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Wang Z, Zhang H, Gao Z, Sang Z, De Clercq E, Pannecouque C, Kang D, Zhan P, Liu X. Structure-based design and optimization lead to the identification of novel dihydrothiopyrano[3,2- d]pyrimidine derivatives as potent HIV-1 inhibitors against drug-resistant variants. Acta Pharm Sin B 2024; 14:1257-1282. [PMID: 38486991 PMCID: PMC10935503 DOI: 10.1016/j.apsb.2023.11.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 11/03/2023] [Accepted: 11/14/2023] [Indexed: 03/17/2024] Open
Abstract
With our continuous endeavors in seeking potent anti-HIV-1 agents, we reported here the discovery, biological characterization, and druggability evaluation of a class of nonnucleoside reverse transcriptase inhibitors. To fully explore the chemical space of the NNRTI-binding pocket, novel series of dihydrothiopyrano [3,2-d]pyrimidines were developed by employing the structure-based design strategy. Most of the derivatives were endowed with prominent antiviral activities against HIV-1 wild-type and resistant strains at nanomolar levels. Among them, compound 23h featuring the aminopiperidine moiety was identified as the most potent inhibitor, with EC50 values ranging from 3.43 to 21.4 nmol/L. Especially, for the challenging double-mutants F227L + V106A and K103N + Y181C, 23h exhibited 2.3- to 14.5-fold more potent activity than the first-line drugs efavirenz and etravirine. Besides, the resistance profiles of 23h achieved remarkable improvement compared to efavirenz and etravirine. The binding target of 23h was further confirmed to be HIV-1 reverse transcriptase. Molecular modeling studies were also performed to elucidate the biological evaluation results and give guidance for the optimization campaign. Furthermore, no apparent inhibition of the major CYP450 enzymes and hERG channel was observed for 23h. Most importantly, 23h was characterized by good pharmacokinetic properties and excellent safety in vivo. Collectively, 23h holds great promise as a potential candidate for its effective antiviral efficacy and favorable drug-like profiles.
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Affiliation(s)
- Zhao Wang
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- China-Belgium Collaborative Research Center for Innovative Antiviral Drugs of Shandong Province, Jinan 250012, China
- Suzhou Research Institute, Shandong University, Suzhou 215123, China
| | - Heng Zhang
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Zhen Gao
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Zihao Sang
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Erik De Clercq
- Rega Institute for Medical Research, Laboratory of Virology and Chemotherapy, K.U. Leuven, Leuven B-3000, Belgium
| | - Christophe Pannecouque
- Rega Institute for Medical Research, Laboratory of Virology and Chemotherapy, K.U. Leuven, Leuven B-3000, Belgium
| | - Dongwei Kang
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- China-Belgium Collaborative Research Center for Innovative Antiviral Drugs of Shandong Province, Jinan 250012, China
| | - Peng Zhan
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- China-Belgium Collaborative Research Center for Innovative Antiviral Drugs of Shandong Province, Jinan 250012, China
| | - Xinyong Liu
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- China-Belgium Collaborative Research Center for Innovative Antiviral Drugs of Shandong Province, Jinan 250012, China
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Baillif B, Cole J, Giangreco I, McCabe P, Bender A. Applying atomistic neural networks to bias conformer ensembles towards bioactive-like conformations. J Cheminform 2023; 15:124. [PMID: 38129933 PMCID: PMC10740246 DOI: 10.1186/s13321-023-00794-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 12/10/2023] [Indexed: 12/23/2023] Open
Abstract
Identifying bioactive conformations of small molecules is an essential process for virtual screening applications relying on three-dimensional structure such as molecular docking. For most small molecules, conformer generators retrieve at least one bioactive-like conformation, with an atomic root-mean-square deviation (ARMSD) lower than 1 Å, among the set of low-energy conformers generated. However, there is currently no general method to prioritise these likely target-bound conformations in the ensemble. In this work, we trained atomistic neural networks (AtNNs) on 3D information of generated conformers of a curated subset of PDBbind ligands to predict the ARMSD to their closest bioactive conformation, and evaluated the early enrichment of bioactive-like conformations when ranking conformers by AtNN prediction. AtNN ranking was compared with bioactivity-unaware baselines such as ascending Sage force field energy ranking, and a slower bioactivity-based baseline ranking by ascending Torsion Fingerprint Deviation to the Maximum Common Substructure to the most similar molecule in the training set (TFD2SimRefMCS). On test sets from random ligand splits of PDBbind, ranking conformers using ComENet, the AtNN encoding the most 3D information, leads to early enrichment of bioactive-like conformations with a median BEDROC of 0.29 ± 0.02, outperforming the best bioactivity-unaware Sage energy ranking baseline (median BEDROC of 0.18 ± 0.02), and performing on a par with the bioactivity-based TFD2SimRefMCS baseline (median BEDROC of 0.31 ± 0.02). The improved performance of the AtNN and TFD2SimRefMCS baseline is mostly observed on test set ligands that bind proteins similar to proteins observed in the training set. On a more challenging subset of flexible molecules, the bioactivity-unaware baselines showed median BEDROCs up to 0.02, while AtNNs and TFD2SimRefMCS showed median BEDROCs between 0.09 and 0.13. When performing rigid ligand re-docking of PDBbind ligands with GOLD using the 1% top-ranked conformers, ComENet ranked conformers showed a higher successful docking rate than bioactivity-unaware baselines, with a rate of 0.48 ± 0.02 compared to CSD probability baseline with a rate of 0.39 ± 0.02. Similarly, on a pharmacophore searching experiment, selecting the 20% top-ranked conformers ranked by ComENet showed higher hit rate compared to baselines. Hence, the approach presented here uses AtNNs successfully to focus conformer ensembles towards bioactive-like conformations, representing an opportunity to reduce computational expense in virtual screening applications on known targets that require input conformations.
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Affiliation(s)
- Benoit Baillif
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, Cambridge, CB2 1EW, UK
| | - Jason Cole
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge, CB2 1EZ, UK
| | - Ilenia Giangreco
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge, CB2 1EZ, UK
- Exscientia plc, The Schrödinger Building, Oxford Science Park, Oxford, OX4 4GE, UK
| | - Patrick McCabe
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge, CB2 1EZ, UK
| | - Andreas Bender
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, Cambridge, CB2 1EW, UK.
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3
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Stylianakis I, Zervos N, Lii JH, Pantazis DA, Kolocouris A. Conformational energies of reference organic molecules: benchmarking of common efficient computational methods against coupled cluster theory. J Comput Aided Mol Des 2023; 37:607-656. [PMID: 37597063 PMCID: PMC10618395 DOI: 10.1007/s10822-023-00513-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 06/03/2023] [Indexed: 08/21/2023]
Abstract
We selected 145 reference organic molecules that include model fragments used in computer-aided drug design. We calculated 158 conformational energies and barriers using force fields, with wide applicability in commercial and free softwares and extensive application on the calculation of conformational energies of organic molecules, e.g. the UFF and DREIDING force fields, the Allinger's force fields MM3-96, MM3-00, MM4-8, the MM2-91 clones MMX and MM+, the MMFF94 force field, MM4, ab initio Hartree-Fock (HF) theory with different basis sets, the standard density functional theory B3LYP, the second-order post-HF MP2 theory and the Domain-based Local Pair Natural Orbital Coupled Cluster DLPNO-CCSD(T) theory, with the latter used for accurate reference values. The data set of the organic molecules includes hydrocarbons, haloalkanes, conjugated compounds, and oxygen-, nitrogen-, phosphorus- and sulphur-containing compounds. We reviewed in detail the conformational aspects of these model organic molecules providing the current understanding of the steric and electronic factors that determine the stability of low energy conformers and the literature including previous experimental observations and calculated findings. While progress on the computer hardware allows the calculations of thousands of conformations for later use in drug design projects, this study is an update from previous classical studies that used, as reference values, experimental ones using a variety of methods and different environments. The lowest mean error against the DLPNO-CCSD(T) reference was calculated for MP2 (0.35 kcal mol-1), followed by B3LYP (0.69 kcal mol-1) and the HF theories (0.81-1.0 kcal mol-1). As regards the force fields, the lowest errors were observed for the Allinger's force fields MM3-00 (1.28 kcal mol-1), ΜΜ3-96 (1.40 kcal mol-1) and the Halgren's MMFF94 force field (1.30 kcal mol-1) and then for the MM2-91 clones MMX (1.77 kcal mol-1) and MM+ (2.01 kcal mol-1) and MM4 (2.05 kcal mol-1). The DREIDING (3.63 kcal mol-1) and UFF (3.77 kcal mol-1) force fields have the lowest performance. These model organic molecules we used are often present as fragments in drug-like molecules. The values calculated using DLPNO-CCSD(T) make up a valuable data set for further comparisons and for improved force field parameterization.
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Affiliation(s)
- Ioannis Stylianakis
- Department of Medicinal Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens, Panepistimioupolis Zografou, 15771, Athens, Greece
| | - Nikolaos Zervos
- Department of Medicinal Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens, Panepistimioupolis Zografou, 15771, Athens, Greece
| | - Jenn-Huei Lii
- Department of Chemistry, National Changhua University of Education, Changhua City, Taiwan
| | - Dimitrios A Pantazis
- Max-Planck-Institut für Kohlenforschung, Kaiser-Wilhelm-Platz 1, 45470, Mülheim an der Ruhr, Germany
| | - Antonios Kolocouris
- Department of Medicinal Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens, Panepistimioupolis Zografou, 15771, Athens, Greece.
- Laboratory of Medicinal Chemistry, Section of Pharmaceutical Chemistry, Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis-Zografou, 15771, Athens, Greece.
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Jain AN, Brueckner AC, Cleves AE, Reibarkh M, Sherer EC. A Distributional Model of Bound Ligand Conformational Strain: From Small Molecules up to Large Peptidic Macrocycles. J Med Chem 2023; 66:1955-1971. [PMID: 36701387 PMCID: PMC9923749 DOI: 10.1021/acs.jmedchem.2c01744] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
The internal conformational strain incurred by ligands upon binding a target site has a critical impact on binding affinity, and expectations about the magnitude of ligand strain guide conformational search protocols. Estimates for bound ligand strain begin with modeled ligand atomic coordinates from X-ray co-crystal structures. By deriving low-energy conformational ensembles to fit X-ray diffraction data, calculated strain energies are substantially reduced compared with prior approaches. We show that the distribution of expected global strain energy values is dependent on molecular size in a superlinear manner. The distribution of strain energy follows a rectified normal distribution whose mean and variance are related to conformational complexity. The modeled strain distribution closely matches calculated strain values from experimental data comprising over 3000 protein-ligand complexes. The distributional model has direct implications for conformational search protocols as well as for directions in molecular design.
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Affiliation(s)
- Ajay N. Jain
- Research
& Development, BioPharmics LLC, Sonoma County, California95404, United States,
| | - Alexander C. Brueckner
- Molecular
Structure & Design, Bristol Myers Squibb, Princeton, New Jersey08543, United States
| | - Ann E. Cleves
- Research
& Development, BioPharmics LLC, Sonoma County, California95404, United States
| | - Mikhail Reibarkh
- Analytical
Research and Development, Merck & Co.
Inc., Rahway, New Jersey07065, United States
| | - Edward C. Sherer
- Analytical
Research and Development, Merck & Co.
Inc., Rahway, New Jersey07065, United States,
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5
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Foloppe N, Chen IJ. The reorganization energy of compounds upon binding to proteins, from dynamic and solvated bound and unbound states. Bioorg Med Chem 2021; 51:116464. [PMID: 34798378 DOI: 10.1016/j.bmc.2021.116464] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 09/26/2021] [Accepted: 10/01/2021] [Indexed: 11/30/2022]
Abstract
The intramolecular reorganization energy (ΔEReorg) of compounds upon binding to proteins is a component of the binding free energy, which has long received particular attention, for fundamental and practical reasons. Understanding ΔEReorg would benefit the science of molecular recognition and drug design. For instance, the tolerable strain energy of compounds upon binding has been elusive. Prior studies found some large ΔEReorg values (e.g. > 10 kcal/mol), received with skepticism since they imply excessive opposition to binding. Indeed, estimating ΔEReorg is technically difficult. Typically, ΔEReorg has been approached by taking two energy-minimized conformers representing the bound and unbound states, and subtracting their conformational energy. This is a drastic oversimplification, liable to conformational collapse of the unbound conformer. Instead, the present work applies extensive molecular dynamics (MD) and the modern OPLS3 force-field to simulate compounds bound and unbound states, in explicit solvent under physically relevant conditions. The thermalized unbound compounds populate multiple conformations, not reducible to one or a few energy-minimized conformers. The intramolecular energies in the bound and unbound states were averaged over pertinent conformational ensembles, and the reorganization enthalpy upon binding (ΔHReorg) deduced by subtraction. This was applied to 76 systems, including 43 approved drugs, carefully selected for i) the quality of the bioactive X-ray structures and ii) the diversity of the chemotypes, their properties and protein targets. It yielded comparatively low ΔHReorg values (median = 1.4 kcal/mol, mean = 3.0 kcal/mol). A new finding is the observation of negative ΔHReorg values. Indeed, reorganization energies do not have to oppose binding, e.g. when intramolecular interactions stabilize preferentially the bound state. Conversely, even with competing water molecules, intramolecular interactions can occur predominantly in the unbound compound, and be replaced by intermolecular counterparts upon protein binding. Such disruption of intramolecular interactions upon binding gives rise to occasional larger ΔHReorg values. Such counterintuitive larger ΔHReorg values may be rationalized as a redistribution of interactions upon binding, qualitatively compatible with binding.
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Affiliation(s)
- Nicolas Foloppe
- Vernalis (R&D) Ltd, Granta Park, Abington, Cambridge CB21 6GB, UK.
| | - I-Jen Chen
- Vernalis (R&D) Ltd, Granta Park, Abington, Cambridge CB21 6GB, UK.
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6
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Kulkarni PU, Shah H, Vyas VK. Hybrid Quantum Mechanics/Molecular Mechanics (QM/MM) Simulation: A Tool for Structure-based Drug Design and Discovery. Mini Rev Med Chem 2021; 22:1096-1107. [PMID: 34620049 DOI: 10.2174/1389557521666211007115250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 04/22/2021] [Accepted: 08/10/2021] [Indexed: 11/22/2022]
Abstract
Quantum mechanics (QM) is physics based theory which explains the physical properties of nature at the level of atoms and sub-atoms. Molecular mechanics (MM) construct molecular systems through the use of classical mechanics. So, hybrid quantum mechanics and molecular mechanics (QM/MM) when combined together can act as computer-based methods which can be used to calculate structure and property data of molecular structures. Hybrid QM/MM combines the strengths of QM with accuracy and MM with speed. QM/MM simulation can also be applied for the study of chemical process in solutions as well as in the proteins, and has a great scope in structure-based drug design (CADD) and discovery. Hybrid QM/MM also applied to HTS, to derive QSAR models and due to availability of many protein crystal structures; it has a great role in computational chemistry, especially in structure- and fragment-based drug design. Fused QM/MM simulations have been developed as a widespread method to explore chemical reactions in condensed phases. In QM/MM simulations, the quantum chemistry theory is used to treat the space in which the chemical reactions occur; however the rest is defined through molecular mechanics force field (MMFF). In this review, we have extensively reviewed recent literature pertaining to the use and applications of hybrid QM/MM simulations for ligand and structure-based computational methods for the design and discovery of therapeutic agents.
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Affiliation(s)
- Prajakta U Kulkarni
- School of Pharmacy, ITM (SLS) Baroda University, Vadodara 391510, Gujarat. India
| | - Harshil Shah
- Department of Pharmaceutical Chemistry, Sardar Patel College of Pharmacy, Bakrol, Anand 388315, Gujarat. India
| | - Vivek K Vyas
- Department of Pharmaceutical Chemistry, Institute of Pharmacy, Nirma University, Ahmedabad 382481, Gujarat. India
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7
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Nayarisseri A, Khandelwal R, Madhavi M, Selvaraj C, Panwar U, Sharma K, Hussain T, Singh SK. Shape-based Machine Learning Models for the Potential Novel COVID-19 Protease Inhibitors Assisted by Molecular Dynamics Simulation. Curr Top Med Chem 2020; 20:2146-2167. [PMID: 32621718 DOI: 10.2174/1568026620666200704135327] [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] [Received: 02/25/2020] [Revised: 03/20/2020] [Accepted: 04/25/2020] [Indexed: 12/17/2022]
Abstract
BACKGROUND The vast geographical expansion of novel coronavirus and an increasing number of COVID-19 affected cases have overwhelmed health and public health services. Artificial Intelligence (AI) and Machine Learning (ML) algorithms have extended their major role in tracking disease patterns, and in identifying possible treatments. OBJECTIVE This study aims to identify potential COVID-19 protease inhibitors through shape-based Machine Learning assisted by Molecular Docking and Molecular Dynamics simulations. METHODS 31 Repurposed compounds have been selected targeting the main coronavirus protease (6LU7) and a machine learning approach was employed to generate shape-based molecules starting from the 3D shape to the pharmacophoric features of their seed compound. Ligand-Receptor Docking was performed with Optimized Potential for Liquid Simulations (OPLS) algorithms to identify highaffinity compounds from the list of selected candidates for 6LU7, which were subjected to Molecular Dynamic Simulations followed by ADMET studies and other analyses. RESULTS Shape-based Machine learning reported remdesivir, valrubicin, aprepitant, and fulvestrant as the best therapeutic agents with the highest affinity for the target protein. Among the best shape-based compounds, a novel compound identified was not indexed in any chemical databases (PubChem, Zinc, or ChEMBL). Hence, the novel compound was named 'nCorv-EMBS'. Further, toxicity analysis showed nCorv-EMBS to be suitable for further consideration as the main protease inhibitor in COVID-19. CONCLUSION Effective ACE-II, GAK, AAK1, and protease 3C blockers can serve as a novel therapeutic approach to block the binding and attachment of the main COVID-19 protease (PDB ID: 6LU7) to the host cell and thus inhibit the infection at AT2 receptors in the lung. The novel compound nCorv- EMBS herein proposed stands as a promising inhibitor to be evaluated further for COVID-19 treatment.
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Affiliation(s)
- Anuraj Nayarisseri
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore-452010, Madhya Pradesh, India,Bioinformatics Research Laboratory, LeGene Biosciences Pvt Ltd., Mahalakshmi Nagar, Indore-452010, Madhya
Pradesh, India,Research Chair for Biomedical Applications of Nanomaterials, Biochemistry Department, College of Science, King
Saud University, Riyadh, Saudi Arabia,Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi-630 003, Tamil Nadu, India
| | - Ravina Khandelwal
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore-452010, Madhya Pradesh, India
| | - Maddala Madhavi
- Department of Zoology, Nizam College, Osmania University, Hyderabad-500001, Telangana State, India
| | - Chandrabose Selvaraj
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi-630 003, Tamil Nadu, India
| | - Umesh Panwar
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi-630 003, Tamil Nadu, India
| | - Khushboo Sharma
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore-452010, Madhya Pradesh, India
| | - Tajamul Hussain
- Center of Excellence in Biotechnology Research, College of Science, King Saud University, Riyadh, Saudi Arabia,Research Chair for Biomedical Applications of Nanomaterials, Biochemistry Department, College of Science, King
Saud University, Riyadh, Saudi Arabia
| | - Sanjeev Kumar Singh
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi-630 003, Tamil Nadu, India
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8
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Zivanovic S, Colizzi F, Moreno D, Hospital A, Soliva R, Orozco M. Exploring the Conformational Landscape of Bioactive Small Molecules. J Chem Theory Comput 2020; 16:6575-6585. [DOI: 10.1021/acs.jctc.0c00304] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Sanja Zivanovic
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology (BIST), Baldiri Reixac, 10, 08028 Barcelona, Spain
| | - Francesco Colizzi
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology (BIST), Baldiri Reixac, 10, 08028 Barcelona, Spain
| | - David Moreno
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology (BIST), Baldiri Reixac, 10, 08028 Barcelona, Spain
| | - Adam Hospital
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology (BIST), Baldiri Reixac, 10, 08028 Barcelona, Spain
| | - Robert Soliva
- Nostrum Biodiscovery, Nexus II Building, 08034 Barcelona, Spain
| | - Modesto Orozco
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology (BIST), Baldiri Reixac, 10, 08028 Barcelona, Spain
- Departament de Bioquímica i Biomedicina, Facultat de Biologia, Universitat de Barcelona, E08028 Barcelona, Spain
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9
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Zsidó BZ, Hetényi C. Molecular Structure, Binding Affinity, and Biological Activity in the Epigenome. Int J Mol Sci 2020; 21:ijms21114134. [PMID: 32531926 PMCID: PMC7311975 DOI: 10.3390/ijms21114134] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 06/07/2020] [Accepted: 06/08/2020] [Indexed: 02/07/2023] Open
Abstract
Development of valid structure–activity relationships (SARs) is a key to the elucidation of pathomechanisms of epigenetic diseases and the development of efficient, new drugs. The present review is based on selected methodologies and applications supplying molecular structure, binding affinity and biological activity data for the development of new SARs. An emphasis is placed on emerging trends and permanent challenges of new discoveries of SARs in the context of proteins as epigenetic drug targets. The review gives a brief overview and classification of the molecular background of epigenetic changes, and surveys both experimental and theoretical approaches in the field. Besides the results of sophisticated, cutting edge techniques such as cryo-electron microscopy, protein crystallography, and isothermal titration calorimetry, examples of frequently used assays and fast screening techniques are also selected. The review features how different experimental methods and theoretical approaches complement each other and result in valid SARs of the epigenome.
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10
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Conformational analysis of macrocycles: comparing general and specialized methods. J Comput Aided Mol Des 2020; 34:231-252. [PMID: 31965404 PMCID: PMC7036058 DOI: 10.1007/s10822-020-00277-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 01/03/2020] [Indexed: 11/24/2022]
Abstract
Abstract Macrocycles represent an important class of medicinally relevant small molecules due to their interesting biological properties. Therefore, a firm understanding of their conformational preferences is important for drug design. Given the importance of macrocycle-protein modelling in drug discovery, we envisaged that a systematic study of both classical and recent specialized methods would provide guidance for other practitioners within the field. In this study we compare the performance of the general, well established conformational analysis methods Monte Carlo Multiple Minimum (MCMM) and Mixed Torsional/Low-Mode sampling (MTLMOD) with two more recent and specialized macrocycle sampling techniques: MacroModel macrocycle Baseline Search (MD/LLMOD) and Prime macrocycle conformational sampling (PRIME-MCS). Using macrocycles extracted from 44 macrocycle-protein X-ray crystallography complexes, we evaluated each method based on their ability to (i) generate unique conformers, (ii) generate unique macrocycle ring conformations, (iii) identify the global energy minimum, (iv) identify conformers similar to the X-ray ligand conformation after Protein Preparation Wizard treatment (X-rayppw), and (v) to the X-rayppw ring conformation. Computational speed was also considered. In addition, conformational coverage, as defined by the number of conformations identified, was studied. In order to study the relative energies of the bioactive conformations, the energy differences between the global energy minima and the energy minimized X-rayppw structures and, the global energy minima and the MCMM-Exhaustive (1,000,000 search steps) generated conformers closest to the X-rayppw structure, were calculated and analysed. All searches were performed using relatively short run times (10,000 steps for MCMM, MTLMOD and MD/LLMOD). To assess the performance of the methods, they were compared to an exhaustive MCMM search using 1,000,000 search steps for each of the 44 macrocycles (requiring ca 200 times more CPU time). Prior to our analysis, we also investigated if the general search methods MCMM and MTLMOD could also be optimized for macrocycle conformational sampling. Taken together, our work concludes that the more general methods can be optimized for macrocycle modelling by slightly adjusting the settings around the ring closure bond. In most cases, MCMM and MTLMOD with either standard or enhanced settings performed well in comparison to the more specialized macrocycle sampling methods MD/LLMOD and PRIME-MCS. When using enhanced settings for MCMM and MTLMOD, the X-rayppw conformation was regenerated with the greatest accuracy. The, MD/LLMOD emerged as the most efficient method for generating the global energy minima. Graphic abstract ![]()
Electronic supplementary material The online version of this article (10.1007/s10822-020-00277-2) contains supplementary material, which is available to authorized users.
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Abstract
There is significant potential for electronic structure methods to improve the quality of the predictions furnished by the tools of computer-aided drug design, which typically rely on empirically derived functions. In this perspective, we consider some recent examples of how quantum mechanics has been applied in predicting protein-ligand geometries, protein-ligand binding affinities and ligand strain on binding. We then outline several significant developments in quantum mechanics methodology likely to influence these approaches: in particular, we note the advent of more computationally expedient ab initio quantum mechanical methods that can provide chemical accuracy for larger molecular systems than hitherto possible. We highlight the emergence of increasingly accurate semiempirical quantum mechanical methods and the associated role of machine learning and molecular databases in their development. Indeed, the convergence of improved algorithms for solving and analyzing electronic structure, modern machine learning methods, and increasingly comprehensive benchmark data sets of molecular geometries and energies provides a context in which the potential of quantum mechanics will be increasingly realized in driving future developments and applications in structure-based drug discovery.
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Affiliation(s)
- Richard A Bryce
- Division of Pharmacy and Optometry, School of Health Sciences, University of Manchester, Manchester, UK.
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12
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Abstract
Quantum mechanics (QM) methods provide a fine description of receptor-ligand interactions and of chemical reactions. Their use in drug design and drug discovery is increasing, especially for complex systems including metal ions in the binding sites, for the design of highly selective inhibitors, for the optimization of bi-specific compounds, to understand enzymatic reactions, and for the study of covalent ligands and prodrugs. They are also used for generating molecular descriptors for predictive QSAR/QSPR models and for the parameterization of force fields. Thanks to the continuous increase of computational power offered by GPUs and to the development of sophisticated algorithms, QM methods are becoming part of the standard tools used in computer-aided drug design (CADD). We present the most used QM methods and software packages, and we discuss recent representative applications in drug design and drug discovery.
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Affiliation(s)
- Martin Kotev
- Global Research Informatics/Cheminformatics and Drug Design, Evotec (France) SAS, Toulouse, France
| | - Laurie Sarrat
- Global Research Informatics/Cheminformatics and Drug Design, Evotec (France) SAS, Toulouse, France
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13
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Abstract
Estimating the range of three-dimensional structures (conformations) that are available to a molecule is a key component of computer-aided drug design. Quantum mechanical simulation offers improved accuracy over forcefield methods, but at a high computational cost. The question is whether this increased cost can be justified in a context in which high-throughput analysis of large numbers of molecules is often key. This chapter discusses the application of quantum mechanics to conformational searching, with a focus on three key challenges: (1) the generation of ensembles that include a good approximation to a molecule's bioactive conformation at as prominent a ranking as possible; (2) rational analysis and modification of a pre-established bioactive conformation in terms of its energetics; and (3) approximation of real solution-phase conformational ensembles in tandem with NMR data. The impact of QM on the high-throughput application (1) is debatable, meaning that for the moment its primary application is still lower-throughput applications such as (2) and (3). The optimal choice of QM method is also discussed. Rigorous benchmarking suggests that DFT methods are only acceptable when used with large basis sets, but a trickle of papers continue to obtain useful results with relatively low-cost methods, leading to a dilemma that the literature has yet to fully resolve.
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14
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Kellici TF, Ntountaniotis D, Liapakis G, Tzakos AG, Mavromoustakos T. The dynamic properties of angiotensin II type 1 receptor inverse agonists in solution and in the receptor site. ARAB J CHEM 2019. [DOI: 10.1016/j.arabjc.2016.11.014] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
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15
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Wahl J, Freyss J, von Korff M, Sander T. Accuracy evaluation and addition of improved dihedral parameters for the MMFF94s. J Cheminform 2019; 11:53. [PMID: 31392432 PMCID: PMC6686419 DOI: 10.1186/s13321-019-0371-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 07/23/2019] [Indexed: 11/10/2022] Open
Abstract
The Platinum dataset of protein-bound ligand conformations was used to benchmark the ability of the MMFF94s force field to generate bioactive conformations by minimization of randomly generated conformers. Torsion angle parameters that generally caused wrong geometries were reparameterized by conducting dihedral scans using ab initio calculations at the MP2 level. This reparameterization resulted in a systematic improvement of generated conformations.
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Affiliation(s)
- Joel Wahl
- Scientific Computing Drug Discovery, Idorsia Pharmaceuticals Ltd, Hegenheimermattweg 89, 4123 Allschwil, Switzerland
| | - Joel Freyss
- Global Information Systems, Idorsia Pharmaceuticals Ltd, Hegenheimermattweg 91, 4123 Allschwil, Switzerland
| | - Modest von Korff
- Scientific Computing Drug Discovery, Idorsia Pharmaceuticals Ltd, Hegenheimermattweg 89, 4123 Allschwil, Switzerland
| | - Thomas Sander
- Scientific Computing Drug Discovery, Idorsia Pharmaceuticals Ltd, Hegenheimermattweg 89, 4123 Allschwil, Switzerland
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16
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Sun Z, Liu Q, Qu G, Feng Y, Reetz MT. Utility of B-Factors in Protein Science: Interpreting Rigidity, Flexibility, and Internal Motion and Engineering Thermostability. Chem Rev 2019; 119:1626-1665. [PMID: 30698416 DOI: 10.1021/acs.chemrev.8b00290] [Citation(s) in RCA: 280] [Impact Index Per Article: 56.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Zhoutong Sun
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, 32 West Seventh Avenue, Tianjin Airport Economic Area, Tianjin 300308, China
| | - Qian Liu
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Ge Qu
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, 32 West Seventh Avenue, Tianjin Airport Economic Area, Tianjin 300308, China
| | - Yan Feng
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Manfred T. Reetz
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, 32 West Seventh Avenue, Tianjin Airport Economic Area, Tianjin 300308, China
- Max-Planck-Institut für Kohlenforschung, Kaiser-Wilhelm-Platz 1, 45470 Mülheim an der Ruhr, Germany
- Chemistry Department, Philipps-University, Hans-Meerwein-Strasse 4, 35032 Marburg, Germany
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17
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Energy windows for computed compound conformers: covering artefacts or truly large reorganization energies? Future Med Chem 2019; 11:97-118. [DOI: 10.4155/fmc-2018-0400] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The generation of 3D conformers of small molecules underpins most computational drug discovery. Thus, the conformer quality is critical and depends on their energetics. A key parameter is the empirical conformational energy window (ΔEw), since only conformers within ΔEw are retained. However, ΔEw values in use appear unrealistically large. We analyze the factors pertaining to the conformer energetics and ΔEw. We argue that more attention must be focused on the problem of collapsed low-energy conformers. That is due to artificial intramolecular stabilization and occurs even with continuum solvation. Consequently, the conformational energy of extended bioactive structures is artefactually increased, which inflates ΔEw. Thus, this Perspective highlights the issues arising from low-energy conformers and suggests improvements via empirical or physics-based strategies.
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18
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Liu G, Wan Y, Wang W, Fang S, Gu S, Ju X. Docking-based 3D-QSAR and pharmacophore studies on diarylpyrimidines as non-nucleoside inhibitors of HIV-1 reverse transcriptase. Mol Divers 2018; 23:107-121. [PMID: 30051344 DOI: 10.1007/s11030-018-9860-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Accepted: 07/13/2018] [Indexed: 11/30/2022]
Abstract
Diarylpyrimidines (DAPYs), a type of effective HIV-1 non-nucleoside reverse transcriptase inhibitors (NNRTIs), have been considered as one of the most successful agents for treating AIDS. A number of structurally diverse DAPYs have been designed and synthesized in the past decade, and most of them exhibited potent anti-HIV-1 activities; however, the structure-activity relationships of recently reported DAPYs and their pharmacophore features that interacted with HIV-1 reverse transcriptase (RT) remain to be studied. In the present study, molecular docking studies were first performed on three novel classes of DAPYs to study their binding pattern in the HIV-1 RT. Based on the docking conformations of these DAPYs, 3D-QSAR models were constructed using CoMSIA and Topomer CoMFA methods, and pharmacophore models were also built using distance comparison technique. All selected DAPYs presented preferred U- or L-shaped conformations while being docked into the non-nucleoside inhibitor-binding pocket of the HIV-1 RT. The best CoMSIA model exhibited powerful predictivity, with satisfactory statistical parameters such as a q2 of 0.572, an r2 of 0.952, and an [Formula: see text] of 0.728. Contour maps of the best CoMSIA model were in accordance with those of the Topomer CoMFA model, giving the insight into the feature requirements of DAPYs for the anti-HIV-1 activity. Three potential pharmacophore models were constructed, and each of them was consisted of five hypothesis features. All results suggested that the aromatic ring on the left wing of DAPYs and the central pyrimidine ring contained key pharmacophore features for the anti-HIV-1 activity, and also indicated that the right wing of DAPYs had potential for further structural modification to improve activity. Eight novel DAPY molecules with potential anti-HIV-1 activities were designed on the basis of the obtained results. The findings in this study might provide important information for further design and development of novel HIV-1 NNRTIs.
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Affiliation(s)
- Genyan Liu
- Key Laboratory for Green Chemical Process of Ministry of Education, School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology, Wuhan, 430205, People's Republic of China.
| | - Youlan Wan
- Key Laboratory for Green Chemical Process of Ministry of Education, School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology, Wuhan, 430205, People's Republic of China
| | - Wenjie Wang
- Key Laboratory for Green Chemical Process of Ministry of Education, School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology, Wuhan, 430205, People's Republic of China
| | - Sai Fang
- Key Laboratory for Green Chemical Process of Ministry of Education, School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology, Wuhan, 430205, People's Republic of China
| | - Shuangxi Gu
- Key Laboratory for Green Chemical Process of Ministry of Education, School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology, Wuhan, 430205, People's Republic of China.
| | - Xiulian Ju
- Key Laboratory for Green Chemical Process of Ministry of Education, School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology, Wuhan, 430205, People's Republic of China
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19
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Habgood M. Conformational ensemble comparison for small molecules in drug discovery. J Comput Aided Mol Des 2018; 32:841-852. [PMID: 29987709 DOI: 10.1007/s10822-018-0132-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Accepted: 07/04/2018] [Indexed: 11/30/2022]
Abstract
Quantification of three-dimensional similarity between small molecules is a fundamental tool of rational drug design. However, there are no widely-adopted scoring approaches for comparing whole conformational ensembles between molecules. Such scores would be desirable for scenarios in which properties of a molecule have been measured (e.g. activity against a target) but the relevant three dimensional structure is not known. In this study, a set of three complementary ensemble comparison scores is proposed. These are the maximum similarity between any pair of conformations; the proportion of the whole set of the conformations that are matched to within a threshold 3D similarity score; and the average value over these matched conformations of the molecular shape descriptor 'σ-fct', introduced by Ballester et al. The utility of this scoring set is demonstrated in three case studies. The first is an attempt to discriminate between the conformational behaviours of a series of compounds with varying types of cyclisations and other conformationally-significant modifications; the second is an analysis of the more and less active members of a series of GPR119 agonists plus an analysis of a series of orexin-1 antagonists; and the third case study is an attempt to obtain enrichment of active against inactive compounds for a subset of the DUD·E dataset, by ensemble comparison against an active reference compound.
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Affiliation(s)
- Matthew Habgood
- Evotec (UK) Ltd., 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire, OX14 4RZ, UK.
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20
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Habgood M. Bioactive focus in conformational ensembles: a pluralistic approach. J Comput Aided Mol Des 2017; 31:1073-1083. [DOI: 10.1007/s10822-017-0089-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Accepted: 11/27/2017] [Indexed: 12/01/2022]
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21
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Peach ML, Cachau RE, Nicklaus MC. Conformational energy range of ligands in protein crystal structures: The difficult quest for accurate understanding. J Mol Recognit 2017; 30:10.1002/jmr.2618. [PMID: 28233410 PMCID: PMC5553890 DOI: 10.1002/jmr.2618] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2016] [Revised: 01/31/2017] [Accepted: 01/31/2017] [Indexed: 12/25/2022]
Abstract
In this review, we address a fundamental question: What is the range of conformational energies seen in ligands in protein-ligand crystal structures? This value is important biophysically, for better understanding the protein-ligand binding process; and practically, for providing a parameter to be used in many computational drug design methods such as docking and pharmacophore searches. We synthesize a selection of previously reported conflicting results from computational studies of this issue and conclude that high ligand conformational energies really are present in some crystal structures. The main source of disagreement between different analyses appears to be due to divergent treatments of electrostatics and solvation. At the same time, however, for many ligands, a high conformational energy is in error, due to either crystal structure inaccuracies or incorrect determination of the reference state. Aside from simple chemistry mistakes, we argue that crystal structure error may mainly be because of the heuristic weighting of ligand stereochemical restraints relative to the fit of the structure to the electron density. This problem cannot be fixed with improvements to electron density fitting or with simple ligand geometry checks, though better metrics are needed for evaluating ligand and binding site chemistry in addition to geometry during structure refinement. The ultimate solution for accurately determining ligand conformational energies lies in ultrahigh-resolution crystal structures that can be refined without restraints.
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Affiliation(s)
- Megan L Peach
- Basic Science Program, Chemical Biology Laboratory, Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Raul E Cachau
- Data Science and Information Technology Program, Advanced Biomedical Computing Center, Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Marc C Nicklaus
- Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD, USA
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22
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Abstract
The generation of conformations for small molecules is a problem of continuing interest in cheminformatics and computational drug discovery. This review will present an overview of methods used to sample conformational space, focusing on those methods designed for organic molecules commonly of interest in drug discovery. Different approaches to both the sampling of conformational space and the scoring of conformational stability will be compared and contrasted, with an emphasis on those methods suitable for conformer sampling of large numbers of drug-like molecules. Particular attention will be devoted to the appropriate utilization of information from experimental solid-state structures in validating and evaluating the performance of these tools. The review will conclude with some areas worthy of further investigation.
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Affiliation(s)
- Paul C D Hawkins
- OpenEye Scientific , 9 Bisbee Court, Suite D, Santa Fe, New Mexico 87508, United States
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23
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Zheng Y, Tice CM, Singh SB. Conformational control in structure-based drug design. Bioorg Med Chem Lett 2017; 27:2825-2837. [DOI: 10.1016/j.bmcl.2017.04.079] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Revised: 04/20/2017] [Accepted: 04/25/2017] [Indexed: 12/19/2022]
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24
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Lodola A, Bertolini S, Biagetti M, Capacchi S, Facchinetti F, Gallo PM, Pappani A, Mor M, Pala D, Rivara S, Visentini F, Corsi M, Capelli AM. Atropisomerism and Conformational Equilibria: Impact on PI3Kδ Inhibition of 2-((6-Amino-9H-purin-9-yl)methyl)-5-methyl-3-(o-tolyl)quinazolin-4(3H)-one (IC87114) and Its Conformationally Restricted Analogs. J Med Chem 2017; 60:4304-4315. [DOI: 10.1021/acs.jmedchem.7b00247] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Alessio Lodola
- Dipartimento
di Scienze degli Alimenti e del Farmaco, Università degli Studi di Parma, Viale delle Scienze 27/A, 43124 Parma, Italy
| | - Serena Bertolini
- Chemistry
Research and Drug Design Department, Chiesi Farmaceutici S.p.A., Largo F. Belloli 11/A, 43122 Parma, Italy
| | - Matteo Biagetti
- Chemistry
Research and Drug Design Department, Chiesi Farmaceutici S.p.A., Largo F. Belloli 11/A, 43122 Parma, Italy
| | - Silvia Capacchi
- Chemistry
Research and Drug Design Department, Chiesi Farmaceutici S.p.A., Largo F. Belloli 11/A, 43122 Parma, Italy
| | - Fabrizio Facchinetti
- Chemistry
Research and Drug Design Department, Chiesi Farmaceutici S.p.A., Largo F. Belloli 11/A, 43122 Parma, Italy
| | - Paola Maria Gallo
- Chemistry
Research and Drug Design Department, Chiesi Farmaceutici S.p.A., Largo F. Belloli 11/A, 43122 Parma, Italy
| | - Alice Pappani
- Chemistry
Research and Drug Design Department, Chiesi Farmaceutici S.p.A., Largo F. Belloli 11/A, 43122 Parma, Italy
| | - Marco Mor
- Dipartimento
di Scienze degli Alimenti e del Farmaco, Università degli Studi di Parma, Viale delle Scienze 27/A, 43124 Parma, Italy
| | - Daniele Pala
- Dipartimento
di Scienze degli Alimenti e del Farmaco, Università degli Studi di Parma, Viale delle Scienze 27/A, 43124 Parma, Italy
| | - Silvia Rivara
- Dipartimento
di Scienze degli Alimenti e del Farmaco, Università degli Studi di Parma, Viale delle Scienze 27/A, 43124 Parma, Italy
| | | | - Mauro Corsi
- Aptuit s.r.l., Via Fleming 4, 37135 Verona, Italy
| | - Anna Maria Capelli
- Chemistry
Research and Drug Design Department, Chiesi Farmaceutici S.p.A., Largo F. Belloli 11/A, 43122 Parma, Italy
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25
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Astolfi A, Felicetti T, Iraci N, Manfroni G, Massari S, Pietrella D, Tabarrini O, Kaatz GW, Barreca ML, Sabatini S, Cecchetti V. Pharmacophore-Based Repositioning of Approved Drugs as Novel Staphylococcus aureus NorA Efflux Pump Inhibitors. J Med Chem 2017; 60:1598-1604. [PMID: 28117588 DOI: 10.1021/acs.jmedchem.6b01439] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
An intriguing opportunity to address antimicrobial resistance is represented by the inhibition of efflux pumps. Focusing on NorA, the most important efflux pump of Staphylococcus aureus, an efflux pump inhibitors (EPIs) library was used for ligand-based pharmacophore modeling studies. By exploitation of the obtained models, an in silico drug repositioning approach allowed for the identification of novel and potent NorA EPIs.
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Affiliation(s)
- Andrea Astolfi
- Department of Pharmaceutical Sciences, University of Perugia , Via del Liceo 1, 06123 Perugia, Italy
| | - Tommaso Felicetti
- Department of Pharmaceutical Sciences, University of Perugia , Via del Liceo 1, 06123 Perugia, Italy
| | - Nunzio Iraci
- Department of Pharmaceutical Sciences, University of Perugia , Via del Liceo 1, 06123 Perugia, Italy
| | - Giuseppe Manfroni
- Department of Pharmaceutical Sciences, University of Perugia , Via del Liceo 1, 06123 Perugia, Italy
| | - Serena Massari
- Department of Pharmaceutical Sciences, University of Perugia , Via del Liceo 1, 06123 Perugia, Italy
| | - Donatella Pietrella
- Department of Pharmaceutical Sciences, University of Perugia , Via del Liceo 1, 06123 Perugia, Italy
| | - Oriana Tabarrini
- Department of Pharmaceutical Sciences, University of Perugia , Via del Liceo 1, 06123 Perugia, Italy
| | - Glenn W Kaatz
- John D. Dingell Department of Veterans Affairs Medical Center and the Department of Internal Medicine, Division of Infectious Diseases, School of Medicine, Wayne State University , Detroit, Michigan 48201, United States
| | - Maria L Barreca
- Department of Pharmaceutical Sciences, University of Perugia , Via del Liceo 1, 06123 Perugia, Italy
| | - Stefano Sabatini
- Department of Pharmaceutical Sciences, University of Perugia , Via del Liceo 1, 06123 Perugia, Italy
| | - Violetta Cecchetti
- Department of Pharmaceutical Sciences, University of Perugia , Via del Liceo 1, 06123 Perugia, Italy
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26
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Supady A, Blum V, Baldauf C. First-Principles Molecular Structure Search with a Genetic Algorithm. J Chem Inf Model 2015; 55:2338-48. [DOI: 10.1021/acs.jcim.5b00243] [Citation(s) in RCA: 68] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Adriana Supady
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, 14195 Berlin, Germany
| | - Volker Blum
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, 14195 Berlin, Germany
- Department of Mechanical Engineering & Materials Science, Duke University, Durham, North Carolina 27708, United States
| | - Carsten Baldauf
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, 14195 Berlin, Germany
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