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Yamin M, Ghouri ZK, Rohman N, Syed JA, Skelton A, Ahmed K. Unravelling pH/pKa influence on pH-responsive drug carriers: Insights from ibuprofen-silica interactions and comparative analysis with carbon nanotubes, sulfasalazine, and alendronate. J Mol Graph Model 2024; 128:108720. [PMID: 38324969 DOI: 10.1016/j.jmgm.2024.108720] [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: 10/06/2023] [Revised: 01/04/2024] [Accepted: 01/23/2024] [Indexed: 02/09/2024]
Abstract
This study employs density functional theory to explore the interaction between ibuprofen (IBU) and silica, emphasizing the influence of the trimethylsilyl (TMS) functional group for designing pH-responsive drug carriers. The surface (S) and drug (D) molecules' neutral (0) or deprotonated (-1) states were taken into consideration during the investigation. The likelihood of these states was determined based on the pKa values and the desired pH conditions. To calculate the pH-dependent interaction energy (EintpH), four different situations have been identified: S0D0, S0D-1, S-1D0, and S-1D-1.The electrostatic component of interaction energy aligns favorably with its theoretical value in both the Debye-Hückel and Grahame models. The investigation has gathered first-hand experimental data on the drug loading and release of pH-responsive mesoporous silica nanoparticles. Effective drug loading was observed in the acidic environment of the stomach (pH 2-5), followed by a release in the slightly basic to neutral pH of the small intestine (pH 7.4), These findings align with existing literature. The results revealed horizontal drug adherence on silica surfaces, improving binding capabilities. Comparisons were made with combinations involving carboxylated carbon nanotubes and ibuprofen, silica, and sulfasalazine, and silica and alendronate, exploring drug loading/release dynamics associated with positive/negative interaction energies. The investigation, supported by experimental data, contributes valuable insights into pH-responsive mesoporous silica nanoparticles, offering new design possibilities for drug carriers.
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Affiliation(s)
- Marriam Yamin
- Department of Biosciences, Salim Habib University, Karachi, Pakistan
| | - Zafar Khan Ghouri
- L. E. J. Nanotechnology Centre, H. E. J. Research Institute of Chemistry, International Centre for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan; Net Zero Industry Innovation Centre, Teesside University, Middlesbrough, Tees Valley TS1 3BX, UK
| | - Nashiour Rohman
- Department of Chemistry, College of Science, Sultan Qaboos University, P. O. Box 36, Al-khoudh, Muscat P. C. 123, Oman
| | - Junaid Ali Syed
- L. E. J. Nanotechnology Centre, H. E. J. Research Institute of Chemistry, International Centre for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan
| | - Adam Skelton
- Department of Pharmaceutical Sciences, University of KwaZulu-Natal, Durban, 4000, South Africa.
| | - Khalid Ahmed
- L. E. J. Nanotechnology Centre, H. E. J. Research Institute of Chemistry, International Centre for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan.
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2
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Chan B, Dawson W, Nakajima T. Sorting drug conformers in enzyme active sites: the XTB way. Phys Chem Chem Phys 2024; 26:12610-12618. [PMID: 38597505 DOI: 10.1039/d4cp00930d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
In the present study, we have used the MEI196 set of interaction energies to investigate low-cost computational chemistry approaches for the calculation of binding between a molecule and its environment. Density functional theory (DFT) methods, when used with the vDZP basis set, yield good agreement with the reference energies. On the other hand, semi-empirical methods are less accurate as expected. By examining different groups of systems within MEI196 that contain species of a similar nature, we find that chemical similarity leads to cancellation of errors in the calculation of relative binding energies. Importantly, the semi-empirical method GFN1-xTB (XTB1) yields reasonable results for this purpose. We have thus further assessed the performance of XTB1 for calculating relative energies of docking poses of substrates in enzyme active sites represented by cluster models or within the ONIOM protocol. The results support the observations on error cancellation. This paves the way for the use of XTB1 in parts of large-scale virtual screening workflows to accelerate the drug discovery process.
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Affiliation(s)
- Bun Chan
- Graduate School of Engineering, Nagasaki University, Bunkyo 1-14, Nagasaki 852-8521, Japan.
- RIKEN Center for Computational Science, 7-1-26, Minatojima-minami-machi, Chuo-ku, Kobe, 650-0047, Japan
| | - William Dawson
- RIKEN Center for Computational Science, 7-1-26, Minatojima-minami-machi, Chuo-ku, Kobe, 650-0047, Japan
| | - Takahito Nakajima
- RIKEN Center for Computational Science, 7-1-26, Minatojima-minami-machi, Chuo-ku, Kobe, 650-0047, Japan
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3
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Yang W, Wang Y, Han D, Tang W, Sun L. Recent advances in application of computer-aided drug design in anti-COVID-19 Virials Drug Discovery. Biomed Pharmacother 2024; 173:116423. [PMID: 38493593 DOI: 10.1016/j.biopha.2024.116423] [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: 12/08/2023] [Revised: 03/05/2024] [Accepted: 03/08/2024] [Indexed: 03/19/2024] Open
Abstract
Corona Virus Disease 2019 (COVID-19) is a global pandemic epidemic caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), which poses a serious threat to human health worldwide and results in significant economic losses. With the continuous emergence of new virus strains, small molecule drugs remain the most effective treatment for COVID-19. The traditional drug development process usually requires several years; however, the development of computer-aided drug design (CADD) offers the opportunity to develop innovative drugs quickly and efficiently. The literature review describes the general process of CADD, the viral proteins that play essential roles in the life cycle of SARS-CoV-2 and can serve as therapeutic targets, and examples of drug screening of viral target proteins by applying CADD methods. Finally, the potential of CADD in COVID-19 therapy, the deficiency, and the possible future development direction are discussed.
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Affiliation(s)
- Weiying Yang
- Department of Emergency Medicine, First Hospital of Jilin University, Changchun 130021, China
| | - Ye Wang
- School of Life Sciences, Jilin University, Changchun 130012, China
| | - Dongfeng Han
- Department of Emergency Medicine, First Hospital of Jilin University, Changchun 130021, China
| | - Wenjing Tang
- Department of Emergency Medicine, First Hospital of Jilin University, Changchun 130021, China
| | - Lichao Sun
- Department of Emergency Medicine, First Hospital of Jilin University, Changchun 130021, China.
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4
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Martin RL, Heifetz A, Bodkin MJ, Townsend-Nicholson A. High-Throughput Structure-Based Drug Design (HT-SBDD) Using Drug Docking, Fragment Molecular Orbital Calculations, and Molecular Dynamic Techniques. Methods Mol Biol 2024; 2716:293-306. [PMID: 37702945 DOI: 10.1007/978-1-0716-3449-3_13] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
Structure-based drug design (SBDD) is rapidly evolving to be a fundamental tool for faster and more cost-effective methods of lead drug discovery. SBDD aims to offer a computational replacement to traditional high-throughput screening (HTS) methods of drug discovery. This "virtual screening" technique utilizes the structural data of a target protein in conjunction with large databases of potential drug candidates and then applies a range of different computational techniques to determine which potential candidates are likely to bind with high affinity and efficacy. It is proposed that high-throughput SBDD (HT-SBDD) will significantly enrich the success rate of HTS methods, which currently fluctuates around ~1%. In this chapter, we focus on the theory and utility of high-throughput drug docking, fragment molecular orbital calculations, and molecular dynamics techniques. We also offer a comparative review of the benefits and limitations of traditional methods against more recent SBDD advances. As HT-SBDD is computationally intensive, we will also cover the important role high-performance computing (HPC) clusters play in the future of computational drug discovery.
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Affiliation(s)
- Reuben L Martin
- Research Department of Structural & Molecular Biology, Division of Biosciences, University College London, London, UK.
- Evotec (UK) Ltd., Abingdon, Oxfordshire, UK.
| | | | | | - Andrea Townsend-Nicholson
- Research Department of Structural & Molecular Biology, Division of Biosciences, University College London, London, UK
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5
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Rohman N, Ahmed K, Skelton AA, Mohiuddin T, Khan I, Selvaraj R, Yamin M. Theoretical insights and implications of pH-dependent drug delivery systems using silica and carbon nanotube. J Mol Graph Model 2023; 125:108609. [PMID: 37647724 DOI: 10.1016/j.jmgm.2023.108609] [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: 06/29/2023] [Revised: 08/14/2023] [Accepted: 08/21/2023] [Indexed: 09/01/2023]
Abstract
In this paper we have studied the density functional theory of four drugs ibuprofen, alendronate, Sulfasalazine and paracetamol with quartz, propylamine, trimethylamine functionalized quartz and carboxyl modified carbon nanotube. The attractive and repulsive interaction energies between drugs and quartz is obtained at various pH values. The attractive and repulsive energies are well correlated with experimental drug loading and releasing behavior by mesoporous silica nanoparticles. Further, a theoretical model is developed that accounts the electrostatic interaction between silica and drug and the model can predict the drug loading and releasing behavior by silica nanoparticles at various pH values. Sulfasalazine can be taken orally and loaded with trimethyl ammonium functionalized mesoporous silica nanoparticles, which keeps the drug in tact with the carrier in the acidic environment of the stomach and releases it into the neutral or basic medium of the small intestine. Alendronate may be loaded and released from propylamine functionalized mesoporous silica nanoparticles in the ranges of 1-5 and > 8, respectively. Ibuprofen is absorbed in an acidic environment and released in basic conditions for carboxyl modified carbon nanotube. The loading and releasing pH ranges for paracetamol in trimethylammonium functionalized mesoporous silica nanoparticles are 4-8 and >8, respectively. We also convert the pH-dependent variant of the diffusion-controlled Higuchi equation. We have changed the original Higuchi equation to produce the pH-dependent variation by incorporating the Nernst-Planck equation into Flick's first law. The updated equation could be used to forecast when medication particles with varying release times will emerge from a nanoparticles matrix.
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Affiliation(s)
- Nashiour Rohman
- Department of Physics, College of Science, Sultan Qaboos University, P. O. Box 36, Al-khoudh, Muscat, P. C. 123, Oman.
| | - Khalid Ahmed
- L. E. J. Nanotechnology Centre, H. E. J. Research Institute of Chemistry, International Centre for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan
| | - Adam A Skelton
- Department of Pharmaceutical Sciences, University of KwaZulu-Natal, Durban, 4000, South Africa.
| | - Tariq Mohiuddin
- Department of Physics, College of Science, Sultan Qaboos University, P. O. Box 36, Al-khoudh, Muscat, P. C. 123, Oman
| | - Imran Khan
- Department of Chemistry, College of Science, Sultan Qaboos University, P. O. Box 36, Al-khoudh, Muscat, P. C. 123, Oman
| | - Rengaraj Selvaraj
- Department of Chemistry, College of Science, Sultan Qaboos University, P. O. Box 36, Al-khoudh, Muscat, P. C. 123, Oman
| | - Marriam Yamin
- Department of Biosciences, Salim Habib University, Karachi, Pakistan
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6
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Freitas de Sousa FJ, Nunes Azevedo FF, Santos de Oliveira FL, Vieira Carletti J, Freire VN, Zanatta G. Quantum biochemistry description of PI3Kα enzyme bound to selective inhibitors. J Biomol Struct Dyn 2023:1-11. [PMID: 37632299 DOI: 10.1080/07391102.2023.2251063] [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: 04/15/2023] [Accepted: 08/17/2023] [Indexed: 08/27/2023]
Abstract
The PI3K class I is composed of four PI3K isoforms that serve as regulatory enzymes governing cellular metabolism, proliferation, and survival. The hyperactivation of PI3Kα is observed in various types of cancer and is linked to poor prognosis. Unfortunately, the development inhibitors selectively targeting one of the isoforms remains challenging, with only few agents in clinical use. The main difficulty arises from the high conservation among residues at the ATP-binding pocket across isoforms, which also serves as target pocket for inhibitors. In this work, molecular dynamics and quantum calculations were performed to investigate the molecular features guiding the binding of selective inhibitors, alpelisib and GDC-0326, into the ATP-binding pocket of PI3Kα. While molecular dynamics allowed crystallographic coordinates to relax, the interaction eergy between each amino acid residues and inhibitors was obtained by combining the Molecular Fractionation with Conjugated Caps scheme with Density Functional Theory calculations. In addition, the atomic charge of ligands in the bound and unbound (free) was calculated. Results indicated that the most relevant residues for the binding of alpelisib are Ile932, Glu859, Val851, Val850, Tyr836, Met922, Ile800, and Ile848, while the most important residues for the binding of GDC-0326 are Ile848, Ile800, Ile932, Gln859, Glu849, and Met922. In addition, residues Trp780, Ile800, Tyr836, Ile848, Gln859 Val850, Val851, Ile932 and Met922 are common hotspots for both inhibitors. Overall, the results from this work contribute to improving the understanding of the molecular mechanisms controlling selectivity and highlight important interactions to be considered during the rational design of new agents.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
| | | | | | | | | | - Geancarlo Zanatta
- Department of Biochemistry and Molecular Biology, Federal University of Ceará, Fortaleza, Brazil
- Department of Biophysics, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
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Azad I, Khan T, Ahmad N, Khan AR, Akhter Y. Updates on drug designing approach through computational strategies: a review. Future Sci OA 2023; 9:FSO862. [PMID: 37180609 PMCID: PMC10167725 DOI: 10.2144/fsoa-2022-0085] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 04/12/2023] [Indexed: 05/16/2023] Open
Abstract
The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.
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Affiliation(s)
- Iqbal Azad
- Department of Chemistry, Integral University, Dasauli, P.O. Bas-ha, Kursi Road, Lucknow, 226026, UP, India
| | - Tahmeena Khan
- Department of Chemistry, Integral University, Dasauli, P.O. Bas-ha, Kursi Road, Lucknow, 226026, UP, India
| | - Naseem Ahmad
- Department of Chemistry, Integral University, Dasauli, P.O. Bas-ha, Kursi Road, Lucknow, 226026, UP, India
| | - Abdul Rahman Khan
- Department of Chemistry, Integral University, Dasauli, P.O. Bas-ha, Kursi Road, Lucknow, 226026, UP, India
| | - Yusuf Akhter
- Department of Biotechnology, Babasaheb Bhimrao Ambedkar University, Vidya Vihar, Raebareli Road, Lucknow, UP, 2260025, India
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8
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Design of Novel Phosphatidylinositol 3-Kinase Inhibitors for Non-Hodgkin's Lymphoma: Molecular Docking, Molecular Dynamics, and Density Functional Theory Studies on Gold Nanoparticles. Molecules 2023; 28:molecules28052289. [PMID: 36903539 PMCID: PMC10005307 DOI: 10.3390/molecules28052289] [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: 02/06/2023] [Revised: 02/25/2023] [Accepted: 02/27/2023] [Indexed: 03/06/2023] Open
Abstract
Non-Hodgkin's lymphomas are a diverse collection of lymphoproliferative cancers that are much less predictable than Hodgkin's lymphomas with a far greater tendency to metastasize to extranodal sites. A quarter of non-Hodgkin's lymphoma cases develop at extranodal sites and the majority of them involve nodal and extranodal sites. The most common subtypes include follicular lymphoma, chronic/small lymphocytic leukaemia, mantel cell lymphoma, and marginal zone lymphoma. Umbralisib is one of the latest PI3Kδ inhibitors in clinical trials for several hematologic cancer indications. In this study, new umbralisib analogues were designed and docked to the active site of PI3Kδ, the main target of the phosphoinositol-3-kinase/Akt/mammalian target of the rapamycin pathway (PI3K/AKT/mTOR). This study resulted in eleven candidates, with strong binding to PI3Kδ with a docking score between -7.66 and -8.42 Kcal/mol. The docking analysis of ligand-receptor interactions between umbralisib analogues bound to PI3K showed that their interactions were mainly controlled by hydrophobic interactions and, to a lesser extent, by hydrogen bonding. In addition, the MM-GBSA binding free energy was calculated. Analogue 306 showed the highest free energy of binding with -52.22 Kcal/mol. To identify the structural changes and the complexes' stability of proposed ligands, molecular dynamic simulation was used. Based on this research finding, the best-designed analogue, analogue 306, formed a stable ligand-protein complex. In addition, pharmacokinetics and toxicity analysis using the QikProp tool demonstrated that analogue 306 had good absorption, distribution, metabolism, and excretion properties. Additionally, it has a promising predicted profile in immune toxicity, carcinogenicity, and cytotoxicity. In addition, analogue 306 had stable interactions with gold nanoparticles that have been studied using density functional theory calculations. The best interaction with gold was observed at the oxygen atom number 5 with -29.42 Kcal/mol. Further in vitro and in vivo investigations are recommended to be carried out to verify the anticancer activity of this analogue.
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9
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Polyakov IV, Nemukhin AV, Domratcheva TM, Kulakova AM, Grigorenko BL. Quantum-based Modeling of Protein-ligand Interaction: The Complex of RutA with Uracil and Molecular Oxygen. Mol Inform 2023; 42:e2200175. [PMID: 36259359 DOI: 10.1002/minf.202200175] [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: 07/25/2022] [Accepted: 10/18/2022] [Indexed: 11/09/2022]
Abstract
Modern quantum-based methods are employed to model interaction of the flavin-dependent enzyme RutA with the uracil and oxygen molecules. This complex presents the structure of reactants for the chain of chemical reactions of monooxygenation in the enzyme active site, which is important in drug metabolism. In this case, application of quantum-based approaches is an essential issue, unlike conventional modeling of protein-ligand interaction with force fields using molecular mechanics and classical molecular dynamics methods. We focus on two difficult problems to characterize the structure of reactants in the RutA-FMN-O2 -uracil complex, where FMN stands for the flavin mononucleotide species. First, location of a small O2 molecule in the triplet spin state in the protein cavities is required. Second, positions of both ligands, O2 and uracil, must be specified in the active site with a comparable accuracy. We show that the methods of molecular dynamics with the interaction potentials of quantum mechanics/molecular mechanics theory (QM/MM MD) allow us to characterize this complex and, in addition, to surmise possible reaction mechanism of uracil oxygenation by RutA.
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Affiliation(s)
- Igor V Polyakov
- Department of Chemistry, Lomonosov Moscow State University, Moscow, 119991, Russia.,Emanuel Institute of Biochemical Physics, Russian Academy of Sciences, Moscow, 119334, Russia
| | - Alexander V Nemukhin
- Department of Chemistry, Lomonosov Moscow State University, Moscow, 119991, Russia.,Emanuel Institute of Biochemical Physics, Russian Academy of Sciences, Moscow, 119334, Russia
| | | | - Anna M Kulakova
- Department of Chemistry, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Bella L Grigorenko
- Department of Chemistry, Lomonosov Moscow State University, Moscow, 119991, Russia.,Emanuel Institute of Biochemical Physics, Russian Academy of Sciences, Moscow, 119334, Russia
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10
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Lau B, Emani PS, Chapman J, Yao L, Lam T, Merrill P, Warrell J, Gerstein MB, Lam HYK. Insights from incorporating quantum computing into drug design workflows. Bioinformatics 2023; 39:6881079. [PMID: 36477833 PMCID: PMC9825754 DOI: 10.1093/bioinformatics/btac789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 10/14/2022] [Accepted: 12/06/2022] [Indexed: 12/12/2022] Open
Abstract
MOTIVATION While many quantum computing (QC) methods promise theoretical advantages over classical counterparts, quantum hardware remains limited. Exploiting near-term QC in computer-aided drug design (CADD) thus requires judicious partitioning between classical and quantum calculations. RESULTS We present HypaCADD, a hybrid classical-quantum workflow for finding ligands binding to proteins, while accounting for genetic mutations. We explicitly identify modules of our drug-design workflow currently amenable to replacement by QC: non-intuitively, we identify the mutation-impact predictor as the best candidate. HypaCADD thus combines classical docking and molecular dynamics with quantum machine learning (QML) to infer the impact of mutations. We present a case study with the coronavirus (SARS-CoV-2) protease and associated mutants. We map a classical machine-learning module onto QC, using a neural network constructed from qubit-rotation gates. We have implemented this in simulation and on two commercial quantum computers. We find that the QML models can perform on par with, if not better than, classical baselines. In summary, HypaCADD offers a successful strategy for leveraging QC for CADD. AVAILABILITY AND IMPLEMENTATION Jupyter Notebooks with Python code are freely available for academic use on GitHub: https://www.github.com/hypahub/hypacadd_notebook. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | | | - Lijing Yao
- HypaHealth, HypaHub Inc., San Jose, CA 95128, USA
| | - Tarsus Lam
- HypaHealth, HypaHub Inc., San Jose, CA 95128, USA
| | - Paul Merrill
- HypaHealth, HypaHub Inc., San Jose, CA 95128, USA
| | - Jonathan Warrell
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
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11
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Zhang Y, Luo M, Wu P, Wu S, Lee TY, Bai C. Application of Computational Biology and Artificial Intelligence in Drug Design. Int J Mol Sci 2022; 23:13568. [PMID: 36362355 PMCID: PMC9658956 DOI: 10.3390/ijms232113568] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 10/29/2022] [Accepted: 11/03/2022] [Indexed: 08/24/2023] Open
Abstract
Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, we propose a methodology for integrating various computational techniques into new drug discovery and design.
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Affiliation(s)
- Yue Zhang
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
| | - Mengqi Luo
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Peng Wu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518055, China
| | - Song Wu
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Tzong-Yi Lee
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
| | - Chen Bai
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
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12
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Targeting and ultrabroad insight into molecular basis of Resistance-nodulation-cell division efflux pumps. Sci Rep 2022; 12:16130. [PMID: 36168028 PMCID: PMC9515154 DOI: 10.1038/s41598-022-20278-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 09/12/2022] [Indexed: 11/09/2022] Open
Abstract
Resistance-nodulation-cell devision (RND) efflux pump variants have attracted a great deal of attention for efflux of many antibiotic classes, which leads to multidrug-resistant bacteria. The present study aimed to discover the interaction between the RND efflux pumps and antibiotics, find the conserved and hot spot residues, and use this information to target the most frequent RND efflux pumps. Protein sequence and 3D conformational alignments, pharmacophore modeling, molecular docking, and molecular dynamics simulation were used in the first level for discovering the function of the residues in interaction with antibiotics. In the second level, pharmacophore-based screening, structural-based screening, multistep docking, GRID MIF, pharmacokinetic modeling, fragment molecular orbital, and MD simulation were utilized alongside the former level information to find the most proper inhibitors. Five conserved residues, containing Ala209, Tyr404, Leu415, Asp416, and Ala417, as well as their counterparts in other OMPs were evaluated as the crucial conserved residues. MD simulation confirmed that a number of these residues had a key role in the performance of the efflux antibiotics; therefore, some of them were hot spot residues. Fourteen ligands were selected, four of which interacted with all the crucial conserved residues. NPC100251 was the fittest OMP inhibitor after pharmacokinetic computations. The second-level MD simulation and FMO supported the efficacy of the NPC100251. It was exhibited that perhaps OMPs worked as the intelligent and programable protein. NPC100251 was the strongest OMPs inhibitor, and may be a potential therapeutic candidate for MDR infections.
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13
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Kumar S, Kumar GS, Maitra SS, Malý P, Bharadwaj S, Sharma P, Dwivedi VD. Viral informatics: bioinformatics-based solution for managing viral infections. Brief Bioinform 2022; 23:6659740. [PMID: 35947964 DOI: 10.1093/bib/bbac326] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 06/26/2022] [Accepted: 07/18/2022] [Indexed: 11/13/2022] Open
Abstract
Several new viral infections have emerged in the human population and establishing as global pandemics. With advancements in translation research, the scientific community has developed potential therapeutics to eradicate or control certain viral infections, such as smallpox and polio, responsible for billions of disabilities and deaths in the past. Unfortunately, some viral infections, such as dengue virus (DENV) and human immunodeficiency virus-1 (HIV-1), are still prevailing due to a lack of specific therapeutics, while new pathogenic viral strains or variants are emerging because of high genetic recombination or cross-species transmission. Consequently, to combat the emerging viral infections, bioinformatics-based potential strategies have been developed for viral characterization and developing new effective therapeutics for their eradication or management. This review attempts to provide a single platform for the available wide range of bioinformatics-based approaches, including bioinformatics methods for the identification and management of emerging or evolved viral strains, genome analysis concerning the pathogenicity and epidemiological analysis, computational methods for designing the viral therapeutics, and consolidated information in the form of databases against the known pathogenic viruses. This enriched review of the generally applicable viral informatics approaches aims to provide an overview of available resources capable of carrying out the desired task and may be utilized to expand additional strategies to improve the quality of translation viral informatics research.
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Affiliation(s)
- Sanjay Kumar
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, India.,Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India
| | - Geethu S Kumar
- Department of Life Science, School of Basic Science and Research, Sharda University, Greater Noida, Uttar Pradesh, India.,Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India
| | | | - Petr Malý
- Laboratory of Ligand Engineering, Institute of Biotechnology of the Czech Academy of Sciences v.v.i., BIOCEV Research Center, Vestec, Czech Republic
| | - Shiv Bharadwaj
- Laboratory of Ligand Engineering, Institute of Biotechnology of the Czech Academy of Sciences v.v.i., BIOCEV Research Center, Vestec, Czech Republic
| | - Pradeep Sharma
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi, India
| | - Vivek Dhar Dwivedi
- Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India.,Institute of Advanced Materials, IAAM, 59053 Ulrika, Sweden
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From molecular dynamics to quantum mechanics of misfolded proteins and amyloid-like macroaggregates applied to neurodegenerative diseases. J Mol Graph Model 2021; 110:108046. [PMID: 34736057 DOI: 10.1016/j.jmgm.2021.108046] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 09/14/2021] [Accepted: 10/05/2021] [Indexed: 11/24/2022]
Abstract
A misfolded protein compared with its native state lacks its biological function resulting in cell dysregulations and often death. Outdated hypotheses on protein folding must be revised: More realistic molecular models, focusing not only on classical molecular dynamics (MD) but also on ab initio quantum mechanics (QM) at the molecular orbitals (MOs) scale, which is not experimentally achievable, are presented to improve our understanding of the thermodynamics of the protein-protein interactions leading to misfolding and neurodegenerative diseases for future drug design. Protein misfolding is characterized by the formation of highly reactive beta-sheets oligomers leading to fibrillar macroscopic aggregates, which are studied with the models given herein that can be useful for the development of new immunotherapies against the Alzheimer's disease and prion, e.g. The example of the prion - an intrinsically disordered protein - is studied, but the models can be generalized to other misfolding diseases. The binding free energy and interactions in a complex of a misfolded prion with a native prion are first analyzed by MD and compared to a complex of two native conformers. A conversion of residues to toxic beta-sheets is observed in the optimized misfolded complex. Then, QM is used to compute, with a much better accuracy than that of MD, the binding free energy of the hydrophobic binding site, responsible of the aggregation, between the bound misfolded and native conformers in the misfolded complex. The latter quantity is significantly negative, so that aggregation is strong and fast. The frontier MOs from QM are used for docking to determine how the first repetitive beta-sheets building blocks of the nanofibrils can be assembled from initial cleaved complexes of the native and misfolded proteins. Successive aggregation of multiple monomers leads to an amyloid-like nanofibril that grows along a principal elongation direction, as also observed experimentally.
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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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Breijyeh Z, Karaman R. Enzyme Models-From Catalysis to Prodrugs. Molecules 2021; 26:molecules26113248. [PMID: 34071328 PMCID: PMC8198240 DOI: 10.3390/molecules26113248] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 05/22/2021] [Accepted: 05/26/2021] [Indexed: 11/24/2022] Open
Abstract
Enzymes are highly specific biological catalysts that accelerate the rate of chemical reactions within the cell. Our knowledge of how enzymes work remains incomplete. Computational methodologies such as molecular mechanics (MM) and quantum mechanical (QM) methods play an important role in elucidating the detailed mechanisms of enzymatic reactions where experimental research measurements are not possible. Theories invoked by a variety of scientists indicate that enzymes work as structural scaffolds that serve to bring together and orient the reactants so that the reaction can proceed with minimum energy. Enzyme models can be utilized for mimicking enzyme catalysis and the development of novel prodrugs. Prodrugs are used to enhance the pharmacokinetics of drugs; classical prodrug approaches focus on alternating the physicochemical properties, while chemical modern approaches are based on the knowledge gained from the chemistry of enzyme models and correlations between experimental and calculated rate values of intramolecular processes (enzyme models). A large number of prodrugs have been designed and developed to improve the effectiveness and pharmacokinetics of commonly used drugs, such as anti-Parkinson (dopamine), antiviral (acyclovir), antimalarial (atovaquone), anticancer (azanucleosides), antifibrinolytic (tranexamic acid), antihyperlipidemia (statins), vasoconstrictors (phenylephrine), antihypertension (atenolol), antibacterial agents (amoxicillin, cephalexin, and cefuroxime axetil), paracetamol, and guaifenesin. This article describes the works done on enzyme models and the computational methods used to understand enzyme catalysis and to help in the development of efficient prodrugs.
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Current and Future Challenges in Modern Drug Discovery. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2021; 2114:1-17. [PMID: 32016883 DOI: 10.1007/978-1-0716-0282-9_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Drug discovery is an expensive, time-consuming, and risky business. To avoid late-stage failure, learnings from past projects and the development of new approaches are crucial. New modalities and emerging new target spaces allow the exploration of unprecedented indications or to address so far undrugable targets. Late-stage attrition is usually attributed to the lack of efficacy or to compound-related safety issues. Efficacy has been shown to be related to a strong genetic link to human disease, a better understanding of the target biology, and the availability of biomarkers to bridge from animals to humans. Compound safety can be improved by ligand optimization, which is becoming increasingly demanding for difficult targets. Therefore, new strategies include the design of allosteric ligands, covalent binders, and other modalities. Design methods currently heavily rely on artificial intelligence and advanced computational methods such as free energy calculations and quantum chemistry. Especially for quantum chemical methods, a more detailed overview is given in this chapter.
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Ahmed K, Inamdar SN, Rohman N, Skelton AA. Acidity constant and DFT-based modelling of pH-responsive alendronate loading and releasing on propylamine-modified silica surface. Phys Chem Chem Phys 2021; 23:2015-2024. [DOI: 10.1039/d0cp04498a] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
A computational methodology that couples the acidity (Ka) and density functional theory (DFT) calculations has been developed to explain the pH-dependent drug loading on and releasing from mesoporous silica nanoparticles.
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Affiliation(s)
- Khalid Ahmed
- Department of Pharmaceutical Sciences
- University of KwaZulu-Natal
- Durban 4000
- South Africa
| | | | - Nashiour Rohman
- Department of Pharmaceutical Sciences
- University of KwaZulu-Natal
- Durban 4000
- South Africa
| | - Adam A. Skelton
- Department of Pharmaceutical Sciences
- University of KwaZulu-Natal
- Durban 4000
- South Africa
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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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20
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Zanni R, Galvez-Llompart M, Garcia-Domenech R, Galvez J. What place does molecular topology have in today’s drug discovery? Expert Opin Drug Discov 2020; 15:1133-1144. [DOI: 10.1080/17460441.2020.1770223] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Riccardo Zanni
- Molecular Topology and Drug Design Unit, Department of Physical Chemistry, University of Valencia, Valencia, Spain
- Departamento de Microbiologia, Facultad de Ciencias, Universidad de Malaga, Málaga, Spain
| | - Maria Galvez-Llompart
- Molecular Topology and Drug Design Unit, Department of Physical Chemistry, University of Valencia, Valencia, Spain
- Instituto de Tecnología Química, UPV-CSIC, Universidad Politécnica de Valencia, Valencia, Spain
| | - Ramon Garcia-Domenech
- Molecular Topology and Drug Design Unit, Department of Physical Chemistry, University of Valencia, Valencia, Spain
| | - Jorge Galvez
- Molecular Topology and Drug Design Unit, Department of Physical Chemistry, University of Valencia, Valencia, Spain
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21
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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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22
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QM Calculations in ADMET Prediction. Methods Mol Biol 2020; 2114:285-305. [PMID: 32016900 DOI: 10.1007/978-1-0716-0282-9_18] [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: 02/24/2023]
Abstract
In recent years, there has been an increase in the application of quantum mechanics (QM) methods to describe properties related to the ADMET profile of small molecules. The application of these methods allows calculating useful descriptors and physiochemical properties contributing to ADMET prediction. Considering that QM methods are the only one that describe the electronic state of a molecules, such methods are particularly useful for studying the metabolism of drugs; furthermore, the introduction of mixed QM and molecular mechanics (QM/MM) is also increasing the understanding of drug interaction with cytochromes from a mechanistic point of view. Finally, combining the increase number of experimental data with machine learning algorithms and QM-derived descriptors allowed the creation of an end-user software capable of affecting the drug discovery process.
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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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Covalent simulations of covalent/irreversible enzyme inhibition in drug discovery: a reliable technical protocol. Future Med Chem 2018; 10:2265-2275. [DOI: 10.4155/fmc-2017-0304] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Aim: Irreversible covalent inhibition of biological targets in disease pathogenesis is an emerging field in drug design. Computational techniques have assumed a critical role in understanding covalent enzyme inhibition. However, a gap currently exists with regards to the reliability and reproducibility of currently available protocols available in literature and open scientific forums. Methodology/results: Appropriate ligand and protein target are selected, docked covalently or noncovalently using respective docking tools. Both components are subjected to premolecular dynamic preparations. This was followed by parameterization of the ligand, protein and covalent complex, respectively. The production runs were initiated and the resulting trajectories are saved and analyzed. Conclusion: This protocol is reliable and reproducible, hence would advance the development of irreversible covalent inhibitors toward disease treatment.
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