151
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Ding W, Nan Y, Wu J, Han C, Xin X, Li S, Liu H, Zhang L. Combining multi-dimensional molecular fingerprints to predict the hERG cardiotoxicity of compounds. Comput Biol Med 2022; 144:105390. [DOI: 10.1016/j.compbiomed.2022.105390] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/06/2022] [Accepted: 03/07/2022] [Indexed: 01/28/2023]
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152
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Venkatraman V, Colligan TH, Lesica GT, Olson DR, Gaiser J, Copeland CJ, Wheeler TJ, Roy A. Drugsniffer: An Open Source Workflow for Virtually Screening Billions of Molecules for Binding Affinity to Protein Targets. Front Pharmacol 2022; 13:874746. [PMID: 35559261 PMCID: PMC9086895 DOI: 10.3389/fphar.2022.874746] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 04/04/2022] [Indexed: 11/13/2022] Open
Abstract
The SARS-CoV2 pandemic has highlighted the importance of efficient and effective methods for identification of therapeutic drugs, and in particular has laid bare the need for methods that allow exploration of the full diversity of synthesizable small molecules. While classical high-throughput screening methods may consider up to millions of molecules, virtual screening methods hold the promise of enabling appraisal of billions of candidate molecules, thus expanding the search space while concurrently reducing costs and speeding discovery. Here, we describe a new screening pipeline, called drugsniffer, that is capable of rapidly exploring drug candidates from a library of billions of molecules, and is designed to support distributed computation on cluster and cloud resources. As an example of performance, our pipeline required ∼40,000 total compute hours to screen for potential drugs targeting three SARS-CoV2 proteins among a library of ∼3.7 billion candidate molecules.
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Affiliation(s)
- Vishwesh Venkatraman
- Department of Chemistry, Norwegian University of Science and Technology, Trondheim, Norway
| | - Thomas H. Colligan
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - George T. Lesica
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Daniel R. Olson
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Jeremiah Gaiser
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Conner J. Copeland
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Travis J. Wheeler
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Amitava Roy
- Department of Computer Science, University of Montana, Missoula, MT, United States
- Rocky Mountain Laboratories, Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, United States
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153
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Inverse Mixed-Solvent Molecular Dynamics for Visualization of the Residue Interaction Profile of Molecular Probes. Int J Mol Sci 2022; 23:ijms23094749. [PMID: 35563139 PMCID: PMC9103889 DOI: 10.3390/ijms23094749] [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: 02/28/2022] [Revised: 04/18/2022] [Accepted: 04/23/2022] [Indexed: 02/01/2023] Open
Abstract
To ensure efficiency in discovery and development, the application of computational technology is essential. Although virtual screening techniques are widely applied in the early stages of drug discovery research, the computational methods used in lead optimization to improve activity and reduce the toxicity of compounds are still evolving. In this study, we propose a method to construct the residue interaction profile of the chemical structure used in the lead optimization by performing “inverse” mixed-solvent molecular dynamics (MSMD) simulation. Contrary to constructing a protein-based, atom interaction profile, we constructed a probe-based, protein residue interaction profile using MSMD trajectories. It provides us the profile of the preferred protein environments of probes without co-crystallized structures. We assessed the method using three probes: benzamidine, catechol, and benzene. As a result, the residue interaction profile of each probe obtained by MSMD was a reasonable physicochemical description of the general non-covalent interaction. Moreover, comparison with the X-ray structure containing each probe as a ligand shows that the map of the interaction profile matches the arrangement of amino acid residues in the X-ray structure.
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154
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Cheshmazar N, Hemmati S, Hamzeh-Mivehroud M, Sokouti B, Zessin M, Schutkowski M, Sippl W, Nozad Charoudeh H, Dastmalchi S. Development of New Inhibitors of HDAC1-3 Enzymes Aided by In Silico Design Strategies. J Chem Inf Model 2022; 62:2387-2397. [PMID: 35467871 DOI: 10.1021/acs.jcim.1c01557] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Histone deacetylases (HDACs) are overexpressed in cancer, and their inhibition shows promising results in cancer therapy. In particular, selective class I HDAC inhibitors such as entinostat are proposed to be more beneficial in breast cancer treatment. Computational drug design is an inevitable part of today's drug discovery projects because of its unequivocal role in saving time and cost. Using three HDAC inhibitors trichostatin, vorinostat, and entinostat as template structures and a diverse fragment library, all synthetically accessible compounds thereof (∼3200) were generated virtually and filtered based on similarity against the templates and PAINS removal. The 298 selected structures were docked into the active site of HDAC I and ranked using a calculated binding affinity. Top-ranking structures were inspected manually, and, considering the ease of synthesis and drug-likeness, two new structures (3a and 3b) were proposed for synthesis and biological evaluation. The synthesized compounds were purified to a degree of more than 95% and structurally verified using various methods. The designed compounds 3a and 3b showed 65-80 and 5% inhibition on HDAC 1, 2, and 3 isoforms at a concentration of 10 μM, respectively. The novel compound 3a may be used as a lead structure for designing new HDAC inhibitors.
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Affiliation(s)
- Narges Cheshmazar
- Student Research Committee, Tabriz University of Medical Sciences, Tabriz 5165665931, Iran.,Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz 5165665813, Iran.,Department of Medicinal Chemistry, School of Pharmacy, Tabriz University of Medical Sciences, Tabriz 5166414766, Iran
| | - Salar Hemmati
- Drug Applied Research Center, Tabriz University of Medical Sciences, Tabriz 51656-65811, Iran
| | - Maryam Hamzeh-Mivehroud
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz 5165665813, Iran.,Department of Medicinal Chemistry, School of Pharmacy, Tabriz University of Medical Sciences, Tabriz 5166414766, Iran
| | - Babak Sokouti
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz 5165665813, Iran
| | - Matthes Zessin
- Department of Enzymology, Institute of Biochemistry, Martin-Luther-University Halle-Wittenberg, 06120 Halle/Saale, Germany
| | - Mike Schutkowski
- Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther-University Halle-Wittenberg, 06120 Halle/Saale, Germany
| | - Wolfgang Sippl
- Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther-University Halle-Wittenberg, 06120 Halle/Saale, Germany
| | | | - Siavoush Dastmalchi
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz 5165665813, Iran.,Department of Medicinal Chemistry, School of Pharmacy, Tabriz University of Medical Sciences, Tabriz 5166414766, Iran.,Faculty of Pharmacy, Near East University, P.O. Box 99138, Nicosia, North Cyprus, Mersin 10, Turkey
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155
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Uddin A, Gupta S, Mohammad T, Shahi D, Hussain A, Alajmi MF, El-Seedi HR, Hassan I, Singh S, Abid M. Target-Based Virtual Screening of Natural Compounds Identifies a Potent Antimalarial With Selective Falcipain-2 Inhibitory Activity. Front Pharmacol 2022; 13:850176. [PMID: 35462917 PMCID: PMC9020225 DOI: 10.3389/fphar.2022.850176] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 03/08/2022] [Indexed: 12/02/2022] Open
Abstract
We employed a comprehensive approach of target-based virtual high-throughput screening to find potential hits from the ZINC database of natural compounds against cysteine proteases falcipain-2 and falcipain-3 (FP2 and FP3). Molecular docking studies showed the initial hits showing high binding affinity and specificity toward FP2 were selected. Furthermore, the enzyme inhibition and surface plasmon resonance assays were performed which resulted in a compound ZINC12900664 (ST72) with potent inhibitory effects on purified FP2. ST72 exhibited strong growth inhibition of chloroquine-sensitive (3D7; EC50 = 2.8 µM) and chloroquine-resistant (RKL-9; EC50 = 6.7 µM) strains of Plasmodium falciparum. Stage-specific inhibition assays revealed a delayed and growth defect during parasite growth and development in parasites treated with ST72. Furthermore, ST72 significantly reduced parasite load and increased host survival in a murine model infected with Plasmodium berghei ANKA. No Evans blue staining in ST72 treatment indicated that ST72 mediated protection of blood–brain barrier integrity in mice infected with P. berghei. ST72 did not show any significant hemolysis or cytotoxicity against human HepG2 cells suggesting a good safety profile. Importantly, ST72 with CQ resulted in improved growth inhibitory activity than individual drugs in both in vitro and in vivo studies.
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Affiliation(s)
- Amad Uddin
- Medicinal Chemistry Laboratory, Department of Biosciences, Jamia Millia Islamia, New Delhi, India
- Special Centre for Molecular Medicine, Jawaharlal Nehru University, New Delhi, India
| | - Sonal Gupta
- Special Centre for Molecular Medicine, Jawaharlal Nehru University, New Delhi, India
| | - Taj Mohammad
- Center for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
| | - Diksha Shahi
- Special Centre for Molecular Medicine, Jawaharlal Nehru University, New Delhi, India
| | - Afzal Hussain
- Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Mohamed F. Alajmi
- Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Hesham R. El-Seedi
- Department of Medicinal Chemistry, Uppsala University, Biomedical Centre, Uppsala, Sweden
| | - Imtaiyaz Hassan
- Center for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
| | - Shailja Singh
- Special Centre for Molecular Medicine, Jawaharlal Nehru University, New Delhi, India
- *Correspondence: Shailja Singh, ; Mohammad Abid,
| | - Mohammad Abid
- Medicinal Chemistry Laboratory, Department of Biosciences, Jamia Millia Islamia, New Delhi, India
- *Correspondence: Shailja Singh, ; Mohammad Abid,
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156
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In Silico Screening of Cinnamon (Cinnamomum burmannii) Bioactive Compounds as Acetylcholinesterase Inhibitors. JURNAL KIMIA SAINS DAN APLIKASI 2022. [DOI: 10.14710/jksa.25.3.97-107] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Alzheimer’s is a progressive and neurodegenerative disease that mainly affects people aged 65 years and older. The pathophysiology of Alzheimer’s is possibly related to the depletion of the neurotransmitter acetylcholine (ACh) due to beta-amyloid plaques and neurofibrillary tangles. Secondary metabolites found in cinnamon bark (Cinnamomum burmannii) have the potential as anticholinesterases to treat Alzheimer’s symptoms. This study aimed to identify the potency of bioactive compounds from cinnamon bark as AChE inhibitors in silico through analysis of binding energy, inhibition constants, and types of interactions. The research was conducted by screening virtually 60 test ligands using the PyRx program and molecular docking using the Autodock Tools program. The results of the ligand-receptor interaction analysis showed that 12 of the 15 tested ligands had potential as AChE inhibitors. Epicatechin and medioresinol are the ligands with the best potential for AChE inhibition with affinity close to the natural ligand or donepezil. Epicatechin has a binding energy of −10.0 kcal/mol and inhibition constant of 0.0459 M, with four hydrogen bonds and seven hydrophobic bonds. Meanwhile, medioresinol has −9.9 kcal/mol binding energy and inhibition constant of 0.0543 M, with one hydrogen bond and thirteen hydrophobic bonds.
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157
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Moon S, Zhung W, Yang S, Lim J, Kim WY. PIGNet: a physics-informed deep learning model toward generalized drug-target interaction predictions. Chem Sci 2022; 13:3661-3673. [PMID: 35432900 PMCID: PMC8966633 DOI: 10.1039/d1sc06946b] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 02/06/2022] [Indexed: 12/21/2022] Open
Abstract
Recently, deep neural network (DNN)-based drug–target interaction (DTI) models were highlighted for their high accuracy with affordable computational costs. Yet, the models' insufficient generalization remains a challenging problem in the practice of in silico drug discovery. We propose two key strategies to enhance generalization in the DTI model. The first is to predict the atom–atom pairwise interactions via physics-informed equations parameterized with neural networks and provides the total binding affinity of a protein–ligand complex as their sum. We further improved the model generalization by augmenting a broader range of binding poses and ligands to training data. We validated our model, PIGNet, in the comparative assessment of scoring functions (CASF) 2016, demonstrating the outperforming docking and screening powers than previous methods. Our physics-informing strategy also enables the interpretation of predicted affinities by visualizing the contribution of ligand substructures, providing insights for further ligand optimization. PIGNet, a deep neural network-based drug–target interaction model guided by physics and extensive data augmentation, shows significantly improved generalization ability and model performance.![]()
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Affiliation(s)
- Seokhyun Moon
- Department of Chemistry, KAIST 291 Daehak-ro, Yuseong-gu Daejeon 34141 Republic of Korea
| | - Wonho Zhung
- Department of Chemistry, KAIST 291 Daehak-ro, Yuseong-gu Daejeon 34141 Republic of Korea
| | - Soojung Yang
- Department of Chemistry, KAIST 291 Daehak-ro, Yuseong-gu Daejeon 34141 Republic of Korea
| | - Jaechang Lim
- HITS Incorporation 124 Teheran-ro, Gangnam-gu Seoul 06234 Republic of Korea
| | - Woo Youn Kim
- Department of Chemistry, KAIST 291 Daehak-ro, Yuseong-gu Daejeon 34141 Republic of Korea .,HITS Incorporation 124 Teheran-ro, Gangnam-gu Seoul 06234 Republic of Korea.,KI for Artificial Intelligence, KAIST 291 Daehak-ro, Yuseong-gu Daejeon 34141 Republic of Korea
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158
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Chou PH, Luo CK, Wali N, Lin WY, Ng SK, Wang CH, Zhao M, Lin SW, Yang PM, Liu PJ, Shie JJ, Wei TT. A chemical probe inhibitor targeting STAT1 restricts cancer stem cell traits and angiogenesis in colorectal cancer. J Biomed Sci 2022; 29:20. [PMID: 35313878 PMCID: PMC8939146 DOI: 10.1186/s12929-022-00803-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 03/14/2022] [Indexed: 01/05/2023] Open
Abstract
Background Colorectal cancer (CRC) is a worldwide cancer with rising annual incidence. New medications for patients with CRC are still needed. Recently, fluorescent chemical probes have been developed for cancer imaging and therapy. Signal transducer and activator of transcription 1 (STAT1) has complex functions in tumorigenesis and its role in CRC still needs further investigation. Methods RNA sequencing datasets in the NCBI GEO repository were analyzed to investigate the expression of STAT1 in patients with CRC. Xenograft mouse models, tail vein injection mouse models, and azoxymethane/dextran sodium sulfate (AOM/DSS) mouse models were generated to study the roles of STAT1 in CRC. A ligand-based high-throughput virtual screening approach combined with SWEETLEAD chemical database analysis was used to discover new STAT1 inhibitors. A newly designed and synthesized fluorescently labeled 4’,5,7-trihydroxyisoflavone (THIF) probe (BODIPY-THIF) elucidated the mechanistic actions of STAT1 and THIF in vitro and in vivo. Colonosphere formation assay and chick chorioallantoic membrane assay were used to evaluate stemness and angiogenesis, respectively. Results Upregulation of STAT1 was observed in patients with CRC and in mouse models of AOM/DSS-induced CRC and metastatic CRC. Knockout of STAT1 in CRC cells reduced tumor growth in vivo. We then combined a high-throughput virtual screening approach and analysis of the SWEETLEAD chemical database and found that THIF, a flavonoid abundant in soybeans, was a novel STAT1 inhibitor. THIF inhibited STAT1 phosphorylation and might bind to the STAT1 SH2 domain, leading to blockade of STAT1-STAT1 dimerization. The results of in vitro and in vivo binding studies of THIF and STAT1 were validated. The pharmacological treatment with BODIPY-THIF or ablation of STAT1 via a CRISPR/Cas9-based strategy abolished stemness and angiogenesis in CRC. Oral administration of BODIPY-THIF attenuated colitis symptoms and tumor growth in the mouse model of AOM/DSS-induced CRC. Conclusions This study demonstrates that STAT1 plays an oncogenic role in CRC. BODIPY-THIF is a new chemical probe inhibitor of STAT1 that reduces stemness and angiogenesis in CRC. BODIPY-THIF can be a potential tool for CRC therapy as well as cancer cell imaging. Supplementary Information The online version contains supplementary material available at 10.1186/s12929-022-00803-4.
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Affiliation(s)
- Pei-Hsuan Chou
- Department and Graduate Institute of Pharmacology, College of Medicine, National Taiwan University, No. 1, Jen-Ai Road, 1st Section, Taipei, 10051, Taiwan
| | - Cong-Kai Luo
- Department and Graduate Institute of Pharmacology, College of Medicine, National Taiwan University, No. 1, Jen-Ai Road, 1st Section, Taipei, 10051, Taiwan
| | - Niaz Wali
- Institute of Chemistry, Academia Sinica, 128 Academia Road, Section 2, Taipei, 11529, Taiwan.,Institute of Biochemical Sciences, College of Life Science, National Taiwan University, Taipei, 10617, Taiwan.,Chemical Biology and Molecular Biophysics, Taiwan International Graduate Program in Chemical Biology and Molecular Biophysics (TIGP-CBMB), Academia Sinica, Taipei, 11529, Taiwan
| | - Wen-Yen Lin
- School of Pharmacy, College of Medicine, National Taiwan University, Taipei, 10051, Taiwan
| | - Shang-Kok Ng
- Department and Graduate Institute of Pharmacology, College of Medicine, National Taiwan University, No. 1, Jen-Ai Road, 1st Section, Taipei, 10051, Taiwan
| | - Chun-Hao Wang
- School of Medicine, College of Medicine, National Taiwan University, Taipei, 10051, Taiwan
| | - Mingtao Zhao
- Center for Cardiovascular Research, The Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, OH, 43210, USA.,The Heart Center, Nationwide Children's Hospital, Columbus, OH, 43210, USA.,Department of Pediatrics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA
| | - Sheng-Wei Lin
- Institute of Biological Chemistry, Academia Sinica, Taipei, 11529, Taiwan
| | - Pei-Ming Yang
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei, 11031, Taiwan
| | - Pin-Jung Liu
- School of Pharmacy, College of Pharmacy, Taipei Medical University, Taipei, 11031, Taiwan
| | - Jiun-Jie Shie
- Institute of Chemistry, Academia Sinica, 128 Academia Road, Section 2, Taipei, 11529, Taiwan.
| | - Tzu-Tang Wei
- Department and Graduate Institute of Pharmacology, College of Medicine, National Taiwan University, No. 1, Jen-Ai Road, 1st Section, Taipei, 10051, Taiwan. .,Chemical Biology and Molecular Biophysics, Taiwan International Graduate Program in Chemical Biology and Molecular Biophysics (TIGP-CBMB), Academia Sinica, Taipei, 11529, Taiwan.
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159
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Pathak RK, Seo YJ, Kim JM. Structural insights into inhibition of PRRSV Nsp4 revealed by structure-based virtual screening, molecular dynamics, and MM-PBSA studies. J Biol Eng 2022; 16:4. [PMID: 35193698 PMCID: PMC8864930 DOI: 10.1186/s13036-022-00284-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 02/02/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Porcine reproductive and respiratory syndrome respiratory sickness in weaned and growing pigs, as well as sow reproductive failure, and its infection is regarded as one of the most serious swine illnesses worldwide. Given the current lack of an effective treatment, in this study, we identified natural compounds capable of inhibiting non-structural protein 4 (Nsp4) of the virus, which is involved in their replication and pathogenesis. RESULTS We screened natural compounds (n = 97,999) obtained from the ZINC database against Nsp4 and selected the top 10 compounds for analysing protein-ligand interactions and physicochemical properties. The five compounds demonstrating strong binding affinity were then subjected to molecular dynamics simulations (100 ns) and binding free energy calculations. Based on analysis, we identified four possible lead compounds that represent potentially effective drug-like inhibitors. CONCLUSIONS These methods identified that these natural compounds are capable of inhibiting Nsp4 and possibly effective as antiviral therapeutics against PRRSV.
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Affiliation(s)
- Rajesh Kumar Pathak
- Department of Animal Science and Technology, Chung-Ang University, Anseong-si, Gyeonggi-do, 17546, Republic of Korea
| | - Young-Jun Seo
- Department of Animal Science and Technology, Chung-Ang University, Anseong-si, Gyeonggi-do, 17546, Republic of Korea
| | - Jun-Mo Kim
- Department of Animal Science and Technology, Chung-Ang University, Anseong-si, Gyeonggi-do, 17546, Republic of Korea.
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160
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Iraci N, Ostacolo C, Medina-Peris A, Ciaglia T, Novoselov AM, Altieri A, Cabañero D, Fernandez-Carvajal A, Campiglia P, Gomez-Monterrey I, Bertamino A, Kurkin AV. In Vitro and In Vivo Pharmacological Characterization of a Novel TRPM8 Inhibitor Chemotype Identified by Small-Scale Preclinical Screening. Int J Mol Sci 2022; 23:ijms23042070. [PMID: 35216186 PMCID: PMC8877448 DOI: 10.3390/ijms23042070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/04/2022] [Accepted: 02/11/2022] [Indexed: 11/16/2022] Open
Abstract
Transient receptor potential melastatin type 8 (TRPM8) is a target for the treatment of different physio-pathological processes. While TRPM8 antagonists are reported as potential drugs for pain, cancer, and inflammation, to date only a limited number of chemotypes have been investigated and thus a limited number of compounds have reached clinical trials. Hence there is high value in searching for new TRPM8 antagonistic to broaden clues to structure-activity relationships, improve pharmacological properties and explore underlying molecular mechanisms. To address this, the EDASA Scientific in-house molecular library has been screened in silico, leading to identifying twenty-one potentially antagonist compounds of TRPM8. Calcium fluorometric assays were used to validate the in-silico hypothesis and assess compound selectivity. Four compounds were identified as selective TRPM8 antagonists, of which two were dual-acting TRPM8/TRPV1 modulators. The most potent TRPM8 antagonists (BB 0322703 and BB 0322720) underwent molecular modelling studies to highlight key structural features responsible for drug–protein interaction. The two compounds were also investigated by patch-clamp assays, confirming low micromolar potencies. The most potent compound (BB 0322703, IC50 1.25 ± 0.26 μM) was then profiled in vivo in a cold allodinya model, showing pharmacological efficacy at 30 μM dose. The new chemotypes identified showed remarkable pharmacological properties paving the way to further investigations for drug discovery and pharmacological purposes.
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Affiliation(s)
- Nunzio Iraci
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Viale Ferdinando Stagno d’Alcontres 31, 98166 Messina, Italy;
| | - Carmine Ostacolo
- Department of Pharmacy, University of Naples Federico II, Via D. Montesano 49, 80131 Naples, Italy; (C.O.); (I.G.-M.)
| | - Alicia Medina-Peris
- Instituto de Investigación, Desarrollo e Innovación en Biotecnología Sanitaria de Elche (IDiBE), Universidad Miguel Hernández de Elche, Avenida de la Universidad, 03202 Elche, Spain; (A.M.-P.); (D.C.); (A.F.-C.)
| | - Tania Ciaglia
- Department of Pharmacy, University of Salerno, Via G. Paolo II, 84084 Fisciano, Italy; (T.C.); (P.C.)
| | - Anton M. Novoselov
- Department of Chemistry, Lomonosov Moscow State University, 1/3 Leninsky Gory, 119991 Moscow, Russia; (A.M.N.); (A.A.)
| | - Andrea Altieri
- Department of Chemistry, Lomonosov Moscow State University, 1/3 Leninsky Gory, 119991 Moscow, Russia; (A.M.N.); (A.A.)
- EDASA Scientific srls, Via Stingi 37, 66050 San Salvo, Italy
| | - David Cabañero
- Instituto de Investigación, Desarrollo e Innovación en Biotecnología Sanitaria de Elche (IDiBE), Universidad Miguel Hernández de Elche, Avenida de la Universidad, 03202 Elche, Spain; (A.M.-P.); (D.C.); (A.F.-C.)
| | - Asia Fernandez-Carvajal
- Instituto de Investigación, Desarrollo e Innovación en Biotecnología Sanitaria de Elche (IDiBE), Universidad Miguel Hernández de Elche, Avenida de la Universidad, 03202 Elche, Spain; (A.M.-P.); (D.C.); (A.F.-C.)
| | - Pietro Campiglia
- Department of Pharmacy, University of Salerno, Via G. Paolo II, 84084 Fisciano, Italy; (T.C.); (P.C.)
| | - Isabel Gomez-Monterrey
- Department of Pharmacy, University of Naples Federico II, Via D. Montesano 49, 80131 Naples, Italy; (C.O.); (I.G.-M.)
| | - Alessia Bertamino
- Department of Pharmacy, University of Salerno, Via G. Paolo II, 84084 Fisciano, Italy; (T.C.); (P.C.)
- Correspondence: (A.B.); (A.V.K.)
| | - Alexander V. Kurkin
- Department of Chemistry, Lomonosov Moscow State University, 1/3 Leninsky Gory, 119991 Moscow, Russia; (A.M.N.); (A.A.)
- Correspondence: (A.B.); (A.V.K.)
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161
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Jiang Y, Liu L, Manning M, Bonahoom M, Lotvola A, Yang Z, Yang ZQ. Structural analysis, virtual screening and molecular simulation to identify potential inhibitors targeting 2'-O-ribose methyltransferase of SARS-CoV-2 coronavirus. J Biomol Struct Dyn 2022; 40:1331-1346. [PMID: 33016237 PMCID: PMC7544923 DOI: 10.1080/07391102.2020.1828172] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 09/12/2020] [Indexed: 12/14/2022]
Abstract
SARS-CoV-2, an emerging coronavirus, has spread rapidly around the world, resulting in over ten million cases and more than half a million deaths as of July 1, 2020. Effective treatments and vaccines for SARS-CoV-2 infection do not currently exist. Previous studies demonstrated that nonstructural protein 16 (nsp16) of coronavirus is an S-adenosyl methionine (SAM)-dependent 2'-O-methyltransferase (2'-O-MTase) that has an important role in viral replication and prevents recognition by the host innate immune system. In the present study, we employed structural analysis, virtual screening, and molecular simulation approaches to identify clinically investigated and approved drugs which can act as promising inhibitors against nsp16 2'-O-MTase of SARS-CoV-2. Comparative analysis of primary amino acid sequences and crystal structures of seven human CoVs defined the key residues for nsp16 2-O'-MTase functions. Virtual screening and docking analysis ranked the potential inhibitors of nsp16 from more than 4,500 clinically investigated and approved drugs. Furthermore, molecular dynamics simulations were carried out on eight top candidates, including Hesperidin, Rimegepant, Gs-9667, and Sonedenoson, to calculate various structural parameters and understand the dynamic behavior of the drug-protein complexes. Our studies provided the foundation to further test and repurpose these candidate drugs experimentally and/or clinically for COVID-19 treatment.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Yuanyuan Jiang
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI, USA
| | - Lanxin Liu
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI, USA
| | - Morenci Manning
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI, USA
| | - Madison Bonahoom
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI, USA
| | - Aaron Lotvola
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI, USA
| | - Zhe Yang
- Department of Biochemistry, Microbiology and Immunology, Wayne State University School of Medicine, Detroit, MI, USA
| | - Zeng-Quan Yang
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI, USA
- Molecular Therapeutics Program, Barbara Ann Karmanos Cancer Institute, Detroit, MI, USA
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162
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Identification of Potential Inhibitors of SARS-CoV-2 Main Protease from Allium roseum L. Molecular Docking Study. CHEMISTRY AFRICA 2022. [PMCID: PMC8607792 DOI: 10.1007/s42250-021-00296-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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163
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Suay-García B, Bueso-Bordils JI, Falcó A, Antón-Fos GM, Alemán-López PA. Virtual Combinatorial Chemistry and Pharmacological Screening: A Short Guide to Drug Design. Int J Mol Sci 2022; 23:ijms23031620. [PMID: 35163543 PMCID: PMC8836228 DOI: 10.3390/ijms23031620] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 01/24/2022] [Accepted: 01/28/2022] [Indexed: 02/04/2023] Open
Abstract
Traditionally, drug development involved the individual synthesis and biological evaluation of hundreds to thousands of compounds with the intention of highlighting their biological activity, selectivity, and bioavailability, as well as their low toxicity. On average, this process of new drug development involved, in addition to high economic costs, a period of several years before hopefully finding a drug with suitable characteristics to drive its commercialization. Therefore, the chemical synthesis of new compounds became the limiting step in the process of searching for or optimizing leads for new drug development. This need for large chemical libraries led to the birth of high-throughput synthesis methods and combinatorial chemistry. Virtual combinatorial chemistry is based on the same principle as real chemistry—many different compounds can be generated from a few building blocks at once. The difference lies in its speed, as millions of compounds can be produced in a few seconds. On the other hand, many virtual screening methods, such as QSAR (Quantitative Sturcture-Activity Relationship), pharmacophore models, and molecular docking, have been developed to study these libraries. These models allow for the selection of molecules to be synthesized and tested with a high probability of success. The virtual combinatorial chemistry–virtual screening tandem has become a fundamental tool in the process of searching for and developing a drug, as it allows the process to be accelerated with extraordinary economic savings.
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Affiliation(s)
- Beatriz Suay-García
- ESI International @ UCHCEU, Departamento de Matemáticas, Física y Ciencias Tecnológicas, Universidad Cardenal Herrera—CEU, CEU Universities San Bartolomé 55, Alfara del Patriarca, 46115 Valencia, Spain;
- Correspondence:
| | - Jose I. Bueso-Bordils
- Departamento de Farmacia, Universidad Cardenal Herrera—CEU, CEU Universities, C/Ramón y Cajal s/n, Alfara del Patriarca, 46115 Valencia, Spain; (G.M.A.-F.); (P.A.A.-L.); (J.I.B.-B.)
| | - Antonio Falcó
- ESI International @ UCHCEU, Departamento de Matemáticas, Física y Ciencias Tecnológicas, Universidad Cardenal Herrera—CEU, CEU Universities San Bartolomé 55, Alfara del Patriarca, 46115 Valencia, Spain;
| | - Gerardo M. Antón-Fos
- Departamento de Farmacia, Universidad Cardenal Herrera—CEU, CEU Universities, C/Ramón y Cajal s/n, Alfara del Patriarca, 46115 Valencia, Spain; (G.M.A.-F.); (P.A.A.-L.); (J.I.B.-B.)
| | - Pedro A. Alemán-López
- Departamento de Farmacia, Universidad Cardenal Herrera—CEU, CEU Universities, C/Ramón y Cajal s/n, Alfara del Patriarca, 46115 Valencia, Spain; (G.M.A.-F.); (P.A.A.-L.); (J.I.B.-B.)
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164
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Ma Y, Li WY, Sun T, Zhang L, Lu XH, Yang B, Wang RL. Structure-based discovery of a specific SHP2 inhibitor with enhanced blood-brain barrier penetration from PubChem database. Bioorg Chem 2022; 121:105648. [PMID: 35180489 DOI: 10.1016/j.bioorg.2022.105648] [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: 06/18/2021] [Revised: 01/22/2022] [Accepted: 01/25/2022] [Indexed: 11/20/2022]
Abstract
The thiophene [2,3-d]pyrimidine structure-like small molecules were discovered from structure-based virtual screening of 1 billion compounds. Base on enzyme activity assay results, a SHP2-specific molecule inhibitor Comp#2 with IC50 of 1.174 μM, 85-fold more selective for SHP2 than the highly related SHP1 (IC50 > 100 μM). The compound can effectively inhibit SHP2-mediated cell signaling and cancer cell proliferation, including cervix cancer, human pancreatic cancer, large cell lung cancer, and mouse glioma cell. Moreover, the in vivo assay indicated that Comp#2 could inhibit cervix cancer tumors growth in BABL/c mice. This work has shown the specific SHP2 inhibitor can inhibit glioblastoma growth in vivo.
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Affiliation(s)
- Ying Ma
- Tianjin Key Laboratory of Technologies Enabling Development of Clinical Therapeutics and Diagnostics, School of Pharmacy, Tianjin Medical University, Tianjin 300070, People's Republic of China
| | - Wei-Ya Li
- Tianjin Key Laboratory of Technologies Enabling Development of Clinical Therapeutics and Diagnostics, School of Pharmacy, Tianjin Medical University, Tianjin 300070, People's Republic of China
| | - Ting Sun
- Research Center of Basic Medical Science, Tianjin Medical University, Tianjin 300070, China
| | - Ling Zhang
- School of Basic Medical Science, Tianjin Medical University, Tianjin 300070, China
| | - Xin-Hua Lu
- New Drug Research & Development Center of North China Pharmaceutical Group Corporation, National Microbial Medicine Engineering & Research Center, Hebei Industry Microbial Metabolic Engineering & Technology Research Center, Shijiazhuang, Hebei 050015, China
| | - Bing Yang
- School of Basic Medical Science, Tianjin Medical University, Tianjin 300070, China.
| | - Run-Ling Wang
- Tianjin Key Laboratory of Technologies Enabling Development of Clinical Therapeutics and Diagnostics, School of Pharmacy, Tianjin Medical University, Tianjin 300070, People's Republic of China.
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165
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In Silico Drug Repurposing Approach: Investigation of Mycobacterium tuberculosis FadD32 Targeted by FDA-Approved Drugs. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27030668. [PMID: 35163931 PMCID: PMC8840176 DOI: 10.3390/molecules27030668] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 12/27/2021] [Accepted: 01/09/2022] [Indexed: 12/17/2022]
Abstract
Background: Despite the enormous efforts made towards combating tuberculosis (TB), the disease remains a major global threat. Hence, new drugs with novel mechanisms against TB are urgently needed. Fatty acid degradation protein D32 (FadD32) has been identified as a promising drug target against TB, the protein is required for the biosynthesis of mycolic acids, hence, essential for the growth and multiplication of the mycobacterium. However, the FadD32 mechanism upon the binding of FDA-approved drugs is not well established. Herein, we applied virtual screening (VS), molecular docking, and molecular dynamic (MD) simulation to identify potential FDA-approved drugs against FadD32. Methodology/Results: VS technique was found promising to identify four FDA-approved drugs (accolate, sorafenib, mefloquine, and loperamide) with higher molecular docking scores, ranging from -8.0 to -10.0 kcal/mol. Post-MD analysis showed that the accolate hit displayed the highest total binding energy of -45.13 kcal/mol. Results also showed that the accolate hit formed more interactions with FadD32 active site residues and all active site residues displayed an increase in total binding contribution. RMSD, RMSF, Rg, and DCCM analysis further supported that the presence of accolate exhibited more structural stability, lower bimolecular flexibility, and more compactness into the FadD32 protein. Conclusions: Our study revealed accolate as the best potential drug against FadD32, hence a prospective anti-TB drug in TB therapy. In addition, we believe that the approach presented in the current study will serve as a cornerstone to identifying new potential inhibitors against a wide range of biological targets.
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166
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Alves LA, Ferreira NCDS, Maricato V, Alberto AVP, Dias EA, Jose Aguiar Coelho N. Graph Neural Networks as a Potential Tool in Improving Virtual Screening Programs. Front Chem 2022; 9:787194. [PMID: 35127645 PMCID: PMC8811035 DOI: 10.3389/fchem.2021.787194] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 12/10/2021] [Indexed: 11/23/2022] Open
Abstract
Despite the increasing number of pharmaceutical companies, university laboratories and funding, less than one percent of initially researched drugs enter the commercial market. In this context, virtual screening (VS) has gained much attention due to several advantages, including timesaving, reduced reagent and consumable costs and the performance of selective analyses regarding the affinity between test molecules and pharmacological targets. Currently, VS is based mainly on algorithms that apply physical and chemistry principles and quantum mechanics to estimate molecule affinities and conformations, among others. Nevertheless, VS has not reached the expected results concerning the improvement of market-approved drugs, comprising less than twenty drugs that have reached this goal to date. In this context, graph neural networks (GNN), a recent deep-learning subtype, may comprise a powerful tool to improve VS results concerning natural products that may be used both simultaneously with standard algorithms or isolated. This review discusses the pros and cons of GNN applied to VS and the future perspectives of this learnable algorithm, which may revolutionize drug discovery if certain obstacles concerning spatial coordinates and adequate datasets, among others, can be overcome.
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Affiliation(s)
- Luiz Anastacio Alves
- Laboratory of Cellular Communication, Oswaldo Cruz Institute – Fiocruz, Rio de Janeiro, Brazil
- *Correspondence: Luiz Anastacio Alves,
| | | | - Victor Maricato
- Laboratory of Cellular Communication, Oswaldo Cruz Institute – Fiocruz, Rio de Janeiro, Brazil
| | | | - Evellyn Araujo Dias
- Laboratory of Cellular Communication, Oswaldo Cruz Institute – Fiocruz, Rio de Janeiro, Brazil
| | - Nt Jose Aguiar Coelho
- National Institute of Industrial Property - INPI and Veiga de Almeida University - UVA, Rio de Janeiro, Brazil
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167
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Xu X, Zhang S, Wang Y, Zhao G, Sun Y, Wang J, Liu L, Liu F, Wang P, Yang J. Virtual Screening Inhibitors of Ubiquitin-specific Protease 7 combining Pharmacophore Modeling and Molecular Docking. Mol Inform 2022; 41:e2100273. [PMID: 35037416 DOI: 10.1002/minf.202100273] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 01/16/2022] [Indexed: 11/07/2022]
Abstract
Ubiquitin-specific protease 7 (USP7) is one of the most extensively studied deubiquitinases. USP7 exhibits a high expression signature in various malignant tumors, suggesting that it is a marker of tumor prognosis and a potential drug target for anti-tumor therapy. In this study, virtual screening based on pharmacophore model and biological evaluation have been applied for the discovery of novel USP7 inhibitors targeting the catalytic active site. The TS-4 was screened from 215,480 small molecules and was found to have USP7 inhibitory activity. Preliminary in vitro studies disclosed its antiproliferative activity on human colon cancer cell lines (HCT-116 and RKO), compared with normal colon cell line (CCD841CoN). Molecular dynamics (MD) simulation revealed the combine mechanism between USP7 with the TS-4. The TS-4 formed stable interactions with Asp295, Phe409 and Tyr514, which were critical to enhance its biological activity. This compound will serve as a promising hit compound for facilitating the further design of novel USP7 inhibitors.
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Affiliation(s)
| | | | | | | | | | | | | | - Fang Liu
- Guangzhou University of Chinese Medicine, CHINA
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168
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Zhao Q, Zhao H, Zheng K, Wang J. HyperAttentionDTI: improving drug-protein interaction prediction by sequence-based deep learning with attention mechanism. Bioinformatics 2022; 38:655-662. [PMID: 34664614 DOI: 10.1093/bioinformatics/btab715] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 09/24/2021] [Accepted: 10/13/2021] [Indexed: 02/06/2023] Open
Abstract
MOTIVATION Identifying drug-target interactions (DTIs) is a crucial step in drug repurposing and drug discovery. Accurately identifying DTIs in silico can significantly shorten development time and reduce costs. Recently, many sequence-based methods are proposed for DTI prediction and improve performance by introducing the attention mechanism. However, these methods only model single non-covalent inter-molecular interactions among drugs and proteins and ignore the complex interaction between atoms and amino acids. RESULTS In this article, we propose an end-to-end bio-inspired model based on the convolutional neural network (CNN) and attention mechanism, named HyperAttentionDTI, for predicting DTIs. We use deep CNNs to learn the feature matrices of drugs and proteins. To model complex non-covalent inter-molecular interactions among atoms and amino acids, we utilize the attention mechanism on the feature matrices and assign an attention vector to each atom or amino acid. We evaluate HpyerAttentionDTI on three benchmark datasets and the results show that our model achieves significantly improved performance compared with the state-of-the-art baselines. Moreover, a case study on the human Gamma-aminobutyric acid receptors confirm that our model can be used as a powerful tool to predict DTIs. AVAILABILITY AND IMPLEMENTATION The codes of our model are available at https://github.com/zhaoqichang/HpyerAttentionDTI and https://zenodo.org/record/5039589. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Qichang Zhao
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Haochen Zhao
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Kai Zheng
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Jianxin Wang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
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169
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Fundamental considerations in drug design. COMPUTER AIDED DRUG DESIGN (CADD): FROM LIGAND-BASED METHODS TO STRUCTURE-BASED APPROACHES 2022:17-55. [PMCID: PMC9212230 DOI: 10.1016/b978-0-323-90608-1.00005-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
The drug discovery paradigm has been very time-consuming, challenging, and expensive; however, the disease conditions originating from bacteria, virus, protozoa, fungus and other microorganisms are steadily shooting up. For instance, COVID-19 is the latest viral infection that affects millions of people and the world’s economy very severely. Therefore, the quest for discovery of novel and potent drug compounds against deadly pathogens is crucial at the moment. Despite a lot of drawbacks in drug discovery and development and its pertaining technology, the advancement must be taken into account so the time duration and cost would be minimized. In this chapter, basic principles in drug design and discovery have been discussed together with advances in drug development.
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170
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Present and future challenges in therapeutic designing using computational approaches. COMPUTATIONAL APPROACHES FOR NOVEL THERAPEUTIC AND DIAGNOSTIC DESIGNING TO MITIGATE SARS-COV-2 INFECTION 2022. [PMCID: PMC9300749 DOI: 10.1016/b978-0-323-91172-6.00020-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Currently, various computational methods are being used for the purpose of therapeutic design. The advent of the Coronavirus disease-2019 (COVID-19) pandemic has created a lot of problems due to which the development of effective treatment options is urgently needed. Computational intelligence is used in the control, prevention, prediction, diagnosis, and treatment of the disease. Several important drug targets have been identified in severe acute respiratory syndrome-Coronavirus-2 using in silico methods. Computer-aided drug design includes a variety of theoretical and computational approaches that are part of modern drug discovery. Advances in machine learning methods and their applications speed up the drug discovery process. Exploration of nucleic acid-based therapeutics is playing an important role in healthcare also. But a lot of challenges have also been seen that complicate the therapeutic design. Therefore, investigation of challenges associated with therapeutic design is important, and the present chapter is aimed to cover various therapeutic design approaches and challenges associated with them. Moreover, the role of computational strategies in the exploration of potential therapeutics against COVID-19 has been investigated.
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171
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Basciu A, Callea L, Motta S, Bonvin AM, Bonati L, Vargiu AV. No dance, no partner! A tale of receptor flexibility in docking and virtual screening. VIRTUAL SCREENING AND DRUG DOCKING 2022. [DOI: 10.1016/bs.armc.2022.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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172
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Nigam A, Pollice R, Aspuru-Guzik A. Parallel tempered genetic algorithm guided by deep neural networks for inverse molecular design. DIGITAL DISCOVERY 2022; 1:390-404. [PMID: 36091415 PMCID: PMC9358752 DOI: 10.1039/d2dd00003b] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 05/03/2022] [Indexed: 12/30/2022]
Abstract
We present JANUS, an evolutionary algorithm for inverse molecular design. It propagates an explorative and an exploitative population exchanging members via parallel tempering and uses active learning via deep neural networks to enhance sampling.
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Affiliation(s)
- AkshatKumar Nigam
- Department of Computer Science, Stanford University, USA
- Department of Computer Science, University of Toronto, Canada
- Department of Chemistry, University of Toronto, Canada
| | - Robert Pollice
- Department of Computer Science, University of Toronto, Canada
- Department of Chemistry, University of Toronto, Canada
| | - Alán Aspuru-Guzik
- Department of Computer Science, University of Toronto, Canada
- Department of Chemistry, University of Toronto, Canada
- Vector Institute for Artificial Intelligence, Toronto, Canada
- Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR), 661 University Ave, Toronto, Ontario M5G, Canada
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173
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Holland CK, Tadfie H. A structure-guided computational screening approach for predicting plant enzyme–metabolite interactions. Methods Enzymol 2022; 676:71-101. [DOI: 10.1016/bs.mie.2022.07.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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174
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Goel M, Raghunathan S, Laghuvarapu S, Priyakumar UD. MoleGuLAR: Molecule Generation Using Reinforcement Learning with Alternating Rewards. J Chem Inf Model 2021; 61:5815-5826. [PMID: 34866384 DOI: 10.1021/acs.jcim.1c01341] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The design of new inhibitors for novel targets is a very important problem especially in the current scenario with the world being plagued by COVID-19. Conventional approaches such as high-throughput virtual screening require extensive combing through existing data sets in the hope of finding possible matches. In this study, we propose a computational strategy for de novo generation of molecules with high binding affinities to the specified target and other desirable properties for druglike molecules using reinforcement learning. A deep generative model built using a stack-augmented recurrent neural network initially trained to generate druglike molecules is optimized using reinforcement learning to start generating molecules with desirable properties like LogP, quantitative estimate of drug likeliness, topological polar surface area, and hydration free energy along with the binding affinity. For multiobjective optimization, we have devised a novel strategy in which the property being used to calculate the reward is changed periodically. In comparison to the conventional approach of taking a weighted sum of all rewards, this strategy shows an enhanced ability to generate a significantly higher number of molecules with desirable properties.
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Affiliation(s)
- Manan Goel
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
| | - Shampa Raghunathan
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India.,École Centrale School of Engineering, Mahindra University, Hyderabad 500 043, India
| | - Siddhartha Laghuvarapu
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
| | - U Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
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175
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Overcoming Depression with 5-HT2A Receptor Ligands. Int J Mol Sci 2021; 23:ijms23010010. [PMID: 35008436 PMCID: PMC8744644 DOI: 10.3390/ijms23010010] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 12/17/2021] [Accepted: 12/18/2021] [Indexed: 01/25/2023] Open
Abstract
Depression is a multifactorial disorder that affects millions of people worldwide, and none of the currently available therapeutics can completely cure it. Thus, there is a need for developing novel, potent, and safer agents. Recent medicinal chemistry findings on the structure and function of the serotonin 2A (5-HT2A) receptor facilitated design and discovery of novel compounds with antidepressant action. Eligible papers highlighting the importance of 5-HT2A receptors in the pathomechanism of the disorder were identified in the content-screening performed on the popular databases (PubMed, Google Scholar). Articles were critically assessed based on their titles and abstracts. The most accurate papers were chosen to be read and presented in the manuscript. The review summarizes current knowledge on the applicability of 5-HT2A receptor signaling modulators in the treatment of depression. It provides an insight into the structural and physiological features of this receptor. Moreover, it presents an overview of recently conducted virtual screening campaigns aiming to identify novel, potent 5-HT2A receptor ligands and additional data on currently synthesized ligands acting through this protein.
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176
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Tilaoui M, Ait Mouse H, Zyad A. Update and New Insights on Future Cancer Drug Candidates From Plant-Based Alkaloids. Front Pharmacol 2021; 12:719694. [PMID: 34975465 PMCID: PMC8716855 DOI: 10.3389/fphar.2021.719694] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 11/23/2021] [Indexed: 02/06/2023] Open
Abstract
Cancer is a complex multifactorial disease that results from alterations in many physiological and biochemical functions. Over the last few decades, it has become clear that cancer cells can acquire multidrug resistance to conventional anticancer drugs, resulting in tumor relapse. Thus, there is a continuous need to discover new and effective anticancer drugs. Natural products from plants have served as a primary source of cancer drugs and continue to provide new plant-derived anticancer drugs. The present review describes plant-based alkaloids, which have been reported as active or potentially active in cancer treatment within the past 4 years (2017-2020), both in preclinical research and/or in clinical trials. In addition, recent insights into the possible molecular mechanism of action of alkaloid prodrugs naturally present in plants are also highlighted.
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Affiliation(s)
- Mounir Tilaoui
- Experimental Oncology and Natural Substances Team, Cellular and Molecular Immuno-pharmacology, Faculty of Sciences and Technology, Sultan Moulay Slimane University, Beni-Mellal, Morocco
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177
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Shafie A, Khan S, Zehra, Mohammad T, Anjum F, Hasan GM, Yadav DK, Hassan MI. Identification of Phytoconstituents as Potent Inhibitors of Casein Kinase-1 Alpha Using Virtual Screening and Molecular Dynamics Simulations. Pharmaceutics 2021; 13:2157. [PMID: 34959438 PMCID: PMC8707374 DOI: 10.3390/pharmaceutics13122157] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 12/02/2021] [Accepted: 12/06/2021] [Indexed: 12/23/2022] Open
Abstract
Casein kinase-1 alpha (CK1α) is a multifunctional protein kinase that belongs to the serine/threonine kinases of the CK1α family. It is involved in various signaling pathways associated with chromosome segregation, cell metabolism, cell cycle progression, apoptosis, autophagy, etc. It has been known to involve in the progression of many diseases, including cancer, neurodegeneration, obesity, and behavioral disorders. The elevated expression of CK1α in diseased conditions facilitates its selective targeting for therapeutic management. Here, we have performed virtual screening of phytoconstituents from the IMPPAT database seeking potential inhibitors of CK1α. First, a cluster of compounds was retrieved based on physicochemical parameters following Lipinski's rules and PAINS filter. Further, high-affinity hits against CK1α were obtained based on their binding affinity score. Furthermore, the ADMET, PAINS, and PASS evaluation was carried out to select more potent hits. Finally, following the interaction analysis, we elucidated three phytoconstituents, Semiglabrinol, Curcusone_A, and Liriodenine, posturing considerable affinity and specificity towards the CK1α binding pocket. The result was further evaluated by molecular dynamics (MD) simulations, dynamical cross-correlation matrix (DCCM), and principal components analysis (PCA), which revealed that binding of the selected compounds, especially Semiglabrinol, stabilizes CK1α and leads to fewer conformational fluctuations. The MM-PBSA analysis suggested an appreciable binding affinity of all three compounds toward CK1α.
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Affiliation(s)
- Alaa Shafie
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia; (A.S.); (F.A.)
| | - Shama Khan
- Drug Discovery and Development Centre (H3D), University of Cape Town, Rondebosch 7701, South Africa;
| | - Zehra
- Department of Computer Science, Jamia Millia Islamia, Jamia Nagar, New Delhi 110025, India;
| | - Taj Mohammad
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, Jamia Nagar, New Delhi 110025, India;
| | - Farah Anjum
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia; (A.S.); (F.A.)
| | - Gulam Mustafa Hasan
- Department of Biochemistry, College of Medicine, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia;
| | - Dharmendra Kumar Yadav
- College of Pharmacy, Gachon University of Medicine and Science, Hambakmoeiro, Yeonsu-gu, Incheon City 21924, Korea
| | - Md. Imtaiyaz Hassan
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, Jamia Nagar, New Delhi 110025, India;
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178
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Maliwal D, Pissurlenkar RRS, Telvekar V. Identification of Novel Potential Anti-Diabetic Candidates targeting Human Pancreatic ɑ-Amylase and Human ɑ-Glycosidase: An Exhaustive Structure-based Screening. CAN J CHEM 2021. [DOI: 10.1139/cjc-2021-0238] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Diabetes is a major health issue that half a billion people affected worldwide. It is a serious, long-term medical condition majorly impacting the lives and well-being of individuals, families, and societies at large. It is amongst the top 10 diseases responsible for the death amongst adults with an expected rise to 10.2% (578 million) by 2030 and 10.9% (700 million) by 2045. The carbohydrates absorbed into the body are hydrolyzed by pancreatic α-amylase and other enzymes, human α-glucosidase. The α-amylase and α-glucosidase are validated therapeutic targets in the treatment of Type II diabetes (T2DM) as they play a vital role in modulating the blood glucose post meal. Herein, we report novel and diverse molecules as potential candidates, with predicted affinity for α-amylase and α-glucosidase. These molecules have been identified via hierarchical multistep docking of small molecules database with the estimated binding free energies. A Glide XP Score cutoff −8.00 kcal/mol was implemented to filter out non potential molecules. Four molecules viz. amb22034702, amb18105639, amb17153304, and amb9760832 have been identified after an exhaustive computational study involving, evaluation of binding interactions and assessment of the pharmacokinetics and toxicity profiles. The in-depth analysis of protein– ligand interactions was performed using a 100ns molecular dynamics (MD) simulation to establish the dynamic stability. Furthermore MM-GBSA based binding free energies were computed for 1000 trajectory snapshots to ascertain the strong binding affinity of these molecules for α-amylase and αglucosidase. The identified molecules can be considered as promising candidates for further drug development through necessary experimental assessments.
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Affiliation(s)
- Deepika Maliwal
- Institute of Chemical Technology, 52735, Department of Pharmaceutical Sciences and Technology, Mumbai, Maharashtra, India
| | | | - Vikas Telvekar
- Institute of Chemical Technology, 52735, Department of Pharmaceutical Sciences and Technology, Mumbai, Maharashtra, India
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179
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Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach. Int J Mol Sci 2021; 22:ijms222413259. [PMID: 34948055 PMCID: PMC8703488 DOI: 10.3390/ijms222413259] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 11/09/2021] [Accepted: 11/14/2021] [Indexed: 12/12/2022] Open
Abstract
Developing new, more effective antibiotics against resistant Mycobacterium tuberculosis that inhibit its essential proteins is an appealing strategy for combating the global tuberculosis (TB) epidemic. Finding a compound that can target a particular cavity in a protein and interrupt its enzymatic activity is the crucial objective of drug design and discovery. Such a compound is then subjected to different tests, including clinical trials, to study its effectiveness against the pathogen in the host. In recent times, new techniques, which involve computational and analytical methods, enhanced the chances of drug development, as opposed to traditional drug design methods, which are laborious and time-consuming. The computational techniques in drug design have been improved with a new generation of software used to develop and optimize active compounds that can be used in future chemotherapeutic development to combat global tuberculosis resistance. This review provides an overview of the evolution of tuberculosis resistance, existing drug management, and the design of new anti-tuberculosis drugs developed based on the contributions of computational techniques. Also, we show an appraisal of available software and databases on computational drug design with an insight into the application of this software and databases in the development of anti-tubercular drugs. The review features a perspective involving machine learning, artificial intelligence, quantum computing, and CRISPR combination with available computational techniques as a prospective pathway to design new anti-tubercular drugs to combat resistant tuberculosis.
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180
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García Y, Vera M, Giraldo JD, Garrido-Miranda K, Jiménez VA, Urbano BF, Pereira ED. Microcystins Detection Methods: A Focus on Recent Advances Using Molecularly Imprinted Polymers. Anal Chem 2021; 94:464-478. [PMID: 34874146 DOI: 10.1021/acs.analchem.1c04090] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Yadiris García
- Departamento de Química Analítica e Inorgánica Facultad de Ciencias Químicas, Universidad de Concepción, Casilla 160-C, 4030000 Concepción, Chile
| | - Myleidi Vera
- Departamento de Polímeros, Facultad de Ciencias Químicas, Universidad de Concepción, Casilla 160-C, 4030000 Concepción, Chile
| | - Juan D Giraldo
- Instituto de Acuicultura, Universidad Austral de Chile, Sede Puerto Montt, Los Pinos s/n Balneario Pelluco, 5480000 Puerto Montt, Chile
| | - Karla Garrido-Miranda
- Center of Waste Management and Bioenergy, Scientific and Technological Bioresource Nucleus, BIOREN-UFRO, Universidad de La Frontera, P.O. Box 54-D, 4811230 Temuco, Chile
| | - Verónica A Jiménez
- Departamento de Ciencias Químicas, Facultad de Ciencias Exactas, Universidad Andres Bello, Sede Concepción, Autopista Concepción-Talcahuano, 4260000 Talcahuano, Chile
| | - Bruno F Urbano
- Departamento de Polímeros, Facultad de Ciencias Químicas, Universidad de Concepción, Casilla 160-C, 4030000 Concepción, Chile
| | - Eduardo D Pereira
- Departamento de Química Analítica e Inorgánica Facultad de Ciencias Químicas, Universidad de Concepción, Casilla 160-C, 4030000 Concepción, Chile
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181
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Jothi R, Hari Prasath N, Gowrishankar S, Pandian SK. Bacterial Quorum-Sensing Molecules as Promising Natural Inhibitors of Candida albicans Virulence Dimorphism: An In Silico and In Vitro Study. Front Cell Infect Microbiol 2021; 11:781790. [PMID: 34926324 PMCID: PMC8677694 DOI: 10.3389/fcimb.2021.781790] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 11/12/2021] [Indexed: 11/25/2022] Open
Abstract
Farnesol, a self-secreted quorum-sensing molecule (QSM) of Candida albicans, has been known to limit yeast-to-hyphal transition by blocking the RAS1-cAMP-PKA pathway. In a similar fashion, certain bacterial QSMs have also been reported to be successful in attenuating C. albicans biofilm and hyphal formation at relatively high cell density. This prompted us to investigate the antihyphal efficacy of certain bacterial QSMs through virtual docking against seminal drug targets, viz., CYCc and RAS1, that have been reported to be the hallmark players in C. albicans dimorphic virulence cascade. Against this backdrop, 64 QSMs belonging to five different bacterial QS signaling systems were subjected to initial virtual screening with farnesol as reference. Data of the virtual screening unveiled QSMs belonging to diketopiperazines (DKPs), i.e., 3-benzyl-6-isobutylidene-2,5-piperazinedione (QSSM 1157) and cyclo(l-Pro-l-Leu) (QSSM 1112), as potential inhibitors of CYCc and RAS1 with binding energies of -8.2 and -7.3 kcal mol-1, respectively. Further, the molecular dynamics simulations (for 50 ns) of CYCc-QSSM 1157 and RAS1-QSSM 1112 complexes revealed the mean ligand root mean square deviation (RMSD) values of 0.35 and 0.27 Å, respectively, which endorsed the rigid nature, less fluctuation in binding stiffness, and conformation of binding complexes. Furthermore, the identified two QSMs were found to be good in solubility, absorption, and permeation and less toxic in nature, as revealed by pharmacokinetics and toxicity analyses. In addition, the in vitro antihyphal assays using liquid and solid media, germ-tube experiment, and microscopic analysis strongly validated DKP-QSSM 1112 as a promising inhibitor of hyphal transition. Taken together, the present study unequivocally proves that DKPs can be used as potent inhibitors of C. albicans virulence dimorphism.
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182
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Thomas M, Boardman A, Garcia-Ortegon M, Yang H, de Graaf C, Bender A. Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2021; 2390:1-59. [PMID: 34731463 DOI: 10.1007/978-1-0716-1787-8_1] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Artificial intelligence (AI) has undergone rapid development in recent years and has been successfully applied to real-world problems such as drug design. In this chapter, we review recent applications of AI to problems in drug design including virtual screening, computer-aided synthesis planning, and de novo molecule generation, with a focus on the limitations of the application of AI therein and opportunities for improvement. Furthermore, we discuss the broader challenges imposed by AI in translating theoretical practice to real-world drug design; including quantifying prediction uncertainty and explaining model behavior.
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Affiliation(s)
- Morgan Thomas
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Andrew Boardman
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Miguel Garcia-Ortegon
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK.,Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, UK
| | - Hongbin Yang
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | | | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK.
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183
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Guterres H, Park SJ, Cao Y, Im W. CHARMM-GUI Ligand Designer for Template-Based Virtual Ligand Design in a Binding Site. J Chem Inf Model 2021; 61:5336-5342. [PMID: 34757752 DOI: 10.1021/acs.jcim.1c01156] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Rational drug design involves a task of finding ligands that would bind to a specific target protein. This work presents CHARMM-GUI Ligand Designer that is an intuitive and interactive web-based tool to design virtual ligands that match the shape and chemical features of a given protein binding site. Ligand Designer provides ligand modification capabilities with 3D visualization that allow researchers to modify and redesign virtual ligands while viewing how the protein-ligand interactions are affected. Virtual ligands can also be parameterized for further molecular dynamics (MD) simulations and free energy calculations. Using 8 targets from 8 different protein classes in the directory of useful decoys, enhanced (DUD-E) data set, we show that Ligand Designer can produce similar ligands to the known active ligands in the crystal structures. Ligand Designer also produces stable protein-ligand complex structures when tested using short MD simulations. We expect that Ligand Designer can be a useful and user-friendly tool to design small molecules in any given potential ligand binding site on a protein of interest.
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Affiliation(s)
- Hugo Guterres
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Sang-Jun Park
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Yiwei Cao
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Wonpil Im
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
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184
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Pihan E, Kotev M, Rabal O, Beato C, Diaz Gonzalez C. Fine tuning for success in structure-based virtual screening. J Comput Aided Mol Des 2021; 35:1195-1206. [PMID: 34799816 DOI: 10.1007/s10822-021-00431-4] [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: 07/09/2021] [Accepted: 11/08/2021] [Indexed: 11/30/2022]
Abstract
Structure-based virtual screening plays a significant role in drug-discovery. The method virtually docks millions of compounds from corporate or public libraries into a binding site of a disease-related protein structure, allowing for the selection of a small list of potential ligands for experimental testing. Many algorithms are available for docking and assessing the affinity of compounds for a targeted protein site. The performance of affinity estimation calculations is highly dependent on the size and nature of the site, therefore a rationale for selecting the best protocol is required. To address this issue, we have developed an automated calibration process, implemented in a Knime workflow. It consists of four steps: preparation of a protein test set with structures and models of the target, preparation of a compound test set with target-related ligands and decoys, automatic test of 24 scoring/rescoring protocols for each target structure and model, and graphical display of results. The automation of the process combined with execution on high performance computing resources greatly reduces the duration of the calibration phase, and the test of many combinations of algorithms on various target conformations results in a rational and optimal choice of the best protocol. Here, we present this tool and exemplify its application in setting-up an optimal protocol for SBVS against Retinoid X Receptor alpha.
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Affiliation(s)
- Emilie Pihan
- Computational Drug Discovery, Evotec (France) SAS, Campus Curie, 195 Route d'Espagne, 31036, Toulouse, France.
| | - Martin Kotev
- Computational Drug Discovery, Evotec (France) SAS, Campus Curie, 195 Route d'Espagne, 31036, Toulouse, France
| | - Obdulia Rabal
- Computational Drug Discovery, Evotec (France) SAS, Campus Curie, 195 Route d'Espagne, 31036, Toulouse, France
| | - Claudia Beato
- Aptuit (Verona) Srl, an Evotec Company, Via Alessandro Fleming, 4, 37135, Verona, Italy
| | - Constantino Diaz Gonzalez
- Computational Drug Discovery, Evotec (France) SAS, Campus Curie, 195 Route d'Espagne, 31036, Toulouse, France
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185
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Rosário-Ferreira N, Baptista SJ, Barreto CAV, Rodrigues FEP, Silva TFD, Ferreira SGF, Vitorino JNM, Melo R, Victor BL, Machuqueiro M, Moreira IS. In Silico End-to-End Protein-Ligand Interaction Characterization Pipeline: The Case of SARS-CoV-2. ACS Synth Biol 2021; 10:3209-3235. [PMID: 34736321 PMCID: PMC8577377 DOI: 10.1021/acssynbio.1c00368] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Indexed: 11/29/2022]
Abstract
SARS-CoV-2 triggered a worldwide pandemic disease, COVID-19, for which an effective treatment has not yet been settled. Among the most promising targets to fight this disease is SARS-CoV-2 main protease (Mpro), which has been extensively studied in the last few months. There is an urgency for developing effective computational protocols that can help us tackle these key viral proteins. Hence, we have put together a robust and thorough pipeline of in silico protein-ligand characterization methods to address one of the biggest biological problems currently plaguing our world. These methodologies were used to characterize the interaction of SARS-CoV-2 Mpro with an α-ketoamide inhibitor and include details on how to upload, visualize, and manage the three-dimensional structure of the complex and acquire high-quality figures for scientific publications using PyMOL (Protocol 1); perform homology modeling with MODELLER (Protocol 2); perform protein-ligand docking calculations using HADDOCK (Protocol 3); run a virtual screening protocol of a small compound database of SARS-CoV-2 candidate inhibitors with AutoDock 4 and AutoDock Vina (Protocol 4); and, finally, sample the conformational space at the atomic level between SARS-CoV-2 Mpro and the α-ketoamide inhibitor with Molecular Dynamics simulations using GROMACS (Protocol 5). Guidelines for careful data analysis and interpretation are also provided for each Protocol.
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Affiliation(s)
- Nícia Rosário-Ferreira
- Coimbra Chemistry Center, Chemistry Department,
Faculty of Science and Technology, University of Coimbra,
Coimbra 3004-535, Portugal
- CNC—Center for Neuroscience and Cell Biology.
University of Coimbra, UC Biotech Building, Cantanhede
3060-197, Portugal
| | - Salete J. Baptista
- CNC—Center for Neuroscience and Cell Biology.
University of Coimbra, UC Biotech Building, Cantanhede
3060-197, Portugal
- Centro de Ciências e Tecnologias Nucleares,
Instituto Superior Técnico, Universidade de Lisboa,
Estrada Nacional 10, ao km 139,7, Bobadela 2695-066, Portugal
| | - Carlos A. V. Barreto
- CNC—Center for Neuroscience and Cell Biology.
University of Coimbra, UC Biotech Building, Cantanhede
3060-197, Portugal
- PhD Programme in Experimental Biology and Biomedicine,
Institute for Interdisciplinary Research (IIIUC), University of
Coimbra, Coimbra 3000-456, Portugal
| | - Filipe E. P. Rodrigues
- BioISI—Biosystems & Integrative Sciences
Institute, Faculty of Sciences, University of Lisboa, Lisboa,
1749-016, Portugal
| | - Tomás F. D. Silva
- BioISI—Biosystems & Integrative Sciences
Institute, Faculty of Sciences, University of Lisboa, Lisboa,
1749-016, Portugal
| | - Sara G. F. Ferreira
- BioISI—Biosystems & Integrative Sciences
Institute, Faculty of Sciences, University of Lisboa, Lisboa,
1749-016, Portugal
| | - João N. M. Vitorino
- BioISI—Biosystems & Integrative Sciences
Institute, Faculty of Sciences, University of Lisboa, Lisboa,
1749-016, Portugal
| | - Rita Melo
- CNC—Center for Neuroscience and Cell Biology.
University of Coimbra, UC Biotech Building, Cantanhede
3060-197, Portugal
- Centro de Ciências e Tecnologias Nucleares,
Instituto Superior Técnico, Universidade de Lisboa,
Estrada Nacional 10, ao km 139,7, Bobadela 2695-066, Portugal
| | - Bruno L. Victor
- BioISI—Biosystems & Integrative Sciences
Institute, Faculty of Sciences, University of Lisboa, Lisboa,
1749-016, Portugal
| | - Miguel Machuqueiro
- BioISI—Biosystems & Integrative Sciences
Institute, Faculty of Sciences, University of Lisboa, Lisboa,
1749-016, Portugal
| | - Irina S. Moreira
- University of Coimbra, Center
for Neurosciences and Cell Biology, Department of Life Sciences, Coimbra 3000-456,
Portugal
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186
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Islam MA, Rallabandi VPS, Mohammed S, Srinivasan S, Natarajan S, Dudekula DB, Park J. Screening of β1- and β2-Adrenergic Receptor Modulators through Advanced Pharmacoinformatics and Machine Learning Approaches. Int J Mol Sci 2021; 22:11191. [PMID: 34681845 PMCID: PMC8538848 DOI: 10.3390/ijms222011191] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 10/14/2021] [Accepted: 10/14/2021] [Indexed: 12/15/2022] Open
Abstract
Cardiovascular diseases (CDs) are a major concern in the human race and one of the leading causes of death worldwide. β-Adrenergic receptors (β1-AR and β2-AR) play a crucial role in the overall regulation of cardiac function. In the present study, structure-based virtual screening, machine learning (ML), and a ligand-based similarity search were conducted for the PubChem database against both β1- and β2-AR. Initially, all docked molecules were screened using the threshold binding energy value. Molecules with a better binding affinity were further used for segregation as active and inactive through ML. The pharmacokinetic assessment was carried out on molecules retained in the above step. Further, similarity searching of the ChEMBL and DrugBank databases was performed. From detailed analysis of the above data, four compounds for each of β1- and β2-AR were found to be promising in nature. A number of critical ligand-binding amino acids formed potential hydrogen bonds and hydrophobic interactions. Finally, a molecular dynamics (MD) simulation study of each molecule bound with the respective target was performed. A number of parameters obtained from the MD simulation trajectories were calculated and substantiated the stability between the protein-ligand complex. Hence, it can be postulated that the final molecules might be crucial for CDs subjected to experimental validation.
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Affiliation(s)
- Md Ataul Islam
- 3BIGS Omicscore Pvt. Ltd., 1, O Shaughnessy Rd, Langford Gardens, Bengaluru, Karnataka 560025, India; (M.A.I.); (V.P.S.R.); (S.M.); (S.S.); (D.B.D.)
| | - V. P. Subramanyam Rallabandi
- 3BIGS Omicscore Pvt. Ltd., 1, O Shaughnessy Rd, Langford Gardens, Bengaluru, Karnataka 560025, India; (M.A.I.); (V.P.S.R.); (S.M.); (S.S.); (D.B.D.)
| | - Sameer Mohammed
- 3BIGS Omicscore Pvt. Ltd., 1, O Shaughnessy Rd, Langford Gardens, Bengaluru, Karnataka 560025, India; (M.A.I.); (V.P.S.R.); (S.M.); (S.S.); (D.B.D.)
| | - Sridhar Srinivasan
- 3BIGS Omicscore Pvt. Ltd., 1, O Shaughnessy Rd, Langford Gardens, Bengaluru, Karnataka 560025, India; (M.A.I.); (V.P.S.R.); (S.M.); (S.S.); (D.B.D.)
| | | | - Dawood Babu Dudekula
- 3BIGS Omicscore Pvt. Ltd., 1, O Shaughnessy Rd, Langford Gardens, Bengaluru, Karnataka 560025, India; (M.A.I.); (V.P.S.R.); (S.M.); (S.S.); (D.B.D.)
| | - Junhyung Park
- 3BIGS Co., Ltd., 156, Gwanggyo-ro, Yeongtong-gu, Suwon-si 16506, Korea;
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187
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de Jesus JPA, Assis LC, de Castro AA, da Cunha EFF, Nepovimova E, Kuca K, de Castro Ramalho T, de Almeida La Porta F. Effect of drug metabolism in the treatment of SARS-CoV-2 from an entirely computational perspective. Sci Rep 2021; 11:19998. [PMID: 34620963 PMCID: PMC8497625 DOI: 10.1038/s41598-021-99451-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 09/01/2021] [Indexed: 12/25/2022] Open
Abstract
Understanding the effects of metabolism on the rational design of novel and more effective drugs is still a considerable challenge. To the best of our knowledge, there are no entirely computational strategies that make it possible to predict these effects. From this perspective, the development of such methodologies could contribute to significantly reduce the side effects of medicines, leading to the emergence of more effective and safer drugs. Thereby, in this study, our strategy is based on simulating the electron ionization mass spectrometry (EI-MS) fragmentation of the drug molecules and combined with molecular docking and ADMET models in two different situations. In the first model, the drug is docked without considering the possible metabolic effects. In the second model, each of the intermediates from the EI-MS results is docked, and metabolism occurs before the drug accesses the biological target. As a proof of concept, in this work, we investigate the main antiviral drugs used in clinical research to treat COVID-19. As a result, our strategy made it possible to assess the biological activity and toxicity of all potential by-products. We believed that our findings provide new chemical insights that can benefit the rational development of novel drugs in the future.
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Affiliation(s)
- João Paulo Almirão de Jesus
- Laboratory of Nanotechnology and Computational Chemistry, Federal Technological University of Paraná, Avenida dos Pioneiros 3131, Londrina, Paraná, CEP 86036-370, Brazil
| | - Letícia Cristina Assis
- Department of Chemistry, Federal University of Lavras, Lavras, Minas Gerais, CEP 37200-000, Brazil
| | | | | | - Eugenie Nepovimova
- Department of Chemistry, Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, 500 03, Hradec Králové, Czech Republic
| | - Kamil Kuca
- Department of Chemistry, Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, 500 03, Hradec Králové, Czech Republic.
| | - Teodorico de Castro Ramalho
- Department of Chemistry, Federal University of Lavras, Lavras, Minas Gerais, CEP 37200-000, Brazil
- Department of Chemistry, Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, 500 03, Hradec Králové, Czech Republic
| | - Felipe de Almeida La Porta
- Laboratory of Nanotechnology and Computational Chemistry, Federal Technological University of Paraná, Avenida dos Pioneiros 3131, Londrina, Paraná, CEP 86036-370, Brazil.
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188
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Şterbuleac D. Molecular dynamics: a powerful tool for studying the medicinal chemistry of ion channel modulators. RSC Med Chem 2021; 12:1503-1518. [PMID: 34671734 PMCID: PMC8459385 DOI: 10.1039/d1md00140j] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 07/21/2021] [Indexed: 01/10/2023] Open
Abstract
Molecular dynamics (MD) simulations allow researchers to investigate the behavior of desired biological targets at ever-decreasing costs with ever-increasing precision. Among the biological macromolecules, ion channels are remarkable transmembrane proteins, capable of performing special biological processes and revealing a complex regulatory matrix, including modulation by small molecules, either endogenous or exogenous. Recently, given the developments in ion channel structure determination and accessibility of bio-computational techniques, MD and related tools are becoming increasingly popular in the intense research area regarding ligand-channel interactions. This review synthesizes and presents the most important fields of MD involvement in investigating channel-molecule interactions, including, but not limited to, deciphering the binding modes of ligands to their ion channel targets and the mechanisms through which chemical compounds exert their effect on channel function. Special attention is devoted to the importance of more elaborate methods, such as free energy calculations, while principles regarding drug design and discovery are highlighted. Several technical aspects involving the creation and simulation of channel-molecule MD systems (ligand parameterization, proper membrane setup, system building, etc.) are also presented.
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Affiliation(s)
- Daniel Şterbuleac
- Department of Health and Human Development, "Ştefan cel Mare" University of Suceava Str. Universităţii 13, 720229, E Building Suceava Romania
- Department of Forestry and Environmental Protection, "Ştefan cel Mare" University of Suceava Str. Universităţii 13, 720229, E Building Suceava Romania
- Integrated Center for Research, Development and Innovation in Advanced Materials, Nanotechnologies and Distributed Systems for Fabrication and Control (MANSiD), "Ştefan cel Mare" University of Suceava Str. Universităţii 13 720229 Suceava Romania
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189
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Mohan A, Rendine N, Mohammed MKS, Jeeva A, Ji HF, Talluri VR. Structure-based virtual screening, in silico docking, ADME properties prediction and molecular dynamics studies for the identification of potential inhibitors against SARS-CoV-2 M pro. Mol Divers 2021; 26:1645-1661. [PMID: 34480682 PMCID: PMC8417657 DOI: 10.1007/s11030-021-10298-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 08/02/2021] [Indexed: 11/30/2022]
Abstract
COVID-19 is a viral pandemic caused by SARS-CoV-2. Due to its highly contagious nature, millions of people are getting affected worldwide knocking down the delicate global socio-economic equilibrium. According to the World Health Organization, COVID-19 has affected over 186 million people with a mortality of around 4 million as of July 09, 2021. Currently, there are few therapeutic options available for COVID-19 control. The rapid mutations in SARS-CoV-2 genome and development of new virulent strains with increased infection and mortality among COVID-19 patients, there is a great need to discover more potential drugs for SARS-CoV-2 on a priority basis. One of the key viral enzymes responsible for the replication and maturation of SARS-CoV-2 is Mpro protein. In the current study, structure-based virtual screening was used to identify four potential ligands against SARS-CoV-2 Mpro from a set of 8,722 ASINEX library compounds. These four compounds were evaluated using ADME filter to check their ADME profile and druggability, and all the four compounds were found to be within the current pharmacological acceptable range. They were individually docked to SARS-CoV-2 Mpro protein to assess their molecular interactions. Further, molecular dynamics (MD) simulations was carried out on protein–ligand complex using Desmond at 100 ns to explore their binding conformational stability. Based on RMSD, RMSF and hydrogen bond interactions, it was found that the stability of protein–ligand complex was maintained throughout the entire 100 ns simulations for all the four compounds. Some of the key ligand amino acid residues participated in stabilizing the protein–ligand interactions includes GLN 189, SER 10, GLU 166, ASN 142 with PHE 66 and TRP 132 of SARS-CoV-2 Mpro. Further optimization of these compounds could lead to promising drug candidates for SARS-CoV-2 Mpro target.
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Affiliation(s)
- Anbuselvam Mohan
- Department of Biotechnology, Selvamm Arts and Science College (Autonomous), Namakkal, Tamil Nadu, 637 003, India
| | - Nicole Rendine
- Department of Chemistry, Drexel University, Philadelphia, PA, 19104, USA
| | | | - Anbuselvam Jeeva
- Department of Animal Science, Bharathidasan University, Tiruchirappalli, Tamil Nadu, 620 024, India
| | - Hai-Feng Ji
- Department of Chemistry, Drexel University, Philadelphia, PA, 19104, USA
| | - Venkateswara Rao Talluri
- Prof.TNA Innovation Centre, Varsha Bioscience and Technology India Private Limited, Jiblakpally, Yadadri District, Hyderabad, Telangana, 508 284, India.
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190
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Trebino MA, Shingare RD, MacMillan JB, Yildiz FH. Strategies and Approaches for Discovery of Small Molecule Disruptors of Biofilm Physiology. Molecules 2021; 26:molecules26154582. [PMID: 34361735 PMCID: PMC8348372 DOI: 10.3390/molecules26154582] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 07/24/2021] [Accepted: 07/26/2021] [Indexed: 12/02/2022] Open
Abstract
Biofilms, the predominant growth mode of microorganisms, pose a significant risk to human health. The protective biofilm matrix, typically composed of exopolysaccharides, proteins, nucleic acids, and lipids, combined with biofilm-grown bacteria’s heterogenous physiology, leads to enhanced fitness and tolerance to traditional methods for treatment. There is a need to identify biofilm inhibitors using diverse approaches and targeting different stages of biofilm formation. This review discusses discovery strategies that successfully identified a wide range of inhibitors and the processes used to characterize their inhibition mechanism and further improvement. Additionally, we examine the structure–activity relationship (SAR) for some of these inhibitors to optimize inhibitor activity.
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Affiliation(s)
- Michael A. Trebino
- Department of Microbiology and Environmental Toxicology, University of California, Santa Cruz, CA 95064, USA;
| | - Rahul D. Shingare
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, CA 95064, USA;
| | - John B. MacMillan
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, CA 95064, USA;
- Correspondence: (J.B.M.); (F.H.Y.)
| | - Fitnat H. Yildiz
- Department of Microbiology and Environmental Toxicology, University of California, Santa Cruz, CA 95064, USA;
- Correspondence: (J.B.M.); (F.H.Y.)
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191
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Gyebi GA, Elfiky AA, Ogunyemi OM, Ibrahim IM, Adegunloye AP, Adebayo JO, Olaiya CO, Ocheje JO, Fabusiwa MM. Structure-based virtual screening suggests inhibitors of 3-Chymotrypsin-Like Protease of SARS-CoV-2 from Vernonia amygdalina and Occinum gratissimum. Comput Biol Med 2021; 136:104671. [PMID: 34332348 PMCID: PMC8294106 DOI: 10.1016/j.compbiomed.2021.104671] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 07/12/2021] [Accepted: 07/17/2021] [Indexed: 01/19/2023]
Abstract
Antiviral culinary plants are potential bioresources for preventive nutraceuticals and/or antiviral drugs in COVID-19. Structure-based virtual screening was undertaken to screen 173 compounds previously reported from Vernonia amygdalina and Occinum gratissimum for direct interaction with the active site of the 3-Chymotrypsin-Like Protease (3CLpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Based on docking scores and comparison with reference inhibitors, a hit-list of 10 top phytocompounds was defined, which also had strong interactions with the catalytic centre of 3CLpro from three related strains of coronavirus (SARS-CoV, MERS-CoV, HKU4). Among these, six compounds (neoandrographolide, vernolide, isorhamnetin, chicoric acid, luteolin, and myricetin) exhibited the highest binding tendencies to the equilibrated conformers of SARS-CoV-2 3CLpro in an in-depth docking analysis to 5 different representative conformations from the cluster analysis of the molecular dynamics simulation (MDS) trajectories of the protein. In silico drug-likeness analyses revealed two drug-like terpenoids viz: neoandrographolide and vernolide as promising inhibitors of SARS-CoV-2 3CLpro. These structures were accommodated within the substrate-binding pocket; and interacted with the catalytic dyad (Cys145 and His41), the oxyanion loop (residues 138-145), and the S1/S2 sub-sites of the enzyme active site through the formation of an array of hydrogen bonds and hydrophobic interactions. Molecular dynamics simulation and binding free energy calculation revealed that the terpenoid-enzyme complexes exhibit strong interactions and structural stability. Therefore, these compounds may stabilize the conformation of the flexible oxyanion loop; and thereby interfere with the tetrahedral oxyanion intermediate formation during the proteolytic activity of the enzyme.
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Affiliation(s)
- Gideon A Gyebi
- Department of Biochemistry, Faculty of Science and Technology, Bingham University, Karu, Nasarawa, Nigeria
| | - Abdo A Elfiky
- Department of Biophysics, Faculty of Sciences, Cairo University, Giza, Egypt.
| | - Oludare M Ogunyemi
- Human Nutraceuticals and Bioinformatics Research Unit, Department of Biochemistry, Salem University, Nigeria.
| | - Ibrahim M Ibrahim
- Department of Biophysics, Faculty of Sciences, Cairo University, Giza, Egypt
| | - Adegbenro P Adegunloye
- Department of Biochemistry, Faculty of Life Sciences, University of Ilorin, Ilorin, Nigeria
| | - Joseph O Adebayo
- Department of Biochemistry, Faculty of Life Sciences, University of Ilorin, Ilorin, Nigeria
| | - Charles O Olaiya
- Nutritional and Industrial Biochemistry Research Unit, Department of Biochemistry, College of Medicine, University of Ibadan, Ibadan, 200005, Nigeria
| | - Joshua O Ocheje
- Human Nutraceuticals and Bioinformatics Research Unit, Department of Biochemistry, Salem University, Nigeria
| | - Modupe M Fabusiwa
- Human Nutraceuticals and Bioinformatics Research Unit, Department of Biochemistry, Salem University, Nigeria
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192
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Sabe VT, Ntombela T, Jhamba LA, Maguire GEM, Govender T, Naicker T, Kruger HG. Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review. Eur J Med Chem 2021; 224:113705. [PMID: 34303871 DOI: 10.1016/j.ejmech.2021.113705] [Citation(s) in RCA: 182] [Impact Index Per Article: 60.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 07/12/2021] [Accepted: 07/12/2021] [Indexed: 12/30/2022]
Abstract
Computer-aided drug design (CADD) is one of the pivotal approaches to contemporary pre-clinical drug discovery, and various computational techniques and software programs are typically used in combination, in a bid to achieve the desired outcome. Several approved drugs have been developed with the aid of CADD. On SciFinder®, we evaluated more than 600 publications through systematic searching and refining, using the terms, virtual screening; software methods; computational studies and publication year, in order to obtain data concerning particular aspects of CADD. The primary focus of this review was on the databases screened, virtual screening and/or molecular docking software program used. Furthermore, we evaluated the studies that subsequently performed molecular dynamics (MD) simulations and we reviewed the software programs applied, the application of density functional theory (DFT) calculations and experimental assays. To represent the latest trends, the most recent data obtained was between 2015 and 2020, consequently the most frequently employed techniques and software programs were recorded. Among these, the ZINC database was the most widely preferred with an average use of 31.2%. Structure-based virtual screening (SBVS) was the most prominently used type of virtual screening and it accounted for an average of 57.6%, with AutoDock being the preferred virtual screening/molecular docking program with 41.8% usage. Following the screening process, 38.5% of the studies performed MD simulations to complement the virtual screening and GROMACS with 39.3% usage, was the popular MD software program. Among the computational techniques, DFT was the least applied whereby it only accounts for 0.02% average use. An average of 36.5% of the studies included reports on experimental evaluations following virtual screening. Ultimately, since the inception and application of CADD in pre-clinical drug discovery, more than 70 approved drugs have been discovered, and this number is steadily increasing over time.
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Affiliation(s)
- Victor T Sabe
- Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa.
| | - Thandokuhle Ntombela
- Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa.
| | - Lindiwe A Jhamba
- HIV Pathogenesis Program, School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa
| | - Glenn E M Maguire
- Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa; School of Chemistry and Physics, University of KwaZulu-Natal, Durban, 4001, South Africa
| | - Thavendran Govender
- Faculty of Science and Agriculture, Department of Chemistry, University of Zululand, KwaDlangezwa, 3886, South Africa
| | - Tricia Naicker
- Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa
| | - Hendrik G Kruger
- Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa.
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Hachisu S. Strategies for discovering resistance-breaking, safe and sustainable commercial herbicides with novel modes of action and chemotypes. PEST MANAGEMENT SCIENCE 2021; 77:3042-3048. [PMID: 33817955 DOI: 10.1002/ps.6397] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 03/26/2021] [Accepted: 04/04/2021] [Indexed: 05/26/2023]
Abstract
Farmers need to manage weeds to grow and harvest crops that are essential to our food and energy supply, and herbicides are the most important tool in the farmers' armory to combat weeds. There is now a crisis in agriculture that has been brought about by herbicides being rendered ineffective by resistant weeds or withdrawn from the market due to safety concerns. Efficacious herbicides with novel modes of action (MoAs) and chemotypes are urgently needed to control resistant weeds and satisfy public and regulators' stringent requirements for safe and sustainable products. This article explores the main strategies being deployed by academic and industrial institutions to discover the next generation of commercial herbicides: phenotypic and in vitro target based approaches. There are early signs that much needed innovation and herbicidal products with novel MoAs are on the horizon from start-ups and established agrochemical companies. © 2021 Society of Chemical Industry. © 2021 Society of Chemical Industry.
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194
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Mohanty S, Rashid MHA, Mohanty C, Swayamsiddha S. Modern computational intelligence based drug repurposing for diabetes epidemic. Diabetes Metab Syndr 2021; 15:102180. [PMID: 34186343 DOI: 10.1016/j.dsx.2021.06.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 06/12/2021] [Accepted: 06/14/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND AND AIM Objectives are to explore recent advances in discovery of new antidiabetic agents using repurposing strategies and to discuss modern technologies used for drug repurposing highlighting diabetic specific web portal. METHODS Recent literature were studied and analyzed from various sources such as Scopus, PubMed, and IEEE Xplore databases. RESULTS Drugs like Niclosamideethanolamine, Methazolamide, Diacerein, Berberine, Clobetasol, etc. with possibility of repurposing to curb diabetes can be potential late-stage clinical candidates, providing access to information on pharmacology, formulation, and probable toxicity if any. CONCLUSIONS With collaboration of artificial intelligence (AI) with pharmacology, the efficiency of drug repurposing can improve significantly.
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Affiliation(s)
- Sweta Mohanty
- School of Applied Science, KIIT University, Bhubaneswar, Odisha, India
| | | | - Chandana Mohanty
- School of Applied Science, KIIT University, Bhubaneswar, Odisha, India.
| | - Swati Swayamsiddha
- School of Electronics Engineering, KIIT University, Bhubaneswar, Odisha, India.
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195
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Bolnykh V, Rossetti G, Rothlisberger U, Carloni P. Expanding the boundaries of ligand–target modeling by exascale calculations. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1535] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Viacheslav Bolnykh
- Laboratory of Computational Chemistry and Biochemistry École Polytechnique Fédérale de Lausanne Lausanne Switzerland
- Computational Biomedicine, Institute of Neuroscience and Medicine (INM‐9)/Institute for Advanced Simulations (IAS‐5) Forschungszentrum Jülich Jülich Germany
| | - Giulia Rossetti
- Computational Biomedicine, Institute of Neuroscience and Medicine (INM‐9)/Institute for Advanced Simulations (IAS‐5) Forschungszentrum Jülich Jülich Germany
- Jülich Supercomputing Centre (JSC) Forschungszentrum Jülich Jülich Germany
- Department of Hematology, Oncology, Hemostaseology and Stem Cell Transplantation University Hospital Aachen RWTH Aachen University Aachen Germany
| | - Ursula Rothlisberger
- Laboratory of Computational Chemistry and Biochemistry École Polytechnique Fédérale de Lausanne Lausanne Switzerland
| | - Paolo Carloni
- Institute for Neuroscience and Medicine and Institute for Advanced Simulations (IAS‐5/INM‐9) “Computational Biomedicine” Forschungszentrum Jülich Jülich Germany
- JARA‐Institute INM‐11 “Molecular Neuroscience and Neuroimaging” Forschungszentrum Jülich Jülich Germany
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196
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Mahmud S, Biswas S, Paul GK, Mita MA, Promi MM, Afrose S, Hasan MR, Zaman S, Uddin MS, Dhama K, Emran TB, Saleh MA, Simal-Gandara J. Plant-Based Phytochemical Screening by Targeting Main Protease of SARS-CoV-2 to Design Effective Potent Inhibitors. BIOLOGY 2021; 10:589. [PMID: 34206970 PMCID: PMC8301192 DOI: 10.3390/biology10070589] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 06/18/2021] [Accepted: 06/22/2021] [Indexed: 02/07/2023]
Abstract
Currently, a worldwide pandemic has been declared in response to the spread of coronavirus disease 2019 (COVID-19), a fatal and fast-spreading viral infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The low availability of efficient vaccines and treatment options has resulted in a high mortality rate, bringing the world economy to its knees. Thus, mechanistic investigations of drugs capable of counteracting this disease are in high demand. The main protease (Mpro) expressed by SARS-CoV-2 has been targeted for the development of potential drug candidates due to the crucial role played by Mpro in viral replication and transcription. We generated a phytochemical library containing 1672 phytochemicals derived from 56 plants, which have been reported as having antiviral, antibacterial, and antifungal activity. A molecular docking program was used to screen the top three candidate compounds: epicatechin-3-O-gallate, psi-taraxasterol, and catechin gallate, which had respective binding affinities of -8.4, -8.5, and -8.8 kcal/mol. Several active sites in the targeted protein, including Cys145, His41, Met49, Glu66, and Met165, were found to interact with the top three candidate compounds. The multiple simulation profile, root-mean-square deviation, root-mean-square fluctuation, radius of gyration, and solvent-accessible surface area values supported the inflexible nature of the docked protein-compound complexes. The toxicity and carcinogenicity profiles were assessed, which showed that epicatechin-3-O-gallate, psi-taraxasterol, and catechin gallate had favorable pharmacological properties with no adverse effects. These findings suggest that these compounds could be developed as part of an effective drug development pathway to treat COVID-19.
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Affiliation(s)
- Shafi Mahmud
- Microbiology Laboratory, Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh; (S.M.); (G.K.P.); (S.Z.); (M.S.U.)
| | - Suvro Biswas
- Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh; (S.B.); (M.A.M.); (M.M.P.); (S.A.); (M.R.H.)
| | - Gobindo Kumar Paul
- Microbiology Laboratory, Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh; (S.M.); (G.K.P.); (S.Z.); (M.S.U.)
| | - Mohasana Akter Mita
- Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh; (S.B.); (M.A.M.); (M.M.P.); (S.A.); (M.R.H.)
| | - Maria Meha Promi
- Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh; (S.B.); (M.A.M.); (M.M.P.); (S.A.); (M.R.H.)
| | - Shamima Afrose
- Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh; (S.B.); (M.A.M.); (M.M.P.); (S.A.); (M.R.H.)
| | - Md. Robiul Hasan
- Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh; (S.B.); (M.A.M.); (M.M.P.); (S.A.); (M.R.H.)
| | - Shahriar Zaman
- Microbiology Laboratory, Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh; (S.M.); (G.K.P.); (S.Z.); (M.S.U.)
| | - Md. Salah Uddin
- Microbiology Laboratory, Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh; (S.M.); (G.K.P.); (S.Z.); (M.S.U.)
| | - Kuldeep Dhama
- Division of Pathology, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India;
| | - Talha Bin Emran
- Department of Pharmacy, BGC Trust University Bangladesh, Chittagong 4381, Bangladesh
| | - Md. Abu Saleh
- Microbiology Laboratory, Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh; (S.M.); (G.K.P.); (S.Z.); (M.S.U.)
| | - Jesus Simal-Gandara
- Nutrition and Bromatology Group, Department of Analytical and Food Chemistry, Faculty of Food Science and Technology, University of Vigo–Ourense Campus, E32004 Ourense, Spain
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Huang D, Bai H, Wang L, Hou Y, Li L, Xia Y, Yan Z, Chen W, Chang L, Li W. The Application and Development of Deep Learning in Radiotherapy: A Systematic Review. Technol Cancer Res Treat 2021; 20:15330338211016386. [PMID: 34142614 PMCID: PMC8216350 DOI: 10.1177/15330338211016386] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
With the massive use of computers, the growth and explosion of data has greatly promoted the development of artificial intelligence (AI). The rise of deep learning (DL) algorithms, such as convolutional neural networks (CNN), has provided radiation oncologists with many promising tools that can simplify the complex radiotherapy process in the clinical work of radiation oncology, improve the accuracy and objectivity of diagnosis, and reduce the workload, thus enabling clinicians to spend more time on advanced decision-making tasks. As the development of DL gets closer to clinical practice, radiation oncologists will need to be more familiar with its principles to properly evaluate and use this powerful tool. In this paper, we explain the development and basic concepts of AI and discuss its application in radiation oncology based on different task categories of DL algorithms. This work clarifies the possibility of further development of DL in radiation oncology.
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Affiliation(s)
- Danju Huang
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Han Bai
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Li Wang
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Yu Hou
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Lan Li
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Yaoxiong Xia
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Zhirui Yan
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Wenrui Chen
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Li Chang
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Wenhui Li
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
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198
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Combination of consensus and ensemble docking strategies for the discovery of human dihydroorotate dehydrogenase inhibitors. Sci Rep 2021; 11:11417. [PMID: 34075175 PMCID: PMC8169699 DOI: 10.1038/s41598-021-91069-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 05/21/2021] [Indexed: 02/06/2023] Open
Abstract
The inconsistencies in the performance of the virtual screening (VS) process, depending on the used software and structural conformation of the protein, is a challenging issue in the drug design and discovery field. Varying performance, especially in terms of early recognition of the potential hit compounds, negatively affects the whole process and leads to unnecessary waste of the time and resources. Appropriate application of the ensemble docking and consensus-scoring approaches can significantly increase reliability of the VS results. Dihydroorotate dehydrogenase (DHODH) is a key enzyme in the pyrimidine biosynthesis pathway. It is considered as a valuable therapeutic target in cancer, autoimmune and viral diseases. Based on the conducted benchmark study and analysis of the effect of different combinations of the applied methods and approaches, here we suggested a structure-based virtual screening (SBVS) workflow that can be used to increase the reliability of VS.
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199
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Moreira-Filho JT, Silva AC, Dantas RF, Gomes BF, Souza Neto LR, Brandao-Neto J, Owens RJ, Furnham N, Neves BJ, Silva-Junior FP, Andrade CH. Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence. Front Immunol 2021; 12:642383. [PMID: 34135888 PMCID: PMC8203334 DOI: 10.3389/fimmu.2021.642383] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 04/30/2021] [Indexed: 12/20/2022] Open
Abstract
Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor.
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Affiliation(s)
- José T. Moreira-Filho
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás – UFG, Goiânia, Brazil
| | - Arthur C. Silva
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás – UFG, Goiânia, Brazil
| | - Rafael F. Dantas
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Barbara F. Gomes
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Lauro R. Souza Neto
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Jose Brandao-Neto
- Diamond Light Source Ltd., Didcot, United Kingdom
- Research Complex at Harwell, Didcot, United Kingdom
| | - Raymond J. Owens
- The Rosalind Franklin Institute, Harwell, United Kingdom
- Division of Structural Biology, The Wellcome Centre for Human Genetic, University of Oxford, Oxford, United Kingdom
| | - Nicholas Furnham
- Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Bruno J. Neves
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás – UFG, Goiânia, Brazil
| | - Floriano P. Silva-Junior
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Carolina H. Andrade
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás – UFG, Goiânia, Brazil
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Mahmud S, Paul GK, Biswas S, Afrose S, Mita MA, Hasan MR, Shimu MSS, Hossain A, Promi MM, Ema FK, Chidambaram K, Chandrasekaran B, Alqahtani AM, Emran TB, Saleh MA. Prospective Role of Peptide-Based Antiviral Therapy Against the Main Protease of SARS-CoV-2. Front Mol Biosci 2021; 8:628585. [PMID: 34041263 PMCID: PMC8142691 DOI: 10.3389/fmolb.2021.628585] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 04/22/2021] [Indexed: 12/12/2022] Open
Abstract
The recently emerged coronavirus (SARS-CoV-2) has created a crisis in world health, and economic sectors as an effective treatment or vaccine candidates are still developing. Besides, negative results in clinical trials and effective cheap solution against this deadly virus have brought new challenges. The viral protein, the main protease from SARS-CoV-2, can be effectively targeted due to its viral replication and pathogenesis role. In this study, we have enlisted 88 peptides from the AVPdb database. The peptide molecules were modeled to carry out the docking interactions. The four peptides molecules, P14, P39, P41, and P74, had more binding energy than the rest of the peptides in multiple docking programs. Interestingly, the active points of the main protease from SARS-CoV-2, Cys145, Leu141, Ser139, Phe140, Leu167, and Gln189, showed nonbonded interaction with the peptide molecules. The molecular dynamics simulation study was carried out for 200 ns to find out the docked complex’s stability where their stability index was proved to be positive compared to the apo and control complex. Our computational works based on peptide molecules may aid the future development of therapeutic options against SARS-CoV-2.
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Affiliation(s)
- Shafi Mahmud
- Microbiology Laboratory, Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi, Bangladesh
| | - Gobindo Kumar Paul
- Microbiology Laboratory, Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi, Bangladesh
| | - Suvro Biswas
- Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi, Bangladesh
| | - Shamima Afrose
- Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi, Bangladesh
| | - Mohasana Akter Mita
- Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi, Bangladesh
| | - Md Robiul Hasan
- Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi, Bangladesh
| | | | - Alomgir Hossain
- Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi, Bangladesh
| | - Maria Meha Promi
- Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi, Bangladesh
| | - Fahmida Khan Ema
- Department of Pharmacy, International Islamic University Chittagong, Chittagong, Bangladesh
| | - Kumarappan Chidambaram
- Department of Pharmacology, College of Pharmacy, King Khalid University, Abha, Saudi Arabia
| | - Balakumar Chandrasekaran
- Department of Medicinal Chemistry, Faculty of Pharmacy, Philadelphia University-Jordan, Amman, Jordan
| | - Ali M Alqahtani
- Department of Pharmacology, College of Pharmacy, King Khalid University, Abha, Saudi Arabia
| | - Talha Bin Emran
- Department of Pharmacy, BGC Trust University Bangladesh, Chittagong, Bangladesh
| | - Md Abu Saleh
- Microbiology Laboratory, Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi, Bangladesh
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