017] [Accepted: 02/01/2017] [Indexed: 12/15/2022] Open
Abstract
In this study, we performed an extensive exploration of the ligand entry mechanism for members of the steroid nuclear hormone receptor family (androgen receptor, estrogen receptor α, glucocorticoid receptor, mineralocorticoid receptor, and progesterone receptor) and their endogenous ligands. The exploration revealed a shared entry path through the helix 3, 7, and 11 regions. Examination of the x-ray structures of the receptor-ligand complexes further showed two distinct folds of the helix 6-7 region, classified as "open" and "closed", which could potentially affect ligand binding. To improve sampling of the helix 6-7 loop, we incorporated motion modes based on principal component analysis of existing crystal structures of the receptors and applied them to the protein-ligand sampling. A detailed comparison with the anisotropic network model (an elastic network model) highlights the importance of flexibility in the entrance region. While the binding (interaction) energy of individual simulations can be used to score different ligands, extensive sampling further allows us to predict absolute binding free energies and analyze reaction kinetics using Markov state models and Perron-cluster cluster analysis, respectively. The predicted relative binding free energies for three ligands binding to the progesterone receptor are in very good agreement with experimental results and the Perron-cluster cluster analysis highlighted the importance of a peripheral binding site. Our analysis revealed that the flexibility of the helix 3, 7, and 11 regions represents the most important factor for ligand binding. Furthermore, the hydrophobicity of the ligand influences the transition between the peripheral and the active binding site.
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34
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Motta S, Bonati L. Modeling Binding with Large Conformational Changes: Key Points in Ensemble-Docking Approaches. J Chem Inf Model 2017; 57:1563-1578. [DOI: 10.1021/acs.jcim.7b00125] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Affiliation(s)
- Stefano Motta
- Department of Earth and Environmental
Sciences, University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milan, Italy
| | - Laura Bonati
- Department of Earth and Environmental
Sciences, University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milan, Italy
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35
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Codutti L, Grimaldi M, Carlomagno T. Structure-Based Design of Scaffolds Targeting PDE10A by INPHARMA-NMR. J Chem Inf Model 2017; 57:1488-1498. [PMID: 28569061 DOI: 10.1021/acs.jcim.7b00246] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Phosphodiesterases (PDE) hydrolyze both cyclic AMP and GMP (cAMP/cGMP) and are responsible for the regulation of their levels in a multitude of cellular functions. PDE10A is expressed in the brain and is a validated target for both schizophrenia and Huntington disease. Here, we address the identification of novel chemical scaffolds that may bind PDE10A via structure-based drug design. For this task, we use INPHARMA, an NMR-based method that measures protein-mediated interligand NOEs between pairs of weakly, competitively binding ligands. INPHARMA is applied to a combination of four chemically diverse PDE10A binding fragments, with the aim of merging their pharmacophoric features into a larger, tighter binding molecule. All four ligands bind the PDE10A cAMP binding domain with affinity in the micromolar range. The application of INPHARMA to identify the correct docking poses of these ligands is challenging due to the nature of the binding pocket and the high content of water-mediated intermolecular contacts. Nevertheless, ensemble docking in the presence of conserved water molecules generates docking poses that are in agreement with all sets of INPHARMA data. These poses are used to build a pharmacophore model with which we search the ZINC database.
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Affiliation(s)
- Luca Codutti
- Centre of Biomolecular Drug Research and Institute of Organic Chemistry, Leibniz Universität Hannover , Schneiderberg 38, D-30167 Hannover, Germany.,European Molecular Biology Laboratory , Meyerhofstr. 1, 69117 Heidelberg, Germany
| | - Manuela Grimaldi
- European Molecular Biology Laboratory , Meyerhofstr. 1, 69117 Heidelberg, Germany
| | - Teresa Carlomagno
- Centre of Biomolecular Drug Research and Institute of Organic Chemistry, Leibniz Universität Hannover , Schneiderberg 38, D-30167 Hannover, Germany.,European Molecular Biology Laboratory , Meyerhofstr. 1, 69117 Heidelberg, Germany.,Group of Structural Chemistry, Helmholtz Centre for Infection Research , Inhoffenstrasse 7, D-38124 Braunschweig, Germany
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36
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Developing an in silico pipeline for faster drug candidate discovery: Virtual high throughput screening with the Signature molecular descriptor using support vector machine models. Chem Eng Sci 2017. [DOI: 10.1016/j.ces.2016.02.037] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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37
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Wieder M, Garon A, Perricone U, Boresch S, Seidel T, Almerico AM, Langer T. Common Hits Approach: Combining Pharmacophore Modeling and Molecular Dynamics Simulations. J Chem Inf Model 2017; 57:365-385. [PMID: 28072524 DOI: 10.1021/acs.jcim.6b00674] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
We present a new approach that incorporates flexibility based on extensive MD simulations of protein-ligand complexes into structure-based pharmacophore modeling and virtual screening. The approach uses the multiple coordinate sets saved during the MD simulations and generates for each frame a pharmacophore model. Pharmacophore models with the same pharmacophore features are pooled. In this way the high number of pharmacophore models that results from the MD simulation is reduced to only a few hundred representative pharmacophore models. Virtual screening runs are performed with every representative pharmacophore model; the screening results are combined and rescored to generate a single hit-list. The score for a particular molecule is calculated based on the number of representative pharmacophore models which classified it as active. Hence, the method is called common hits approach (CHA). The steps between the MD simulation and the final hit-list are performed automatically and without user interaction. We test the performance of CHA for virtual screening using screening databases with active and inactive compounds for 40 protein-ligand systems. The results of the CHA are compared to the (i) median screening performance of all representative pharmacophore models of protein-ligand systems, as well as to the virtual screening performance of (ii) a random classifier, (iii) the pharmacophore model derived from the experimental structure in the PDB, and (iv) the representative pharmacophore model appearing most frequently during the MD simulation. For the 34 (out of 40) protein-ligand complexes, for which at least one of the approaches was able to perform better than a random classifier, the highest enrichment was achieved using CHA in 68% of the cases, compared to 12% for the PDB pharmacophore model and 20% for the representative pharmacophore model appearing most frequently. The availabilithy of diverse sets of different pharmacophore models is utilized to analyze some additional questions of interest in 3D pharmacophore-based virtual screening.
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Affiliation(s)
- Marcus Wieder
- Faculty of Life Sciences, Department of Pharmaceutical Chemistry, University of Vienna , Althanstraße 14, 1090 Vienna, Austria.,Faculty of Chemistry, Department of Computational Biological Chemistry, University of Vienna , Währingerstraße 17, 1090 Vienna, Austria
| | - Arthur Garon
- Faculty of Life Sciences, Department of Pharmaceutical Chemistry, University of Vienna , Althanstraße 14, 1090 Vienna, Austria
| | - Ugo Perricone
- Faculty of Life Sciences, Department of Pharmaceutical Chemistry, University of Vienna , Althanstraße 14, 1090 Vienna, Austria.,Department of Biological, Chemical and Pharmaceutical Sciences and Technologies (STEBICEF), University of Palermo , Via Archirafi 32, Palermo, Italy
| | - Stefan Boresch
- Faculty of Chemistry, Department of Computational Biological Chemistry, University of Vienna , Währingerstraße 17, 1090 Vienna, Austria
| | - Thomas Seidel
- Faculty of Life Sciences, Department of Pharmaceutical Chemistry, University of Vienna , Althanstraße 14, 1090 Vienna, Austria
| | - Anna Maria Almerico
- Department of Biological, Chemical and Pharmaceutical Sciences and Technologies (STEBICEF), University of Palermo , Via Archirafi 32, Palermo, Italy
| | - Thierry Langer
- Faculty of Life Sciences, Department of Pharmaceutical Chemistry, University of Vienna , Althanstraße 14, 1090 Vienna, Austria
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38
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Xie L, Shen L, Chen ZN, Yang M. Efficient free energy calculations by combining two complementary tempering sampling methods. J Chem Phys 2017; 146:024103. [PMID: 28088161 DOI: 10.1063/1.4973607] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Affiliation(s)
- Liangxu Xie
- Department of Chemistry, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Lin Shen
- Department of Chemistry, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Zhe-Ning Chen
- Department of Chemistry, The University of Hong Kong, Pokfulam Road, Hong Kong, China
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Yangqiao West Road 155, Fuzhou, Fujian 350002, China
| | - Mingjun Yang
- Department of Chemistry, The University of Hong Kong, Pokfulam Road, Hong Kong, China
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39
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Incerti M, Russo S, Callegari D, Pala D, Giorgio C, Zanotti I, Barocelli E, Vicini P, Vacondio F, Rivara S, Castelli R, Tognolini M, Lodola A. Metadynamics for Perspective Drug Design: Computationally Driven Synthesis of New Protein-Protein Interaction Inhibitors Targeting the EphA2 Receptor. J Med Chem 2017; 60:787-796. [PMID: 28005388 DOI: 10.1021/acs.jmedchem.6b01642] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Metadynamics (META-D) is emerging as a powerful method for the computation of the multidimensional free-energy surface (FES) describing the protein-ligand binding process. Herein, the FES of unbinding of the antagonist N-(3α-hydroxy-5β-cholan-24-oyl)-l-β-homotryptophan (UniPR129) from its EphA2 receptor was reconstructed by META-D simulations. The characterization of the free-energy minima identified on this FES proposes a binding mode fully consistent with previously reported and new structure-activity relationship data. To validate this binding mode, new N-(3α-hydroxy-5β-cholan-24-oyl)-l-β-homotryptophan derivatives were designed, synthesized, and tested for their ability to displace ephrin-A1 from the EphA2 receptor. Among them, two antagonists, namely compounds 21 and 22, displayed high affinity versus the EphA2 receptor and resulted endowed with better physicochemical and pharmacokinetic properties than the parent compound. These findings highlight the importance of free-energy calculations in drug design, confirming that META-D simulations can be used to successfully design novel bioactive compounds.
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Affiliation(s)
- Matteo Incerti
- Dipartimento di Farmacia, Università degli Studi di Parma , Parco Area delle Scienze 27/A, 43124 Parma, Italy
| | - Simonetta Russo
- Dipartimento di Farmacia, Università degli Studi di Parma , Parco Area delle Scienze 27/A, 43124 Parma, Italy
| | - Donatella Callegari
- Dipartimento di Farmacia, Università degli Studi di Parma , Parco Area delle Scienze 27/A, 43124 Parma, Italy
| | - Daniele Pala
- Dipartimento di Farmacia, Università degli Studi di Parma , Parco Area delle Scienze 27/A, 43124 Parma, Italy
| | - Carmine Giorgio
- Dipartimento di Farmacia, Università degli Studi di Parma , Parco Area delle Scienze 27/A, 43124 Parma, Italy
| | - Ilaria Zanotti
- Dipartimento di Farmacia, Università degli Studi di Parma , Parco Area delle Scienze 27/A, 43124 Parma, Italy
| | - Elisabetta Barocelli
- Dipartimento di Farmacia, Università degli Studi di Parma , Parco Area delle Scienze 27/A, 43124 Parma, Italy
| | - Paola Vicini
- Dipartimento di Farmacia, Università degli Studi di Parma , Parco Area delle Scienze 27/A, 43124 Parma, Italy
| | - Federica Vacondio
- Dipartimento di Farmacia, Università degli Studi di Parma , Parco Area delle Scienze 27/A, 43124 Parma, Italy
| | - Silvia Rivara
- Dipartimento di Farmacia, Università degli Studi di Parma , Parco Area delle Scienze 27/A, 43124 Parma, Italy
| | - Riccardo Castelli
- Dipartimento di Farmacia, Università degli Studi di Parma , Parco Area delle Scienze 27/A, 43124 Parma, Italy
| | - Massimiliano Tognolini
- Dipartimento di Farmacia, Università degli Studi di Parma , Parco Area delle Scienze 27/A, 43124 Parma, Italy
| | - Alessio Lodola
- Dipartimento di Farmacia, Università degli Studi di Parma , Parco Area delle Scienze 27/A, 43124 Parma, Italy.,Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University , Newcastle upon Tyne NE1 8ST, United Kingdom
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40
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Abstract
Today, the development of new drugs is a challenging task of science. Researchers already applied molecular docking in the drug design field to simulate ligand- receptor interactions. Docking is a term used for computational schemes that attempt to find the “best” matching between two molecules in a complex formed from constituent molecules. It has a wide range of uses and applications in drug discovery. However, some defects still exist; the accuracy and speed of docking calculation is a challenge to explore and these methods can be enhanced as a solution to docking problem. The molecular docking problem can be defined as follows: Given the atomic coordinates of two molecules, predict their “correct” bound association. The chapter discusses common challenges critical aspects of docking method such as ligand- and receptor- conformation, flexibility and cavity detection, etc. It emphasis to the challenges and inadequacies with the theories behind as well as the examples.
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41
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Abstract
Molecular docking was earlier considered to predict the binding affinity of the receptor and ligand molecules. With the progress in computational power and developing approaches, new horizons are now opening for accurate prediction of molecular binding affinity. In the current book chapter, recent strategies for Computer-Aided Drug Designing (CADD) including virtual screening and molecular docking, encompassing molecular dynamics simulations, and binding free energy calculation methods are discussed. Brief overview of different binding free energy methods MMPBSA, MMGBSA, LIE and TI have also been given along with the recent Relaxed Complex Scheme protocol.
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Affiliation(s)
| | - Akhil Kumar
- CSIR-Central Institute of Medicinal and Aromatic Plants, India
| | | | - Ashok Sharma
- CSIR-Central Institute of Medicinal and Aromatic Plants, India
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42
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Shao Q, Xu Z, Wang J, Shi J, Zhu W. Energetics and structural characterization of the “DFG-flip” conformational transition of B-RAF kinase: a SITS molecular dynamics study. Phys Chem Chem Phys 2017; 19:1257-1267. [DOI: 10.1039/c6cp06624k] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A combination of a homology modeling technique and an enhanced sampling molecular dynamics simulation implemented using the SITS method is employed to compute a detailed map of the free-energy landscape and explore the conformational transition pathway of B-RAF kinase.
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Affiliation(s)
- Qiang Shao
- Drug Discovery and Design Center
- Key Laboratory of Receptor Research
- Shanghai Institute of Materia Medica
- Chinese Academy of Sciences
- Shanghai
| | - Zhijian Xu
- Drug Discovery and Design Center
- Key Laboratory of Receptor Research
- Shanghai Institute of Materia Medica
- Chinese Academy of Sciences
- Shanghai
| | - Jinan Wang
- Drug Discovery and Design Center
- Key Laboratory of Receptor Research
- Shanghai Institute of Materia Medica
- Chinese Academy of Sciences
- Shanghai
| | - Jiye Shi
- UCB Biopharma SPRL
- Chemin du Foriest
- Braine-l’Alleud
- Belgium
| | - Weiliang Zhu
- Drug Discovery and Design Center
- Key Laboratory of Receptor Research
- Shanghai Institute of Materia Medica
- Chinese Academy of Sciences
- Shanghai
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43
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Marques SM, Daniel L, Buryska T, Prokop Z, Brezovsky J, Damborsky J. Enzyme Tunnels and Gates As Relevant Targets in Drug Design. Med Res Rev 2016; 37:1095-1139. [PMID: 27957758 DOI: 10.1002/med.21430] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2016] [Revised: 10/11/2016] [Accepted: 11/07/2016] [Indexed: 12/28/2022]
Abstract
Many enzymes contain tunnels and gates that are essential to their function. Gates reversibly switch between open and closed conformations and thereby control the traffic of small molecules-substrates, products, ions, and solvent molecules-into and out of the enzyme's structure via molecular tunnels. Many transient tunnels and gates undoubtedly remain to be identified, and their functional roles and utility as potential drug targets have received comparatively little attention. Here, we describe a set of general concepts relating to the structural properties, function, and classification of these interesting structural features. In addition, we highlight the potential of enzyme tunnels and gates as targets for the binding of small molecules. The different types of binding that are possible and the potential pharmacological benefits of such targeting are discussed. Twelve examples of ligands bound to the tunnels and/or gates of clinically relevant enzymes are used to illustrate the different binding modes and to explain some new strategies for drug design. Such strategies could potentially help to overcome some of the problems facing medicinal chemists and lead to the discovery of more effective drugs.
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Affiliation(s)
- Sergio M Marques
- Loschmidt Laboratories, Faculty of Science, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment, RECETOX, Masaryk University, Kamenice 5, 625 00, Brno, Czech Republic
| | - Lukas Daniel
- Loschmidt Laboratories, Faculty of Science, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment, RECETOX, Masaryk University, Kamenice 5, 625 00, Brno, Czech Republic.,International Centre for Clinical Research, St. Anne's University Hospital, Pekarska 53, 656 91, Brno, Czech Republic
| | - Tomas Buryska
- Loschmidt Laboratories, Faculty of Science, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment, RECETOX, Masaryk University, Kamenice 5, 625 00, Brno, Czech Republic.,International Centre for Clinical Research, St. Anne's University Hospital, Pekarska 53, 656 91, Brno, Czech Republic
| | - Zbynek Prokop
- Loschmidt Laboratories, Faculty of Science, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment, RECETOX, Masaryk University, Kamenice 5, 625 00, Brno, Czech Republic.,International Centre for Clinical Research, St. Anne's University Hospital, Pekarska 53, 656 91, Brno, Czech Republic
| | - Jan Brezovsky
- Loschmidt Laboratories, Faculty of Science, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment, RECETOX, Masaryk University, Kamenice 5, 625 00, Brno, Czech Republic.,International Centre for Clinical Research, St. Anne's University Hospital, Pekarska 53, 656 91, Brno, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Faculty of Science, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment, RECETOX, Masaryk University, Kamenice 5, 625 00, Brno, Czech Republic.,International Centre for Clinical Research, St. Anne's University Hospital, Pekarska 53, 656 91, Brno, Czech Republic
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44
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Leelananda SP, Lindert S. Computational methods in drug discovery. Beilstein J Org Chem 2016; 12:2694-2718. [PMID: 28144341 PMCID: PMC5238551 DOI: 10.3762/bjoc.12.267] [Citation(s) in RCA: 285] [Impact Index Per Article: 35.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Accepted: 11/22/2016] [Indexed: 12/11/2022] Open
Abstract
The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.
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Affiliation(s)
- Sumudu P Leelananda
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH 43210, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH 43210, USA
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45
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Riley TP, Ayres CM, Hellman LM, Singh NK, Cosiano M, Cimons JM, Anderson MJ, Piepenbrink KH, Pierce BG, Weng Z, Baker BM. A generalized framework for computational design and mutational scanning of T-cell receptor binding interfaces. Protein Eng Des Sel 2016; 29:595-606. [PMID: 27624308 PMCID: PMC5181382 DOI: 10.1093/protein/gzw050] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Revised: 08/19/2016] [Accepted: 08/23/2016] [Indexed: 11/13/2022] Open
Abstract
T-cell receptors (TCRs) have emerged as a new class of therapeutics, most prominently for cancer where they are the key components of new cellular therapies as well as soluble biologics. Many studies have generated high affinity TCRs in order to enhance sensitivity. Recent outcomes, however, have suggested that fine manipulation of TCR binding, with an emphasis on specificity may be more valuable than large affinity increments. Structure-guided design is ideally suited for this role, and here we studied the generality of structure-guided design as applied to TCRs. We found that a previous approach, which successfully optimized the binding of a therapeutic TCR, had poor accuracy when applied to a broader set of TCR interfaces. We thus sought to develop a more general purpose TCR design framework. After assembling a large dataset of experimental data spanning multiple interfaces, we trained a new scoring function that accounted for unique features of each interface. Together with other improvements, such as explicit inclusion of molecular flexibility, this permitted the design new affinity-enhancing mutations in multiple TCRs, including those not used in training. Our approach also captured the impacts of mutations and substitutions in the peptide/MHC ligand, and recapitulated recent findings regarding TCR specificity, indicating utility in more general mutational scanning of TCR-pMHC interfaces.
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Affiliation(s)
- Timothy P Riley
- Department of Chemistry & Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, 251 Nieuwland Science Hall, Notre Dame, IN 46556, USA
| | - Cory M Ayres
- Department of Chemistry & Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, 251 Nieuwland Science Hall, Notre Dame, IN 46556, USA
| | - Lance M Hellman
- Department of Chemistry & Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, 251 Nieuwland Science Hall, Notre Dame, IN 46556, USA
| | - Nishant K Singh
- Department of Chemistry & Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, 251 Nieuwland Science Hall, Notre Dame, IN 46556, USA
| | - Michael Cosiano
- Department of Chemistry & Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, 251 Nieuwland Science Hall, Notre Dame, IN 46556, USA
| | - Jennifer M Cimons
- Department of Chemistry & Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, 251 Nieuwland Science Hall, Notre Dame, IN 46556, USA
| | - Michael J Anderson
- Department of Chemistry & Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, 251 Nieuwland Science Hall, Notre Dame, IN 46556, USA
| | - Kurt H Piepenbrink
- Department of Chemistry & Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, 251 Nieuwland Science Hall, Notre Dame, IN 46556, USA
| | - Brian G Pierce
- Institute for Bioscience and Biotechnology Research, University of Maryland, 9600 Gudelsky Drive, Rockville, MD 20850, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Brian M Baker
- Department of Chemistry & Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, 251 Nieuwland Science Hall, Notre Dame, IN 46556, USA
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46
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Incorporation of side chain flexibility into protein binding pockets using MTflex. Bioorg Med Chem 2016; 24:4978-4987. [DOI: 10.1016/j.bmc.2016.08.030] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Revised: 08/16/2016] [Accepted: 08/18/2016] [Indexed: 01/15/2023]
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47
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Blinded predictions of binding modes and energies of HSP90-α ligands for the 2015 D3R grand challenge. Bioorg Med Chem 2016; 24:4890-4899. [DOI: 10.1016/j.bmc.2016.07.044] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Revised: 07/19/2016] [Accepted: 07/20/2016] [Indexed: 01/14/2023]
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48
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Paul DS, Gautham N. MOLS 2.0: software package for peptide modeling and protein-ligand docking. J Mol Model 2016; 22:239. [PMID: 27638416 DOI: 10.1007/s00894-016-3106-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Accepted: 09/01/2016] [Indexed: 11/25/2022]
Abstract
We previously developed an algorithm to perform conformational searches of proteins and peptides, and to perform the docking of ligands to protein receptors. In order to identify optimal conformations and docked poses, this algorithm uses mutually orthogonal Latin squares (MOLS) to rationally sample the vast conformational (or docking) space, and then analyzes this relatively small sample using a variant of mean field theory. The conformational search part of the algorithm was denoted MOLS 1.0. The docking portion of the algorithm, which allows only "flexible ligand/rigid receptor" docking, was denoted MOLSDOCK. Both are FORTRAN-based command-line-only molecular docking computer programs, though a GUI was developed later for MOLS 1.0. Both the conformational search and the rigid receptor docking parts of the algorithm have been extensively validated. We have now further enhanced the capabilities of the program by incorporating "induced fit" side-chain receptor flexibility for docking peptide ligands. Benchmarking and extensive testing is now being carried out for the flexible receptor portion of the docking. Additionally, to make both the peptide conformational search and docking algorithms (the latter including both flexible ligand/rigid receptor and flexible ligand/flexible receptor techniques) more accessible to the research community, we have developed MOLS 2.0, which incorporates a new Java-based graphical user interface (GUI). Here, we give a detailed description of MOLS 2.0. The source code and binary for MOLS 2.0 are distributed free (under a GNU Lesser General Public License) to the scientific community. They are freely available for download at https://sourceforge.net/projects/mols2-0/files/ .
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Affiliation(s)
- D Sam Paul
- Centre of Advanced Study in Crystallography and Biophysics, University of Madras, Guindy Campus, Chennai, 600025, India
| | - N Gautham
- Centre of Advanced Study in Crystallography and Biophysics, University of Madras, Guindy Campus, Chennai, 600025, India.
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49
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Exploiting computationally derived out-of-the-box protein conformations for drug design. Future Med Chem 2016; 8:1887-1897. [PMID: 27629935 DOI: 10.4155/fmc-2016-0098] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Structural plasticity is an intrinsic property of proteins that allows each gene product to accomplish its tasks in a strictly regulated manner at a precise time and cellular location. Moreover, protein motions allow protein-ligand and protein-protein recognition. The knowledge of the conformational ensemble that a drug target populates may be crucial for the design of small molecules that can differently modulate its function. X-ray crystallography and NMR have endlessly provided snapshots of protein states. However, experimental structure determination is not always straightforward. Therefore, attempts have been made to depict protein conformational landscapes through molecular dynamics and enhanced sampling methods. Here, we review how accounting for protein dynamics through in silico generated out-of-the-box protein conformations has started to impact on drug discovery.
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50
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Ren W, Ren Y, Dong M, Gao Y. Design, Synthesis, and Thrombin Inhibitory Activity Evaluation of Some Novel Benzimidazole Derivatives. Helv Chim Acta 2016. [DOI: 10.1002/hlca.201500527] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Weixin Ren
- College of Chemical and Environmental Engineering; Shanghai Institute of Technology; 100 Haiquan Road Shanghai 201418 P. R. China
| | - Yujie Ren
- College of Chemical and Environmental Engineering; Shanghai Institute of Technology; 100 Haiquan Road Shanghai 201418 P. R. China
| | - Minghui Dong
- College of Chemical and Environmental Engineering; Shanghai Institute of Technology; 100 Haiquan Road Shanghai 201418 P. R. China
| | - Yonghong Gao
- College of Chemical and Environmental Engineering; Shanghai Institute of Technology; 100 Haiquan Road Shanghai 201418 P. R. China
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