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Sabale P, Sayyad N, Ali A, Sabale V, Kaleem M, Asar TO, Ali A, Mujtaba MA, Anwer MK. Design, synthesis, molecular docking and in vitro anticancer activities of 1-(4-(benzamido)phenyl)-3-arylurea derivatives. RSC Adv 2024; 14:23785-23795. [PMID: 39077323 PMCID: PMC11284930 DOI: 10.1039/d4ra02882a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Accepted: 07/13/2024] [Indexed: 07/31/2024] Open
Abstract
In both premenopausal and postmenopausal women, oestrogens play a critical role in the development of breast cancer. Aromatase is an enzyme that catalyses the final step in the biosynthesis of estrogen and has emerged as a promising target for therapeutic intervention. This study aimed to design and evaluate novel 1-(4-(benzamido)phenyl)-3-arylurea derivatives as potential aromatase inhibitors. Through molecular docking, promising leads were identified and synthesized. Spectroscopic techniques confirmed their structural integrity. Cytotoxicity against various cancer cell lines was assessed using MTT assay. Docking investigations against the aromatase enzyme (3s7s) elucidated binding interactions and energies. Compound 6g, exhibiting a binding energy of -8.6 kcal mol-1 and interacting with ALA306 and THR310 residues, showed the most promising activity. It demonstrated GI50 values ranging from 14.46 μM, 13.97 μM, 11.35 μM, 11.58 μM, and 15.77 μM against A-498, NCI-H23, MDAMB-231, MCF-7, and A-549 respectively. Lastly, the physicochemical, and ADMET properties of the compound were predicted. These findings highlight the potential of 1-(4-(benzamido)phenyl)-3-arylureas as a new class of antitumor agents targeting aromatase. Their versatility and superior activity compared to standard chemotherapeutic agents, like doxorubicin, warrant further investigation for the development of broader-spectrum anticancer drugs.
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Affiliation(s)
- Prafulla Sabale
- Department of Pharmaceutical Sciences, Rashtrasant Tukadoji Maharaj Nagpur University Mahatma Jyotiba Fuley Shaikshanik Parisar Nagpur-440033 India +919158537050
| | - Nusrat Sayyad
- Department of Pharmaceutical Sciences, Rashtrasant Tukadoji Maharaj Nagpur University Mahatma Jyotiba Fuley Shaikshanik Parisar Nagpur-440033 India +919158537050
| | - Abuzer Ali
- Department of Pharmacognosy, College of Pharmacy, Taif University P.O. Box 11099 Taif 21944 Saudi Arabia
| | - Vidya Sabale
- Department of Pharmaceutics, Dadasaheb Balpande College of Pharmacy, Rashtrasant Tukadoji Maharaj Nagpur University Nagpur Maharashtra 440037 India
| | - Mohammed Kaleem
- Department of Pharmacology, Dadasaheb Balpande College of Pharmacy, Rashtrasant Tukadoji Maharaj Nagpur University Nagpur Maharashtra 440037 India
| | - Turky Omar Asar
- Department of Biology, College of Science and Arts at Alkamil, University of Jeddah Saudi Arabia
| | - Amena Ali
- Department of Pharmaceutical Chemistry, College of Pharmacy, Taif University P.O. Box 11099 Taif 21944 Saudi Arabia
| | - Md Ali Mujtaba
- Department of Pharmaceutics, Faculty of Pharmacy, Northern Border University Arar Saudi Arabia
| | - Md Khalid Anwer
- Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University P.O. Box 173 Al-Kharj 11942 Saudi Arabia
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2
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Ahamad S, Junaid IT, Gupta D. Computational Design of Novel Tau-Tubulin Kinase 1 Inhibitors for Neurodegenerative Diseases. Pharmaceuticals (Basel) 2024; 17:952. [PMID: 39065802 PMCID: PMC11280166 DOI: 10.3390/ph17070952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Revised: 06/14/2024] [Accepted: 06/20/2024] [Indexed: 07/28/2024] Open
Abstract
The tau-tubulin kinase 1 (TTBK1) protein is a casein kinase 1 superfamily member located at chromosome 6p21.1. It is expressed explicitly in the brain, particularly in the cytoplasm of cortical and hippocampal neurons. TTBK1 has been implicated in the phosphorylation and aggregation of tau in Alzheimer's disease (AD). Considering its significance in AD, TTBK1 has emerged as a promising target for AD treatment. In the present study, we identified novel TTBK1 inhibitors using various computational techniques. We performed a virtual screening-based docking study followed by E-pharmacophore modeling, cavity-based pharmacophore, and ligand design techniques and found ZINC000095101333, LD7, LD55, and LD75 to be potential novel TTBK1 lead inhibitors. The docking results were complemented by Molecular Mechanics/Generalized Born Surface Area (MMGBSA) calculations. The molecular dynamics (MD) simulation studies at a 500 ns scale were carried out to monitor the behavior of the protein toward the identified ligands. Pharmacological and ADME/T studies were carried out to check the drug-likeness of the compounds. In summary, we identified a new series of compounds that could effectively bind the TTBK1 receptor. The newly designed compounds are promising candidates for developing therapeutics targeting TTBK1 for AD.
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Affiliation(s)
- Shahzaib Ahamad
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi 110067, India
| | - Iqbal Taliy Junaid
- Malaria Biology, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi 110067, India;
| | - Dinesh Gupta
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi 110067, India
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3
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Behera DU, Gaur M, Sahoo M, Subudhi E, Subudhi BB. Development of pharmacophore models for AcrB protein and the identification of potential adjuvant candidates for overcoming efflux-mediated colistin resistance. RSC Med Chem 2024; 15:127-138. [PMID: 38283226 PMCID: PMC10809322 DOI: 10.1039/d3md00483j] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 10/26/2023] [Indexed: 01/30/2024] Open
Abstract
Growing multi-drug resistance (MDR) among ESKAPE pathogens is a huge challenge. Increased resistance to last-resort antibiotics, like colistin, has further aggravated this. Efflux is identified as a major route of colistin resistance. So, finding an FDA-approved efflux inhibitor for potential application as an adjuvant to colistin was the primary objective of this study. E. coli-AcrB pump inhibitors and substrates were used to develop and validate the pharmacophoric model. Drugs confirming this pharmacophore were subjected to molecular docking to identify hits for the AcrB binding pocket. The efflux inhibition potential of the top hit was validated through the in vitro evaluation of the minimum inhibitory concentration (MIC) in combination with colistin. The checkerboard assay was done to demonstrate synergism, which was further corroborated by the Time-kill assay. Ten common pharmacophore hypotheses were successfully generated using substrate/inhibitors. Following enrichment analysis, AHHNR.100 was identified as the top-ranked hypothesis, and 207 unique compounds were found to conform to this hypothesis. The multi-step docking of these compounds against the AcrB protein revealed argatroban as the top non-antibiotic hit. This significantly inhibited the efflux activity of colistin-resistant clinical isolates K. pneumoniae (n = 1) and M. morganii (n = 2). Further, their combination with colistin enhanced the susceptibility of these isolates, and the effect was found to be synergistic. Accordingly, the time-kill assay of this combination showed 8-log and 2-log reductions against K. pneumoniae and M. morganii, respectively. In conclusion, this study found argatroban as a bacterial efflux inhibitor that can be potentially used to overcome efflux-mediated resistance.
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Affiliation(s)
- Dibyajyoti Uttameswar Behera
- Centre for Biotechnology, School of Pharmaceutical Sciences, Siksha 'O' Anusandhan (Deemed to be University) Kalinga Nagar, Ghatikia Bhubaneswar-751003 Odisha India +91 9861075829
| | - Mahendra Gaur
- Drug Development and Analysis Laboratory, School of Pharmaceutical Sciences, Siksha 'O' Anusandhan (Deemed to be University) Kalinga Nagar, Ghatikia Bhubaneswar-751003 Odisha India +91 7978085389
- Department of Biotechnology & Food Technology, Punjabi University Patiala 147002 India
| | - Maheswata Sahoo
- Centre for Biotechnology, School of Pharmaceutical Sciences, Siksha 'O' Anusandhan (Deemed to be University) Kalinga Nagar, Ghatikia Bhubaneswar-751003 Odisha India +91 9861075829
| | - Enketeswara Subudhi
- Centre for Biotechnology, School of Pharmaceutical Sciences, Siksha 'O' Anusandhan (Deemed to be University) Kalinga Nagar, Ghatikia Bhubaneswar-751003 Odisha India +91 9861075829
| | - Bharat Bhusan Subudhi
- Drug Development and Analysis Laboratory, School of Pharmaceutical Sciences, Siksha 'O' Anusandhan (Deemed to be University) Kalinga Nagar, Ghatikia Bhubaneswar-751003 Odisha India +91 7978085389
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4
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Sharma T, Mondal T, Khan S, Churqui MP, Nyström K, Thombare K, Baig MH, Dong JJ. Identifying novel inhibitors targeting Exportin-1 for the potential treatment of COVID-19. Arch Microbiol 2024; 206:69. [PMID: 38240823 DOI: 10.1007/s00203-023-03761-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 11/09/2023] [Accepted: 11/19/2023] [Indexed: 01/23/2024]
Abstract
The nuclear export protein 1 (XPO1) mediates the nucleocytoplasmic transport of proteins and ribonucleic acids (RNAs) and plays a prominent role in maintaining cellular homeostasis. XPO1 has emerged as a promising therapeutic approach to interfere with the lifecycle of many viruses. In our earlier study, we proved the inhibition of XPO1 as a therapeutic strategy for managing SARS-COV-2 and its variants. In this study, we have utilized pharmacophore-assisted computational methods to identify prominent XPO1 inhibitors. After several layers of screening, a few molecules were shortlisted for further experimental validation on the in vitro SARS-CoV-2 cell infection model. It was observed that these compounds reduced spike positivity, suggesting inhibition of SARS-COV-2 infection. The outcome of this study could be considered further for developing novel antiviral therapeutic strategies against SARS-CoV-2.
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Affiliation(s)
- Tanuj Sharma
- Department of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Gangnam-gu, Seoul, 06273, Republic of Korea
| | - Tanmoy Mondal
- Department of Laboratory Medicine, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
- Department of Clinical Chemistry, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Sajid Khan
- Department of Biochemistry, Aligarh Muslim University, Aligarh, India
| | - Marianela Patzi Churqui
- Department of Infectious Diseases, Sahlgrenska Academy, University of Gothenburg, 41345, Gothenburg, Sweden
| | - Kristina Nyström
- Department of Infectious Diseases, Sahlgrenska Academy, University of Gothenburg, 41345, Gothenburg, Sweden
| | - Ketan Thombare
- Department of Laboratory Medicine, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
- Department of Clinical Chemistry, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Mohammad Hassan Baig
- Department of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Gangnam-gu, Seoul, 06273, Republic of Korea.
| | - Jae-June Dong
- Department of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Gangnam-gu, Seoul, 06273, Republic of Korea.
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5
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Del Hoyo D, Salinas M, Lomas A, Ulzurrun E, Campillo NE, Sorzano CO. Scipion-Chem: An Open Platform for Virtual Drug Screening. J Chem Inf Model 2023; 63:7873-7885. [PMID: 38052452 PMCID: PMC10751785 DOI: 10.1021/acs.jcim.3c01085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 11/07/2023] [Accepted: 11/10/2023] [Indexed: 12/07/2023]
Abstract
Virtual drug screening (VDS) tackles the problem of drug discovery by computationally reducing the number of potential pharmacological molecules that need to be tested experimentally to find a new drug. To do so, several approaches have been developed through the years, typically focusing on either the physicochemical characteristics of the receptor structure (structure-based virtual screening) or those of the potential ligands (ligand-based virtual screening). Scipion is a workflow engine well suited for structural studies of biological macromolecules. Here, we present Scipion-chem, a new branch oriented to VDS. A total of 11 plugins have already been integrated from the most common programs used in the field. They can be used through the Scipion graphical user interface to execute and analyze typical VDS tasks. In addition, we have developed several consensus protocols that combine results from the different integrated programs to generate more robust predictions. Backstage, Scipion also facilitates the interoperability of those different software packages while tracking all of the intermediate files, parameters, and user decisions. In summary, in this article, we present Scipion-chem. This accessible, interoperable, and traceable platform provides the user with all of the tools to carry out a successful VDS workflow. Scipion-chem is openly available at https://github.com/scipion-chem.
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Affiliation(s)
- Daniel Del Hoyo
- National
Center of Biotechnology (CNB-CSIC), Madrid 28049, Spain
| | - Martin Salinas
- National
Center of Biotechnology (CNB-CSIC), Madrid 28049, Spain
| | - Alba Lomas
- National
Center of Biotechnology (CNB-CSIC), Madrid 28049, Spain
| | | | - Nuria E. Campillo
- Center
for Biological Research (CIB-CSIC), Madrid 28040, Spain
- Institute
of Mathematical Sciences (ICMAT-CSIC), Madrid 28049, Spain
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6
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Rajagopal K, Kalusalingam A, Bharathidasan AR, Sivaprakash A, Shanmugam K, Sundaramoorthy M, Byran G. In Silico Drug Design of Anti-Breast Cancer Agents. Molecules 2023; 28:molecules28104175. [PMID: 37241915 DOI: 10.3390/molecules28104175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 04/18/2023] [Accepted: 05/12/2023] [Indexed: 05/28/2023] Open
Abstract
Cancer is a condition marked by abnormal cell proliferation that has the potential to invade or indicate other health issues. Human beings are affected by more than 100 different types of cancer. Some cancer promotes rapid cell proliferation, whereas others cause cells to divide and develop more slowly. Some cancers, such as leukemia, produce visible tumors, while others, such as breast cancer, do not. In this work, in silico investigations were carried out to investigate the binding mechanisms of four major analogs, which are marine sesquiterpene, sesquiterpene lactone, heteroaromatic chalcones, and benzothiophene against the target estrogen receptor-α for targeting breast cancer using Schrödinger suite 2021-4. The Glide module handled the molecular docking experiments, the QikProp module handled the ADMET screening, and the Prime MM-GB/SA module determined the binding energy of the ligands. The benzothiophene analog BT_ER_15f (G-score -15.922 Kcal/mol) showed the best binding activity against the target protein estrogen receptor-α when compared with the standard drug tamoxifen which has a docking score of -13.560 Kcal/mol. TRP383 (tryptophan) has the highest interaction time with the ligand, and hence it could act for a long time. Based on in silico investigations, the benzothiophene analog BT_ER_15f significantly binds with the active site of the target protein estrogen receptor-α. Similar to the outcomes of molecular docking, the target and ligand complex interaction motif established a high affinity of lead candidates in a dynamic system. This study shows that estrogen receptor-α targets inhibitors with better potential and low toxicity when compared to the existing market drugs, which can be made from a benzothiophene derivative. It may result in considerable activity and be applied to more research on breast cancer.
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Affiliation(s)
- Kalirajan Rajagopal
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education & Research, The Nilgiris, Ooty 643001, Tamilnadu, India
| | - Anandarajagopal Kalusalingam
- Centre of Excellence for Pharmaceutical Sciences, School of Pharmacy, KPJ Healthcare University College, Nilai 71800, Negeri Sembilan, Malaysia
| | - Anubhav Raj Bharathidasan
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education & Research, The Nilgiris, Ooty 643001, Tamilnadu, India
| | - Aadarsh Sivaprakash
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education & Research, The Nilgiris, Ooty 643001, Tamilnadu, India
| | - Krutheesh Shanmugam
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education & Research, The Nilgiris, Ooty 643001, Tamilnadu, India
| | - Monall Sundaramoorthy
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education & Research, The Nilgiris, Ooty 643001, Tamilnadu, India
| | - Gowramma Byran
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education & Research, The Nilgiris, Ooty 643001, Tamilnadu, India
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Sharma H, Sharma P, Urquiza U, Chastain LR, Ihnat MA. Exploration of a Large Virtual Chemical Space: Identification of Potent Inhibitors of Lactate Dehydrogenase-A against Pancreatic Cancer. J Chem Inf Model 2023; 63:1028-1043. [PMID: 36646658 PMCID: PMC9930117 DOI: 10.1021/acs.jcim.2c01544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
It is imperative to explore the gigantic available chemical space to identify new scaffolds for drug lead discovery. Identifying potent hits from virtual screening of large chemical databases is challenging and computationally demanding. Rather than the traditional two-dimensional (2D)/three-dimensional (3D) approaches on smaller chemical libraries of a few hundred thousand compounds, we screened a ZINC library of 15 million compounds using multiple computational methods. Here, we present the successful application of a virtual screening methodology that identifies several chemotypes as starting hits against lactate dehydrogenase-A (LDHA). From 29 compounds identified from virtual screening, 17 (58%) showed IC50 values < 63 μM, two showed single-digit micromolar inhibition, and the most potent hit compound had IC50 down to 117 nM. We enriched the database and employed an ensemble approach by combining 2D fingerprint similarity searches, pharmacophore modeling, molecular docking, and molecular dynamics. WaterMap calculations were carried out to explore the thermodynamics of surface water molecules and gain insights into the LDHA binding pocket. The present work has led to the discovery of two new chemical classes, including compounds with a succinic acid monoamide moiety or a hydroxy pyrimidinone ring system. Selected hits block lactate production in cells and inhibit pancreatic cancer cell lines with cytotoxicity IC50 down to 12.26 μM against MIAPaCa-2 cells and 14.64 μM against PANC-1, which, under normoxic conditions, is already comparable or more potent than most currently available known LDHA inhibitors.
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Affiliation(s)
- Horrick Sharma
- Department
of Pharmaceutical Sciences, College of Pharmacy, Southwestern Oklahoma State University, Weatherford, Oklahoma73096, United States,, . Phone: (+1)580-774-3064. Fax: (+1)(580)-774-7020
| | - Pragya Sharma
- Department
of Biological Sciences, Southwestern Oklahoma
State University, Weatherford, Oklahoma73096, United States
| | - Uzziah Urquiza
- Department
of Biological Sciences, Southwestern Oklahoma
State University, Weatherford, Oklahoma73096, United States
| | - Lerin R. Chastain
- Department
of Pharmaceutical Sciences, College of Pharmacy, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma73117, United States
| | - Michael A. Ihnat
- Department
of Pharmaceutical Sciences, College of Pharmacy, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma73117, United States
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Spiegel J, Senderowitz H. Towards an Enrichment Optimization Algorithm (EOA)-based Target Specific Docking Functions for Virtual Screening. Mol Inform 2022; 41:e2200034. [PMID: 35790469 PMCID: PMC9786651 DOI: 10.1002/minf.202200034] [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: 02/10/2022] [Accepted: 07/05/2022] [Indexed: 12/30/2022]
Abstract
Docking-based virtual screening (VS) is a common starting point in many drug discovery projects. While ligand-based approaches may sometimes provide better results, the advantage of docking lies in its ability to provide reliable ligand binding modes and approximated binding free energies, two factors that are important for hit selection and optimization. Most docking programs were developed to be as general as possible and consequently their performances on specific targets may be sub-optimal. With this in mind, in this work we present a method for the development of target-specific scoring functions using our recently reported Enrichment Optimization Algorithm (EOA). EOA derives QSAR models in the form of multiple linear regression (MLR) equations by optimizing an enrichment-like metric. Since EOA requires target-specific active and inactive (or decoy) compounds, we retrieved such data for six targets from the DUD-E database, and used them to re-derive the weights associated with the components that make up GOLD's ChemPLP scoring function yielding target-specific, modified functions. We then used the original ChemPLP function in small-scale VS experiments on the six targets and subsequently rescored the resulting poses with the modified functions. In addition, we used the modified functions for compounds re-docking. We found that in many although not all cases, either rescoring the original ChemPLP poses or repeating the entire docking process with the modified functions, yielded better results in terms of AUC and EF1% , two metrics, common for the evaluation of VS performances. While work on additional datasets and docking tools is clearly required, we propose that the results obtained thus far hint to the potential benefits in using EOA-based optimization for the derivation of target-specific functions in the context of virtual screening. To this end, we discuss the downsides of the methods and how it could be improved.
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Affiliation(s)
- Jacob Spiegel
- Department of ChemistryBar-Ilan UniversityRamat-Gan5290002Israel
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9
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Kumar S, Kumar GS, Maitra SS, Malý P, Bharadwaj S, Sharma P, Dwivedi VD. Viral informatics: bioinformatics-based solution for managing viral infections. Brief Bioinform 2022; 23:6659740. [PMID: 35947964 DOI: 10.1093/bib/bbac326] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 06/26/2022] [Accepted: 07/18/2022] [Indexed: 11/13/2022] Open
Abstract
Several new viral infections have emerged in the human population and establishing as global pandemics. With advancements in translation research, the scientific community has developed potential therapeutics to eradicate or control certain viral infections, such as smallpox and polio, responsible for billions of disabilities and deaths in the past. Unfortunately, some viral infections, such as dengue virus (DENV) and human immunodeficiency virus-1 (HIV-1), are still prevailing due to a lack of specific therapeutics, while new pathogenic viral strains or variants are emerging because of high genetic recombination or cross-species transmission. Consequently, to combat the emerging viral infections, bioinformatics-based potential strategies have been developed for viral characterization and developing new effective therapeutics for their eradication or management. This review attempts to provide a single platform for the available wide range of bioinformatics-based approaches, including bioinformatics methods for the identification and management of emerging or evolved viral strains, genome analysis concerning the pathogenicity and epidemiological analysis, computational methods for designing the viral therapeutics, and consolidated information in the form of databases against the known pathogenic viruses. This enriched review of the generally applicable viral informatics approaches aims to provide an overview of available resources capable of carrying out the desired task and may be utilized to expand additional strategies to improve the quality of translation viral informatics research.
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Affiliation(s)
- Sanjay Kumar
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, India.,Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India
| | - Geethu S Kumar
- Department of Life Science, School of Basic Science and Research, Sharda University, Greater Noida, Uttar Pradesh, India.,Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India
| | | | - Petr Malý
- Laboratory of Ligand Engineering, Institute of Biotechnology of the Czech Academy of Sciences v.v.i., BIOCEV Research Center, Vestec, Czech Republic
| | - Shiv Bharadwaj
- Laboratory of Ligand Engineering, Institute of Biotechnology of the Czech Academy of Sciences v.v.i., BIOCEV Research Center, Vestec, Czech Republic
| | - Pradeep Sharma
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi, India
| | - Vivek Dhar Dwivedi
- Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India.,Institute of Advanced Materials, IAAM, 59053 Ulrika, Sweden
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10
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Sachdev KR, Lynch KJ, Barreto GE. Exploration of novel ligands to target C-C Motif Chemokine Receptor 2 (CCR2) as a promising pharmacological treatment against traumatic brain injury. Biomed Pharmacother 2022; 151:113155. [PMID: 35598371 DOI: 10.1016/j.biopha.2022.113155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 05/13/2022] [Accepted: 05/15/2022] [Indexed: 11/02/2022] Open
Abstract
It is widely reported that the overexpression of the C-C Motif Chemokine Receptor 2 (CCR2) has negative implications in neuroinflammatory diseases such as traumatic brain injury (TBI), although promising drugs to tackle this have been less forthcoming. As of 2016, only 2 drugs specifically targeting this receptor have made their way to market, with unsuccessful outcome unfortunately, suggesting that the search for more specific and precise ligands is utterly necessary. In this paper we hypothesized that by targeting Glu291, Met295, Trp98, Leu45 and Val189 amino acids, essential in the binding of CCR2 with C-C Motif Chemokine Ligand 2 (CCL2), the endogenous substrate, mitigates its activity in TBI. We used a pharmacophore model to screen for suitable ligands that may bind to CCR2, which returned 871 ligands. Docking and molecular dynamics results uncovered that two ligands (A102) and (A435) contained several of those important residues and showed a stability and compactness when in complex with CCR2, with these results confirmed by MMGBSA calculations with A102 recording a better interaction compared to A435. Finally, a PPI network was built to explore downstream signaling being regulated by both ligands in TBI, showing amyloid precursor protein (APP) as a key target and neuroactive-ligand receptor interaction (1.80E-27) the top functional annotated category. In conclusion, for the first time we report novel ligands A102 and A435 targeting CCR2 as a potential new pharmacological approach to target inflammation post-TBI.
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Affiliation(s)
- Kilian R Sachdev
- Department of Biological Sciences, University of Limerick, Limerick, Ireland
| | - Kevin J Lynch
- Department of Biological Sciences, University of Limerick, Limerick, Ireland
| | - George E Barreto
- Department of Biological Sciences, University of Limerick, Limerick, Ireland.
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MUHAMMED MT, AKI-YALCIN E. Pharmacophore Modeling in Drug Discovery: Methodology and Current Status. JOURNAL OF THE TURKISH CHEMICAL SOCIETY, SECTION A: CHEMISTRY 2021. [DOI: 10.18596/jotcsa.927426] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
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12
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Shawky AM, Ibrahim NA, Abourehab MAS, Abdalla AN, Gouda AM. Pharmacophore-based virtual screening, synthesis, biological evaluation, and molecular docking study of novel pyrrolizines bearing urea/thiourea moieties with potential cytotoxicity and CDK inhibitory activities. J Enzyme Inhib Med Chem 2021; 36:15-33. [PMID: 33103497 PMCID: PMC7594867 DOI: 10.1080/14756366.2020.1837124] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
In the current study, virtual screening of a small library of 1302 pyrrolizines bearing urea/thiourea moieties was performed. The top-scoring hits were synthesised and evaluated for their cytotoxicity against three cancer (MCF-7, A2780, and HT29) and one normal (MRC-5) cell lines. The results of the MTT assay revealed potent cytotoxic activities for most of the new compounds (IC50 = 0.16–34.13 μM). The drug-likeness study revealed that all the new compounds conform to Lipinski’s rule. Mechanistic studies of compounds 18 b, 19a, and 20a revealed the induction of apoptosis and cell cycle arrest at the G1 phase in MCF-7 cells. The three compounds also displayed potent inhibitory activity against CDK-2 (IC50 = 25.53–115.30 nM). Moreover, the docking study revealed a nice fitting of compound 19a into the active sites of CDK-2/6/9. These preliminary results suggested that compound 19a could serve as a promising scaffold in the discovery of new potent anticancer agents.
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Affiliation(s)
- Ahmed M Shawky
- Science and Technology Unit (STU), Umm Al-Qura University, Makkah, Saudi Arabia
| | - Nashwa A Ibrahim
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Mohammed A S Abourehab
- Department of Pharmaceutics, Faculty of Pharmacy, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Ashraf N Abdalla
- Department of Pharmacology and Toxicology, Faculty of Pharmacy, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Ahmed M Gouda
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Umm Al-Qura University, Makkah, Saudi Arabia.,Department of Medicinal Chemistry, Faculty of Pharmacy, Beni-Suef University, Beni-Suef, Egypt
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13
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Grimm M, Liu Y, Yang X, Bu C, Xiao Z, Cao Y. LigMate: A Multifeature Integration Algorithm for Ligand-Similarity-Based Virtual Screening. J Chem Inf Model 2020; 60:6044-6053. [DOI: 10.1021/acs.jcim.9b01210] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Maximilian Grimm
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China
| | - Yang Liu
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China
| | - Xiaocong Yang
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China
| | - Chunya Bu
- College of Biological Science and Engineering, Beijing University of Agriculture, Beijing 102206, China
| | - Zhixiong Xiao
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China
| | - Yang Cao
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China
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14
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Sebastian J, Rathinasamy K. Benserazide Perturbs Kif15‐kinesin Binding Protein Interaction with Prolonged Metaphase and Defects in Chromosomal Congression: A Study Based on
in silico
Modeling and Cell Culture. Mol Inform 2019; 39:e1900035. [DOI: 10.1002/minf.201900035] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Accepted: 07/12/2019] [Indexed: 02/02/2023]
Affiliation(s)
- Jomon Sebastian
- School of BiotechnologyNational Institute of Technology Calicut Calicut-673601 India
| | - Krishnan Rathinasamy
- School of BiotechnologyNational Institute of Technology Calicut Calicut-673601 India
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15
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Esposito C, Wiedmer L, Caflisch A. In Silico Identification of JMJD3 Demethylase Inhibitors. J Chem Inf Model 2018; 58:2151-2163. [DOI: 10.1021/acs.jcim.8b00539] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- C. Esposito
- Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
| | - L. Wiedmer
- Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
| | - A. Caflisch
- Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
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16
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Monsen RC, Trent JO. G-quadruplex virtual drug screening: A review. Biochimie 2018; 152:134-148. [PMID: 29966734 PMCID: PMC6134840 DOI: 10.1016/j.biochi.2018.06.024] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Accepted: 06/28/2018] [Indexed: 12/18/2022]
Abstract
Over the past two decades biologists and bioinformaticians have unearthed substantial evidence supporting a role for G-quadruplexes as important mediators of biological processes. This includes telomere damage signaling, transcriptional activity, and splicing. Both their structural heterogeneity and their abundance in oncogene promoters makes them ideal targets for drug discovery. Currently, there are hundreds of deposited DNA and RNA quadruplex atomic structures which have allowed researchers to begin using in silico drug screening approaches to develop novel stabilizing ligands. Here we provide a review of the past decade of G-quadruplex virtual drug discovery approaches and campaigns. With this we introduce relevant virtual screening platforms followed by a discussion of best practices to assist future G4 VS campaigns.
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Affiliation(s)
- Robert C Monsen
- Department of Biochemistry and Molecular Biology, University of Louisville, Louisville, KY, 40206, USA
| | - John O Trent
- James Graham Brown Cancer Center, University of Louisville, Louisville, KY, 40206, USA; Department of Biochemistry and Molecular Biology, University of Louisville, Louisville, KY, 40206, USA; Department of Medicine, University of Louisville, Louisville, KY, 40206, USA.
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17
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Vázquez J, Deplano A, Herrero A, Ginex T, Gibert E, Rabal O, Oyarzabal J, Herrero E, Luque FJ. Development and Validation of Molecular Overlays Derived from Three-Dimensional Hydrophobic Similarity with PharmScreen. J Chem Inf Model 2018; 58:1596-1609. [PMID: 30010337 DOI: 10.1021/acs.jcim.8b00216] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Molecular alignment is a standard procedure for three-dimensional (3D) similarity measurements and pharmacophore elucidation. This process is influenced by several factors, such as the physicochemical descriptors utilized to account for the molecular determinants of biological activity and the reference templates. Relying on the hypothesis that the maximal achievable binding affinity for a drug-like molecule is largely due to desolvation, we explore a novel strategy for 3D molecular overlays that exploits the partitioning of molecular hydrophobicity into atomic contributions in conjunction with information about the distribution of hydrogen-bond (HB) donor/acceptor groups. A brief description of the method, as implemented in the software package PharmScreen, including the derivation of the fractional hydrophobic contributions within the quantum mechanical version of the Miertus-Scrocco-Tomasi (MST) continuum model, and the procedure utilized for the optimal superposition between molecules, is presented. The computational procedure is calibrated by using a data set of 402 molecules pertaining to 14 distinct targets taken from the literature and validated against the AstraZeneca test, which comprises 121 experimentally derived sets of molecular overlays. The results point out the suitability of the MST-based hydrophobic parameters for generating molecular overlays, as correct predictions were obtained for 94%, 79%, and 54% of the molecules classified into easy, moderate, and hard sets, respectively. Moreover, the results point out that this accuracy is attained at a much lower degree of identity between the templates used by hydrophobic/HB fields and electrostatic/steric ones. These findings support the usefulness of the hydrophobic/HB descriptors to generate complementary overlays that may be valuable to rationalize structure-activity relationships and for virtual screening campaigns.
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Affiliation(s)
- Javier Vázquez
- Pharmacelera , Plaça Pau Vila, 1, Sector C 2a , Edifici Palau de Mar, Barcelona 08039 , Spain.,Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Institute of Biomedicine (IBUB), and Institute of Theoretical and Computational Chemistry (IQTC-UB) , University of Barcelona , Av. Prat de la Riba 171 , Santa Coloma de Gramenet E-08921 , Spain
| | - Alessandro Deplano
- Pharmacelera , Plaça Pau Vila, 1, Sector C 2a , Edifici Palau de Mar, Barcelona 08039 , Spain
| | - Albert Herrero
- Pharmacelera , Plaça Pau Vila, 1, Sector C 2a , Edifici Palau de Mar, Barcelona 08039 , Spain
| | - Tiziana Ginex
- Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Institute of Biomedicine (IBUB), and Institute of Theoretical and Computational Chemistry (IQTC-UB) , University of Barcelona , Av. Prat de la Riba 171 , Santa Coloma de Gramenet E-08921 , Spain
| | - Enric Gibert
- Pharmacelera , Plaça Pau Vila, 1, Sector C 2a , Edifici Palau de Mar, Barcelona 08039 , Spain
| | - Obdulia Rabal
- Small Molecule Discovery Platform, Molecular Therapeutics Program, Center for Applied Medical Research (CIMA) , University of Navarra , Avda. Pio XII 55 , Pamplona E-31008 , Spain
| | - Julen Oyarzabal
- Small Molecule Discovery Platform, Molecular Therapeutics Program, Center for Applied Medical Research (CIMA) , University of Navarra , Avda. Pio XII 55 , Pamplona E-31008 , Spain
| | - Enric Herrero
- Pharmacelera , Plaça Pau Vila, 1, Sector C 2a , Edifici Palau de Mar, Barcelona 08039 , Spain
| | - F Javier Luque
- Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Institute of Biomedicine (IBUB), and Institute of Theoretical and Computational Chemistry (IQTC-UB) , University of Barcelona , Av. Prat de la Riba 171 , Santa Coloma de Gramenet E-08921 , Spain
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18
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Xia J, Reid TE, Wu S, Zhang L, Wang XS. Maximal Unbiased Benchmarking Data Sets for Human Chemokine Receptors and Comparative Analysis. J Chem Inf Model 2018; 58:1104-1120. [PMID: 29698608 DOI: 10.1021/acs.jcim.8b00004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Chemokine receptors (CRs) have long been druggable targets for the treatment of inflammatory diseases and HIV-1 infection. As a powerful technique, virtual screening (VS) has been widely applied to identifying small molecule leads for modern drug targets including CRs. For rational selection of a wide variety of VS approaches, ligand enrichment assessment based on a benchmarking data set has become an indispensable practice. However, the lack of versatile benchmarking sets for the whole CRs family that are able to unbiasedly evaluate every single approach including both structure- and ligand-based VS somewhat hinders modern drug discovery efforts. To address this issue, we constructed Maximal Unbiased Benchmarking Data sets for human Chemokine Receptors (MUBD-hCRs) using our recently developed tools of MUBD-DecoyMaker. The MUBD-hCRs encompasses 13 subtypes out of 20 chemokine receptors, composed of 404 ligands and 15756 decoys so far and is readily expandable in the future. It had been thoroughly validated that MUBD-hCRs ligands are chemically diverse while its decoys are maximal unbiased in terms of "artificial enrichment", "analogue bias". In addition, we studied the performance of MUBD-hCRs, in particular CXCR4 and CCR5 data sets, in ligand enrichment assessments of both structure- and ligand-based VS approaches in comparison with other benchmarking data sets available in the public domain and demonstrated that MUBD-hCRs is very capable of designating the optimal VS approach. MUBD-hCRs is a unique and maximal unbiased benchmarking set that covers major CRs subtypes so far.
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Affiliation(s)
- Jie Xia
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica , Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100050 , China.,State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences , Peking University , Beijing 100191 , China
| | - Terry-Elinor Reid
- Molecular Modeling and Drug Discovery Core Laboratory for District of Columbia Center for AIDS Research (DC CFAR), Department of Pharmaceutical Sciences, College of Pharmacy , Howard University , Washington , D.C. 20059 , United States
| | - Song Wu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica , Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100050 , China
| | - Liangren Zhang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences , Peking University , Beijing 100191 , China
| | - Xiang Simon Wang
- Molecular Modeling and Drug Discovery Core Laboratory for District of Columbia Center for AIDS Research (DC CFAR), Department of Pharmaceutical Sciences, College of Pharmacy , Howard University , Washington , D.C. 20059 , United States
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19
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Lin A, Horvath D, Afonina V, Marcou G, Reymond JL, Varnek A. Mapping of the Available Chemical Space versus the Chemical Universe of Lead-Like Compounds. ChemMedChem 2018; 13:540-554. [PMID: 29154440 DOI: 10.1002/cmdc.201700561] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Revised: 11/07/2017] [Indexed: 12/15/2022]
Abstract
This is, to our knowledge, the most comprehensive analysis to date based on generative topographic mapping (GTM) of fragment-like chemical space (40 million molecules with no more than 17 heavy atoms, both from the theoretically enumerated GDB-17 and real-world PubChem/ChEMBL databases). The challenge was to prove that a robust map of fragment-like chemical space can actually be built, in spite of a limited (≪105 ) maximal number of compounds ("frame set") usable for fitting the GTM manifold. An evolutionary map building strategy has been updated with a "coverage check" step, which discards manifolds failing to accommodate compounds outside the frame set. The evolved map has a good propensity to separate actives from inactives for more than 20 external structure-activity sets. It was proven to properly accommodate the entire collection of 40 m compounds. Next, it served as a library comparison tool to highlight biases of real-world molecules (PubChem and ChEMBL) versus the universe of all possible species represented by FDB-17, a fragment-like subset of GDB-17 containing 10 million molecules. Specific patterns, proper to some libraries and absent from others (diversity holes), were highlighted.
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Affiliation(s)
- Arkadii Lin
- Laboratory of Chemoinformatics, Faculty of Chemistry, University of Strasbourg, 4 Blaise Pascal str., 67081, Strasbourg, France
| | - Dragos Horvath
- Laboratory of Chemoinformatics, Faculty of Chemistry, University of Strasbourg, 4 Blaise Pascal str., 67081, Strasbourg, France
| | - Valentina Afonina
- Laboratory of Chemoinformatics, Faculty of Chemistry, University of Strasbourg, 4 Blaise Pascal str., 67081, Strasbourg, France.,Laboratory of Chemoinformatics and Molecular Modeling, Department of Organic Chemistry, A.M. Butlerov Institute of Chemistry, Kazan Federal University, 18 Kremlyovskaya str., 420008, Kazan, Russia
| | - Gilles Marcou
- Laboratory of Chemoinformatics, Faculty of Chemistry, University of Strasbourg, 4 Blaise Pascal str., 67081, Strasbourg, France
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry, University of Berne, 3 Freiestrasse, 3012, Berne, Switzerland
| | - Alexandre Varnek
- Laboratory of Chemoinformatics, Faculty of Chemistry, University of Strasbourg, 4 Blaise Pascal str., 67081, Strasbourg, France
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20
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Preto J, Gentile F, Winter P, Churchill C, Omar SI, Tuszynski JA. Molecular Dynamics and Related Computational Methods with Applications to Drug Discovery. SPRINGER PROCEEDINGS IN MATHEMATICS & STATISTICS 2018. [DOI: 10.1007/978-3-319-76599-0_14] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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21
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Sahlgren C, Meinander A, Zhang H, Cheng F, Preis M, Xu C, Salminen TA, Toivola D, Abankwa D, Rosling A, Karaman DŞ, Salo-Ahen OMH, Österbacka R, Eriksson JE, Willför S, Petre I, Peltonen J, Leino R, Johnson M, Rosenholm J, Sandler N. Tailored Approaches in Drug Development and Diagnostics: From Molecular Design to Biological Model Systems. Adv Healthc Mater 2017; 6. [PMID: 28892296 DOI: 10.1002/adhm.201700258] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2017] [Revised: 05/04/2017] [Indexed: 12/13/2022]
Abstract
Approaches to increase the efficiency in developing drugs and diagnostics tools, including new drug delivery and diagnostic technologies, are needed for improved diagnosis and treatment of major diseases and health problems such as cancer, inflammatory diseases, chronic wounds, and antibiotic resistance. Development within several areas of research ranging from computational sciences, material sciences, bioengineering to biomedical sciences and bioimaging is needed to realize innovative drug development and diagnostic (DDD) approaches. Here, an overview of recent progresses within key areas that can provide customizable solutions to improve processes and the approaches taken within DDD is provided. Due to the broadness of the area, unfortunately all relevant aspects such as pharmacokinetics of bioactive molecules and delivery systems cannot be covered. Tailored approaches within (i) bioinformatics and computer-aided drug design, (ii) nanotechnology, (iii) novel materials and technologies for drug delivery and diagnostic systems, and (iv) disease models to predict safety and efficacy of medicines under development are focused on. Current developments and challenges ahead are discussed. The broad scope reflects the multidisciplinary nature of the field of DDD and aims to highlight the convergence of biological, pharmaceutical, and medical disciplines needed to meet the societal challenges of the 21st century.
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Affiliation(s)
- Cecilia Sahlgren
- Faculty of Science and Engineering; Cell Biology; Åbo Akademi University; FI-20520 Turku Finland
- Turku Centre for Biotechnology; Åbo Akademi University and University of Turku; FI-20520 Turku Finland
- Department of Biomedical Engineering; Technical University of Eindhoven; 5613 DR Eindhoven Netherlands
| | - Annika Meinander
- Faculty of Science and Engineering; Cell Biology; Åbo Akademi University; FI-20520 Turku Finland
| | - Hongbo Zhang
- Faculty of Science and Engineering; Pharmaceutical Sciences Laboratory; Åbo Akademi University; FI-20520 Turku Finland
| | - Fang Cheng
- Faculty of Science and Engineering; Cell Biology; Åbo Akademi University; FI-20520 Turku Finland
| | - Maren Preis
- Faculty of Science and Engineering; Pharmaceutical Sciences Laboratory; Åbo Akademi University; FI-20520 Turku Finland
| | - Chunlin Xu
- Faculty of Science and Engineering; Natural Materials Technology; Åbo Akademi University; FI-20500 Turku Finland
| | - Tiina A. Salminen
- Faculty of Science and Engineering; Structural Bioinformatics Laboratory; Åbo Akademi University; FI-20520 Turku Finland
| | - Diana Toivola
- Faculty of Science and Engineering; Cell Biology; Åbo Akademi University; FI-20520 Turku Finland
- Turku Center for Disease Modeling; University of Turku; FI-20520 Turku Finland
| | - Daniel Abankwa
- Department of Biomedical Engineering; Technical University of Eindhoven; 5613 DR Eindhoven Netherlands
| | - Ari Rosling
- Faculty of Science and Engineering; Polymer Technologies; Åbo Akademi University; FI-20500 Turku Finland
| | - Didem Şen Karaman
- Faculty of Science and Engineering; Pharmaceutical Sciences Laboratory; Åbo Akademi University; FI-20520 Turku Finland
| | - Outi M. H. Salo-Ahen
- Faculty of Science and Engineering; Pharmaceutical Sciences Laboratory; Åbo Akademi University; FI-20520 Turku Finland
- Faculty of Science and Engineering; Structural Bioinformatics Laboratory; Åbo Akademi University; FI-20520 Turku Finland
| | - Ronald Österbacka
- Faculty of Science and Engineering; Physics; Åbo Akademi University; FI-20500 Turku Finland
| | - John E. Eriksson
- Faculty of Science and Engineering; Cell Biology; Åbo Akademi University; FI-20520 Turku Finland
- Turku Centre for Biotechnology; Åbo Akademi University and University of Turku; FI-20520 Turku Finland
| | - Stefan Willför
- Faculty of Science and Engineering; Natural Materials Technology; Åbo Akademi University; FI-20500 Turku Finland
| | - Ion Petre
- Faculty of Science and Engineering; Computer Science; Åbo Akademi University; FI-20500 Turku Finland
| | - Jouko Peltonen
- Faculty of Science and Engineering; Physical Chemistry; Åbo Akademi University; FI-20500 Turku Finland
| | - Reko Leino
- Faculty of Science and Engineering; Organic Chemistry; Johan Gadolin Process Chemistry Centre; Åbo Akademi University; FI-20500 Turku Finland
| | - Mark Johnson
- Faculty of Science and Engineering; Structural Bioinformatics Laboratory; Åbo Akademi University; FI-20520 Turku Finland
| | - Jessica Rosenholm
- Faculty of Science and Engineering; Pharmaceutical Sciences Laboratory; Åbo Akademi University; FI-20520 Turku Finland
| | - Niklas Sandler
- Faculty of Science and Engineering; Pharmaceutical Sciences Laboratory; Åbo Akademi University; FI-20520 Turku Finland
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22
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Sahlgren C, Meinander A, Zhang H, Cheng F, Preis M, Xu C, Salminen TA, Toivola D, Abankwa D, Rosling A, Karaman DŞ, Salo-Ahen OMH, Österbacka R, Eriksson JE, Willför S, Petre I, Peltonen J, Leino R, Johnson M, Rosenholm J, Sandler N. Tailored Approaches in Drug Development and Diagnostics: From Molecular Design to Biological Model Systems. Adv Healthc Mater 2017. [DOI: 10.1002/adhm.201700258 10.1002/adhm.201700258] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/26/2023]
Affiliation(s)
- Cecilia Sahlgren
- Faculty of Science and Engineering; Cell Biology; Åbo Akademi University; FI-20520 Turku Finland
- Turku Centre for Biotechnology; Åbo Akademi University and University of Turku; FI-20520 Turku Finland
- Department of Biomedical Engineering; Technical University of Eindhoven; 5613 DR Eindhoven Netherlands
| | - Annika Meinander
- Faculty of Science and Engineering; Cell Biology; Åbo Akademi University; FI-20520 Turku Finland
| | - Hongbo Zhang
- Faculty of Science and Engineering; Pharmaceutical Sciences Laboratory; Åbo Akademi University; FI-20520 Turku Finland
| | - Fang Cheng
- Faculty of Science and Engineering; Cell Biology; Åbo Akademi University; FI-20520 Turku Finland
| | - Maren Preis
- Faculty of Science and Engineering; Pharmaceutical Sciences Laboratory; Åbo Akademi University; FI-20520 Turku Finland
| | - Chunlin Xu
- Faculty of Science and Engineering; Natural Materials Technology; Åbo Akademi University; FI-20500 Turku Finland
| | - Tiina A. Salminen
- Faculty of Science and Engineering; Structural Bioinformatics Laboratory; Åbo Akademi University; FI-20520 Turku Finland
| | - Diana Toivola
- Faculty of Science and Engineering; Cell Biology; Åbo Akademi University; FI-20520 Turku Finland
- Turku Center for Disease Modeling; University of Turku; FI-20520 Turku Finland
| | - Daniel Abankwa
- Department of Biomedical Engineering; Technical University of Eindhoven; 5613 DR Eindhoven Netherlands
| | - Ari Rosling
- Faculty of Science and Engineering; Polymer Technologies; Åbo Akademi University; FI-20500 Turku Finland
| | - Didem Şen Karaman
- Faculty of Science and Engineering; Pharmaceutical Sciences Laboratory; Åbo Akademi University; FI-20520 Turku Finland
| | - Outi M. H. Salo-Ahen
- Faculty of Science and Engineering; Pharmaceutical Sciences Laboratory; Åbo Akademi University; FI-20520 Turku Finland
- Faculty of Science and Engineering; Structural Bioinformatics Laboratory; Åbo Akademi University; FI-20520 Turku Finland
| | - Ronald Österbacka
- Faculty of Science and Engineering; Physics; Åbo Akademi University; FI-20500 Turku Finland
| | - John E. Eriksson
- Faculty of Science and Engineering; Cell Biology; Åbo Akademi University; FI-20520 Turku Finland
- Turku Centre for Biotechnology; Åbo Akademi University and University of Turku; FI-20520 Turku Finland
| | - Stefan Willför
- Faculty of Science and Engineering; Natural Materials Technology; Åbo Akademi University; FI-20500 Turku Finland
| | - Ion Petre
- Faculty of Science and Engineering; Computer Science; Åbo Akademi University; FI-20500 Turku Finland
| | - Jouko Peltonen
- Faculty of Science and Engineering; Physical Chemistry; Åbo Akademi University; FI-20500 Turku Finland
| | - Reko Leino
- Faculty of Science and Engineering; Organic Chemistry; Johan Gadolin Process Chemistry Centre; Åbo Akademi University; FI-20500 Turku Finland
| | - Mark Johnson
- Faculty of Science and Engineering; Structural Bioinformatics Laboratory; Åbo Akademi University; FI-20520 Turku Finland
| | - Jessica Rosenholm
- Faculty of Science and Engineering; Pharmaceutical Sciences Laboratory; Åbo Akademi University; FI-20520 Turku Finland
| | - Niklas Sandler
- Faculty of Science and Engineering; Pharmaceutical Sciences Laboratory; Åbo Akademi University; FI-20520 Turku Finland
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23
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Affiliation(s)
- Xavier Barril
- a Facultat de Farmacia and Institut de Biomedicina (IBUB) , Universitat de Barcelona , Barcelona , Spain.,b Catalan Institution for Research and Advanced Studies (ICREA) , Barcelona , Spain
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24
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Ramakrishnan C, Mary Thangakani A, Velmurugan D, Anantha Krishnan D, Sekijima M, Akiyama Y, Gromiha MM. Identification of type I and type II inhibitors of c-Yes kinase using in silico and experimental techniques. J Biomol Struct Dyn 2017; 36:1566-1576. [DOI: 10.1080/07391102.2017.1329098] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Chandrasekaran Ramakrishnan
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai 600036, Tamilnadu, India
| | - Anthony Mary Thangakani
- Centre of Advanced Study in Crystallography and Biophysics, University of Madras, Guindy Campus, Chennai 600025, Tamilnadu, India
| | - Devadasan Velmurugan
- Centre of Advanced Study in Crystallography and Biophysics, University of Madras, Guindy Campus, Chennai 600025, Tamilnadu, India
| | - Dhanabalan Anantha Krishnan
- Centre of Advanced Study in Crystallography and Biophysics, University of Madras, Guindy Campus, Chennai 600025, Tamilnadu, India
| | - Masakazu Sekijima
- Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama 226-8501, Japan
- Advanced Computational Drug Discovery Unit (ACDD), Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama 226-8501, Japan
- Department of Computer Science, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro-ku, Tokyo 152-8550, Japan
| | - Yutaka Akiyama
- Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama 226-8501, Japan
- Advanced Computational Drug Discovery Unit (ACDD), Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama 226-8501, Japan
- Department of Computer Science, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro-ku, Tokyo 152-8550, Japan
| | - M. Michael Gromiha
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai 600036, Tamilnadu, India
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25
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Matsuoka M, Kumar A, Muddassar M, Matsuyama A, Yoshida M, Zhang KYJ. Discovery of Fungal Denitrification Inhibitors by Targeting Copper Nitrite Reductase from Fusarium oxysporum. J Chem Inf Model 2017; 57:203-213. [DOI: 10.1021/acs.jcim.6b00649] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Masaki Matsuoka
- Chemical
Genomics Research Group, Center for Sustainable Resource Science, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Ashutosh Kumar
- Structural
Bioinformatics Team, Center for Life Science Technologies, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
| | - Muhammad Muddassar
- Structural
Bioinformatics Team, Center for Life Science Technologies, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
| | - Akihisa Matsuyama
- Chemical
Genomics Research Group, Center for Sustainable Resource Science, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
- Chemical
Genetics Laboratory, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Minoru Yoshida
- Chemical
Genomics Research Group, Center for Sustainable Resource Science, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
- Chemical
Genetics Laboratory, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
- CREST Research
Project, Japan Science and Technology Corporation, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan
| | - Kam Y. J. Zhang
- Structural
Bioinformatics Team, Center for Life Science Technologies, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
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Selvaraj G, Kaliamurthi S, Cakmak ZE, Cakmak T. Computational screening of dipeptidyl peptidase IV inhibitors from micoroalgal metabolites by pharmacophore modeling and molecular docking. PHYCOLOGICAL RESEARCH 2016. [DOI: 10.1111/pre.12141] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Gurudeeban Selvaraj
- Department of Molecular Biology and Genetics, Faculty of Engineering and Natural Sciences; Istanbul Medeniyet University; Istanbul Turkey
| | - Satyavani Kaliamurthi
- Department of Molecular Biology and Genetics, Faculty of Engineering and Natural Sciences; Istanbul Medeniyet University; Istanbul Turkey
| | - Zeynep E. Cakmak
- Department of Molecular Biology and Genetics, Faculty of Engineering and Natural Sciences; Istanbul Medeniyet University; Istanbul Turkey
- Department of Biology, Faculty of Arts and Sciences; Kirikkale University; Kirikkale Turkey
| | - Turgay Cakmak
- Department of Molecular Biology and Genetics, Faculty of Engineering and Natural Sciences; Istanbul Medeniyet University; Istanbul Turkey
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27
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Kumar A, Zhang KYJ. Prospective evaluation of shape similarity based pose prediction method in D3R Grand Challenge 2015. J Comput Aided Mol Des 2016; 30:685-693. [DOI: 10.1007/s10822-016-9931-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2016] [Accepted: 07/25/2016] [Indexed: 01/23/2023]
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28
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Zoete V, Daina A, Bovigny C, Michielin O. SwissSimilarity: A Web Tool for Low to Ultra High Throughput Ligand-Based Virtual Screening. J Chem Inf Model 2016; 56:1399-404. [PMID: 27391578 DOI: 10.1021/acs.jcim.6b00174] [Citation(s) in RCA: 182] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
SwissSimilarity is a new web tool for rapid ligand-based virtual screening of small to unprecedented ultralarge libraries of small molecules. Screenable compounds include drugs, bioactive and commercial molecules, as well as 205 million of virtual compounds readily synthesizable from commercially available synthetic reagents. Predictions can be carried out on-the-fly using six different screening approaches, including 2D molecular fingerprints as well as superpositional and fast nonsuperpositional 3D similarity methodologies. SwissSimilarity is part of a large initiative of the SIB Swiss Institute of Bioinformatics to provide online tools for computer-aided drug design, such as SwissDock, SwissBioisostere or SwissTargetPrediction with which it can interoperate, and is linked to other well-established online tools and databases. User interface and backend have been designed for simplicity and ease of use, to provide proficient virtual screening capabilities to specialists and nonexperts in the field. SwissSimilarity is accessible free of charge or login at http://www.swisssimilarity.ch .
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Affiliation(s)
- Vincent Zoete
- SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Génopode , CH-1015 Lausanne, Switzerland
| | - Antoine Daina
- SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Génopode , CH-1015 Lausanne, Switzerland
| | - Christophe Bovigny
- SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Génopode , CH-1015 Lausanne, Switzerland
| | - Olivier Michielin
- SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Génopode , CH-1015 Lausanne, Switzerland.,Ludwig Institute for Cancer Research, Centre Hospitalier Universitaire Vaudois , CH-1011 Lausanne, Switzerland.,Department of Oncology, University of Lausanne and Centre Hospitalier Universitaire Vaudois , CH-1011 Lausanne, Switzerland
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29
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Niinivehmas SP, Manivannan E, Rauhamäki S, Huuskonen J, Pentikäinen OT. Identification of estrogen receptor α ligands with virtual screening techniques. J Mol Graph Model 2016; 64:30-39. [PMID: 26774287 DOI: 10.1016/j.jmgm.2015.12.006] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2015] [Revised: 12/22/2015] [Accepted: 12/29/2015] [Indexed: 11/16/2022]
Abstract
Utilization of computer-aided molecular discovery methods in virtual screening (VS) is a cost-effective approach to identify novel bioactive small molecules. Unfortunately, no universal VS strategy can guarantee high hit rates for all biological targets, but each target requires distinct, fine-tuned solutions. Here, we have studied in retrospective manner the effectiveness and usefulness of common pharmacophore hypothesis, molecular docking and negative image-based screening as potential VS tools for a widely applied drug discovery target, estrogen receptor α (ERα). The comparison of the methods helps to demonstrate the differences in their ability to identify active molecules. For example, structure-based methods identified an already known active ligand from the widely-used bechmarking decoy molecule set. Although prospective VS against one commercially available database with around 100,000 drug-like molecules did not retrieve many testworthy hits, one novel hit molecule with pIC50 value of 6.6, was identified. Furthermore, our small in-house compound collection of easy-to-synthesize molecules was virtually screened against ERα, yielding to five hit candidates, which were found to be active in vitro having pIC50 values from 5.5 to 6.5.
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Affiliation(s)
- Sanna P Niinivehmas
- Department of Biological and Environmental Science & Nanoscience Center, University of Jyvaskyla, P.O. Box 35, FI-40014, Finland
| | - Elangovan Manivannan
- Department of Biological and Environmental Science & Nanoscience Center, University of Jyvaskyla, P.O. Box 35, FI-40014, Finland; School of Pharmacy, Devi Ahilya University, Indore 452001, Madhya Pradesh, India
| | - Sanna Rauhamäki
- Department of Biological and Environmental Science & Nanoscience Center, University of Jyvaskyla, P.O. Box 35, FI-40014, Finland
| | - Juhani Huuskonen
- Department of Chemistry & Nanoscience Center, University of Jyvaskyla, P.O. Box 35, FI-40014, Finland
| | - Olli T Pentikäinen
- Department of Biological and Environmental Science & Nanoscience Center, University of Jyvaskyla, P.O. Box 35, FI-40014, Finland.
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30
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Bradley AR, Wall ID, von Delft F, Green DVS, Deane CM, Marsden BD. WONKA: objective novel complex analysis for ensembles of protein-ligand structures. J Comput Aided Mol Des 2015; 29:963-73. [PMID: 26387008 PMCID: PMC4621702 DOI: 10.1007/s10822-015-9866-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2015] [Accepted: 09/04/2015] [Indexed: 01/16/2023]
Abstract
WONKA is a tool for the systematic analysis of an ensemble of protein-ligand structures. It makes the identification of conserved and unusual features within such an ensemble straightforward. WONKA uses an intuitive workflow to process structural co-ordinates. Ligand and protein features are summarised and then presented within an interactive web application. WONKA's power in consolidating and summarising large amounts of data is described through the analysis of three bromodomain datasets. Furthermore, and in contrast to many current methods, WONKA relates analysis to individual ligands, from which we find unusual and erroneous binding modes. Finally the use of WONKA as an annotation tool to share observations about structures is demonstrated. WONKA is freely available to download and install locally or can be used online at http://wonka.sgc.ox.ac.uk.
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Affiliation(s)
- A R Bradley
- SGC, Nuffield Department of Medicine, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Headington, Oxford, OX3 7DQ, UK
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, 1 South Parks Road, Oxford, OX1 TG, UK
| | - I D Wall
- Computational & Structural Chemistry, GlaxoSmithKline, Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire, SG1 2NY, UK
| | - F von Delft
- SGC, Nuffield Department of Medicine, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Headington, Oxford, OX3 7DQ, UK
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot, OX11 0QX, UK
- Department of Biochemistry, University of Johannesburg, Aukland Park, 2006, South Africa
| | - D V S Green
- Computational & Structural Chemistry, GlaxoSmithKline, Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire, SG1 2NY, UK
| | - C M Deane
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, 1 South Parks Road, Oxford, OX1 TG, UK
| | - B D Marsden
- SGC, Nuffield Department of Medicine, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Headington, Oxford, OX3 7DQ, UK.
- Kennedy Institute of Rheumatology, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Roosevelt Drive, Headington, Oxford, OX3 7FY, UK.
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31
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Spyrakis F, Cavasotto CN. Open challenges in structure-based virtual screening: Receptor modeling, target flexibility consideration and active site water molecules description. Arch Biochem Biophys 2015; 583:105-19. [DOI: 10.1016/j.abb.2015.08.002] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2015] [Revised: 08/03/2015] [Accepted: 08/03/2015] [Indexed: 01/05/2023]
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32
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Structure versus function—The impact of computational methods on the discovery of specific GPCR–ligands. Bioorg Med Chem 2015; 23:3907-12. [DOI: 10.1016/j.bmc.2015.03.026] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2015] [Revised: 03/06/2015] [Accepted: 03/09/2015] [Indexed: 12/26/2022]
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33
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Dobi K, Flachner B, Pukáncsik M, Máthé E, Bognár M, Szaszkó M, Magyar C, Hajdú I, Lőrincz Z, Simon I, Fülöp F, Cseh S, Dormán G. Combination of Pharmacophore Matching, 2D Similarity Search, andIn VitroBiological Assays in the Selection of Potential 5-HT6Antagonists from Large Commercial Repositories. Chem Biol Drug Des 2015; 86:864-80. [DOI: 10.1111/cbdd.12563] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2014] [Revised: 02/06/2015] [Accepted: 03/10/2015] [Indexed: 12/13/2022]
Affiliation(s)
| | | | | | - Enikő Máthé
- Targetex Kft.; Kápolna köz 4/a Dunakeszi H-2120 Hungary
| | | | - Mária Szaszkó
- Targetex Kft.; Kápolna köz 4/a Dunakeszi H-2120 Hungary
| | - Csaba Magyar
- Institute of Enzymology; Research Centre for Natural Sciences; Hungarian Academy of Sciences; Magyar Tudósok körútja 2. Budapest H-1117 Hungary
| | - István Hajdú
- Targetex Kft.; Kápolna köz 4/a Dunakeszi H-2120 Hungary
- Institute of Enzymology; Research Centre for Natural Sciences; Hungarian Academy of Sciences; Magyar Tudósok körútja 2. Budapest H-1117 Hungary
| | - Zsolt Lőrincz
- Targetex Kft.; Kápolna köz 4/a Dunakeszi H-2120 Hungary
| | - István Simon
- Institute of Enzymology; Research Centre for Natural Sciences; Hungarian Academy of Sciences; Magyar Tudósok körútja 2. Budapest H-1117 Hungary
| | - Ferenc Fülöp
- Institute of Pharmaceutical Chemistry; University of Szeged; Eötvös u. 6. Szeged H-6720 Hungary
| | - Sándor Cseh
- Targetex Kft.; Kápolna köz 4/a Dunakeszi H-2120 Hungary
| | - György Dormán
- Targetex Kft.; Kápolna köz 4/a Dunakeszi H-2120 Hungary
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34
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Kumar A, Zhang KYJ. Hierarchical virtual screening approaches in small molecule drug discovery. Methods 2015; 71:26-37. [PMID: 25072167 PMCID: PMC7129923 DOI: 10.1016/j.ymeth.2014.07.007] [Citation(s) in RCA: 95] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2014] [Revised: 07/16/2014] [Accepted: 07/17/2014] [Indexed: 02/06/2023] Open
Abstract
Virtual screening has played a significant role in the discovery of small molecule inhibitors of therapeutic targets in last two decades. Various ligand and structure-based virtual screening approaches are employed to identify small molecule ligands for proteins of interest. These approaches are often combined in either hierarchical or parallel manner to take advantage of the strength and avoid the limitations associated with individual methods. Hierarchical combination of ligand and structure-based virtual screening approaches has received noteworthy success in numerous drug discovery campaigns. In hierarchical virtual screening, several filters using ligand and structure-based approaches are sequentially applied to reduce a large screening library to a number small enough for experimental testing. In this review, we focus on different hierarchical virtual screening strategies and their application in the discovery of small molecule modulators of important drug targets. Several virtual screening studies are discussed to demonstrate the successful application of hierarchical virtual screening in small molecule drug discovery.
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Affiliation(s)
- Ashutosh Kumar
- Structural Bioinformatics Team, Center for Life Science Technologies, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
| | - Kam Y J Zhang
- Structural Bioinformatics Team, Center for Life Science Technologies, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan.
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35
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Xia J, Tilahun EL, Reid TE, Zhang L, Wang XS. Benchmarking methods and data sets for ligand enrichment assessment in virtual screening. Methods 2015; 71:146-57. [PMID: 25481478 PMCID: PMC4278665 DOI: 10.1016/j.ymeth.2014.11.015] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2014] [Revised: 11/22/2014] [Accepted: 11/24/2014] [Indexed: 11/21/2022] Open
Abstract
Retrospective small-scale virtual screening (VS) based on benchmarking data sets has been widely used to estimate ligand enrichments of VS approaches in the prospective (i.e. real-world) efforts. However, the intrinsic differences of benchmarking sets to the real screening chemical libraries can cause biased assessment. Herein, we summarize the history of benchmarking methods as well as data sets and highlight three main types of biases found in benchmarking sets, i.e. "analogue bias", "artificial enrichment" and "false negative". In addition, we introduce our recent algorithm to build maximum-unbiased benchmarking sets applicable to both ligand-based and structure-based VS approaches, and its implementations to three important human histone deacetylases (HDACs) isoforms, i.e. HDAC1, HDAC6 and HDAC8. The leave-one-out cross-validation (LOO CV) demonstrates that the benchmarking sets built by our algorithm are maximum-unbiased as measured by property matching, ROC curves and AUCs.
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Affiliation(s)
- Jie Xia
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, PR China; Molecular Modeling and Drug Discovery Core for District of Columbia Developmental Center for AIDS Research (DC D-CFAR), Laboratory of Cheminformatics and Drug Design, Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, Washington, DC 20059, USA
| | - Ermias Lemma Tilahun
- Molecular Modeling and Drug Discovery Core for District of Columbia Developmental Center for AIDS Research (DC D-CFAR), Laboratory of Cheminformatics and Drug Design, Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, Washington, DC 20059, USA
| | - Terry-Elinor Reid
- Molecular Modeling and Drug Discovery Core for District of Columbia Developmental Center for AIDS Research (DC D-CFAR), Laboratory of Cheminformatics and Drug Design, Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, Washington, DC 20059, USA
| | - Liangren Zhang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, PR China.
| | - Xiang Simon Wang
- Molecular Modeling and Drug Discovery Core for District of Columbia Developmental Center for AIDS Research (DC D-CFAR), Laboratory of Cheminformatics and Drug Design, Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, Washington, DC 20059, USA.
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36
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Horvath D, Lisurek M, Rupp B, Kühne R, Specker E, von Kries J, Rognan D, Andersson CD, Almqvist F, Elofsson M, Enqvist PA, Gustavsson AL, Remez N, Mestres J, Marcou G, Varnek A, Hibert M, Quintana J, Frank R. Design of a general-purpose European compound screening library for EU-OPENSCREEN. ChemMedChem 2014; 9:2309-26. [PMID: 25044981 DOI: 10.1002/cmdc.201402126] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2014] [Indexed: 01/08/2023]
Abstract
This work describes a collaborative effort to define and apply a protocol for the rational selection of a general-purpose screening library, to be used by the screening platforms affiliated with the EU-OPENSCREEN initiative. It is designed as a standard source of compounds for primary screening against novel biological targets, at the request of research partners. Given the general nature of the potential applications of this compound collection, the focus of the selection strategy lies on ensuring chemical stability, absence of reactive compounds, screening-compliant physicochemical properties, loose compliance to drug-likeness criteria (as drug design is a major, but not exclusive application), and maximal diversity/coverage of chemical space, aimed at providing hits for a wide spectrum of drugable targets. Finally, practical availability/cost issues cannot be avoided. The main goal of this publication is to inform potential future users of this library about its conception, sources, and characteristics. The outline of the selection procedure, notably of the filtering rules designed by a large committee of European medicinal chemists and chemoinformaticians, may be of general methodological interest for the screening/medicinal chemistry community. The selection task of 200K molecules out of a pre-filtered set of 1.4M candidates was shared by five independent European research groups, each picking a subset of 40K compounds according to their own in-house methodology and expertise. An in-depth analysis of chemical space coverage of the library serves not only to characterize the collection, but also to compare the various chemoinformatics-driven selection procedures of maximal diversity sets. Compound selections contributed by various participating groups were mapped onto general-purpose self-organizing maps (SOMs) built on the basis of marketed drugs and bioactive reference molecules. In this way, the occupancy of chemical space by the EU-OPENSCREEN library could be directly compared with distributions of known bioactives of various classes. This mapping highlights the relevance of the selection and shows how the consensus reached by merging the five different 40K selections contributes to achieve this relevance. The approach also allows one to readily identify subsets of target- or target-class-oriented compounds from the EU-OPENSCREEN library to suit the needs of the diverse range of potential users. The final EU-OPENSCREEN library, assembled by merging five independent selections of 40K compounds from various expert groups, represents an excellent example of a Europe-wide collaborative effort toward the common objective of building best-in-class European open screening platforms.
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Affiliation(s)
- Dragos Horvath
- Laboratoire de Chémoinformatique, UMR 7140 CNRS (LCS) - Université de Strasbourg, 1 rue Blaise Pascal, 6700 Strasbourg (France).
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Xia J, Jin H, Liu Z, Zhang L, Wang XS. An unbiased method to build benchmarking sets for ligand-based virtual screening and its application to GPCRs. J Chem Inf Model 2014; 54:1433-50. [PMID: 24749745 PMCID: PMC4038372 DOI: 10.1021/ci500062f] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
![]()
Benchmarking data
sets have become common in recent years for the
purpose of virtual screening, though the main focus had been placed
on the structure-based virtual screening (SBVS) approaches. Due to
the lack of crystal structures, there is great need for unbiased benchmarking
sets to evaluate various ligand-based virtual screening (LBVS) methods
for important drug targets such as G protein-coupled receptors (GPCRs).
To date these ready-to-apply data sets for LBVS are fairly limited,
and the direct usage of benchmarking sets designed for SBVS could
bring the biases to the evaluation of LBVS. Herein, we propose an
unbiased method to build benchmarking sets for LBVS and validate it
on a multitude of GPCRs targets. To be more specific, our methods
can (1) ensure chemical diversity of ligands, (2) maintain the physicochemical
similarity between ligands and decoys, (3) make the decoys dissimilar
in chemical topology to all ligands to avoid false negatives, and
(4) maximize spatial random distribution of ligands and decoys. We
evaluated the quality of our Unbiased Ligand Set (ULS) and Unbiased
Decoy Set (UDS) using three common LBVS approaches, with Leave-One-Out
(LOO) Cross-Validation (CV) and a metric of average AUC of the ROC
curves. Our method has greatly reduced the “artificial enrichment”
and “analogue bias” of a published GPCRs benchmarking
set, i.e., GPCR Ligand Library (GLL)/GPCR Decoy Database (GDD). In
addition, we addressed an important issue about the ratio of decoys
per ligand and found that for a range of 30 to 100 it does not affect
the quality of the benchmarking set, so we kept the original ratio
of 39 from the GLL/GDD.
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Affiliation(s)
- Jie Xia
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University , Beijing 100191, China
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38
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Structure-based design of small-molecule protein–protein interaction modulators: the story so far. Future Med Chem 2014; 6:343-57. [DOI: 10.4155/fmc.13.204] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
As the pivotal role of protein–protein interactions in cell growth, transcriptional activity, intracellular trafficking, signal transduction and pathological conditions has been assessed, experimental and in silico strategies have been developed to design protein–protein interaction modulators. State-of-the-art structure-based design methods, mainly pharmacophore modeling and docking, which have succeeded in the identification of enzyme inhibitors, receptor agonists and antagonists, and new tools specifically conceived to target protein–protein interfaces (e.g., hot-spot and druggable pocket prediction methods) have been applied in the search for small-molecule protein–protein interaction modulators. Many successful applications of structure-based design approaches that, despite the challenge of targeting protein–protein interfaces with small molecules, have led to the identification of micromolar and submicromolar hits are reviewed here.
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Integrating molecular docking, CoMFA analysis, and machine-learning classification with virtual screening toward identification of novel scaffolds as Plasmodium falciparum enoyl acyl carrier protein reductase inhibitor. Med Chem Res 2014. [DOI: 10.1007/s00044-014-0910-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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41
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Zauhar RJ, Gianti E, Welsh WJ. Fragment-based Shape Signatures: a new tool for virtual screening and drug discovery. J Comput Aided Mol Des 2013; 27:1009-36. [PMID: 24366428 PMCID: PMC3880490 DOI: 10.1007/s10822-013-9698-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2013] [Accepted: 12/03/2013] [Indexed: 12/13/2022]
Abstract
Since its introduction in 2003, the Shape Signatures method has been successfully applied in a number of drug design projects. Because it uses a ray-tracing approach to directly measure molecular shape and properties (as opposed to relying on chemical structure), it excels at scaffold hopping, and is extraordinarily easy to use. Despite its advantages, a significant drawback of the method has hampered its application to certain classes of problems; namely, when the chemical structures considered are large and contain heterogeneous ring-systems, the method produces descriptors that tend to merely measure the overall size of the molecule, and begin to lose selective power. To remedy this, the approach has been reformulated to automatically decompose compounds into fragments using ring systems as anchors, and to likewise partition the ray-trace in accordance with the fragment assignments. Subsequently, descriptors are generated that are fragment-based, and query and target molecules are compared by mapping query fragments onto target fragments in all ways consistent with the underlying chemical connectivity. This has proven to greatly extend the selective power of the method, while maintaining the ease of use and scaffold-hopping capabilities that characterized the original implementation. In this work, we provide a full conceptual description of the next generation Shape Signatures, and we underline the advantages of the method by discussing its practical applications to ligand-based virtual screening. The new approach can also be applied in receptor-based mode, where protein-binding sites (partitioned into subsites) can be matched against the new fragment-based Shape Signatures descriptors of library compounds.
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Affiliation(s)
- Randy J Zauhar
- Department of Chemistry and Biochemistry, University of the Sciences, 600 S. 43rd Street, Philadelphia, PA, 19104, USA,
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42
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Electrostatic similarities between protein and small molecule ligands facilitate the design of protein-protein interaction inhibitors. PLoS One 2013; 8:e75762. [PMID: 24130741 PMCID: PMC3794991 DOI: 10.1371/journal.pone.0075762] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2013] [Accepted: 08/19/2013] [Indexed: 12/26/2022] Open
Abstract
One of the underlying principles in drug discovery is that a biologically active compound is complimentary in shape and molecular recognition features to its receptor. This principle infers that molecules binding to the same receptor may share some common features. Here, we have investigated whether the electrostatic similarity can be used for the discovery of small molecule protein-protein interaction inhibitors (SMPPIIs). We have developed a method that can be used to evaluate the similarity of electrostatic potentials between small molecules and known protein ligands. This method was implemented in a software called EleKit. Analyses of all available (at the time of research) SMPPII structures indicate that SMPPIIs bear some similarities of electrostatic potential with the ligand proteins of the same receptor. This is especially true for the more polar SMPPIIs. Retrospective analysis of several successful SMPPIIs has shown the applicability of EleKit in the design of new SMPPIIs.
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Karthikeyan M, Vyas R. Chemical structure representations and applications in computational toxicity. Methods Mol Biol 2013; 929:167-92. [PMID: 23007430 DOI: 10.1007/978-1-62703-050-2_8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
Efficient storage and retrieval of chemical structures is one of the most important prerequisite for solving any computational-based problem in life sciences. Several resources including research publications, text books, and articles are available on chemical structure representation. Chemical substances that have same molecular formula but several structural formulae, conformations, and skeleton framework/scaffold/functional groups of the molecule convey various characteristics of the molecule. Today with the aid of sophisticated mathematical models and informatics tools, it is possible to design a molecule of interest with specified characteristics based on their applications in pharmaceuticals, agrochemicals, biotechnology, nanomaterials, petrochemicals, and polymers. This chapter discusses both traditional and current state of art representation of chemical structures and their applications in chemical information management, bioactivity- and toxicity-based predictive studies.
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Affiliation(s)
- Muthukumarasamy Karthikeyan
- National Chemical Laboratory, Digital Information Resource Centre & Centre of Excellence in Scientific Computing, Pune, India.
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In silico screening for identification of novel HIV-1 integrase inhibitors using QSAR and docking methodologies. Med Chem Res 2013. [DOI: 10.1007/s00044-013-0490-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Abstract
During the last 30 years, significant progress has been made in the development of novel antiviral drugs, mainly crystallizing in the establishment of potent antiretroviral therapies and the approval of drugs inhibiting hepatitis C virus replication. Although major targets of antiviral intervention involve intracellular processes required for the synthesis of viral proteins and nucleic acids, a number of inhibitors blocking virus assembly, budding, maturation, entry or uncoating act on virions or viral capsids. In this review, we focus on the drug discovery process while presenting the currently used methodologies to identify novel antiviral drugs by using a computer-based approach. We provide examples illustrating structure-based antiviral drug development, specifically neuraminidase inhibitors against influenza virus (e.g. oseltamivir and zanamivir) and human immunodeficiency virus type 1 protease inhibitors (i.e. the development of darunavir from early peptidomimetic compounds such as saquinavir). A number of drugs in preclinical development acting against picornaviruses, hepatitis B virus and human immunodeficiency virus and their mechanism of action are presented to show how viral capsids can be exploited as targets of antiviral therapy.
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Affiliation(s)
- Luis Menéndez-Arias
- Centro de Biología Molecular "Severo Ochoa" (Consejo Superior de Investigaciones Científicas & Universidad Autónoma de Madrid), c/Nicolás Cabrera 1, Campus de Cantoblanco, 28049, Madrid, Spain,
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Wills LP, Trager RE, Beeson GC, Lindsey CC, Peterson YK, Beeson CC, Schnellmann RG. The β2-adrenoceptor agonist formoterol stimulates mitochondrial biogenesis. J Pharmacol Exp Ther 2012; 342:106-18. [PMID: 22490378 PMCID: PMC3383035 DOI: 10.1124/jpet.112.191528] [Citation(s) in RCA: 74] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2012] [Accepted: 04/05/2012] [Indexed: 12/15/2022] Open
Abstract
Mitochondrial dysfunction is a common mediator of disease and organ injury. Although recent studies show that inducing mitochondrial biogenesis (MB) stimulates cell repair and regeneration, only a limited number of chemicals are known to induce MB. To examine the impact of the β-adrenoceptor (β-AR) signaling pathway on MB, primary renal proximal tubule cells (RPTC) and adult feline cardiomyocytes were exposed for 24 h to multiple β-AR agonists: isoproterenol (nonselective β-AR agonist), (±)-(R*,R*)-[4-[2-[[2-(3-chlorophenyl)-2-hydroxyethyl]amino]propyl]phenoxy] acetic acid sodium hydrate (BRL 37344) (selective β(3)-AR agonist), and formoterol (selective β(2)-AR agonist). The Seahorse Biosciences (North Billerica, MA) extracellular flux analyzer was used to quantify carbonylcyanide p-trifluoromethoxyphenylhydrazone (FCCP)-uncoupled oxygen consumption rate (OCR), a marker of maximal electron transport chain activity. Isoproterenol and BRL 37244 did not alter mitochondrial respiration at any of the concentrations examined. Formoterol exposure resulted in increases in both FCCP-uncoupled OCR and mitochondrial DNA (mtDNA) copy number. The effect of formoterol on OCR in RPTC was inhibited by the β-AR antagonist propranolol and the β(2)-AR inverse agonist 3-(isopropylamino)-1-[(7-methyl-4-indanyl)oxy]butan-2-ol hydrochloride (ICI-118,551). Mice exposed to formoterol for 24 or 72 h exhibited increases in kidney and heart mtDNA copy number, peroxisome proliferator-activated receptor γ coactivator 1α, and multiple genes involved in the mitochondrial electron transport chain (F0 subunit 6 of transmembrane F-type ATP synthase, NADH dehydrogenase subunit 1, NADH dehydrogenase subunit 6, and NADH dehydrogenase [ubiquinone] 1β subcomplex subunit 8). Cheminformatic modeling, virtual chemical library screening, and experimental validation identified nisoxetine from the Sigma Library of Pharmacologically Active Compounds and two compounds from the ChemBridge DIVERSet that increased mitochondrial respiratory capacity. These data provide compelling evidence for the use and development of β(2)-AR ligands for therapeutic MB.
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Affiliation(s)
- Lauren P Wills
- Center for Cell Death, Injury, and Regeneration, Department of Pharmaceutical and Biomedical Sciences, Medical University of South Carolina, Charleston, SC 29425, USA
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Chepelev LL, Hastings J, Ennis M, Steinbeck C, Dumontier M. Self-organizing ontology of biochemically relevant small molecules. BMC Bioinformatics 2012; 13:3. [PMID: 22221313 PMCID: PMC3267649 DOI: 10.1186/1471-2105-13-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2011] [Accepted: 01/06/2012] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND The advent of high-throughput experimentation in biochemistry has led to the generation of vast amounts of chemical data, necessitating the development of novel analysis, characterization, and cataloguing techniques and tools. Recently, a movement to publically release such data has advanced biochemical structure-activity relationship research, while providing new challenges, the biggest being the curation, annotation, and classification of this information to facilitate useful biochemical pattern analysis. Unfortunately, the human resources currently employed by the organizations supporting these efforts (e.g. ChEBI) are expanding linearly, while new useful scientific information is being released in a seemingly exponential fashion. Compounding this, currently existing chemical classification and annotation systems are not amenable to automated classification, formal and transparent chemical class definition axiomatization, facile class redefinition, or novel class integration, thus further limiting chemical ontology growth by necessitating human involvement in curation. Clearly, there is a need for the automation of this process, especially for novel chemical entities of biological interest. RESULTS To address this, we present a formal framework based on Semantic Web technologies for the automatic design of chemical ontology which can be used for automated classification of novel entities. We demonstrate the automatic self-assembly of a structure-based chemical ontology based on 60 MeSH and 40 ChEBI chemical classes. This ontology is then used to classify 200 compounds with an accuracy of 92.7%. We extend these structure-based classes with molecular feature information and demonstrate the utility of our framework for classification of functionally relevant chemicals. Finally, we discuss an iterative approach that we envision for future biochemical ontology development. CONCLUSIONS We conclude that the proposed methodology can ease the burden of chemical data annotators and dramatically increase their productivity. We anticipate that the use of formal logic in our proposed framework will make chemical classification criteria more transparent to humans and machines alike and will thus facilitate predictive and integrative bioactivity model development.
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Affiliation(s)
| | - Janna Hastings
- European Bioinformatics Institute, Wellcome Trust Genome Centre, Hinxton, UK
| | - Marcus Ennis
- European Bioinformatics Institute, Wellcome Trust Genome Centre, Hinxton, UK
| | - Christoph Steinbeck
- European Bioinformatics Institute, Wellcome Trust Genome Centre, Hinxton, UK
| | - Michel Dumontier
- Department of Biology, Carleton University, Ottawa, Canada
- School of Computer Science, Carleton University, Ottawa, Canada
- Institute of Biochemistry, Carleton University, Ottawa, Canada
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Kortagere S, Lill M, Kerrigan J. Role of computational methods in pharmaceutical sciences. Methods Mol Biol 2012; 929:21-48. [PMID: 23007425 DOI: 10.1007/978-1-62703-050-2_3] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2022]
Abstract
Over the past two decades computational methods have eased up the financial and experimental burden of early drug discovery process. The in silico methods have provided support in terms of databases, data mining of large genomes, network analysis, systems biology on the bioinformatics front and structure-activity relationship, similarity analysis, docking, and pharmacophore methods for lead design and optimization. This review highlights some of the applications of bioinformatics and chemoinformatics methods that have enriched the field of drug discovery. In addition, the review also provided insights into the use of free energy perturbation methods for efficiently computing binding energy. These in silico methods are complementary and can be easily integrated into the traditional in vitro and in vivo methods to test pharmacological hypothesis.
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Affiliation(s)
- Sandhya Kortagere
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA.
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Löwer M, Proschak E. Structure-Based Pharmacophores for Virtual Screening. Mol Inform 2011; 30:398-404. [DOI: 10.1002/minf.201100007] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2011] [Accepted: 04/06/2011] [Indexed: 11/11/2022]
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