201
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Rajda K, Podlewska S. Similar, or dissimilar, that is the question. How different are methods for comparison of compounds similarity? Comput Biol Chem 2020; 88:107367. [PMID: 32956952 DOI: 10.1016/j.compbiolchem.2020.107367] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 08/13/2020] [Accepted: 08/24/2020] [Indexed: 10/23/2022]
Abstract
Comparison of compounds similarity is one of the main strategies of virtual screening protocols. Both similarity and dissimilarity concepts are of great importance during the search for new active compounds. Similarity is important due to the assumption that underlies the process of searching for new drug candidates: structurally similar compounds should induce similar biological response. On the other hand, we are also interested in dissimilarity, as we usually aim to find structurally novel ligands. In the study, we compared several approaches of evaluating compound similarity. Various representations and metrics were applied and we indicated the rate of variation of the results that can occur when shifting from one strategy to another. We compared both general similarity of datasets using different approaches, as well as examined the changes in the set of nearest neighbors when changing one compound representation into another, and the influence of representation/metric settings on the clustering outcome. We hope that the study will be of great help during the preparation of virtual screening experiments, stressing the need for careful selection of the way, the compound similarity is assessed. The differences in the results that can be obtained via the application of particular strategy can significantly influence the outcome of comparison studies; therefore, its settings should be carefully selected beforerunning the comparison.
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Affiliation(s)
- Krzysztof Rajda
- Wroclaw University of Science and Technology, Faculty of Computer Science and Management, 50-371 Wrocław, I. Łukasiewicza Street 5, Poland
| | - Sabina Podlewska
- Jagiellonian University Medical College, Department of Technology and Biotechnology of Drugs, 30-688 Kraków, 9 Medyczna Street, Poland; Maj Institute of Pharmacology, Polish Academy of Sciences, Department of Medicinal Chemistry, 31-343 Kraków, Smętna Street 12, Poland.
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202
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Forouzesh N, Onufriev AV. MMGB/SA Consensus Estimate of the Binding Free Energy Between the Novel Coronavirus Spike Protein to the Human ACE2 Receptor. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.08.25.267625. [PMID: 32869029 PMCID: PMC7457614 DOI: 10.1101/2020.08.25.267625] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
The ability to estimate protein-protein binding free energy in a computationally efficient via a physics-based approach is beneficial to research focused on the mechanism of viruses binding to their target proteins. Implicit solvation methodology may be particularly useful in the early stages of such research, as it can offer valuable insights into the binding process, quickly. Here we evaluate the potential of the related molecular mechanics generalized Born surface area (MMGB/SA) approach to estimate the binding free energy ΔGbind between the SARS-CoV-2 spike receptor-binding domain and the human ACE2 receptor. The calculations are based on a recent flavor of the generalized Born model, GBNSR6. Two estimates of ΔGbind are performed: one based on standard bondi radii, and the other based on a newly developed set of atomic radii (OPT1), optimized specifically for protein-ligand binding. We take the average of the resulting two ΔGbind values as the consensus estimate. For the well-studied Ras-Raf protein-protein complex, which has similar binding free energy to that of the SARS-CoV-2/ACE2 complex, the consensus ΔGbind = -11.8 ± 1 kcal/mol, vs. experimental -9.7 ± 0.2 kcal/mol. The consensus estimates for the SARS-CoV-2/ACE2 complex is ΔGbind = -9.4 ± 1.5 kcal/mol, which is in near quantitative agreement with experiment (-10.6 kcal/mol). The availability of a conceptually simple MMGB/SA-based protocol for analysis of the SARS-CoV-2 /ACE2 binding may be beneficial in light of the need to move forward fast.
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Affiliation(s)
- Negin Forouzesh
- Department of Computer Science, California State University, Los Angeles, Los Angeles, CA 90032, USA
| | - Alexey V Onufriev
- Department of Computer Science, Virginia Polytechnic Institute & State University, Blacksburg, VA 24061, USA
- Department of Physics, Virginia Polytechnic Institute & State University, Blacksburg, VA 24061, USA
- Center for Soft Matter and Biological Physics, Virginia Polytechnic Institute & State University, Blacksburg, VA 24061, USA
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203
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Wong KM, Tai HK, Siu SWI. GWOVina: A grey wolf optimization approach to rigid and flexible receptor docking. Chem Biol Drug Des 2020; 97:97-110. [PMID: 32679606 PMCID: PMC7818481 DOI: 10.1111/cbdd.13764] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 06/18/2020] [Accepted: 07/05/2020] [Indexed: 12/19/2022]
Abstract
Protein–ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein. In this paper, we introduce an efficient flexible docking method, gwovina, which is a variant of the Vina implementation using the grey wolf optimizer (GWO) and random walk for the global search, and the Dunbrack rotamer library for side‐chain sampling. The new method was validated for rigid and flexible‐receptor docking using four independent datasets. In rigid docking, gwovina showed comparable docking performance to Vina in terms of ligand pose RMSD, success rate, and affinity prediction. In flexible‐receptor docking, gwovina has improved success rate compared to Vina and AutoDockFR. It ran 2 to 7 times faster than Vina and 40 to 100 times faster than AutoDockFR. Therefore, gwovina can play a role in solving the complex flexible‐receptor docking cases and is suitable for virtual screening of compound libraries. gwovina is freely available at https://cbbio.cis.um.edu.mo/software/gwovina for testing.
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Affiliation(s)
- Kin Meng Wong
- Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Taipa, Macau, China
| | - Hio Kuan Tai
- Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Taipa, Macau, China
| | - Shirley W I Siu
- Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Taipa, Macau, China
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204
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Sun Z. SAMPL7 TrimerTrip host-guest binding poses and binding affinities from spherical-coordinates-biased simulations. J Comput Aided Mol Des 2020; 35:105-115. [PMID: 32776199 DOI: 10.1007/s10822-020-00335-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Accepted: 08/04/2020] [Indexed: 12/21/2022]
Abstract
Host-guest binding remains a major challenge in modern computational modelling. The newest 7th statistical assessment of the modeling of proteins and ligands (SAMPL) challenge contains a new series of host-guest systems. The TrimerTrip host binds to 16 structurally diverse guests. Previously, we have successfully employed the spherical coordinates as the collective variables coupled with the enhanced sampling technique metadynamics to enhance the sampling of the binding/unbinding event, search for possible binding poses and calculate the binding affinities in all three host-guest binding cases of the 6th SAMPL challenge. In this work, we report a retrospective study on the TrimerTrip host-guest systems by employing the same protocol to investigate the TrimerTrip host in the SAMPL7 challenge. As no binding pose is provided by the SAMPL7 host, our simulations initiate from randomly selected configurations and are proceeded long enough to obtain converged free energy estimates and search for possible binding poses. The calculated binding affinities are in good agreement with the experimental reference, and the obtained binding poses serve as a nice starting point for end-point or alchemical free energy calculations. Note that as the work is performed after the close of the SAMPL7 challenge, we do not participate in the challenge and the results are not formally submitted to the SAMPL7 challenge.
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Affiliation(s)
- Zhaoxi Sun
- State Key Laboratory of Precision Spectroscopy, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China.
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205
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Miao Y, Bhattarai A, Wang J. Ligand Gaussian Accelerated Molecular Dynamics (LiGaMD): Characterization of Ligand Binding Thermodynamics and Kinetics. J Chem Theory Comput 2020; 16:5526-5547. [PMID: 32692556 DOI: 10.1021/acs.jctc.0c00395] [Citation(s) in RCA: 105] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Calculations of ligand binding free energies and kinetic rates are important for drug design. However, such tasks have proven challenging in computational chemistry and biophysics. To address this challenge, we have developed a new computational method, ligand Gaussian accelerated molecular dynamics (LiGaMD), which selectively boosts the ligand nonbonded interaction potential energy based on the Gaussian accelerated molecular dynamics (GaMD) enhanced sampling technique. Another boost potential could be applied to the remaining potential energy of the entire system in a dual-boost algorithm (LiGaMD_Dual) to facilitate ligand binding. LiGaMD has been demonstrated on host-guest and protein-ligand binding model systems. Repetitive guest binding and unbinding in the β-cyclodextrin host were observed in hundreds-of-nanosecond LiGaMD_Dual simulations. The calculated guest binding free energies agreed excellently with experimental data with <1.0 kcal/mol errors. Compared with converged microsecond-time scale conventional molecular dynamics simulations, the sampling errors of LiGaMD_Dual simulations were also <1.0 kcal/mol. Accelerations of ligand kinetic rate constants in LiGaMD simulations were properly estimated using Kramers' rate theory. Furthermore, LiGaMD allowed us to capture repetitive dissociation and binding of the benzamidine inhibitor in trypsin within 1 μs simulations. The calculated ligand binding free energy and kinetic rate constants compared well with the experimental data. In summary, LiGaMD provides a powerful enhanced sampling approach for characterizing ligand binding thermodynamics and kinetics simultaneously, which is expected to facilitate computer-aided drug design.
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Affiliation(s)
- Yinglong Miao
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047, United States
| | - Apurba Bhattarai
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047, United States
| | - Jinan Wang
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047, United States
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206
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Hassanzadeh P. Towards the quantum-enabled technologies for development of drugs or delivery systems. J Control Release 2020; 324:260-279. [DOI: 10.1016/j.jconrel.2020.04.050] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Revised: 04/28/2020] [Accepted: 04/29/2020] [Indexed: 12/20/2022]
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207
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Yoshino R, Yasuo N, Sekijima M. Identification of key interactions between SARS-CoV-2 main protease and inhibitor drug candidates. Sci Rep 2020; 10:12493. [PMID: 32719454 PMCID: PMC7385649 DOI: 10.1038/s41598-020-69337-9] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Accepted: 07/01/2020] [Indexed: 01/08/2023] Open
Abstract
The number of cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (COVID-19) has reached over 114,000. SARS-CoV-2 caused a pandemic in Wuhan, China, in December 2019 and is rapidly spreading globally. It has been reported that peptide-like anti-HIV-1 drugs are effective against SARS-CoV Main protease (Mpro). Due to the close phylogenetic relationship between SARS-CoV and SARS-CoV-2, their main proteases share many structural and functional features. Thus, these drugs are also regarded as potential drug candidates targeting SARS-CoV-2 Mpro. However, the mechanism of action of SARS-CoV-2 Mpro at the atomic-level is unknown. In the present study, we revealed key interactions between SARS-CoV-2 Mpro and three drug candidates by performing pharmacophore modeling and 1 μs molecular dynamics (MD) simulations. His41, Gly143, and Glu166 formed interactions with the functional groups that were common among peptide-like inhibitors in all MD simulations. These interactions are important targets for potential drugs against SARS-CoV-2 Mpro.
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Affiliation(s)
- Ryunosuke Yoshino
- Transborder Medical Research Center, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8577, Japan
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8577, Japan
| | - Nobuaki Yasuo
- Tokyo Tech Academy for Convergence of Materials and Informatics (TAC-MI), Tokyo Institute of Technology, J3-23-4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan
| | - Masakazu Sekijima
- Tokyo Tech Academy for Convergence of Materials and Informatics (TAC-MI), Tokyo Institute of Technology, J3-23-4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan.
- School of Computing, Tokyo Institute of Technology, J3-23-4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan.
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208
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Coricello A, Mesiti F, Lupia A, Maruca A, Alcaro S. Inside Perspective of the Synthetic and Computational Toolbox of JAK Inhibitors: Recent Updates. Molecules 2020; 25:E3321. [PMID: 32707925 PMCID: PMC7435994 DOI: 10.3390/molecules25153321] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 07/18/2020] [Accepted: 07/20/2020] [Indexed: 01/10/2023] Open
Abstract
The mechanisms of inflammation and cancer are intertwined by complex networks of signaling pathways. Dysregulations in the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway underlie several pathogenic conditions related to chronic inflammatory states, autoimmune diseases and cancer. Historically, the potential application of JAK inhibition has been thoroughly explored, thus triggering an escalation of favorable results in this field. So far, five JAK inhibitors have been approved by the Food and Drug Administration (FDA) for the treatment of different diseases. Considering the complexity of JAK-depending processes and their involvement in multiple disorders, JAK inhibitors are the perfect candidates for drug repurposing and for the assessment of multitarget strategies. Herein we reviewed the recent progress concerning JAK inhibition, including the innovations provided by the release of JAKs crystal structures and the improvement of synthetic strategies aimed to simplify of the industrial scale-up.
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Affiliation(s)
- Adriana Coricello
- Dipartimento di Scienze della Salute, Università "Magna Græcia" di Catanzaro, Viale Europa, 88100 Catanzaro, Italy
| | - Francesco Mesiti
- Dipartimento di Scienze della Salute, Università "Magna Græcia" di Catanzaro, Viale Europa, 88100 Catanzaro, Italy
- Net4Science srl, Università 'Magna Græcia' di Catanzaro, Campus Universitario 'S. Venuta', Viale Europa, 88100 Catanzaro, Italy
| | - Antonio Lupia
- Net4Science srl, Università 'Magna Græcia' di Catanzaro, Campus Universitario 'S. Venuta', Viale Europa, 88100 Catanzaro, Italy
| | - Annalisa Maruca
- Dipartimento di Scienze della Salute, Università "Magna Græcia" di Catanzaro, Viale Europa, 88100 Catanzaro, Italy
- Net4Science srl, Università 'Magna Græcia' di Catanzaro, Campus Universitario 'S. Venuta', Viale Europa, 88100 Catanzaro, Italy
| | - Stefano Alcaro
- Dipartimento di Scienze della Salute, Università "Magna Græcia" di Catanzaro, Viale Europa, 88100 Catanzaro, Italy
- Net4Science srl, Università 'Magna Græcia' di Catanzaro, Campus Universitario 'S. Venuta', Viale Europa, 88100 Catanzaro, Italy
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209
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Gilabert JF, Gracia Carmona O, Hogner A, Guallar V. Combining Monte Carlo and Molecular Dynamics Simulations for Enhanced Binding Free Energy Estimation through Markov State Models. J Chem Inf Model 2020; 60:5529-5539. [PMID: 32644807 DOI: 10.1021/acs.jcim.0c00406] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
We present a multistep protocol, combining Monte Carlo and molecular dynamics simulations, for the estimation of absolute binding free energies, one of the most significant challenges in computer-aided drug design. The protocol is based on an initial short enhanced Monte Carlo simulation, followed by clustering of the ligand positions, which serve to identify the most relevant states of the unbinding process. From these states, extensive molecular dynamics simulations are run to estimate an equilibrium probability distribution obtained with Markov State Models, which is subsequently used to estimate the binding free energy. We tested the procedure on two different protein systems, the Plasminogen kringle domain 1 and Urokinase, each with multiple ligands, for an aggregated molecular dynamics length of 760 μs. Our results indicate that the initial sampling of the unbinding events largely facilitates the convergence of the subsequent molecular dynamics exploration. Moreover, the protocol is capable to properly rank the set of ligands examined, albeit with a significant computational cost for the, more realistic, Urokinase complexes. Overall, this work demonstrates the usefulness of combining enhanced sampling methods with regular simulation techniques as a way to obtain more reliable binding affinity estimates.
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Affiliation(s)
- Joan F Gilabert
- Barcelona Supercomputing Center, Jordi Girona 29, E-08034 Barcelona, Spain
| | | | - Anders Hogner
- Medicinal Chemistry, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Victor Guallar
- Barcelona Supercomputing Center, Jordi Girona 29, E-08034 Barcelona, Spain.,ICREA, Passeig Lluís Companys 23, E-08010 Barcelona, Spain
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210
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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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211
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Forouzesh N, Mukhopadhyay A, Watson LT, Onufriev AV. Multidimensional Global Optimization and Robustness Analysis in the Context of Protein-Ligand Binding. J Chem Theory Comput 2020; 16:4669-4684. [PMID: 32450041 PMCID: PMC8594251 DOI: 10.1021/acs.jctc.0c00142] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Accuracy of protein-ligand binding free energy calculations utilizing implicit solvent models is critically affected by parameters of the underlying dielectric boundary, specifically, the atomic and water probe radii. Here, a global multidimensional optimization pipeline is developed to find optimal atomic radii specifically for protein-ligand binding calculations in implicit solvent. The computational pipeline has these three key components: (1) a massively parallel implementation of a deterministic global optimization algorithm (VTDIRECT95), (2) an accurate yet reasonably fast generalized Born implicit solvent model (GBNSR6), and (3) a novel robustness metric that helps distinguish between nearly degenerate local minima via a postprocessing step of the optimization. A graph-based "kT-connectivity" approach to explore and visualize the multidimensional energy landscape is proposed: local minima that can be reached from the global minimum without exceeding a given energy threshold (kT) are considered to be connected. As an illustration of the capabilities of the optimization pipeline, we apply it to find a global optimum in the space of just five radii: four atomic (O, H, N, and C) radii and water probe radius. The optimized radii, ρW = 1.37 Å, ρC = 1.40 Å, ρH = 1.55 Å, ρN = 2.35 Å, and ρO = 1.28 Å, lead to a closer agreement of electrostatic binding free energies with the explicit solvent reference than two commonly used sets of radii previously optimized for small molecules. At the same time, the ability of the optimizer to find the global optimum reveals fundamental limits of the common two-dielectric implicit solvation model: the computed electrostatic binding free energies are still almost 4 kcal/mol away from the explicit solvent reference. The proposed computational approach opens the possibility to further improve the accuracy of practical computational protocols for binding free energy calculations.
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Affiliation(s)
- Negin Forouzesh
- Department of Computer Science, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States
| | - Abhishek Mukhopadhyay
- Department of Physics, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States
| | - Layne T Watson
- Department of Computer Science, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States
- Department of Mathematics, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States
- Department of Aerospace and Ocean Engineering, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States
- Center for Soft Matter and Biological Physics, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States
| | - Alexey V Onufriev
- Department of Computer Science, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States
- Department of Physics, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States
- Center for Soft Matter and Biological Physics, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States
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212
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Serafim MSM, Kronenberger T, Oliveira PR, Poso A, Honório KM, Mota BEF, Maltarollo VG. The application of machine learning techniques to innovative antibacterial discovery and development. Expert Opin Drug Discov 2020; 15:1165-1180. [PMID: 32552005 DOI: 10.1080/17460441.2020.1776696] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
INTRODUCTION After the initial wave of antibiotic discovery, few novel classes of antibiotics have emerged, with the latest dating back to the 1980's. Furthermore, the pace of antibiotic drug discovery is unable to keep up with the increasing prevalence of antibiotic drug resistance. However, the increasing amount of available data promotes the use of machine learning techniques (MLT) in drug discovery projects (e.g. construction of regression/classification models and ranking/virtual screening of compounds). AREAS COVERED In this review, the authors cover some of the applications of MLT in medicinal chemistry, focusing on the development of new antibiotics, the prediction of resistance and its mechanisms. The aim of this review is to illustrate the main advantages and disadvantages and the major trends from studies over the past 5 years. EXPERT OPINION The application of MLT to antibacterial drug discovery can aid the selection of new and potent lead compounds, with desirable pharmacokinetic and toxic profiles for further optimization. The increasing volume of available data along with the constant improvement in computational power and algorithms has meant that we are experiencing a transition in the way we face modern issues such as drug resistance, where our decisions are data-driven and experiments can be focused by data-suggested hypotheses.
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Affiliation(s)
- Mateus Sá Magalhães Serafim
- Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG) , Belo Horizonte, Brazil
| | - Thales Kronenberger
- Department of Internal Medicine VIII, University Hospital of Tübingen , Tübingen, Germany
| | | | - Antti Poso
- Department of Internal Medicine VIII, University Hospital of Tübingen , Tübingen, Germany.,School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland , Kuopio, Finland
| | - Káthia Maria Honório
- Escola de Artes, Ciências e Humanidades, Universidade de São Paulo (USP) , São Paulo, Brazil.,Centro de Ciências Naturais e Humanas, Universidade Federal do ABC , Santo André, Brazil
| | - Bruno Eduardo Fernandes Mota
- Departamento de Análises Clínicas e Toxicológicas, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG) , Belo Horizonte, Brazil
| | - Vinícius Gonçalves Maltarollo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG) , Belo Horizonte, Brazil
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213
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Bera I, Payghan PV. Use of Molecular Dynamics Simulations in Structure-Based Drug Discovery. Curr Pharm Des 2020; 25:3339-3349. [PMID: 31480998 DOI: 10.2174/1381612825666190903153043] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Accepted: 09/01/2019] [Indexed: 12/31/2022]
Abstract
BACKGROUND Traditional drug discovery is a lengthy process which involves a huge amount of resources. Modern-day drug discovers various multidisciplinary approaches amongst which, computational ligand and structure-based drug designing methods contribute significantly. Structure-based drug designing techniques require the knowledge of structural information of drug target and drug-target complexes. Proper understanding of drug-target binding requires the flexibility of both ligand and receptor to be incorporated. Molecular docking refers to the static picture of the drug-target complex(es). Molecular dynamics, on the other hand, introduces flexibility to understand the drug binding process. OBJECTIVE The aim of the present study is to provide a systematic review on the usage of molecular dynamics simulations to aid the process of structure-based drug design. METHOD This review discussed findings from various research articles and review papers on the use of molecular dynamics in drug discovery. All efforts highlight the practical grounds for which molecular dynamics simulations are used in drug designing program. In summary, various aspects of the use of molecular dynamics simulations that underline the basis of studying drug-target complexes were thoroughly explained. RESULTS This review is the result of reviewing more than a hundred papers. It summarizes various problems that use molecular dynamics simulations. CONCLUSION The findings of this review highlight how molecular dynamics simulations have been successfully implemented to study the structure-function details of specific drug-target complexes. It also identifies the key areas such as stability of drug-target complexes, ligand binding kinetics and identification of allosteric sites which have been elucidated using molecular dynamics simulations.
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Affiliation(s)
- Indrani Bera
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, United States
| | - Pavan V Payghan
- Structural Biology and Bioinformatics Department, CSIR-IICB, Kolkata, India.,Department of Pharmaceutical Sciences, Washington State University College of Pharmacy and Pharmaceutical Sciences, Spokane, WA, United States
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214
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Saikia S, Bordoloi M. Molecular Docking: Challenges, Advances and its Use in Drug Discovery Perspective. Curr Drug Targets 2020; 20:501-521. [PMID: 30360733 DOI: 10.2174/1389450119666181022153016] [Citation(s) in RCA: 203] [Impact Index Per Article: 50.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Revised: 06/08/2018] [Accepted: 08/28/2018] [Indexed: 01/21/2023]
Abstract
Molecular docking is a process through which small molecules are docked into the macromolecular structures for scoring its complementary values at the binding sites. It is a vibrant research area with dynamic utility in structure-based drug-designing, lead optimization, biochemical pathway and for drug designing being the most attractive tools. Two pillars for a successful docking experiment are correct pose and affinity prediction. Each program has its own advantages and drawbacks with respect to their docking accuracy, ranking accuracy and time consumption so a general conclusion cannot be drawn. Moreover, users don't always consider sufficient diversity in their test sets which results in certain programs to outperform others. In this review, the prime focus has been laid on the challenges of docking and troubleshooters in existing programs, underlying algorithmic background of docking, preferences regarding the use of docking programs for best results illustrated with examples, comparison of performance for existing tools and algorithms, state of art in docking, recent trends of diseases and current drug industries, evidence from clinical trials and post-marketing surveillance are discussed. These aspects of the molecular drug designing paradigm are quite controversial and challenging and this review would be an asset to the bioinformatics and drug designing communities.
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Affiliation(s)
- Surovi Saikia
- Natural Products Chemistry Group, CSIR North East Institute of Science & Technology, Jorhat-785006, Assam, India
| | - Manobjyoti Bordoloi
- Natural Products Chemistry Group, CSIR North East Institute of Science & Technology, Jorhat-785006, Assam, India
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215
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Hage-Melim LIDS, Federico LB, de Oliveira NKS, Francisco VCC, Correia LC, de Lima HB, Gomes SQ, Barcelos MP, Francischini IAG, da Silva CHTDP. Virtual screening, ADME/Tox predictions and the drug repurposing concept for future use of old drugs against the COVID-19. Life Sci 2020; 256:117963. [PMID: 32535080 PMCID: PMC7289103 DOI: 10.1016/j.lfs.2020.117963] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 06/02/2020] [Accepted: 06/09/2020] [Indexed: 12/27/2022]
Abstract
The new Coronavirus (SARS-CoV-2) is the cause of a serious infection in the respiratory tract called COVID-19. Structures of the main protease of SARS-CoV-2 (Mpro), responsible for the replication of the virus, have been solved and quickly made available, thus allowing the design of compounds that could interact with this protease and thus to prevent the progression of the disease by avoiding the viral peptide to be cleaved, so that smaller viral proteins can be released into the host's plasma. These structural data are extremely important for in silico design and development of compounds as well, being possible to quick and effectively identify potential inhibitors addressed to such enzyme's structure. Therefore, in order to identify potential inhibitors for Mpro, we used virtual screening approaches based with the structure of the enzyme and two compounds libraries, targeted to SARS-CoV-2, containing compounds with predicted activity against Mpro. In this way, we selected, through docking studies, the 100 top-ranked compounds, which followed to subsequent studies of pharmacokinetic and toxicity predictions. After all the simulations and predictions here performed, we obtained 10 top-ranked compounds that were again in silico analyzed inside the Mpro catalytic site, together some drugs that are being currently investigated for treatment of COVID-19. After proposing and analyzing the interaction modes of these compounds, we submitted one molecule then selected as template to a 2D similarity study in a database containing drugs approved by FDA and we have found and indicated Apixaban as a potential drug for future treatment of COVID-19. The new coronavirus (SARS-CoV-2) is the cause of a serious infection in the respiratory tract called COVID-19. The main protease SARS-CoV-2 (Mpro) is essential in the process of maturation and infectivity of the virus. In silico methodologies are extremely important to identify potential inhibitors for the target structure quickly and effectively. The drug repurposing is an important concept for future use of old drugs.
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Affiliation(s)
| | - Leonardo Bruno Federico
- Computational Laboratory of Pharmaceutical Chemistry, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | | | | | - Lenir Cabral Correia
- Laboratory of Pharmaceutical and Medicinal Chemistry (PharMedChem), Federal University of Amapá, Macapá, Amapá, Brazil
| | - Henrique Barros de Lima
- Laboratory of Pharmaceutical and Medicinal Chemistry (PharMedChem), Federal University of Amapá, Macapá, Amapá, Brazil
| | - Suzane Quintana Gomes
- Computational Laboratory of Pharmaceutical Chemistry, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil; Department of Chemistry, School of Philosophy, Sciences and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Mariana Pegrucci Barcelos
- Computational Laboratory of Pharmaceutical Chemistry, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil; Department of Chemistry, School of Philosophy, Sciences and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Isaque Antônio Galindo Francischini
- Computational Laboratory of Pharmaceutical Chemistry, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Carlos Henrique Tomich de Paula da Silva
- Computational Laboratory of Pharmaceutical Chemistry, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil; Department of Chemistry, School of Philosophy, Sciences and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
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216
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Bagheri S, Behnejad H, Firouzi R, Karimi-Jafari MH. Using the Semiempirical Quantum Mechanics in Improving the Molecular Docking: A Case Study with CDK2. Mol Inform 2020; 39:e2000036. [PMID: 32485047 DOI: 10.1002/minf.202000036] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Accepted: 05/28/2020] [Indexed: 11/12/2022]
Abstract
In this study, we use some modified semiempirical quantum mechanics (SQM) methods for improving the molecular docking process. To this end, the three popular SQM Hamiltonians, PM6, PM6-D3H4X, and PM7 are employed for geometry optimization of some binding modes of ligands docked into the human cyclin-dependent kinase 2 (CDK2) by two widely used docking tools, AutoDock and AutoDock Vina. The results were analyzed with two different evaluation metrics: the symmetry-corrected heavy-atom RMSD and the fraction of recovered ligand-protein contacts. It is shown that the evaluation of the fraction of recovered contacts is more useful to measure the similarity between two structures when interacting with a protein. It was also found that AutoDock is more successful than AutoDock Vina in producing the correct ligand poses (RMSD≤2.0 Å) and ranking of the poses. It is also demonstrated that the ligand optimization at the SQM level improves the docking results and the SQM structures have a significantly better fit to the observed crystal structures. Finally, the SQM optimizations reduce the number of close contacts in the docking poses and successfully remove most of the clash or bad contacts between ligand and protein.
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Affiliation(s)
- Saleh Bagheri
- Department of Physical Chemistry, School of Chemistry, College of Science, University of Tehran, Tehran, Iran
| | - Hassan Behnejad
- Department of Physical Chemistry, School of Chemistry, College of Science, University of Tehran, Tehran, Iran
| | - Rohoullah Firouzi
- Department of Physical Chemistry, Chemistry and Chemical Engineering Research Center of Iran, Tehran, Iran
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217
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Molecular Interpretation of Pharmaceuticals’ Adsorption on Carbon Nanomaterials: Theory Meets Experiments. Processes (Basel) 2020. [DOI: 10.3390/pr8060642] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
The ability of carbon-based nanomaterials (CNM) to interact with a variety of pharmaceutical drugs can be exploited in many applications. In particular, they have been studied both as carriers for in vivo drug delivery and as sorbents for the treatment of water polluted by pharmaceuticals. In recent years, the large number of experimental studies was also assisted by computational work as a tool to provide understanding at molecular level of structural and thermodynamic aspects of adsorption processes. Quantum mechanical methods, especially based on density functional theory (DFT) and classical molecular dynamics (MD) simulations were mainly applied to study adsorption/release of various drugs. This review aims to compare results obtained by theory and experiments, focusing on the adsorption of three classes of compounds: (i) simple organic model molecules; (ii) antimicrobials; (iii) cytostatics. Generally, a good agreement between experimental data (e.g. energies of adsorption, spectroscopic properties, adsorption isotherms, type of interactions, emerged from this review) and theoretical results can be reached, provided that a selection of the correct level of theory is performed. Computational studies are shown to be a valuable tool for investigating such systems and ultimately provide useful insights to guide CNMs materials development and design.
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218
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Tanida Y, Matsuura A. Alchemical free energy calculations via metadynamics: Application to the theophylline-RNA aptamer complex. J Comput Chem 2020; 41:1804-1819. [PMID: 32449538 DOI: 10.1002/jcc.26221] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 04/03/2020] [Accepted: 04/26/2020] [Indexed: 11/08/2022]
Abstract
We propose a computational workflow for robust and accurate prediction of both binding poses and their affinities at early stage in designing drug candidates. Small, rigid ligands with few intramolecular degrees of freedom, for example, fragment-like molecules, have multiple binding poses, even at a single binding site, and their affinities are often close to each other. We explore various structures of ligand binding to a target through metadynamics using a small number of collective variables, followed by reweighting to obtain the atomic coordinates. After identifying each binding pose by cluster analysis, we perform alchemical free energy calculations on each structure to obtain the overall value. We applied this protocol in computing free energy of binding for the theophylline-RNA aptamer complex. Of the six (meta)stable structures found, the most favorable binding structure is consistent with the structure obtained by NMR. The overall free energy of binding reproduces the experimental values very well.
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219
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Ding X, Wu Y, Wang Y, Vilseck JZ, Brooks CL. Accelerated CDOCKER with GPUs, Parallel Simulated Annealing, and Fast Fourier Transforms. J Chem Theory Comput 2020; 16:3910-3919. [PMID: 32374996 DOI: 10.1021/acs.jctc.0c00145] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Fast Fourier transform (FFT)-based protein ligand docking together with parallel simulated annealing for both rigid and flexible receptor docking are implemented on graphical processing unit (GPU) accelerated platforms to significantly enhance the throughput of the CDOCKER and flexible CDOCKER - the docking algorithms in the CHARMM program for biomolecule modeling. The FFT-based approach for docking, first applied in protein-protein docking to efficiently search for the binding position and orientation of proteins, is adapted here to search ligand translational and rotational spaces given a ligand conformation in protein-ligand docking. Running on GPUs, our implementation of FFT docking in CDOCKER achieves a 15 000 fold speedup in the ligand translational and rotational space search in protein-ligand docking problems. With this significant speedup it becomes practical to exhaustively search ligand translational and rotational space when docking a rigid ligand into a protein receptor. We demonstrate in this paper that this provides an efficient way to calculate an upper bound for docking accuracy in the assessment of scoring functions for protein-ligand docking, which can be useful for improving scoring functions. The parallel molecular dynamics (MD) simulated annealing, also running on GPUs, aims to accelerate the search algorithm in CDOCKER by running MD simulated annealing in parallel on GPUs. When utilized as part of the general CDOCKER docking protocol, acceleration in excess of 20 times is achieved. With this acceleration, we demonstrate that the performance of CDOCKER for redocking is significantly improved compared with three other popular protein-ligand docking programs on two widely used protein ligand complex data sets: the Astex diverse set and the SB2012 test set. The flexible CDOCKER is similarly improved by the parallel MD simulated annealing on GPUs. Based on the results presented here, we suggest that the accelerated CDOCKER platform provides a highly competitive docking engine for both rigid-receptor and flexible-receptor docking studies and will further facilitate continued improvement in the physics-based scoring function employed in CDOCKER docking studies.
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220
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Ali I, Mukhtar SD, Ali HS, Scotti MT, Scotti L. Advances in Nanoparticles as Anticancer Drug Delivery Vector: Need of this Century. Curr Pharm Des 2020; 26:1637-1649. [DOI: 10.2174/1381612826666200203124330] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 12/02/2019] [Indexed: 12/17/2022]
Abstract
Background:
Nanotechnology has contributed a great deal to the field of medical science. Smart drugdelivery
vectors, combined with stimuli-based characteristics, are becoming increasingly important. The use of
external and internal stimulating factors can have enormous benefits and increase the targeting efficiency of
nanotechnology platforms. The pH values of tumor vascular tissues are acidic in nature, allowing the improved
targeting of anticancer drug payloads using drug-delivery vectors. Nanopolymers are smart drug-delivery vectors
that have recently been developed and recommended for use by scientists because of their potential targeting
capabilities, non-toxicity and biocompatibility, and make them ideal nanocarriers for personalized drug delivery.
Method:
The present review article provides an overview of current advances in the use of nanoparticles (NPs) as
anticancer drug-delivery vectors.
Results:
This article reviews the molecular basis for the use of NPs in medicine, including personalized medicine,
personalized therapy, emerging vistas in anticancer therapy, nanopolymer targeting, passive and active targeting
transports, pH-responsive drug carriers, biological barriers, computer-aided drug design, future challenges and
perspectives, biodegradability and safety.
Conclusions:
This article will benefit academia, researchers, clinicians, and government authorities by providing a
basis for further research advancements.
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Affiliation(s)
- Imran Ali
- Department of Chemistry, College of Sciences, Taibah University, Al-Medina Al-Munawara – 41477, Saudi Arabia
| | - Sofi D. Mukhtar
- Department of Chemistry, Jamia Millia Islamia (Central University) New Delhi-110025, India
| | - Heyam S. Ali
- Department of Pharmaceutics, University of Khartoum, Khartoum, Sudan
| | - Marcus T. Scotti
- Cheminformatics Laboratory- Postgraduate Program in Natural Products and Synthetic Bioactive, Federal University of Paraíba-Campus I 58051-970, João Pessoa, PB, Brazil
| | - Luciana Scotti
- Teaching and Research Management - University Hospital, Cheminformatics Laboratory- Postgraduate Program in Natural Products and Synthetic Bioactive, Federal University of Paraíba-Campus I, 58051-970, João Pessoa, PB, Brazil
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221
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Future avenues for Alzheimer's disease detection and therapy: liquid biopsy, intracellular signaling modulation, systems pharmacology drug discovery. Neuropharmacology 2020; 185:108081. [PMID: 32407924 DOI: 10.1016/j.neuropharm.2020.108081] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 03/01/2020] [Accepted: 03/30/2020] [Indexed: 12/20/2022]
Abstract
When Alzheimer's disease (AD) disease-modifying therapies will be available, global healthcare systems will be challenged by a large-scale demand for clinical and biological screening. Validation and qualification of globally accessible, minimally-invasive, and time-, cost-saving blood-based biomarkers need to be advanced. Novel pathophysiological mechanisms (and related candidate biomarkers) - including neuroinflammation pathways (TREM2 and YKL-40), axonal degeneration (neurofilament light chain protein), synaptic dysfunction (neurogranin, synaptotagmin, α-synuclein, and SNAP-25) - may be integrated into an expanding pathophysiological and biomarker matrix and, ultimately, integrated into a comprehensive blood-based liquid biopsy, aligned with the evolving ATN + classification system and the precision medicine paradigm. Liquid biopsy-based diagnostic and therapeutic algorithms are increasingly employed in Oncology disease-modifying therapies and medical practice, showing an enormous potential for AD and other brain diseases as well. For AD and other neurodegenerative diseases, newly identified aberrant molecular pathways have been identified as suitable therapeutic targets and are currently investigated by academia/industry-led R&D programs, including the nerve-growth factor pathway in basal forebrain cholinergic neurons, the sigma1 receptor, and the GTPases of the Rho family. Evidence for a clinical long-term effect on cognitive function and brain health span of cholinergic compounds, drug candidates for repositioning programs, and non-pharmacological multidomain interventions (nutrition, cognitive training, and physical activity) is developing as well. Ultimately, novel pharmacological paradigms, such as quantitative systems pharmacology-based integrative/explorative approaches, are gaining momentum to optimize drug discovery and accomplish effective pathway-based strategies for precision medicine. This article is part of the special issue on 'The Quest for Disease-Modifying Therapies for Neurodegenerative Disorders'.
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222
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Zhu F, Zhang X, Allen JE, Jones D, Lightstone FC. Binding Affinity Prediction by Pairwise Function Based on Neural Network. J Chem Inf Model 2020; 60:2766-2772. [PMID: 32338892 DOI: 10.1021/acs.jcim.0c00026] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We present a new approach to estimate the binding affinity from given three-dimensional poses of protein-ligand complexes. In this scheme, every protein-ligand atom pair makes an additive free-energy contribution. The sum of these pairwise contributions then gives the total binding free energy or the logarithm of the dissociation constant. The pairwise contribution is calculated by a function implemented via a neural network that takes the properties of the two atoms and their distance as input. The pairwise function is trained using a portion of the PDBbind 2018 data set. The model achieves good accuracy for affinity predictions when evaluated with PDBbind 2018 and with the CASF-2016 benchmark, comparing favorably to many scoring functions such as that of AutoDock Vina. The framework here may be extended to incorporate other factors to further improve its accuracy and power.
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Affiliation(s)
- Fangqiang Zhu
- Biochemical and Biophysical Systems Group, Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94550, United States
| | - Xiaohua Zhang
- Biochemical and Biophysical Systems Group, Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94550, United States
| | - Jonathan E Allen
- Global Security, Computing Applications Division, Computing Directorate, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94550, United States
| | - Derek Jones
- Global Security, Computing Applications Division, Computing Directorate, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94550, United States
| | - Felice C Lightstone
- Biochemical and Biophysical Systems Group, Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94550, United States
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223
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Sohraby F, Aryapour H. Rational drug repurposing for cancer by inclusion of the unbiased molecular dynamics simulation in the structure-based virtual screening approach: Challenges and breakthroughs. Semin Cancer Biol 2020; 68:249-257. [PMID: 32360530 DOI: 10.1016/j.semcancer.2020.04.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Revised: 03/07/2020] [Accepted: 04/22/2020] [Indexed: 12/13/2022]
Abstract
Managing cancer is now one of the biggest concerns of health organizations. Many strategies have been developed in drug discovery pipelines to help rectify this problem and two of the best ones are drug repurposing and computational methods. The combination of these approaches can have immense impact on the course of drug discovery. In silico drug repurposing can significantly reduce the time, the cost and the effort of drug development. Computational methods such as structure-based drug design (SBDD) and virtual screening can predict the potentials of small molecule binders, such as drugs, for having favorable effect on a particular molecular target. However, the demand for accuracy and efficiency of SBDD requires more sophisticated and complicated approaches such as unbiased molecular dynamics (UMD) simulation that has been recently introduced. As a complementary strategy, the knowledge acquired from UMD simulations can increase the chance of finding the right candidates and the pipeline of its administration is introduced and discussed in this review. An elaboration of this pipeline is also made by detailing an example, the binding and unbinding pathways of dasatinib-c-Src kinase complex, which shows that how influential this method can be in rational drug repurposing in cancer treatment.
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Affiliation(s)
- Farzin Sohraby
- Department of Biology, Faculty of Science, Golestan University, Gorgan, Iran
| | - Hassan Aryapour
- Department of Biology, Faculty of Science, Golestan University, Gorgan, Iran.
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224
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Pennington LD, Aquila BM, Choi Y, Valiulin RA, Muegge I. Positional Analogue Scanning: An Effective Strategy for Multiparameter Optimization in Drug Design. J Med Chem 2020; 63:8956-8976. [PMID: 32330036 DOI: 10.1021/acs.jmedchem.9b02092] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Minimizing the number and duration of design cycles needed to optimize hit or lead compounds into high-quality chemical probes or drug candidates is an ongoing challenge in biomedical research. Small structure modifications to hit or lead compounds can have meaningful impacts on pharmacological profiles due to significant effects on molecular and physicochemical properties and intra- and intermolecular interactions. Rapid pharmacological profiling of an efficiently prepared series of positional analogues stemming from the systematic exchange of methine groups with heteroatoms or other substituents in aromatic or heteroaromatic ring-containing hit or lead compounds is one approach toward minimizing design cycles (e.g., exchange of aromatic or heteroaromatic CH groups with N atoms or CF, CMe, or COH groups). In this Perspective, positional analogue scanning is shown to be an effective strategy for multiparameter optimization in drug design, whereby substantial improvements in a variety of pharmacological parameters can be achieved.
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225
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Guterres H, Im W. Improving Protein-Ligand Docking Results with High-Throughput Molecular Dynamics Simulations. J Chem Inf Model 2020; 60:2189-2198. [PMID: 32227880 DOI: 10.1021/acs.jcim.0c00057] [Citation(s) in RCA: 122] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Structure-based virtual screening relies on classical scoring functions that often fail to reliably discriminate binders from nonbinders. In this work, we present a high-throughput protein-ligand complex molecular dynamics (MD) simulation that uses the output from AutoDock Vina to improve docking results in distinguishing active from decoy ligands in a directory of useful decoy-enhanced (DUD-E) dataset. MD trajectories are processed by evaluating ligand-binding stability using root-mean-square deviations. We select 56 protein targets (of 7 different protein classes) and 560 ligands (280 actives, 280 decoys) and show 22% improvement in ROC AUC (area under the curve, receiver operating characteristics curve), from an initial value of 0.68 (AutoDock Vina) to a final value of 0.83. The MD simulation demonstrates a robust performance across all seven different protein classes. In addition, some predicted ligand-binding modes are moderately refined during MD simulations. These results systematically validate the reliability of a physics-based approach to evaluate protein-ligand binding interactions.
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Affiliation(s)
- Hugo Guterres
- Departments of Biological Sciences, Chemistry, and Bioengineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Wonpil Im
- Departments of Biological Sciences, Chemistry, and Bioengineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States.,School of Computational Sciences, Korea Institute for Advanced Study, Seoul 02455, Republic of Korea
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226
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Discovery of Novel Inhibitor for WNT/β-Catenin Pathway by Tankyrase 1/2 Structure-Based Virtual Screening. Molecules 2020; 25:molecules25071680. [PMID: 32268564 PMCID: PMC7180783 DOI: 10.3390/molecules25071680] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 03/26/2020] [Accepted: 03/31/2020] [Indexed: 12/22/2022] Open
Abstract
Aberrant activation of the WNT/β-catenin signaling pathway is implicated in various types of cancers. Inhibitors targeting the Wnt signaling pathway are intensively studied in the current cancer research field, the outcomes of which remain to be determined. In this study, we have attempted to discover novel potent WNT/β-catenin pathway inhibitors through tankyrase 1/2 structure-based virtual screening. After screening more than 13.4 million compounds through molecular docking, we experimentally verified one compound, LZZ-02, as the most potent inhibitor out of 11 structurally representative top hits. LiCl-induced HEK293 cells containing TOPFlash reporter showed that LZZ-02 inhibited the transcriptional activity of β-catenin with an IC50 of 10 ± 1.2 μM. Mechanistically, LZZ-02 degrades the expression of β-catenin by stabilizing axin 2, thereby diminishing downstream proteins levels, including c-Myc and cyclin D1. LZZ-02 also inhibits the growth of colonic carcinoma cell harboring constitutively active β-catenin. More importantly, LZZ-02 effectively shrinks tumor xenograft derived from colonic cell lines. Our study successfully identified a novel tankyrase 1/2 inhibitor and shed light on a novel strategy for developing inhibitors targeting the WNT/β-catenin signaling axis.
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227
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Yin Z. Virtual screening by 2-D fingerprints, shape and docking for discovering new chemotypes of activator protein-1 inhibitors. J Biomol Struct Dyn 2020; 39:2455-2462. [PMID: 32223539 DOI: 10.1080/07391102.2020.1749130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Activator protein-1 Fos/Jun proteins bind to cognate DNA and regulate gene expression. Small-molecule inhibitors targeting activator protein-1 DNA binding have been developed in the past decades for therapeutic applications. Recent structural and in silico studies suggest a putative inhibitor binding pocket on the activator protein-1 structure, and computational modeling suggests a consensus binding mode of three classes of known small-molecule inhibitors. Here, virtual screening of two-dimensional fingerprints, shape, and docking was performed in search of novel chemotypes of activator protein-1 inhibitors. A standard score (Z3-score) coalescing top-ranked ligands from the three methods was used and the top-scoring ligands were clustered by similarity. A representative ligand from each large cluster was evaluated using metadynamics simulations and molecular mechanics calculations. Representative hits showed diverse chemotypes and their modeled binding pose on activator protein-1 reproduced key interactions with binding site residues.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Zhou Yin
- Waksman Institute and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ, USA
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228
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Distinguishing drug/non-drug-like small molecules in drug discovery using deep belief network. Mol Divers 2020; 25:827-838. [PMID: 32193758 DOI: 10.1007/s11030-020-10065-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2020] [Accepted: 02/26/2020] [Indexed: 10/24/2022]
Abstract
The advent of computational methods for efficient prediction of the druglikeness of small molecules and their ever-burgeoning applications in the fields of medicinal chemistry and drug industries have been a profound scientific development, since only a few amounts of the small molecule libraries were identified as approvable drugs. In this study, a deep belief network was utilized to construct a druglikeness classification model. For this purpose, small molecules and approved drugs from the ZINC database were selected for the unsupervised pre-training step and supervised training step. Various binary fingerprints such as Macc 166 bit, PubChem 881 bit, and Morgan 2048 bit as data features were investigated. The report revealed that using an unsupervised pre-training phase can lead to a good performance model and generalizability capability. Accuracy, precision, and recall of the model for Macc features were 97%, 96%, and 99%, respectively. For more consideration about the generalizability of the model, the external data by expression and investigational drugs in drug banks as drug data and randomly selected data from the ZINC database as non-drug were created. The results confirmed the good performance and generalizability capability of the model. Also, the outcomes depicted that a large proportion of misclassified non-drug small molecules ascertain the bioavailability conditions and could be investigated as a drug in the future. Furthermore, our model attempted to tap potential opportunities as a drug filter in drug discovery.
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229
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Kostal J, Voutchkova-Kostal A. Going All In: A Strategic Investment in In Silico Toxicology. Chem Res Toxicol 2020; 33:880-888. [PMID: 32166946 DOI: 10.1021/acs.chemrestox.9b00497] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
As vast numbers of new chemicals are introduced to market annually, we are faced with the grand challenge of protecting humans and the environment while minimizing economically and ethically costly animal testing. In silico models promise to be the solution we seek, but we find ourselves at crossroads of future development efforts that would ensure standalone applicability and reliability of these tools. A conscientious effort that prioritizes experimental testing to support the needs of in silico models (versus regulatory needs) is called for to achieve this goal. Using economic analogy in the title of this work, we argue that a prudent investment is to go all-in to support in silico model development, rather than gamble our future by keeping the status quo of a "balanced portfolio" of testing approaches. We discuss two paths to future in silico toxicology-one based on big-data statistics ("broadsword"), and the other based on direct modeling of molecular interactions ("scalpel")-and offer rationale that the latter approach is more transparent, is better aligned with our quest for fundamental knowledge, and has a greater potential to succeed if we are willing to transform our toxicity-testing paradigm.
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Affiliation(s)
- Jakub Kostal
- Department of Chemistry, The George Washington University, 800 22nd Street NW, Washington, D.C. 20052-0066, United States
| | - Adelina Voutchkova-Kostal
- Department of Chemistry, The George Washington University, 800 22nd Street NW, Washington, D.C. 20052-0066, United States
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230
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Lin X, Li X, Lin X. A Review on Applications of Computational Methods in Drug Screening and Design. Molecules 2020; 25:E1375. [PMID: 32197324 PMCID: PMC7144386 DOI: 10.3390/molecules25061375] [Citation(s) in RCA: 235] [Impact Index Per Article: 58.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 03/16/2020] [Accepted: 03/16/2020] [Indexed: 12/27/2022] Open
Abstract
Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure- and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design.
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Affiliation(s)
- Xiaoqian Lin
- Institute of Single Cell Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, China;
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Xiu Li
- School of Chemistry and Material Science, Shanxi Normal University, Linfen 041004, China;
| | - Xubo Lin
- Institute of Single Cell Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, China;
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
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231
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Shi S, Li J, Sun F, Chen Y, Pang C, Geng Y, Qi J, Guo S, Wang X, Zhang H, Zhan Y, An H. Molecular Mechanisms and Structural Basis of Retigabine Analogues in Regulating KCNQ2 Channel. J Membr Biol 2020; 253:167-181. [DOI: 10.1007/s00232-020-00113-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Accepted: 03/05/2020] [Indexed: 12/22/2022]
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232
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Farhadi Z, Farhadi T, Hashemian SM. Virtual screening for potential inhibitors of β(1,3)-D-glucan synthase as drug candidates against fungal cell wall. J Drug Assess 2020; 9:52-59. [PMID: 32284908 PMCID: PMC7144292 DOI: 10.1080/21556660.2020.1734010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 02/07/2020] [Indexed: 01/17/2023] Open
Abstract
Background To enhance the outcome in patients with invasive candidiasis, initiation of an efficient antifungal treatment in a suitable dosage is necessary. Echinocandins (e.g. caspofungin) inhibit the enzyme β(1,3)-D-glucan synthase of the fungal cell wall. Compared to azoles and other antifungal agents, echinocandins have lower adverse effects and toxicity in humans. Echinocandins are available in injectable (intravenous) form. Methods In this study, to identify the novel oral drug-like compounds that affect the fungal cell wall, downloaded oral drug-like compounds from the ZINC database were processed with a virtual screening procedure. The docking free energies were calculated and compared with the known inhibitor caspofungin. Four molecules were selected as the most potent ligands and subjected to hydrogen bonds analysis. Results Considering the hydrogen bond analysis, two compounds (ZINC71336662 and ZINC40910772) were predicted to better interact with the active site of β(1,3)-D-glucan synthase compared with caspofungin. Conclusion The introduced compound in this study may be valuable to analyze experimentally as a novel oral drug candidate targeting fungal cell walls.
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Affiliation(s)
- Zinat Farhadi
- Chronic Respiratory Diseases Research Center (CRDRC), National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran.,Behavioral Disease Counseling Center, Marvdasht Health Center, Shiraz University of Medical Sciences, Shiraz, Iran.,Department of Microbiology, Shiraz Branch, Islamic Azad University, Shiraz, Iran
| | - Tayebeh Farhadi
- Chronic Respiratory Diseases Research Center (CRDRC), National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyed MohammadReza Hashemian
- Chronic Respiratory Diseases Research Center (CRDRC), National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran.,Critical Care Department, Farhikhtegan Hospital, Tehran Medical Branch, Islamic Azad University, Tehran, Iran
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233
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He X, Liu S, Lee TS, Ji B, Man VH, York DM, Wang J. Fast, Accurate, and Reliable Protocols for Routine Calculations of Protein-Ligand Binding Affinities in Drug Design Projects Using AMBER GPU-TI with ff14SB/GAFF. ACS OMEGA 2020; 5:4611-4619. [PMID: 32175507 PMCID: PMC7066661 DOI: 10.1021/acsomega.9b04233] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 02/13/2020] [Indexed: 05/12/2023]
Abstract
Accurate prediction of the absolute or relative protein-ligand binding affinity is one of the major tasks in computer-aided drug design projects, especially in the stage of lead optimization. In principle, the alchemical free energy (AFE) methods such as thermodynamic integration (TI) or free-energy perturbation (FEP) can fulfill this task, but in practice, a lot of hurdles prevent them from being routinely applied in daily drug design projects, such as the demanding computing resources, slow computing processes, unavailable or inaccurate force field parameters, and difficult and unfriendly setting up and post-analysis procedures. In this study, we have exploited practical protocols of applying the CPU (central processing unit)-TI and newly developed GPU (graphic processing unit)-TI modules and other tools in the AMBER software package, combined with ff14SB/GAFF1.8 force fields, to conduct efficient and accurate AFE calculations on protein-ligand binding free energies. We have tested 134 protein-ligand complexes in total for four target proteins (BACE, CDK2, MCL1, and PTP1B) and obtained overall comparable performance with the commercial Schrodinger FEP+ program (WangJ. Am. Chem. Soc.2015, 137, 2695-2703). The achieved accuracy fits within the requirements for computations to generate effective guidance for experimental work in drug lead optimization, and the needed wall time is short enough for practical application. Our verified protocol provides a practical solution for routine AFE calculations in real drug design projects.
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Affiliation(s)
- Xibing He
- Department
of Pharmaceutical Sciences and Computational Chemical Genomics Screening
Center, School of Pharmacy, University of
Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Shuhan Liu
- Department
of Pharmaceutical Sciences and Computational Chemical Genomics Screening
Center, School of Pharmacy, University of
Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Tai-Sung Lee
- Laboratory
for Biomolecular Simulation Research, Center for Integrative Proteomics
Research, and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Beihong Ji
- Department
of Pharmaceutical Sciences and Computational Chemical Genomics Screening
Center, School of Pharmacy, University of
Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Viet H. Man
- Department
of Pharmaceutical Sciences and Computational Chemical Genomics Screening
Center, School of Pharmacy, University of
Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Darrin M. York
- Laboratory
for Biomolecular Simulation Research, Center for Integrative Proteomics
Research, and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Junmei Wang
- Department
of Pharmaceutical Sciences and Computational Chemical Genomics Screening
Center, School of Pharmacy, University of
Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
- E-mail: . Phone: (412) 383-3268. Fax: (412) 383-7436
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234
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Park CS, Iwabata K, Sridhar U, Tsuei M, Singh K, Kim YK, Thayumanavan S, Abbott NL. A New Strategy for Reporting Specific Protein Binding Events at Aqueous-Liquid Crystal Interfaces in the Presence of Non-Specific Proteins. ACS APPLIED MATERIALS & INTERFACES 2020; 12:7869-7878. [PMID: 31825195 PMCID: PMC7368459 DOI: 10.1021/acsami.9b16867] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Aqueous-liquid crystal (LC) interfaces offer promise as responsive interfaces at which biomolecular recognition events can be amplified into macroscopic signals. However, the design of LC interfaces that distinguish between specific and non-specific protein interactions remains an unresolved challenge. Herein, we report the synthesis of amphiphilic monomers, dimers, and trimers conjugated to sulfonamide ligands via triazole rings, their assembly at aqueous-LC interfaces, and the orientational response of LCs to the interactions of carbonic anhydrase II (CAII) and serum albumin with the oligomer-decorated LC interfaces. Of six oligomers synthesized, only dimers without amide methylation were found to assemble at aqueous interfaces of nematic 4-cyano-4'-pentylbiphenyl (5CB) to induce perpendicular LC orientations. At dimer-decorated LC interfaces, we found that concentrations of CAII less than 4 μM did not measurably perturb the LC but prevented non-specific adsorption and penetration of serum albumin into the dimer-decorated interface that otherwise triggered bright, globular LC optical domains. These experiments and others (including competitive adsorption of CAII, BSA, and lysozyme) support our hypothesis that specific binding of CAII to the dimer prevents LC anchoring transitions triggered by non-specific adsorption of serum albumin. We illustrate the utility of the approach by reporting (i) the relative activity of two small-molecule inhibitors (6-ethoxy-2-benzothiazolesulfonamide and benzenesulfonamide) of CAII to sulfonamide and (ii) proteolytic digestion of a protein (CAII) by thermolysin. Overall, the results in this paper provide new insight into the interactions of proteins at aqueous-LC interfaces and fresh ideas for either blocking non-specific interactions of proteins at surfaces or reporting specific binding events at LC interfaces in the presence of non-specific proteins.
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Affiliation(s)
- Chul Soon Park
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Kazuki Iwabata
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Uma Sridhar
- Department of Chemistry, University of Massachusetts Amherst, Amherst, MA 01003, USA
| | - Michael Tsuei
- Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Khushboo Singh
- Department of Chemistry, University of Massachusetts Amherst, Amherst, MA 01003, USA
| | - Young-ki Kim
- Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14853, USA
- Department of Chemical Engineering, Pohang University of Science and Technology, Pohang, Gyeongbuk 37673, Korea
| | - S. Thayumanavan
- Department of Chemistry, University of Massachusetts Amherst, Amherst, MA 01003, USA
| | - Nicholas L. Abbott
- Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14853, USA
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235
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Xu Y, He Z, Liu H, Chen Y, Gao Y, Zhang S, Wang M, Lu X, Wang C, Zhao Z, Liu Y, Zhao J, Yu Y, Yang M. 3D-QSAR, molecular docking, and molecular dynamics simulation study of thieno[3,2- b]pyrrole-5-carboxamide derivatives as LSD1 inhibitors. RSC Adv 2020; 10:6927-6943. [PMID: 35493862 PMCID: PMC9049714 DOI: 10.1039/c9ra10085g] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 02/01/2020] [Indexed: 12/28/2022] Open
Abstract
Histone Lysine Specific Demethylase 1 (LSD1) is overexpressed in many cancers and becomes a new target for anticancer drugs. In recent years, small molecule inhibitors with various structures targeting LSD1 have been reported. Here we report the binding interaction modes of a series of thieno[3,2-b]pyrrole-5-carboxamide LSD1 inhibitors using molecular docking, and three-dimensional quantitative structure-activity relationships (3D-QSAR). Comparative molecular field analysis (CoMFA q 2 = 0.783, r 2 = 0.944, r pred 2 = 0.851) and comparative molecular similarity indices analysis (CoMSIA q 2 = 0.728, r 2 = 0.982, r pred 2 = 0.814) were used to establish 3D-QSAR models, which had good verification and prediction capabilities. Based on the contour maps and the information of molecular docking, 8 novel small molecules were designed in silico, among which compounds D4, D5 and D8 with high predictive activity were subjected to further molecular dynamics simulations (MD), and their possible binding modes were explored. It was found that Asn535 plays a crucial role in stabilizing the inhibitors. Furthermore, ADME and bioavailability prediction for D4, D5 and D8 were carried out. The results would provide valuable guidance for designing new reversible LSD1 inhibitors in the future.
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Affiliation(s)
- Yongtao Xu
- School of Biomedical Engineering, Xinxiang Medical University Xinxiang Henan 453003 China
- Xinxiang Key Laboratory of Biomedical Information Research Xinxiang Henan 453003 China
- Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data Xinxiang Henan 453003 China
| | - Zihao He
- School of Biomedical Engineering, Xinxiang Medical University Xinxiang Henan 453003 China
- Xinxiang Key Laboratory of Biomedical Information Research Xinxiang Henan 453003 China
- Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data Xinxiang Henan 453003 China
| | - Hongyi Liu
- School of Biomedical Engineering, Xinxiang Medical University Xinxiang Henan 453003 China
- Xinxiang Key Laboratory of Biomedical Information Research Xinxiang Henan 453003 China
- Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data Xinxiang Henan 453003 China
| | - Yifan Chen
- School of Biomedical Engineering, Xinxiang Medical University Xinxiang Henan 453003 China
- Xinxiang Key Laboratory of Biomedical Information Research Xinxiang Henan 453003 China
- Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data Xinxiang Henan 453003 China
| | - Yunlong Gao
- School of Biomedical Engineering, Xinxiang Medical University Xinxiang Henan 453003 China
- Xinxiang Key Laboratory of Biomedical Information Research Xinxiang Henan 453003 China
- Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data Xinxiang Henan 453003 China
| | - Songjie Zhang
- School of Biomedical Engineering, Xinxiang Medical University Xinxiang Henan 453003 China
- Xinxiang Key Laboratory of Biomedical Information Research Xinxiang Henan 453003 China
- Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data Xinxiang Henan 453003 China
| | - Meiting Wang
- School of Biomedical Engineering, Xinxiang Medical University Xinxiang Henan 453003 China
- Xinxiang Key Laboratory of Biomedical Information Research Xinxiang Henan 453003 China
- Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data Xinxiang Henan 453003 China
| | - Xiaoyuan Lu
- School of Biomedical Engineering, Xinxiang Medical University Xinxiang Henan 453003 China
| | - Chang Wang
- School of Biomedical Engineering, Xinxiang Medical University Xinxiang Henan 453003 China
| | - Zongya Zhao
- School of Biomedical Engineering, Xinxiang Medical University Xinxiang Henan 453003 China
| | - Yan Liu
- School of Biomedical Engineering, Xinxiang Medical University Xinxiang Henan 453003 China
| | - Junqiang Zhao
- School of Biomedical Engineering, Xinxiang Medical University Xinxiang Henan 453003 China
| | - Yi Yu
- School of Biomedical Engineering, Xinxiang Medical University Xinxiang Henan 453003 China
| | - Min Yang
- School of Biomedical Engineering, Xinxiang Medical University Xinxiang Henan 453003 China
- Xinxiang Key Laboratory of Biomedical Information Research Xinxiang Henan 453003 China
- Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data Xinxiang Henan 453003 China
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236
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Majewski M, Barril X. Structural Stability Predicts the Binding Mode of Protein–Ligand Complexes. J Chem Inf Model 2020; 60:1644-1651. [DOI: 10.1021/acs.jcim.9b01062] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Maciej Majewski
- Institut de Biomedicina de la Universitat de Barcelona (IBUB) and Facultat de Farmacia, Universitat de Barcelona, Av. Joan XXIII 27-31, Barcelona 08028, Spain
| | - Xavier Barril
- Institut de Biomedicina de la Universitat de Barcelona (IBUB) and Facultat de Farmacia, Universitat de Barcelona, Av. Joan XXIII 27-31, Barcelona 08028, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluis Companys 23, Barcelona 08010, Spain
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237
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Rifai EA, Ferrario V, Pleiss J, Geerke DP. Combined Linear Interaction Energy and Alchemical Solvation Free-Energy Approach for Protein-Binding Affinity Computation. J Chem Theory Comput 2020; 16:1300-1310. [PMID: 31894691 PMCID: PMC7017367 DOI: 10.1021/acs.jctc.9b00890] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Calculating free energies of binding (ΔGbind) between ligands and their target protein is of major interest to drug discovery and safety, yet it is still associated with several challenges and difficulties. Linear interaction energy (LIE) is an efficient in silico method for ΔGbind computation. LIE models can be trained and used to directly calculate binding affinities from interaction energies involving ligands in the bound and unbound states only, and LIE can be combined with statistical weighting to calculate ΔGbind for flexible proteins that may bind their ligands in multiple orientations. Here, we investigate if LIE predictions can be effectively improved by explicitly including the entropy of (de)solvation into our free-energy calculations. For that purpose, we combine LIE calculations for the protein-ligand-bound state with explicit free-energy perturbation to rigorously compute the unbound ligand's solvation free energy. We show that for 28 Cytochrome P450 2A6 (CYP2A6) ligands, coupling LIE with alchemical solvation free-energy calculation helps to improve obtained correlation between computed and reference (experimental) binding data.
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Affiliation(s)
- Eko Aditya Rifai
- AIMMS Division of Molecular and Computational Toxicology, Department of Chemistry and Pharmaceutical Sciences , Vrije Universiteit Amsterdam , De Boelelaan 1108 , 1081 HZ Amsterdam , The Netherlands
| | - Valerio Ferrario
- Institute of Biochemistry and Technical Biochemistry , Universität Stuttgart , Allmandring 31 , 70569 Stuttgart , Germany
| | - Jürgen Pleiss
- Institute of Biochemistry and Technical Biochemistry , Universität Stuttgart , Allmandring 31 , 70569 Stuttgart , Germany
| | - Daan P Geerke
- AIMMS Division of Molecular and Computational Toxicology, Department of Chemistry and Pharmaceutical Sciences , Vrije Universiteit Amsterdam , De Boelelaan 1108 , 1081 HZ Amsterdam , The Netherlands
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238
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QM Implementation in Drug Design: Does It Really Help? Methods Mol Biol 2020. [PMID: 32016884 DOI: 10.1007/978-1-0716-0282-9_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/13/2023]
Abstract
Computational chemistry allows one to characterize the structure, dynamics, and energetics of protein-ligand interactions, which makes it a valuable tool in drug discovery in both academic research and pharmaceutical industry. Molecular mechanics (MM)-based approaches are widely utilized to assist the discovery of new drug candidates. However, the complexity of protein-ligand interactions challenges the accuracy and efficiency of the commonly used empirical methods. Aiming to provide better accuracy in the description of protein-ligand interactions, quantum mechanics (QM)-based approaches are becoming increasingly explored. In principle, QM calculation includes all contributions to the energy, accounting for terms usually missing in empirical force fields, and provides a greater degree of transferability. The usefulness of QM in drug design cannot be overemphasized. In this chapter, we present recent developments and applications of fragment-based QM method in studying the protein-ligand and protein-protein interactions. We critically discuss the performance of the fragment-based QM method at different ab initio levels while trying to answer a critical question: do QM-based methods really help in drug design?
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239
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Discovery of Novel Inhibitors Targeting Multi-UDP-hexose Pyrophosphorylases as Anticancer Agents. Molecules 2020; 25:molecules25030645. [PMID: 32028604 PMCID: PMC7038226 DOI: 10.3390/molecules25030645] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 01/25/2020] [Accepted: 01/27/2020] [Indexed: 02/06/2023] Open
Abstract
To minimize treatment toxicities, recent anti-cancer research efforts have switched from broad-based chemotherapy to targeted therapy, and emerging data show that altered cellular metabolism in cancerous cells can be exploited as new venues for targeted intervention. In this study, we focused on, among the altered metabolic processes in cancerous cells, altered glycosylation due to its documented roles in cancer tumorigenesis, metastasis and drug resistance. We hypothesize that the enzymes required for the biosynthesis of UDP-hexoses, glycosyl donors for glycan synthesis, could serve as therapeutic targets for cancers. Through structure-based virtual screening and kinetic assay, we identified a drug-like chemical fragment, GAL-012, that inhibit a small family of UDP-hexose pyrophosphorylases-galactose pyro-phosphorylase (GALT), UDP-glucose pyrophosphorylase (UGP2) and UDP-N-acetylglucosamine pyrophosphorylase (AGX1/UAP1) with an IC50 of 30 µM. The computational docking studies supported the interaction of GAL-012 to the binding sites of GALT at Trp190 and Ser192, UGP2 at Gly116 and Lys127, and AGX1/UAP1 at Asn327 and Lys407, respectively. One of GAL-012 derivatives GAL-012-2 also demonstrated the inhibitory activity against GALT and UGP2. Moreover, we showed that GAL-012 suppressed the growth of PC3 cells in a dose-dependent manner with an EC50 of 75 µM with no effects on normal skin fibroblasts at 200 µM. Western blot analysis revealed reduced expression of pAKT (Ser473), pAKT (Thr308) by 77% and 72%, respectively in the treated cells. siRNA experiments against the respective genes encoding the pyrophosphorylases were also performed and the results further validated the proposed roles in cancer growth inhibition. Finally, synergistic relationships between GAL-012 and tunicamycin, as well as bortezomib (BTZ) in killing cultured cancer cells were observed, respectively. With its unique scaffold and relatively small size, GAL-012 serves as a promising early chemotype for optimization to become a safe, effective, multi-target anti-cancer drug candidate which could be used alone or in combination with known therapeutics.
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240
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Parks CD, Gaieb Z, Chiu M, Yang H, Shao C, Walters WP, Jansen JM, McGaughey G, Lewis RA, Bembenek SD, Ameriks MK, Mirzadegan T, Burley SK, Amaro RE, Gilson MK. D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies. J Comput Aided Mol Des 2020; 34:99-119. [PMID: 31974851 PMCID: PMC7261493 DOI: 10.1007/s10822-020-00289-y] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 01/13/2020] [Indexed: 12/11/2022]
Abstract
The Drug Design Data Resource (D3R) aims to identify best practice methods for computer aided drug design through blinded ligand pose prediction and affinity challenges. Herein, we report on the results of Grand Challenge 4 (GC4). GC4 focused on proteins beta secretase 1 and Cathepsin S, and was run in an analogous manner to prior challenges. In Stage 1, participant ability to predict the pose and affinity of BACE1 ligands were assessed. Following the completion of Stage 1, all BACE1 co-crystal structures were released, and Stage 2 tested affinity rankings with co-crystal structures. We provide an analysis of the results and discuss insights into determined best practice methods.
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Affiliation(s)
- Conor D Parks
- Drug Design Data Resource, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Zied Gaieb
- Drug Design Data Resource, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Michael Chiu
- Drug Design Data Resource, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Huanwang Yang
- RCSB Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, New Brunswick, NJ, 08903, USA
- San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Chenghua Shao
- RCSB Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, New Brunswick, NJ, 08903, USA
- San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA, 92093, USA
| | | | - Johanna M Jansen
- Novartis Institutes for BioMedical Research, Emeryville, CA, 94608, USA
| | | | - Richard A Lewis
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, 4002, Basel, Switzerland
| | | | | | | | - Stephen K Burley
- RCSB Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, New Brunswick, NJ, 08903, USA
- San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Rommie E Amaro
- Drug Design Data Resource, University of California, San Diego, La Jolla, CA, 92093, USA.
- Department of Chemistry and Biochemistry, UC San Diego, La Jolla, CA, 92093-0340, USA.
| | - Michael K Gilson
- Drug Design Data Resource, University of California, San Diego, La Jolla, CA, 92093, USA.
- Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego, 9500 Gilman Drive, MC0751, La Jolla, CA, 92093, USA.
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Bhattacharyya MK, Dutta D, Nashre-ul-Islam SM, Frontera A, Sharma P, Verma AK, Das A. Energetically significant antiparallel π-stacking contacts in Co(II), Ni(II) and Cu(II) coordination compounds of pyridine-2,6-dicarboxylates: Antiproliferative evaluation and theoretical studies. Inorganica Chim Acta 2020. [DOI: 10.1016/j.ica.2019.119233] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Understanding the potency of malarial ligand (D44) in plasmodium FKBP35 and modelled halogen atom (Br, Cl, F) functional groups. J Mol Graph Model 2020; 97:107553. [PMID: 32035313 DOI: 10.1016/j.jmgm.2020.107553] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 01/10/2020] [Accepted: 01/27/2020] [Indexed: 11/21/2022]
Abstract
The present study clearly depicts the understanding of the D44 in Plasmodium FKBP35 around the hinge region. To analyse the binding stability of D44 ligand and to understand the role of halogen bond, hydrogen bond interaction formed between the hinge region amino acids: Isoleucine (Ile74), Phenylalanine (Phe54), Aspartic acid (Asp55) Phenylalanine (Phe64),Tyrosine (Tyr100), Tryptophan (TRP 77) and ligand D44 was portrayed specifically through interaction energy calculations at HF, M062X, MP2 level of theories for different basis set (6-311G**, 6-31+G*, LANL2DZ). The investigation will provide an apparent picture regarding the non-covalent interaction that hold the contact of ligand and amino acids in the hinge region and the implication of modelled functional groups (Br, Cl, F, OSO and NH2) on ligand, which will help chemist in synthesizing new novel ligands. HOMO, LUMO chart calculated for D44 ligands reveals graphic illustration of orbital's that stimulate for contact. The aim and natural bond orbital analysis identified key contribution of individual hydrogen/halogen bonds that contribute for the binding strength through stabilization energy, ρ and ∇2ρ values. Overall this study finds out that the Stability of D44 in Plasmodium FKBP35 was enhanced by the Halogen atom (Br, Cl, F) functional groups; which provide an innovative pathway for the selection of functional groups that opt for the hinge region side chains on the ligand.
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243
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Flood DT, Kingston C, Vantourout JC, Dawson PE, Baran PS. DNA Encoded Libraries: A Visitor's Guide. Isr J Chem 2020. [DOI: 10.1002/ijch.201900133] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Dillon T. Flood
- Department of ChemistryScripps Research 10550 North Torrey Pines Road La Jolla, California 93037
| | - Cian Kingston
- Department of ChemistryScripps Research 10550 North Torrey Pines Road La Jolla, California 93037
| | - Julien C. Vantourout
- Department of ChemistryScripps Research 10550 North Torrey Pines Road La Jolla, California 93037
| | - Philip E. Dawson
- Department of ChemistryScripps Research 10550 North Torrey Pines Road La Jolla, California 93037
| | - Phil S. Baran
- Department of ChemistryScripps Research 10550 North Torrey Pines Road La Jolla, California 93037
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244
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Sakae Y, Zhang BW, Levy RM, Deng N. Absolute Protein Binding Free Energy Simulations for Ligands with Multiple Poses, a Thermodynamic Path That Avoids Exhaustive Enumeration of the Poses. J Comput Chem 2020; 41:56-68. [PMID: 31621932 PMCID: PMC7140983 DOI: 10.1002/jcc.26078] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Revised: 08/07/2019] [Accepted: 08/31/2019] [Indexed: 12/28/2022]
Abstract
We propose a free energy calculation method for receptor-ligand binding, which have multiple binding poses that avoids exhaustive enumeration of the poses. For systems with multiple binding poses, the standard procedure is to enumerate orientations of the binding poses, restrain the ligand to each orientation, and then, calculate the binding free energies for each binding pose. In this study, we modify a part of the thermodynamic cycle in order to sample a broader conformational space of the ligand in the binding site. This modification leads to more accurate free energy calculation without performing separate free energy simulations for each binding pose. We applied our modification to simple model host-guest systems as a test, which have only two binding poses, by using a single decoupling method (SDM) in implicit solvent. The results showed that the binding free energies obtained from our method without knowing the two binding poses were in good agreement with the benchmark results obtained by explicit enumeration of the binding poses. Our method is applicable to other alchemical binding free energy calculation methods such as the double decoupling method (DDM) in explicit solvent. We performed a calculation for a protein-ligand system with explicit solvent using our modified thermodynamic path. The results of the free energy simulation along our modified path were in good agreement with the results of conventional DDM, which requires a separate binding free energy calculation for each of the binding poses of the example of phenol binding to T4 lysozyme in explicit solvent. © 2019 Wiley Periodicals, Inc.
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Affiliation(s)
- Yoshitake Sakae
- Center for Biophysics and Computational Biology, Temple University, Philadelphia, Pennsylvania, 19122
| | - Bin W Zhang
- Center for Biophysics and Computational Biology, Temple University, Philadelphia, Pennsylvania, 19122
| | - Ronald M Levy
- Center for Biophysics and Computational Biology, Temple University, Philadelphia, Pennsylvania, 19122
- Department of Chemistry, Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania, 19122
| | - Nanjie Deng
- Department of Chemistry and Physical Sciences, Pace University, New York, New York, 10038
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245
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Lodola A, Callegari D, Scalvini L, Rivara S, Mor M. Design and SAR Analysis of Covalent Inhibitors Driven by Hybrid QM/MM Simulations. Methods Mol Biol 2020; 2114:307-337. [PMID: 32016901 DOI: 10.1007/978-1-0716-0282-9_19] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Quantum mechanics/molecular mechanics (QM/MM) hybrid technique is emerging as a reliable computational method to investigate and characterize chemical reactions occurring in enzymes. From a drug discovery perspective, a thorough understanding of enzyme catalysis appears pivotal to assist the design of inhibitors able to covalently bind one of the residues belonging to the enzyme catalytic machinery. Thanks to the current advances in computer power, and the availability of more efficient algorithms for QM-based simulations, the use of QM/MM methodology is becoming a viable option in the field of covalent inhibitor design. In the present review, we summarized our experience in the field of QM/MM simulations applied to drug design problems which involved the optimization of agents working on two well-known drug targets, namely fatty acid amide hydrolase (FAAH) and epidermal growth factor receptor (EGFR). In this context, QM/MM simulations gave valuable information in terms of geometry (i.e., of transition states and metastable intermediates) and reaction energetics that allowed to correctly predict inhibitor binding orientation and substituent effect on enzyme inhibition. What is more, enzyme reaction modelling with QM/MM provided insights that were translated into the synthesis of new covalent inhibitor featured by a unique combination of intrinsic reactivity, on-target activity, and selectivity.
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Affiliation(s)
- Alessio Lodola
- Drug Design and Discovery Group, Department of Food and Drug, University of Parma, Parma, Italy.
| | - Donatella Callegari
- Drug Design and Discovery Group, Department of Food and Drug, University of Parma, Parma, Italy
| | - Laura Scalvini
- Drug Design and Discovery Group, Department of Food and Drug, University of Parma, Parma, Italy
| | - Silvia Rivara
- Drug Design and Discovery Group, Department of Food and Drug, University of Parma, Parma, Italy
| | - Marco Mor
- Drug Design and Discovery Group, Department of Food and Drug, University of Parma, Parma, Italy
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246
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Shubhangi, Paul AK. Getting insights of molecular interactions for potential drug candidates against S. aureus: Pharmacophore modeling, molecular screening and docking studies. J Mol Graph Model 2020; 94:107487. [DOI: 10.1016/j.jmgm.2019.107487] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 10/18/2019] [Accepted: 10/29/2019] [Indexed: 11/25/2022]
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247
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Stolbov LA, Druzhilovskiy DS, Filimonov DA, Nicklaus MC, Poroikov VV. (Q)SAR Models of HIV-1 Protein Inhibition by Drug-Like Compounds. Molecules 2019; 25:molecules25010087. [PMID: 31881687 PMCID: PMC6983201 DOI: 10.3390/molecules25010087] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 12/17/2019] [Accepted: 12/18/2019] [Indexed: 12/17/2022] Open
Abstract
Despite the achievements of antiretroviral therapy, discovery of new anti-HIV medicines remains an essential task because the existing drugs do not provide a complete cure for the infected patients, exhibit severe adverse effects, and lead to the appearance of resistant strains. To predict the interaction of drug-like compounds with multiple targets for HIV treatment, ligand-based drug design approach is widely applied. In this study, we evaluated the possibilities and limitations of (Q)SAR analysis aimed at the discovery of novel antiretroviral agents inhibiting the vital HIV enzymes. Local (Q)SAR models are based on the analysis of structure–activity relationships for molecules from the same chemical class, which significantly restrict their applicability domain. In contrast, global (Q)SAR models exploit data from heterogeneous sets of drug-like compounds, which allows their application to databases containing diverse structures. We compared the information for HIV-1 integrase, protease and reverse transcriptase inhibitors available in the EBI ChEMBL, NIAID HIV/OI/TB Therapeutics, and Clarivate Analytics Integrity databases as the sources for (Q)SAR training sets. Using the PASS and GUSAR software, we developed and validated a variety of (Q)SAR models, which can be further used for virtual screening of new antiretrovirals in the SAVI library. The developed models are implemented in the freely available web resource AntiHIV-Pred.
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Affiliation(s)
- Leonid A. Stolbov
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10 bldg. 8, Pogodinskaya str., 119121 Moscow, Russia; (L.A.S.); (D.S.D.); (D.A.F.)
| | - Dmitry S. Druzhilovskiy
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10 bldg. 8, Pogodinskaya str., 119121 Moscow, Russia; (L.A.S.); (D.S.D.); (D.A.F.)
| | - Dmitry A. Filimonov
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10 bldg. 8, Pogodinskaya str., 119121 Moscow, Russia; (L.A.S.); (D.S.D.); (D.A.F.)
| | - Marc C. Nicklaus
- Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA;
| | - Vladimir V. Poroikov
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10 bldg. 8, Pogodinskaya str., 119121 Moscow, Russia; (L.A.S.); (D.S.D.); (D.A.F.)
- Correspondence:
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248
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Yoshino R, Yasuo N, Sekijima M. Molecular Dynamics Simulation reveals the mechanism by which the Influenza Cap-dependent Endonuclease acquires resistance against Baloxavir marboxil. Sci Rep 2019; 9:17464. [PMID: 31767949 PMCID: PMC6877583 DOI: 10.1038/s41598-019-53945-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Accepted: 10/24/2019] [Indexed: 12/11/2022] Open
Abstract
Baloxavir marboxil (BXM), an antiviral drug for influenza virus, inhibits RNA replication by binding to RNA replication cap-dependent endonuclease (CEN) of influenza A and B viruses. Although this drug was only approved by the FDA in October 2018, drug resistant viruses have already been detected from clinical trials owing to an I38 mutation of CEN. To investigate the reduction of drug sensitivity by the I38 mutant variants, we performed a molecular dynamics (MD) simulation on the CEN-BXM complex structure to analyze variations in the mode of interaction. Our simulation results suggest that the side chain methyl group of I38 in CEN engages in a CH-pi interaction with the aromatic ring of BXM. This interaction is abolished in various I38 mutant variants. Moreover, MD simulation on various mutation models and binding free energy prediction by MM/GBSA method suggest that the I38 mutation precludes any interaction with the aromatic ring of BXA and thereby reduces BXA sensitivity.
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Affiliation(s)
- Ryunosuke Yoshino
- Transborder Medical Research Center, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8577, Japan
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8577, Japan
| | - Nobuaki Yasuo
- Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology, 4259-J3-23 Nagatsutacho, Midori-ku, Yokohama, Kanagawa, 226-8501, Japan
| | - Masakazu Sekijima
- Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology, 4259-J3-23 Nagatsutacho, Midori-ku, Yokohama, Kanagawa, 226-8501, Japan.
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249
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Shubhangi, Kumar N, Kanagaraj R, Lal K, Paul AK. Modeling molecular interactions of propounded pyrazole based drug candidates against bacterial DNA gyrase: Validation by syntheses and biological studies. J Mol Struct 2019. [DOI: 10.1016/j.molstruc.2019.05.125] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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250
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Affiliation(s)
| | - Chloe Luyet
- Department of Chemical Engineering and Materials Science, Wayne State University, Detroit, MI, USA
| | - Jeffrey J. Potoff
- Department of Chemical Engineering and Materials Science, Wayne State University, Detroit, MI, USA
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