1
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Zhao M, Yu W, MacKerell AD. Enhancing SILCS-MC via GPU Acceleration and Ligand Conformational Optimization with Genetic and Parallel Tempering Algorithms. J Phys Chem B 2024. [PMID: 39031121 DOI: 10.1021/acs.jpcb.4c03045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/22/2024]
Abstract
In the domain of computer-aided drug design, achieving precise and accurate estimates of ligand-protein binding is paramount in the context of screening extensive drug libraries and performing ligand optimization. A fundamental aspect of the SILCS (site identification by ligand competitive saturation) methodology lies in the generation of comprehensive 3D free-energy functional group affinity maps (FragMaps), encompassing the entirety of the target molecule structure. These FragMaps offer an intricate landscape of functional group affinities across the protein, bilayer, or RNA, acting as the basis for subsequent SILCS-Monte Carlo (MC) simulations wherein ligands are docked to the target molecule. To augment the efficiency and breadth of ligand sampling capabilities, we implemented an improved SILCS-MC methodology. By harnessing the parallel computing capability of GPUs, our approach facilitates concurrent calculations over multiple ligands and binding sites, markedly enhancing the computational efficiency. Moreover, the integration of a genetic algorithm (GA) with MC allows us to employ an evolutionary approach to perform ligand sampling, assuring enhanced convergence characteristics. In addition, the potential utility of parallel tempering (PT) to improve sampling was investigated. Implementation of SILCS-MC on GPU architecture is shown to accelerate the speed of SILCS-MC calculations by over 2-orders of magnitude. Use of GA and PT yield improvements over Markov-chain MC, increasing the precision of the resultant docked orientations and binding free energies, though the extent of improvements is relatively small. Accordingly, significant improvements in speed are obtained through the GPU implementation with minor improvements in the precision of the docking obtained via the tested GA and PT algorithms.
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Affiliation(s)
- Mingtian Zhao
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland, School of Pharmacy, 20 Penn Street, Baltimore, Maryland 21201, United States
| | - Wenbo Yu
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland, School of Pharmacy, 20 Penn Street, Baltimore, Maryland 21201, United States
| | - Alexander D MacKerell
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland, School of Pharmacy, 20 Penn Street, Baltimore, Maryland 21201, United States
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2
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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] [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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3
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Schmitz B, Frieg B, Homeyer N, Jessen G, Gohlke H. Extracting binding energies and binding modes from biomolecular simulations of fragment binding to endothiapepsin. Arch Pharm (Weinheim) 2024; 357:e2300612. [PMID: 38319801 DOI: 10.1002/ardp.202300612] [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: 10/20/2023] [Revised: 12/18/2023] [Accepted: 01/10/2024] [Indexed: 02/08/2024]
Abstract
Fragment-based drug discovery (FBDD) aims to discover a set of small binding fragments that may be subsequently linked together. Therefore, in-depth knowledge of the individual fragments' structural and energetic binding properties is essential. In addition to experimental techniques, the direct simulation of fragment binding by molecular dynamics (MD) simulations became popular to characterize fragment binding. However, former studies showed that long simulation times and high computational demands per fragment are needed, which limits applicability in FBDD. Here, we performed short, unbiased MD simulations of direct fragment binding to endothiapepsin, a well-characterized model system of pepsin-like aspartic proteases. To evaluate the strengths and limitations of short MD simulations for the structural and energetic characterization of fragment binding, we predicted the fragments' absolute free energies and binding poses based on the direct simulations of fragment binding and compared the predictions to experimental data. The predicted absolute free energies are in fair agreement with the experiment. Combining the MD data with binding mode predictions from molecular docking approaches helped to correctly identify the most promising fragments for further chemical optimization. Importantly, all computations and predictions were done within 5 days, suggesting that MD simulations may become a viable tool in FBDD projects.
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Affiliation(s)
- Birte Schmitz
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Benedikt Frieg
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- John von Neumann Institute for Computing (NIC), Jülich Supercomputing Centre (JSC), and Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Forschungszentrum Jülich, Jülich, Germany
| | - Nadine Homeyer
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Gisela Jessen
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Holger Gohlke
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- John von Neumann Institute for Computing (NIC), Jülich Supercomputing Centre (JSC), and Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Forschungszentrum Jülich, Jülich, Germany
- Institute of Bio- and Geosciences (IBG-4: Bioinformatics), Forschungszentrum Jülich, Jülich, Germany
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4
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Fragment screening using biolayer interferometry reveals ligands targeting the SHP-motif binding site of the AAA+ ATPase p97. Commun Chem 2022; 5:169. [PMID: 36697690 PMCID: PMC9814400 DOI: 10.1038/s42004-022-00782-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 11/17/2022] [Indexed: 12/12/2022] Open
Abstract
Biosensor techniques have become increasingly important for fragment-based drug discovery during the last years. The AAA+ ATPase p97 is an essential protein with key roles in protein homeostasis and a possible target for cancer chemotherapy. Currently available p97 inhibitors address its ATPase activity and globally impair p97-mediated processes. In contrast, inhibition of cofactor binding to the N-domain by a protein-protein-interaction inhibitor would enable the selective targeting of specific p97 functions. Here, we describe a biolayer interferometry-based fragment screen targeting the N-domain of p97 and demonstrate that a region known as SHP-motif binding site can be targeted with small molecules. Guided by molecular dynamics simulations, the binding sites of selected screening hits were postulated and experimentally validated using protein- and ligand-based NMR techniques, as well as X-ray crystallography, ultimately resulting in the first structure of a small molecule in complex with the N-domain of p97. The identified fragments provide insights into how this region could be targeted and present first chemical starting points for the development of a protein-protein interaction inhibitor preventing the binding of selected cofactors to p97.
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5
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Pavan M, Bassani D, Bolcato G, Bissaro M, Sturles M, Moro S. Computational strategies to identify new drug candidates against neuroinflammation. Curr Med Chem 2022; 29:4756-4775. [PMID: 35135446 DOI: 10.2174/0929867329666220208095122] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 12/09/2021] [Accepted: 12/13/2021] [Indexed: 11/22/2022]
Abstract
The even more increasing application of computational approaches in these last decades has deeply modified the process of discovery and commercialization of new therapeutic entities. This is especially true in the field of neuroinflammation, in which both the peculiar anatomical localization and the presence of the blood-brain barrier makeit mandatory to finely tune the candidates' physicochemical properties from the early stages of the discovery pipeline. The aim of this review is therefore to provide a general overview to the readers about the topic of neuroinflammation, together with the most common computational strategies that can be exploited to discover and design small molecules controlling neuroinflammation, especially those based on the knowledge of the three-dimensional structure of the biological targets of therapeutic interest. The techniques used to describe the molecular recognition mechanisms, such as molecular docking and molecular dynamics, will therefore be eviscerated, highlighting their advantages and their limitations. Finally, we report several case studies in which computational methods have been applied in drug discovery on neuroinflammation, focusing on the last decade's research.
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Affiliation(s)
- Matteo Pavan
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Davide Bassani
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Giovanni Bolcato
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Maicol Bissaro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Mattia Sturles
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Stefano Moro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
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6
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Ferrari F, Bissaro M, Fabbian S, De Almeida Roger J, Mammi S, Moro S, Bellanda M, Sturlese M. HT-SuMD: making molecular dynamics simulations suitable for fragment-based screening. A comparative study with NMR. J Enzyme Inhib Med Chem 2021; 36:1-14. [PMID: 33115279 PMCID: PMC7598995 DOI: 10.1080/14756366.2020.1838499] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 10/13/2020] [Accepted: 10/13/2020] [Indexed: 01/21/2023] Open
Abstract
Fragment-based lead discovery (FBLD) is one of the most efficient methods to develop new drugs. We present here a new computational protocol called High-Throughput Supervised Molecular Dynamics (HT-SuMD), which makes it possible to automatically screen up to thousands of fragments, representing therefore a new valuable resource to prioritise fragments in FBLD campaigns. The protocol was applied to Bcl-XL, an oncological protein target involved in the regulation of apoptosis through protein-protein interactions. Initially, HT-SuMD performances were validated against a robust NMR-based screening, using the same set of 100 fragments. These independent results showed a remarkable agreement between the two methods. Then, a virtual screening on a larger library of additional 300 fragments was carried out and the best hits were validated by NMR. Remarkably, all the in silico selected fragments were confirmed as Bcl-XL binders. This represents, to date, the largest computational fragments screening entirely based on MD.
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Affiliation(s)
- Francesca Ferrari
- Department of Chemical Sciences, University of Padova, Padova, Italy
| | - Maicol Bissaro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy
| | - Simone Fabbian
- Department of Chemical Sciences, University of Padova, Padova, Italy
| | - Jessica De Almeida Roger
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy
| | - Stefano Mammi
- Department of Chemical Sciences, University of Padova, Padova, Italy
| | - Stefano Moro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy
| | - Massimo Bellanda
- Department of Chemical Sciences, University of Padova, Padova, Italy
| | - Mattia Sturlese
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy
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7
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Varela‐Rial A, Majewski M, De Fabritiis G. Structure based virtual screening: Fast and slow. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1544] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Alejandro Varela‐Rial
- Acellera Labs Barcelona Spain
- Computational Science Laboratory Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB) Barcelona Spain
| | - Maciej Majewski
- Computational Science Laboratory Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB) Barcelona Spain
| | - Gianni De Fabritiis
- Computational Science Laboratory Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB) Barcelona Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA) Barcelona Spain
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8
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Privat C, Granadino-Roldán JM, Bonet J, Santos Tomas M, Perez JJ, Rubio-Martinez J. Fragment dissolved molecular dynamics: a systematic and efficient method to locate binding sites. Phys Chem Chem Phys 2021; 23:3123-3134. [PMID: 33491698 DOI: 10.1039/d0cp05471b] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Diverse computational methods to support fragment-based drug discovery (FBDD) are available in the literature. Despite their demonstrated efficacy in supporting FBDD campaigns, they exhibit some drawbacks such as protein denaturation or ligand aggregation that have not yet been clearly overcome in the framework of biomolecular simulations. In the present work, we discuss a systematic semi-automatic novel computational procedure, designed to surpass these difficulties. The method, named fragment dissolved Molecular Dynamics (fdMD), utilizes simulation boxes of solvated small fragments, adding a repulsive Lennard-Jones potential term to avoid aggregation, which can be easily used to solvate the targets of interest. This method has the advantage of solvating the target with a low number of ligands, thus preventing the denaturation of the target, while simultaneously generating a database of ligand-solvated boxes that can be used in further studies. A number of scripts are made available to analyze the results and obtain the descriptors proposed as a means to trustfully discard spurious binding sites. To test our method, four test cases of different complexity have been solvated with ligand boxes and four molecular dynamics runs of 200 ns length have been run for each system, which have been extended up to 1 μs when needed. The reported results point out that the selected number of replicas are enough to identify the correct binding sites irrespective of the initial structure, even in the case of proteins having several close binding sites for the same ligand. We also propose a set of descriptors to analyze the results, among which the average MMGBSA and the average KDEEP energies have emerged as the most robust ones.
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Affiliation(s)
- Cristian Privat
- Departament de Ciència dels Materials i Química Física, Universitat de Barcelona (UB) and the Institut de Quimica Teorica i Computacional (IQTCUB), Martí i Franqués 1, 08028 Barcelona, Spain.
| | - José M Granadino-Roldán
- Departamento de Química Física y Analítica, Facultad de Ciencias Experimentales, Universidad de Jaén, Campus "Las Lagunillas" s/n, 23071, Jaén, Spain
| | - Jordi Bonet
- Departament de Ciència dels Materials i Química Física, Universitat de Barcelona (UB) and the Institut de Quimica Teorica i Computacional (IQTCUB), Martí i Franqués 1, 08028 Barcelona, Spain.
| | - Maria Santos Tomas
- Department of Architecture Technology, Universitat Politecnica de Catalunya, Av. Diagonal 649, 08028 Barcelona, Spain
| | - Juan J Perez
- Deparment of Chemical Engineering, Universitat Politecnica de Catalunya, Av. Diagonal 647, 08028 Barcelona, Spain
| | - Jaime Rubio-Martinez
- Departament de Ciència dels Materials i Química Física, Universitat de Barcelona (UB) and the Institut de Quimica Teorica i Computacional (IQTCUB), Martí i Franqués 1, 08028 Barcelona, Spain.
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9
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Bissaro M, Sturlese M, Moro S. The rise of molecular simulations in fragment-based drug design (FBDD): an overview. Drug Discov Today 2020; 25:1693-1701. [PMID: 32592867 PMCID: PMC7314695 DOI: 10.1016/j.drudis.2020.06.023] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Revised: 05/24/2020] [Accepted: 06/19/2020] [Indexed: 12/31/2022]
Abstract
Fragment-based drug discovery (FBDD) is an innovative approach, progressively more applied in the academic and industrial context, to enhance hit identification for previously considered undruggable biological targets. In particular, FBDD discovers low-molecular-weight (LMW) ligands (<300Da) able to bind to therapeutically relevant macromolecules in an affinity range from the micromolar (μM) to millimolar (mM). X-ray crystallography (XRC) and nuclear magnetic resonance (NMR) spectroscopy are commonly the methods of choice to obtain 3D information about the bound ligand-protein complex, but this can occasionally be problematic, mainly for early, low-affinity fragments. The recent development of computational fragment-based approaches provides a further strategy for improving the identification of fragment hits. In this review, we summarize the state of the art of molecular dynamics simulations approaches used in FBDD, and discuss limitations and future perspectives for these approaches.
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Affiliation(s)
- Maicol Bissaro
- Molecular Modeling Section, Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy
| | - Mattia Sturlese
- Molecular Modeling Section, Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy
| | - Stefano Moro
- Molecular Modeling Section, Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy.
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10
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Herrera-Nieto P, Pérez A, De Fabritiis G. Small Molecule Modulation of Intrinsically Disordered Proteins Using Molecular Dynamics Simulations. J Chem Inf Model 2020; 60:5003-5010. [DOI: 10.1021/acs.jcim.0c00381] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Pablo Herrera-Nieto
- Computational Science Laboratory, Universitat Pompeu Fabra, 08003 Barcelona, Spain
| | - Adrià Pérez
- Computational Science Laboratory, Universitat Pompeu Fabra, 08003 Barcelona, Spain
| | - Gianni De Fabritiis
- Computational Science Laboratory, Universitat Pompeu Fabra, 08003 Barcelona, Spain
- Acellera Ltd., 08005 Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats, 08010 Barcelona, Spain
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11
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Peng C, Wang J, Xu Z, Cai T, Zhu W. Accurate prediction of relative binding affinities of a series of HIV-1 protease inhibitors using semi-empirical quantum mechanical charge. J Comput Chem 2020; 41:1773-1780. [PMID: 32352193 DOI: 10.1002/jcc.26218] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Revised: 04/03/2020] [Accepted: 04/18/2020] [Indexed: 11/05/2022]
Abstract
A major challenge in computer-aided drug design is the accurate estimation of ligand binding affinity. Here, a new approach that combines the adaptive steered molecular dynamics (ASMD) and partial atomic charges calculated by semi-empirical quantum mechanics (SQMPC), namely ASMD-SQMPC, is suggested to predict the ligand binding affinities, with 24 HIV-1 protease inhibitors as testing examples. In the ASMD-SQMPC, the relative binding free energy (ΔG) is reflected by the average maximum potential of mean force (<PMF>max ) between bound and unbound states. The correlation coefficient (R2 ) between the <PMF>max and experimentally determined ΔG is 0.86, showing a significant improvement compared with the conventional ASMD (R2 = 0.52). Therefore, this study provides an efficient approach to predict the relative ΔG and reveals the significance of precise partial atomic charges in the theoretical simulations.
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Affiliation(s)
- Cheng Peng
- CAS Key Laboratory of Receptor Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, China.,University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, China
| | - Jinan Wang
- CAS Key Laboratory of Receptor Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, China
| | - Zhijian Xu
- CAS Key Laboratory of Receptor Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, China.,University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, China
| | - Tingting Cai
- CAS Key Laboratory of Receptor Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, China
| | - Weiliang Zhu
- CAS Key Laboratory of Receptor Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, China.,University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, China.,Open Studio for Druggability Research of Marine Natural Products, Pilot National Laboratory for Marine Science and Technology (Qingdao), 1 Wenhai Road, Aoshanwei, Jimo, Qingdao, China
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12
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Pérez A, Herrera-Nieto P, Doerr S, De Fabritiis G. AdaptiveBandit: A Multi-armed Bandit Framework for Adaptive Sampling in Molecular Simulations. J Chem Theory Comput 2020; 16:4685-4693. [DOI: 10.1021/acs.jctc.0c00205] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Adrià Pérez
- Computational Science Laboratory, Universitat Pompeu Fabra, 08003 Barcelona, Spain
| | - Pablo Herrera-Nieto
- Computational Science Laboratory, Universitat Pompeu Fabra, 08003 Barcelona, Spain
| | - Stefan Doerr
- Computational Science Laboratory, Universitat Pompeu Fabra, 08003 Barcelona, Spain
- Acellera Labs, 08005 Barcelona, Spain
| | - Gianni De Fabritiis
- Computational Science Laboratory, Universitat Pompeu Fabra, 08003 Barcelona, Spain
- Acellera Labs, 08005 Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats, 08010 Barcelona, Spain
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13
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Chitrala KN, Yang X, Busbee B, Singh NP, Bonati L, Xing Y, Nagarkatti P, Nagarkatti M. Computational prediction and in vitro validation of VEGFR1 as a novel protein target for 2,3,7,8-tetrachlorodibenzo-p-dioxin. Sci Rep 2019; 9:6810. [PMID: 31048752 PMCID: PMC6497656 DOI: 10.1038/s41598-019-43232-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Accepted: 04/18/2019] [Indexed: 11/09/2022] Open
Abstract
The toxic manifestations of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), an environmental contaminant, primarily depend on its ability to activate aryl hydrocarbon receptor (AhR), which is a ligand-dependent transcription factor belonging to the superfamily of basic-helix-loop-helix DNA-binding proteins. In the present study, we aimed to identify novel protein receptor targets for TCDD using computational and in vitro validation experiments. Interestingly, results from computational methods predicted that Vascular Endothelial Growth Factor Receptor 1 (VEGFR1) could be one of the potential targets for TCDD in both mouse and humans. Results from molecular docking studies showed that human VEGFR1 (hVEGFR1) has less affinity towards TCDD compared to the mouse VEGFR1 (mVEGFR1). In vitro validation results showed that TCDD can bind and phosphorylate hVEGFR1. Further, results from molecular dynamic simulation studies showed that hVEGFR1 interaction with TCDD is stable throughout the simulation time. Overall, the present study has identified VEGFR1 as a novel target for TCDD, which provides the basis for further elucidating the role of TCDD in angiogenesis.
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Affiliation(s)
- Kumaraswamy Naidu Chitrala
- Department of Pathology, Microbiology and Immunology, University of South Carolina School of Medicine, Columbia, SC, 29208, USA
| | - Xiaoming Yang
- Department of Pathology, Microbiology and Immunology, University of South Carolina School of Medicine, Columbia, SC, 29208, USA
| | - Brandon Busbee
- Department of Pathology, Microbiology and Immunology, University of South Carolina School of Medicine, Columbia, SC, 29208, USA
| | - Narendra P Singh
- Department of Pathology, Microbiology and Immunology, University of South Carolina School of Medicine, Columbia, SC, 29208, USA
| | - Laura Bonati
- Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Yongna Xing
- McArdle Laboratory for Cancer Research, University of Wisconsin-Madison, Madison, WI, USA
| | - Prakash Nagarkatti
- Department of Pathology, Microbiology and Immunology, University of South Carolina School of Medicine, Columbia, SC, 29208, USA
| | - Mitzi Nagarkatti
- Department of Pathology, Microbiology and Immunology, University of South Carolina School of Medicine, Columbia, SC, 29208, USA.
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14
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Solvents to Fragments to Drugs: MD Applications in Drug Design. Molecules 2018; 23:molecules23123269. [PMID: 30544890 PMCID: PMC6321499 DOI: 10.3390/molecules23123269] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 12/02/2018] [Accepted: 12/03/2018] [Indexed: 01/24/2023] Open
Abstract
Simulations of molecular dynamics (MD) are playing an increasingly important role in structure-based drug discovery (SBDD). Here we review the use of MD for proteins in aqueous solvation, organic/aqueous mixed solvents (MDmix) and with small ligands, to the classic SBDD problems: Binding mode and binding free energy predictions. The simulation of proteins in their condensed state reveals solvent structures and preferential interaction sites (hot spots) on the protein surface. The information provided by water and its cosolvents can be used very effectively to understand protein ligand recognition and to improve the predictive capability of well-established methods such as molecular docking. The application of MD simulations to the study of the association of proteins with drug-like compounds is currently only possible for specific cases, as it remains computationally very expensive and labor intensive. MDmix simulations on the other hand, can be used systematically to address some of the common tasks in SBDD. With the advent of new tools and faster computers we expect to see an increase in the application of mixed solvent MD simulations to a plethora of protein targets to identify new drug candidates.
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15
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Schuetz DA, Bernetti M, Bertazzo M, Musil D, Eggenweiler HM, Recanatini M, Masetti M, Ecker GF, Cavalli A. Predicting Residence Time and Drug Unbinding Pathway through Scaled Molecular Dynamics. J Chem Inf Model 2018; 59:535-549. [DOI: 10.1021/acs.jcim.8b00614] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Doris A. Schuetz
- Department of Pharmaceutical Chemistry, University of Vienna, UZA 2, Althanstrasse 14, 1090 Vienna, Austria
| | - Mattia Bernetti
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum—Università di Bologna, via Belmeloro 6, I-40126 Bologna, Italy
| | - Martina Bertazzo
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum—Università di Bologna, via Belmeloro 6, I-40126 Bologna, Italy
- Computational Sciences, Istituto Italiano di Tecnologia, via Morego 30, 16163 Genova, Italy
| | - Djordje Musil
- Discovery Technologies, Merck KGaA, Frankfurter Straße 250, 64293 Darmstadt, Germany
| | | | - Maurizio Recanatini
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum—Università di Bologna, via Belmeloro 6, I-40126 Bologna, Italy
| | - Matteo Masetti
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum—Università di Bologna, via Belmeloro 6, I-40126 Bologna, Italy
| | - Gerhard F. Ecker
- Department of Pharmaceutical Chemistry, University of Vienna, UZA 2, Althanstrasse 14, 1090 Vienna, Austria
| | - Andrea Cavalli
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum—Università di Bologna, via Belmeloro 6, I-40126 Bologna, Italy
- Computational Sciences, Istituto Italiano di Tecnologia, via Morego 30, 16163 Genova, Italy
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16
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Weiss D, Karpiak J, Huang XP, Sassano MF, Lyu J, Roth BL, Shoichet BK. Selectivity Challenges in Docking Screens for GPCR Targets and Antitargets. J Med Chem 2018; 61:6830-6845. [PMID: 29990431 PMCID: PMC6105036 DOI: 10.1021/acs.jmedchem.8b00718] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Indexed: 12/14/2022]
Abstract
To investigate large library docking's ability to find molecules with joint activity against on-targets and selectivity versus antitargets, the dopamine D2 and serotonin 5-HT2A receptors were targeted, seeking selectivity against the histamine H1 receptor. In a second campaign, κ-opioid receptor ligands were sought with selectivity versus the μ-opioid receptor. While hit rates ranged from 40% to 63% against the on-targets, they were just as good against the antitargets, even though the molecules were selected for their putative lack of binding to the off-targets. Affinities, too, were often as good or better for the off-targets. Even though it was occasionally possible to find selective molecules, such as a mid-nanomolar D2/5-HT2A ligand with 21-fold selectivity versus the H1 receptor, this was the exception. Whereas false-negatives are tolerable in docking screens against on-targets, they are intolerable against antitargets; addressing this problem may demand new strategies in the field.
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Affiliation(s)
- Dahlia
R. Weiss
- Department
of Pharmaceutical Chemistry, University
of California—San Francisco, San Francisco, California 94158-2550, United States
| | - Joel Karpiak
- Department
of Pharmaceutical Chemistry, University
of California—San Francisco, San Francisco, California 94158-2550, United States
| | - Xi-Ping Huang
- Department
of Pharmacology and National Institute of Mental Health Psychoactive
Drug Screening Program, School of Medicine, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Maria F. Sassano
- Department
of Pharmacology and National Institute of Mental Health Psychoactive
Drug Screening Program, School of Medicine, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Jiankun Lyu
- Department
of Pharmaceutical Chemistry, University
of California—San Francisco, San Francisco, California 94158-2550, United States
| | - Bryan L. Roth
- Department
of Pharmacology and National Institute of Mental Health Psychoactive
Drug Screening Program, School of Medicine, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Brian K. Shoichet
- Department
of Pharmaceutical Chemistry, University
of California—San Francisco, San Francisco, California 94158-2550, United States
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17
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Rachman MM, Barril X, Hubbard RE. Predicting how drug molecules bind to their protein targets. Curr Opin Pharmacol 2018; 42:34-39. [PMID: 30041063 DOI: 10.1016/j.coph.2018.07.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Accepted: 07/01/2018] [Indexed: 01/27/2023]
Abstract
There have been substantial advances in the application of molecular modelling and simulation to drug discovery in recent years, as massive increases in computer power are coupled with continued development in the underlying methods and understanding of how to apply them. Here, we survey recent advances in one particular area-predicting how a known ligand binds to a particular protein. We focus on the four contributing classes of calculation: predicting where a binding site is on a protein; characterizing where chemical functional groups will bind to that site; molecular docking to generate a binding mode for a ligand and dynamics simulations to refine that pose and allow for protein conformation change. Examples of successful application are provided for each class.
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Affiliation(s)
- Moira M Rachman
- Facultat de Farmàcia and Institut de Biomedicina, Universitat de Barcelona, Av. Joan XXIII, 27-31, 08028 Barcelona, Spain
| | - Xavier Barril
- Facultat de Farmàcia and Institut de Biomedicina, Universitat de Barcelona, Av. Joan XXIII, 27-31, 08028 Barcelona, Spain; Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluís Companys 23, 08010 Barcelona, Spain
| | - Roderick E Hubbard
- YSBL, University of York, Heslington, York YO10 5DD, UK; Vernalis (R&D) Ltd, Granta Park, Abington, Cambridge CB21 6GB, UK.
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18
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Perricone U, Gulotta MR, Lombino J, Parrino B, Cascioferro S, Diana P, Cirrincione G, Padova A. An overview of recent molecular dynamics applications as medicinal chemistry tools for the undruggable site challenge. MEDCHEMCOMM 2018; 9:920-936. [PMID: 30108981 PMCID: PMC6072422 DOI: 10.1039/c8md00166a] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Accepted: 04/19/2018] [Indexed: 12/14/2022]
Abstract
Molecular dynamics (MD) has become increasingly popular due to the development of hardware and software solutions and the improvement in algorithms, which allowed researchers to scale up calculations in order to speed them up. MD simulations are usually used to address protein folding issues or protein-ligand complex stability through energy profile analysis over time. In recent years, the development of new tools able to deeply explore a potential energy surface (PES) has allowed researchers to focus on the dynamic nature of the binding recognition process and binding-induced protein conformational changes. Moreover, modern approaches have been demonstrated to be effective and reliable in calculating some kinetic and thermodynamic parameters behind the host-guest recognition process. Starting from all of these considerations, several efforts have been made in order to integrate MD within the virtual screening process in drug discovery. Knowledge retrieved from MD can, in fact, be exploited as a starting point to build pharmacophores or docking constraints in the early stage of the screening campaign as well as to define key features, in order to unravel hidden binding modes and help the optimisation of the molecular structure of a lead compound. Based on these outcomes, researchers are nowadays using MD as an invaluable tool to discover and target previously considered undruggable binding sites, including protein-protein interactions and allosteric sites on a protein surface. As a matter of fact, the use of MD has been recognised as vital to the discovery of selective protein-protein interaction modulators. The use of a dynamic overview on how the host-guest recognition occurs and of the relative conformational modifications induced allows researchers to optimise small molecules and small peptides capable of tightly interacting within the cleft between two proteins. In this review, we aim to present the most recent applications of MD as an integrated tool to be used in the rational design of small molecules or small peptides able to modulate undruggable targets, such as allosteric sites and protein-protein interactions.
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Affiliation(s)
- Ugo Perricone
- Computational and Medicinal Chemistry Group , Fondazione Ri.MED , Via Bandiera 11 , 90133 Palermo , Italy .
| | - Maria Rita Gulotta
- Computational and Medicinal Chemistry Group , Fondazione Ri.MED , Via Bandiera 11 , 90133 Palermo , Italy .
- Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche (STEBICEF) , Università degli Studi di Palermo , Via Archirafi 32 , 90123 Palermo , Italy
| | - Jessica Lombino
- Computational and Medicinal Chemistry Group , Fondazione Ri.MED , Via Bandiera 11 , 90133 Palermo , Italy .
- Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche (STEBICEF) , Università degli Studi di Palermo , Via Archirafi 32 , 90123 Palermo , Italy
| | - Barbara Parrino
- Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche (STEBICEF) , Università degli Studi di Palermo , Via Archirafi 32 , 90123 Palermo , Italy
| | - Stella Cascioferro
- Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche (STEBICEF) , Università degli Studi di Palermo , Via Archirafi 32 , 90123 Palermo , Italy
| | - Patrizia Diana
- Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche (STEBICEF) , Università degli Studi di Palermo , Via Archirafi 32 , 90123 Palermo , Italy
| | - Girolamo Cirrincione
- Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche (STEBICEF) , Università degli Studi di Palermo , Via Archirafi 32 , 90123 Palermo , Italy
| | - Alessandro Padova
- Computational and Medicinal Chemistry Group , Fondazione Ri.MED , Via Bandiera 11 , 90133 Palermo , Italy .
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19
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Juritz EI, Bascur JP, Almonacid DE, González-Nilo FD. Novel Insights for Inhibiting Mutant Heterodimer IDH1 wt-R132H in Cancer: An In-Silico Approach. Mol Diagn Ther 2018; 22:369-380. [PMID: 29651790 DOI: 10.1007/s40291-018-0331-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
BACKGROUND Isocitrate dehydrogenase 1 (IDH1) is a dimeric enzyme responsible for supplying the cell's nicotinamide adenine dinucleotide phosphate (NADPH) reserves via dehydrogenation of isocitrate (ICT) and reduction of NADP+. Mutations in position R132 trigger cancer by enabling IDH1 to produce D-2-hydroxyglutarate (2-HG) and reduce inhibition by ICT. Mutant IDH1 can be found as a homodimer or a heterodimer. OBJECTIVE We propose a novel strategy to inhibit IDH1 R132 variants as a means not to decrease the concentration of 2-HG but to provoke a cytotoxic effect, as the cell malignancy at this point no longer depends on 2-HG. We aim to inhibit the activity of the mutant heterodimer to block the wild-type subunit. Limiting the NADPH reserves in a cancerous cell will enhance its susceptibility to the oxidative stress provoked by chemotherapy. METHODS We performed a virtual screening using all US FDA-approved drugs to replicate the loss of inhibition of mutant IDH1 by ICT. We characterized our results based on molecular interactions and correlated them with the described phenotypes. RESULTS We replicated the loss of inhibition by ICT in mutant IDH1. We identified 20 drugs with the potential to inhibit the heterodimeric isoform. Six of them are used in cancer treatment. CONCLUSIONS We present 20 FDA-approved drugs with the potential to inhibit IDH1 wild-type activity in mutated cells. We believe this work may provide important insights into current and new approaches to dealing with IDH1 mutations. In addition, it may be used as a basis for additional studies centered on drugs presenting differential sensitivities to different IDH1 isoforms.
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Affiliation(s)
- Ezequiel Iván Juritz
- Center for Bioinformatics and Integrative Biology, Facultad de Ciencias de la Vida, Universidad Andrés Bello, 8370146, Santiago, Chile.
| | - Juan Pablo Bascur
- Center for Bioinformatics and Integrative Biology, Facultad de Ciencias de la Vida, Universidad Andrés Bello, 8370146, Santiago, Chile
| | - Daniel Eduardo Almonacid
- Center for Bioinformatics and Integrative Biology, Facultad de Ciencias de la Vida, Universidad Andrés Bello, 8370146, Santiago, Chile.,uBiome, Inc., San Francisco, CA, USA
| | - Fernando Danilo González-Nilo
- Center for Bioinformatics and Integrative Biology, Facultad de Ciencias de la Vida, Universidad Andrés Bello, 8370146, Santiago, Chile.,Centro Interdisciplinario de Neurociencia de Valparaíso, Facultad de Ciencias, Universidad de Valparaíso, 2366103, Valparaíso, Chile
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