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Ling C, Zeng T, Dang Q, Liang Y, Liu X, Xie S. Predicting drug-target interactions using matrix factorization with self-paced learning and dual similarity information. Technol Health Care 2024; 32:49-64. [PMID: 38759038 PMCID: PMC11191455 DOI: 10.3233/thc-248005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2024]
Abstract
BACKGROUND Drug repositioning (DR) refers to a method used to find new targets for existing drugs. This method can effectively reduce the development cost of drugs, save time on drug development, and reduce the risks of drug design. The traditional experimental methods related to DR are time-consuming, expensive, and have a high failure rate. Several computational methods have been developed with the increase in data volume and computing power. In the last decade, matrix factorization (MF) methods have been widely used in DR issues. However, these methods still have some challenges. (1) The model easily falls into a bad local optimal solution due to the high noise and high missing rate in the data. (2) Single similarity information makes the learning power of the model insufficient in terms of identifying the potential associations accurately. OBJECTIVE We proposed self-paced learning with dual similarity information and MF (SPLDMF), which introduced the self-paced learning method and more information related to drugs and targets into the model to improve prediction performance. METHODS Combining self-paced learning first can effectively alleviate the model prone to fall into a bad local optimal solution because of the high noise and high data missing rate. Then, we incorporated more data into the model to improve the model's capacity for learning. RESULTS Our model achieved the best results on each dataset tested. For example, the area under the receiver operating characteristic curve and the precision-recall curve of SPLDMF was 0.982 and 0.815, respectively, outperforming the state-of-the-art methods. CONCLUSION The experimental results on five benchmark datasets and two extended datasets demonstrated the effectiveness of our approach in predicting drug-target interactions.
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Affiliation(s)
- Caijin Ling
- Faculty of Information Technology, Macau University of Science and Technology, Taipa, Macao, China
| | - Ting Zeng
- Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems, Macau University of Science and Technology, Taipa, Macao, China
| | - Qi Dang
- Faculty of Information Technology, Macau University of Science and Technology, Taipa, Macao, China
| | - Yong Liang
- Peng Cheng Laboratory, Shenzhen, Guangdong, China
| | - Xiaoying Liu
- Computer Engineering Technical College, Guangdong Polytechnic of Science and Technology, Zhuhai, Guangdong, China
| | - Shengli Xie
- Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing, Guangzhou, Guangdong, China
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Craven HM, Nettesheim G, Cicuta P, Blagborough AM, Merrick CJ. Effects of the G-quadruplex-binding drugs quarfloxin and CX-5461 on the malaria parasite Plasmodium falciparum. Int J Parasitol Drugs Drug Resist 2023; 23:106-119. [PMID: 38041930 PMCID: PMC10711401 DOI: 10.1016/j.ijpddr.2023.11.007] [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: 08/15/2023] [Revised: 11/15/2023] [Accepted: 11/20/2023] [Indexed: 12/04/2023]
Abstract
Plasmodium falciparum is the deadliest causative agent of human malaria. This parasite has historically developed resistance to most drugs, including the current frontline treatments, so new therapeutic targets are needed. Our previous work on guanine quadruplexes (G4s) in the parasite's DNA and RNA has highlighted their influence on parasite biology, and revealed G4 stabilising compounds as promising candidates for repositioning. In particular, quarfloxin, a former anticancer agent, kills blood-stage parasites at all developmental stages, with fast rates of kill and nanomolar potency. Here we explored the molecular mechanism of quarfloxin and its related derivative CX-5461. In vitro, both compounds bound to P. falciparum-encoded G4 sequences. In cellulo, quarfloxin was more potent than CX-5461, and could prevent establishment of blood-stage malaria in vivo in a murine model. CX-5461 showed clear DNA damaging activity, as reported in human cells, while quarfloxin caused weaker signatures of DNA damage. Both compounds caused transcriptional dysregulation in the parasite, but the affected genes were largely different, again suggesting different modes of action. Therefore, CX-5461 may act primarily as a DNA damaging agent in both Plasmodium parasites and mammalian cells, whereas the complete antimalarial mode of action of quarfloxin may be parasite-specific and remains somewhat elusive.
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Affiliation(s)
- Holly M Craven
- Department of Pathology, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QP, UK
| | - Guilherme Nettesheim
- Department of Physics, Cavendish Laboratory University of Cambridge, J.J. Thomson Avenue, Cambridge, CB3 0HE, UK
| | - Pietro Cicuta
- Department of Physics, Cavendish Laboratory University of Cambridge, J.J. Thomson Avenue, Cambridge, CB3 0HE, UK
| | - Andrew M Blagborough
- Department of Pathology, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QP, UK
| | - Catherine J Merrick
- Department of Pathology, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QP, UK.
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Bolz SN, Schroeder M. Promiscuity in drug discovery on the verge of the structural revolution: recent advances and future chances. Expert Opin Drug Discov 2023; 18:973-985. [PMID: 37489516 DOI: 10.1080/17460441.2023.2239700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 07/19/2023] [Indexed: 07/26/2023]
Abstract
INTRODUCTION Promiscuity denotes the ability of ligands and targets to specifically interact with multiple binding partners. Despite negative aspects like side effects, promiscuity is receiving increasing attention in drug discovery as it can enhance drug efficacy and provides a molecular basis for drug repositioning. The three-dimensional structure of ligand-target complexes delivers exclusive insights into the molecular mechanisms of promiscuity and structure-based methods enable the identification of promiscuous interactions. With the recent breakthrough in protein structure prediction, novel possibilities open up to reveal unknown connections in ligand-target interaction networks. AREAS COVERED This review highlights the significance of structure in the identification and characterization of promiscuity and evaluates the potential of protein structure prediction to advance our knowledge of drug-target interaction networks. It discusses the definition and relevance of promiscuity in drug discovery and explores different approaches to detecting promiscuous ligands and targets. EXPERT OPINION Examination of structural data is essential for understanding and quantifying promiscuity. The recent advancements in structure prediction have resulted in an abundance of targets that are well-suited for structure-based methods like docking. In silico approaches may eventually completely transform our understanding of drug-target networks by complementing the millions of predicted protein structures with billions of predicted drug-target interactions.
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Affiliation(s)
- Sarah Naomi Bolz
- Biotechnology Center (BIOTEC), CMCB, Technische Universität Dresden, Dresden, Germany
| | - Michael Schroeder
- Biotechnology Center (BIOTEC), CMCB, Technische Universität Dresden, Dresden, Germany
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Kumbhar N, Nimal S, Patil D, Kaiser VF, Haupt J, Gacche RN. Repurposing of neprilysin inhibitor 'sacubitrilat' as an anti-cancer drug by modulating epigenetic and apoptotic regulators. Sci Rep 2023; 13:9952. [PMID: 37336927 DOI: 10.1038/s41598-023-36872-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 06/12/2023] [Indexed: 06/21/2023] Open
Abstract
Modifications in the epigenetic landscape have been considered a hallmark of cancer. Histone deacetylation is one of the crucial epigenetic modulations associated with the aggressive progression of various cancer subtypes. Herein, we have repurposed the neprilysin inhibitor sacubitrilat as a potent anticancer agent using in-silico protein-ligand interaction profiler (PLIP) analysis, molecular docking, and in vitro studies. The screening of PLIP profiles between vorinostat/panobinostat and HDACs/LTA4H followed by molecular docking resulted in five (Sacubitrilat, B65, BDS, BIR, and NPV) FDA-approved, experimental and investigational drugs. Sacubitrilat has demonstrated promising anticancer activity against colorectal cancer (SW-480) and triple-negative breast cancer (MDA-MB-231) cells, with IC50 values of 14.07 μg/mL and 23.02 μg/mL, respectively. FACS analysis revealed that sacubitrilat arrests the cell cycle at the G0/G1 phase and induces apoptotic-mediated cell death in SW-480 cells. In addition, sacubitrilat inhibited HDAC isoforms at the transcriptomic level by 0.7-0.9 fold and at the proteomic level by 0.5-0.6 fold as compared to the control. Sacubitrilat increased the protein expression of tumor-suppressor (p53) and pro-apoptotic makers (Bax and Bid) by 0.2-2.5 fold while decreasing the expression of anti-apoptotic Bcl2 and Nrf2 proteins by 0.2-0.5 fold with respect to control. The observed cleaved PARP product indicates that sacubitrilat induces apoptotic-mediated cell death. This study may pave the way to identify the anticancer potential of sacubitrilat and can be explored in human clinical trials.
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Affiliation(s)
- Navanath Kumbhar
- Department of Biotechnology, Savitribai Phule Pune University, Pune, Maharashtra (MS), 411007, India
| | - Snehal Nimal
- Department of Biotechnology, Savitribai Phule Pune University, Pune, Maharashtra (MS), 411007, India
| | - Deeksha Patil
- Department of Microbiology, Savitribai Phule Pune University, Pune, Maharashtra (MS), 411007, India
| | | | | | - Rajesh N Gacche
- Department of Biotechnology, Savitribai Phule Pune University, Pune, Maharashtra (MS), 411007, India.
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Lang X, Liu J, Zhang G, Feng X, Dan W. Knowledge Mapping of Drug Repositioning's Theme and Development. Drug Des Devel Ther 2023; 17:1157-1174. [PMID: 37096060 PMCID: PMC10122475 DOI: 10.2147/dddt.s405906] [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: 01/25/2023] [Accepted: 04/11/2023] [Indexed: 04/26/2023] Open
Abstract
Background In recent years, the emergence of new diseases and resistance to known diseases have led to increasing demand for new drugs. By means of bibliometric analysis, this paper studied the relevant articles on drug repositioning in recent years and analyzed the current research foci and trends. Methodology The Web of Science database was searched to collect all relevant literature on drug repositioning from 2001 to 2022. These data were imported into CiteSpace and bibliometric online analysis platforms for bibliometric analysis. The processed data and visualized images predict the development trends in the research field. Results The quality and quantity of articles published after 2011 have improved significantly, with 45 of them cited more than 100 times. Articles posted by journals from different countries have high citation values. Authors from other institutions have also collaborated to analyze drug rediscovery. Keywords found in the literature include molecular docking (N=223), virtual screening (N=170), drug discovery (N=126), machine learning (N=125), and drug-target interaction (N=68); these words represent the core content of drug repositioning. Conclusion The key focus of drug research and development is related to the discovery of new indications for drugs. Researchers are starting to retarget drugs after analyzing online databases and clinical trials. More and more drugs are being targeted at other diseases to treat more patients, based on saving money and time. It is worth noting that researchers need more financial and technical support to complete drug development.
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Affiliation(s)
- Xiaona Lang
- Pharmacy Department, Tianjin Hospital, Tianjin, People’s Republic of China
| | - Jinlei Liu
- Cardiology Department, Guang ‘anmen Hospital, Chinese Academy of Traditional Chinese Medicine, Beijing, People’s Republic of China
| | - Guangzhong Zhang
- Dermatological Department, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of China
| | - Xin Feng
- Pharmacy Department, Tianjin Hospital, Tianjin, People’s Republic of China
| | - Wenchao Dan
- Dermatological Department, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of China
- Correspondence: Wenchao Dan, Dermatological Department, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of China, Tel +86 13652001152, Email
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Hou L, Zhang Y, Ju H, Cherukupalli S, Jia R, Zhang J, Huang B, Loregian A, Liu X, Zhan P. Contemporary medicinal chemistry strategies for the discovery and optimization of influenza inhibitors targeting vRNP constituent proteins. Acta Pharm Sin B 2022; 12:1805-1824. [PMID: 35847499 PMCID: PMC9279641 DOI: 10.1016/j.apsb.2021.11.018] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 11/02/2021] [Accepted: 11/12/2021] [Indexed: 11/21/2022] Open
Abstract
Influenza is an acute respiratory infectious disease caused by the influenza virus, affecting people globally and causing significant social and economic losses. Due to the inevitable limitations of vaccines and approved drugs, there is an urgent need to discover new anti-influenza drugs with different mechanisms. The viral ribonucleoprotein complex (vRNP) plays an essential role in the life cycle of influenza viruses, representing an attractive target for drug design. In recent years, the functional area of constituent proteins in vRNP are widely used as targets for drug discovery, especially the PA endonuclease active site, the RNA-binding site of PB1, the cap-binding site of PB2 and the nuclear export signal of NP protein. Encouragingly, the PA inhibitor baloxavir has been marketed in Japan and the United States, and several drug candidates have also entered clinical trials, such as favipiravir. This article reviews the compositions and functions of the influenza virus vRNP and the research progress on vRNP inhibitors, and discusses the representative drug discovery and optimization strategies pursued.
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Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers 2021; 25:1315-1360. [PMID: 33844136 PMCID: PMC8040371 DOI: 10.1007/s11030-021-10217-3] [Citation(s) in RCA: 256] [Impact Index Per Article: 85.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/22/2021] [Indexed: 02/06/2023]
Abstract
Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.
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Affiliation(s)
- Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Devesh Srivastava
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Swati Tiwari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India.
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Repurposing drugs during the COVID-19 pandemic and beyond. Pharm Pat Anal 2021; 10:9-12. [PMID: 33445955 PMCID: PMC7814678 DOI: 10.4155/ppa-2020-0031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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9
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Wang C, Kurgan L. Survey of Similarity-Based Prediction of Drug-Protein Interactions. Curr Med Chem 2021; 27:5856-5886. [PMID: 31393241 DOI: 10.2174/0929867326666190808154841] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 04/16/2018] [Accepted: 10/23/2018] [Indexed: 12/20/2022]
Abstract
Therapeutic activity of a significant majority of drugs is determined by their interactions with proteins. Databases of drug-protein interactions (DPIs) primarily focus on the therapeutic protein targets while the knowledge of the off-targets is fragmented and partial. One way to bridge this knowledge gap is to employ computational methods to predict protein targets for a given drug molecule, or interacting drugs for given protein targets. We survey a comprehensive set of 35 methods that were published in high-impact venues and that predict DPIs based on similarity between drugs and similarity between protein targets. We analyze the internal databases of known PDIs that these methods utilize to compute similarities, and investigate how they are linked to the 12 publicly available source databases. We discuss contents, impact and relationships between these internal and source databases, and well as the timeline of their releases and publications. The 35 predictors exploit and often combine three types of similarities that consider drug structures, drug profiles, and target sequences. We review the predictive architectures of these methods, their impact, and we explain how their internal DPIs databases are linked to the source databases. We also include a detailed timeline of the development of these predictors and discuss the underlying limitations of the current resources and predictive tools. Finally, we provide several recommendations concerning the future development of the related databases and methods.
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Affiliation(s)
- Chen Wang
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, United States
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, United States
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Uncovering New Drug Properties in Target-Based Drug-Drug Similarity Networks. Pharmaceutics 2020; 12:pharmaceutics12090879. [PMID: 32947845 PMCID: PMC7557376 DOI: 10.3390/pharmaceutics12090879] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 09/09/2020] [Accepted: 09/10/2020] [Indexed: 01/19/2023] Open
Abstract
Despite recent advances in bioinformatics, systems biology, and machine learning, the accurate prediction of drug properties remains an open problem. Indeed, because the biological environment is a complex system, the traditional approach—based on knowledge about the chemical structures—can not fully explain the nature of interactions between drugs and biological targets. Consequently, in this paper, we propose an unsupervised machine learning approach that uses the information we know about drug–target interactions to infer drug properties. To this end, we define drug similarity based on drug–target interactions and build a weighted Drug–Drug Similarity Network according to the drug–drug similarity relationships. Using an energy-model network layout, we generate drug communities associated with specific, dominant drug properties. DrugBank confirms the properties of 59.52% of the drugs in these communities, and 26.98% are existing drug repositioning hints we reconstruct with our DDSN approach. The remaining 13.49% of the drugs seem not to match the dominant pharmacologic property; thus, we consider them potential drug repurposing hints. The resources required to test all these repurposing hints are considerable. Therefore we introduce a mechanism of prioritization based on the betweenness/degree node centrality. Using betweenness/degree as an indicator of drug repurposing potential, we select Azelaic acid and Meprobamate as a possible antineoplastic and antifungal, respectively. Finally, we use a test procedure based on molecular docking to analyze Azelaic acid and Meprobamate’s repurposing.
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Anwar A, Khan NA, Siddiqui R. Repurposing of Drugs Is a Viable Approach to Develop Therapeutic Strategies against Central Nervous System Related Pathogenic Amoebae. ACS Chem Neurosci 2020; 11:2378-2384. [PMID: 32073257 DOI: 10.1021/acschemneuro.9b00613] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Brain-eating amoebae including Acanthamoeba spp., Naegleria fowleri, and Balamuthia mandrillaris cause rare infections of the central nervous system that almost always result in death. The high mortality rate, lack of interest for drug development from pharmaceutical industries, and no available effective drugs present an alarming challenge. The current drugs employed in the management and therapy of these devastating diseases are amphotericin B, miltefosine, chlorhexidine, pentamidine, and voriconazole which are generally used in combination. However, clinical evidence shows that these drugs have limited efficacy and high host cell cytotoxicity. Repurposing of drugs is a practical approach to utilize commercially available, U.S. Food and Drug Administration approved drugs for one disease against rare diseases caused by brain-eating amoebae. In this Perspective, we highlight some of the success stories of drugs repositioned against neglected parasitic diseases and identify future potential for effective and sustainable drug development against brain-eating amoebae infections.
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Affiliation(s)
- Ayaz Anwar
- Department of Biological Sciences, School of Science and Technology, Sunway University, Subang Jaya 47500, Selangor, Malaysia
| | - Naveed Ahmed Khan
- Department of Biology, Chemistry and Environmental Sciences, College of Arts and Sciences, American University of Sharjah, Sharjah 26666, United Arab Emirates
| | - Ruqaiyyah Siddiqui
- Department of Biology, Chemistry and Environmental Sciences, College of Arts and Sciences, American University of Sharjah, Sharjah 26666, United Arab Emirates
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Three Alkaloids from an Apocynaceae Species, Aspidosperma spruceanum as Antileishmaniasis Agents by In Silico Demo-case Studies. PLANTS 2020; 9:plants9080983. [PMID: 32756456 PMCID: PMC7465237 DOI: 10.3390/plants9080983] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 07/28/2020] [Accepted: 07/30/2020] [Indexed: 12/27/2022]
Abstract
This paper is focused on demonstrating with a real case that Ethnobotany added to Bioinformatics is a promising tool for new drugs search. It encourages the in silico investigation of "challua kaspi", a medicinal kichwa Amazonian plant (Aspidosperma spruceanum) against a Neglected Tropical Disease, leishmaniasis. The illness affects over 150 million people especially in subtropical regions, there is no vaccination and conventional treatments are unsatisfactory. In attempts to find potent and safe inhibitors of its etiological agent, Leishmania, we recovered the published traditional knowledge on kichwa antimalarials and selected three A. spruceanum alkaloids, (aspidoalbine, aspidocarpine and tubotaiwine), to evaluate by molecular docking their activity upon five Leishmania targets: DHFR-TS, PTR1, PK, HGPRT and SQS enzymes. Our simulation results suggest that aspidoalbine interacts competitively with the five targets, with a greater affinity for the active site of PTR1 than some physiological ligands. Our virtual data also point to the demonstration of few side effects. The predicted binding free energy has a greater affinity to Leishmania proteins than to their homologous in humans (TS, DHR, PKLR, HGPRT and SQS), and there is no match with binding pockets of physiological importance. Keys for the in silico protocols applied are included in order to offer a standardized method replicable in other cases. Apocynaceae having ethnobotanical use can be virtually tested as molecular antileishmaniasis new drugs.
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Prakash AV, Park JW, Seong JW, Kang TJ. Repositioned Drugs for Inflammatory Diseases such as Sepsis, Asthma, and Atopic Dermatitis. Biomol Ther (Seoul) 2020; 28:222-229. [PMID: 32133828 PMCID: PMC7216745 DOI: 10.4062/biomolther.2020.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2020] [Revised: 02/11/2020] [Accepted: 02/17/2020] [Indexed: 12/14/2022] Open
Abstract
The process of drug discovery and drug development consumes billions of dollars to bring a new drug to the market. Drug development is time consuming and sometimes, the failure rates are high. Thus, the pharmaceutical industry is looking for a better option for new drug discovery. Drug repositioning is a good alternative technology that has demonstrated many advantages over de novo drug development, the most important one being shorter drug development timelines. In the last two decades, drug repositioning has made tremendous impact on drug development technologies. In this review, we focus on the recent advances in drug repositioning technologies and discuss the repositioned drugs used for inflammatory diseases such as sepsis, asthma, and atopic dermatitis.
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Affiliation(s)
- Annamneedi Venkata Prakash
- Convergence Research Center, Department of Pharmacy and Institute of Chronic Disease, Sahmyook University, Seoul 01795, Republic of Korea
| | - Jun Woo Park
- Convergence Research Center, Department of Pharmacy and Institute of Chronic Disease, Sahmyook University, Seoul 01795, Republic of Korea
| | - Ju-Won Seong
- Convergence Research Center, Department of Pharmacy and Institute of Chronic Disease, Sahmyook University, Seoul 01795, Republic of Korea
| | - Tae Jin Kang
- Convergence Research Center, Department of Pharmacy and Institute of Chronic Disease, Sahmyook University, Seoul 01795, Republic of Korea
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Xie L, He S, Zhang Z, Lin K, Bo X, Yang S, Feng B, Wan K, Yang K, Yang J, Ding Y. Domain-adversarial multi-task framework for novel therapeutic property prediction of compounds. Bioinformatics 2020; 36:2848-2855. [PMID: 31999334 DOI: 10.1093/bioinformatics/btaa063] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2019] [Revised: 12/23/2019] [Accepted: 01/23/2020] [Indexed: 12/11/2022] Open
Abstract
MOTIVATION With the rapid development of high-throughput technologies, parallel acquisition of large-scale drug-informatics data provides significant opportunities to improve pharmaceutical research and development. One important application is the purpose prediction of small-molecule compounds with the objective of specifying the therapeutic properties of extensive purpose-unknown compounds and repurposing the novel therapeutic properties of FDA-approved drugs. Such a problem is extremely challenging because compound attributes include heterogeneous data with various feature patterns, such as drug fingerprints, drug physicochemical properties and drug perturbation gene expressions. Moreover, there is a complex non-linear dependency among heterogeneous data. In this study, we propose a novel domain-adversarial multi-task framework for integrating shared knowledge from multiple domains. The framework first uses an adversarial strategy to learn target representations and then models non-linear dependency among several domains. RESULTS Experiments on two real-world datasets illustrate that our approach achieves an obvious improvement over competitive baselines. The novel therapeutic properties of purpose-unknown compounds that we predicted have been widely reported or brought to clinics. Furthermore, our framework can integrate various attributes beyond the three domains examined herein and can be applied in industry for screening significant numbers of small-molecule drug candidates. AVAILABILITY AND IMPLEMENTATION The source code and datasets are available at https://github.com/JohnnyY8/DAMT-Model. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Lingwei Xie
- School of Informatics, Xiamen University, Xiamen 361005, China
| | - Song He
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Zhongnan Zhang
- School of Informatics, Xiamen University, Xiamen 361005, China
| | - Kunhui Lin
- School of Informatics, Xiamen University, Xiamen 361005, China
| | - Xiaochen Bo
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Shu Yang
- Department of Computer Science, UC Santa Barbara, Santa Barbara, CA 93106, USA
| | - Boyuan Feng
- Department of Computer Science, UC Santa Barbara, Santa Barbara, CA 93106, USA
| | - Kun Wan
- Department of Computer Science, UC Santa Barbara, Santa Barbara, CA 93106, USA
| | - Kang Yang
- School of Informatics, Xiamen University, Xiamen 361005, China
| | - Jie Yang
- School of Informatics, Xiamen University, Xiamen 361005, China
| | - Yufei Ding
- Department of Computer Science, UC Santa Barbara, Santa Barbara, CA 93106, USA
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15
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Bellera CL, Alberca LN, Sbaraglini ML, Talevi A. In Silico Drug Repositioning for Chagas Disease. Curr Med Chem 2020; 27:662-675. [PMID: 31622200 DOI: 10.2174/0929867326666191016114839] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2017] [Revised: 09/12/2019] [Accepted: 09/23/2019] [Indexed: 12/18/2022]
Abstract
Chagas disease is an infectious tropical disease included within the group of neglected tropical diseases. Though historically endemic to Latin America, it has lately spread to high-income countries due to human migration. At present, there are only two available drugs, nifurtimox and benznidazole, approved for this treatment, both with considerable side-effects (which often result in treatment interruption) and limited efficacy in the chronic stage of the disease in adults. Drug repositioning involves finding novel therapeutic indications for known drugs, including approved, withdrawn, abandoned and investigational drugs. It is today a broadly applied approach to develop innovative medications, since indication shifts are built on existing safety, ADME and manufacturing information, thus greatly shortening development timeframes. Drug repositioning has been signaled as a particularly interesting strategy to search for new therapeutic solutions for neglected and rare conditions, which traditionally present limited commercial interest and are mostly covered by the public sector and not-for-profit initiatives and organizations. Here, we review the applications of computer-aided technologies as systematic approaches to drug repositioning in the field of Chagas disease. In silico screening represents the most explored approach, whereas other rational methods such as network-based and signature-based approximations have still not been applied.
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Affiliation(s)
- Carolina L Bellera
- Laboratory of Bioactive Research and Development (LIDeB), Faculty of Exact Sciences, University of La Plata (UNLP), La Plata, Argentina
| | - Lucas N Alberca
- Laboratory of Bioactive Research and Development (LIDeB), Faculty of Exact Sciences, University of La Plata (UNLP), La Plata, Argentina
| | - María L Sbaraglini
- Laboratory of Bioactive Research and Development (LIDeB), Faculty of Exact Sciences, University of La Plata (UNLP), La Plata, Argentina
| | - Alan Talevi
- Laboratory of Bioactive Research and Development (LIDeB), Faculty of Exact Sciences, University of La Plata (UNLP), La Plata, Argentina
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16
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Talevi A, Carrillo C, Comini M. The Thiol-polyamine Metabolism of Trypanosoma cruzi: Molecular Targets and Drug Repurposing Strategies. Curr Med Chem 2019; 26:6614-6635. [PMID: 30259812 DOI: 10.2174/0929867325666180926151059] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 07/23/2018] [Accepted: 09/10/2018] [Indexed: 12/18/2022]
Abstract
Chagas´ disease continues to be a challenging and neglected public health problem in many American countries. The etiologic agent, Trypanosoma cruzi, develops intracellularly in the mammalian host, which hinders treatment efficacy. Progress in the knowledge of parasite biology and host-pathogen interaction has not been paralleled by the development of novel, safe and effective therapeutic options. It is then urgent to seek for novel therapeutic candidates and to implement drug discovery strategies that may accelerate the discovery process. The most appealing targets for pharmacological intervention are those essential for the pathogen and, whenever possible, absent or significantly different from the host homolog. The thiol-polyamine metabolism of T. cruzi offers interesting candidates for a rational design of selective drugs. In this respect, here we critically review the state of the art of the thiolpolyamine metabolism of T. cruzi and the pharmacological potential of its components. On the other hand, drug repurposing emerged as a valid strategy to identify new biological activities for drugs in clinical use, while significantly shortening the long time and high cost associated with de novo drug discovery approaches. Thus, we also discuss the different drug repurposing strategies available with a special emphasis in their applications to the identification of drug candidates targeting essential components of the thiol-polyamine metabolism of T. cruzi.
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Affiliation(s)
- Alan Talevi
- Medicinal Chemistry, Department of Biological Sciences, Faculty of Exact Sciences, University of La Plata, La Plata, Argentina
| | - Carolina Carrillo
- Instituto de Ciencias y Tecnología Dr. César Milstein (ICT Milstein) - CONICET. Ciudad Autónoma de Buenos Aires, Argentina
| | - Marcelo Comini
- Institut Pasteur de Montevideo, Mataojo 2020, Montevideo 11400, Uruguay
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17
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Thatikonda S, Pooladanda V, Godugu C. Repurposing an old drug for new use: Niclosamide in psoriasis-like skin inflammation. J Cell Physiol 2019; 235:5270-5283. [PMID: 31846070 DOI: 10.1002/jcp.29413] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Accepted: 12/05/2019] [Indexed: 12/13/2022]
Abstract
Drug discovery is an onerous, extremely expensive, and time-consuming process. Instead, drug repurposing is an attractive strategy for exploiting novel indications for a drug beyond its original use. The untapped potential of drug repurposing compensates the barriers associated with the drug discovery pipeline. Psoriasis is an autoimmune skin disease, where hyperproliferation of keratinocytes and exaggerated immune responses are the important hallmarks of the disease. Extensive in vitro and preclinical research has demonstrated that niclosamide was found to exert potent anticancer and anti-inflammatory properties by targeting STAT3, p65 NF-κB, and NFATc-1 signaling paradigm with minimal host toxicity. From the disease perspective, the static intracellular molecular network in both cancer and psoriasis share overlapping pathological features in terms of hyperproliferation and chronic inflammation, which is mediated by the aforementioned signaling cascade. The plausible mechanistic relevance has prompted us to investigate the implementation of niclosamide for repositioning in psoriasis. Our in vitro and in vivo findings suggest that niclosamide inhibits keratinocytes hyperproliferation by reactive oxygen species-mediated apoptosis through the loss of mitochondrial membrane potential, cell cycle arrest at Sub G1 phase, and DNA fragmentation. Furthermore, niclosamide treatment resulted in abrogation of lipopolysaccharide-induced inflammatory cytokine levels in murine macrophages. Additionally, our results provided a preclinical rationale in imiquimod (IMQ)-induced BALB/c mouse model, where niclosamide diligently mitigated the IMQ-induced epidermal hyperplasia and inflammation by downregulating STAT3, p65 NF-κB, and NFATc-1 transcription factors along with Akt, Ki-67, and ICAM-1 protein expression.
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Affiliation(s)
- Sowjanya Thatikonda
- Department of Regulatory Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Telangana, India
| | - Venkatesh Pooladanda
- Department of Regulatory Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Telangana, India
| | - Chandraiah Godugu
- Department of Regulatory Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Telangana, India
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18
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Arakelyan A, Nersisyan L, Nikoghosyan M, Hakobyan S, Simonyan A, Hopp L, Loeffler-Wirth H, Binder H. Transcriptome-Guided Drug Repositioning. Pharmaceutics 2019; 11:E677. [PMID: 31842375 PMCID: PMC6969900 DOI: 10.3390/pharmaceutics11120677] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 11/17/2019] [Accepted: 12/11/2019] [Indexed: 02/06/2023] Open
Abstract
Drug repositioning can save considerable time and resources and significantly speed up the drug development process. The increasing availability of drug action and disease-associated transcriptome data makes it an attractive source for repositioning studies. Here, we have developed a transcriptome-guided approach for drug/biologics repositioning based on multi-layer self-organizing maps (ml-SOM). It allows for analyzing multiple transcriptome datasets by segmenting them into layers of drug action- and disease-associated transcriptome data. A comparison of expression changes in clusters of functionally related genes across the layers identifies "drug target" spots in disease layers and evaluates the repositioning possibility of a drug. The repositioning potential for two approved biologics drugs (infliximab and brodalumab) confirmed the drugs' action for approved diseases (ulcerative colitis and Crohn's disease for infliximab and psoriasis for brodalumab). We showed the potential efficacy of infliximab for the treatment of sarcoidosis, but not chronic obstructive pulmonary disease (COPD). Brodalumab failed to affect dysregulated functional gene clusters in Crohn's disease (CD) and systemic juvenile idiopathic arthritis (SJIA), clearly indicating that it may not be effective in the treatment of these diseases. In conclusion, ml-SOM offers a novel approach for transcriptome-guided drug repositioning that could be particularly useful for biologics drugs.
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Affiliation(s)
- Arsen Arakelyan
- Institute of Biomedicine and Pharmacy, Russian-Armenian University, 0051 Yerevan, Armenia; (M.N.); (A.S.)
| | - Lilit Nersisyan
- Group of Bioinformatics, Institute of Molecular Biology NAS RA, 0014 Yerevan, Armenia; (L.N.); (S.H.)
| | - Maria Nikoghosyan
- Institute of Biomedicine and Pharmacy, Russian-Armenian University, 0051 Yerevan, Armenia; (M.N.); (A.S.)
- Group of Bioinformatics, Institute of Molecular Biology NAS RA, 0014 Yerevan, Armenia; (L.N.); (S.H.)
| | - Siras Hakobyan
- Group of Bioinformatics, Institute of Molecular Biology NAS RA, 0014 Yerevan, Armenia; (L.N.); (S.H.)
| | - Arman Simonyan
- Institute of Biomedicine and Pharmacy, Russian-Armenian University, 0051 Yerevan, Armenia; (M.N.); (A.S.)
- Group of Bioinformatics, Institute of Molecular Biology NAS RA, 0014 Yerevan, Armenia; (L.N.); (S.H.)
| | - Lydia Hopp
- Interdisciplinary Centre for Bioinformatics, University of Leipzig, D-04107 Leipzig, Germany; (L.H.); (H.L.-W.)
| | - Henry Loeffler-Wirth
- Interdisciplinary Centre for Bioinformatics, University of Leipzig, D-04107 Leipzig, Germany; (L.H.); (H.L.-W.)
| | - Hans Binder
- Interdisciplinary Centre for Bioinformatics, University of Leipzig, D-04107 Leipzig, Germany; (L.H.); (H.L.-W.)
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19
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Ghadermarzi S, Li X, Li M, Kurgan L. Sequence-Derived Markers of Drug Targets and Potentially Druggable Human Proteins. Front Genet 2019; 10:1075. [PMID: 31803227 PMCID: PMC6872670 DOI: 10.3389/fgene.2019.01075] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Accepted: 10/09/2019] [Indexed: 12/16/2022] Open
Abstract
Recent research shows that majority of the druggable human proteome is yet to be annotated and explored. Accurate identification of these unexplored druggable proteins would facilitate development, screening, repurposing, and repositioning of drugs, as well as prediction of new drug–protein interactions. We contrast the current drug targets against the datasets of non-druggable and possibly druggable proteins to formulate markers that could be used to identify druggable proteins. We focus on the markers that can be extracted from protein sequences or names/identifiers to ensure that they can be applied across the entire human proteome. These markers quantify key features covered in the past works (topological features of PPIs, cellular functions, and subcellular locations) and several novel factors (intrinsic disorder, residue-level conservation, alternative splicing isoforms, domains, and sequence-derived solvent accessibility). We find that the possibly druggable proteins have significantly higher abundance of alternative splicing isoforms, relatively large number of domains, higher degree of centrality in the protein-protein interaction networks, and lower numbers of conserved and surface residues, when compared with the non-druggable proteins. We show that the current drug targets and possibly druggable proteins share involvement in the catalytic and signaling functions. However, unlike the drug targets, the possibly druggable proteins participate in the metabolic and biosynthesis processes, are enriched in the intrinsic disorder, interact with proteins and nucleic acids, and are localized across the cell. To sum up, we formulate several markers that can help with finding novel druggable human proteins and provide interesting insights into the cellular functions and subcellular locations of the current drug targets and potentially druggable proteins.
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Affiliation(s)
- Sina Ghadermarzi
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, United States
| | - Xingyi Li
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, United States
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20
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Xuan P, Song Y, Zhang T, Jia L. Prediction of Potential Drug-Disease Associations through Deep Integration of Diversity and Projections of Various Drug Features. Int J Mol Sci 2019; 20:ijms20174102. [PMID: 31443472 PMCID: PMC6747548 DOI: 10.3390/ijms20174102] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 08/19/2019] [Accepted: 08/20/2019] [Indexed: 11/17/2022] Open
Abstract
Identifying new indications for existing drugs may reduce costs and expedites drug development. Drug-related disease predictions typically combined heterogeneous drug-related and disease-related data to derive the associations between drugs and diseases, while recently developed approaches integrate multiple kinds of drug features, but fail to take the diversity implied by these features into account. We developed a method based on non-negative matrix factorization, DivePred, for predicting potential drug–disease associations. DivePred integrated disease similarity, drug–disease associations, and various drug features derived from drug chemical substructures, drug target protein domains, drug target annotations, and drug-related diseases. Diverse drug features reflect the characteristics of drugs from different perspectives, and utilizing the diversity of multiple kinds of features is critical for association prediction. The various drug features had higher dimensions and sparse characteristics, whereas DivePred projected high-dimensional drug features into the low-dimensional feature space to generate dense feature representations of drugs. Furthermore, DivePred’s optimization term enhanced diversity and reduced redundancy of multiple kinds of drug features. The neighbor information was exploited to infer the likelihood of drug–disease associations. Experiments indicated that DivePred was superior to several state-of-the-art methods for prediction drug-disease association. During the validation process, DivePred identified more drug-disease associations in the top part of prediction result than other methods, benefitting further biological validation. Case studies of acetaminophen, ciprofloxacin, doxorubicin, hydrocortisone, and ampicillin demonstrated that DivePred has the ability to discover potential candidate disease indications for drugs.
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Affiliation(s)
- Ping Xuan
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
| | - Yingying Song
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
| | - Tiangang Zhang
- School of Mathematical Science, Heilongjiang University, Harbin 150080, China.
| | - Lan Jia
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
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21
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Forouzesh A, Samadi Foroushani S, Forouzesh F, Zand E. Reliable Target Prediction of Bioactive Molecules Based on Chemical Similarity Without Employing Statistical Methods. Front Pharmacol 2019; 10:835. [PMID: 31404334 PMCID: PMC6676798 DOI: 10.3389/fphar.2019.00835] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2019] [Accepted: 07/01/2019] [Indexed: 12/11/2022] Open
Abstract
The prediction of biological targets of bioactive molecules from machine-readable materials can be routinely performed by computational target prediction tools (CTPTs). However, the prediction of biological targets of bioactive molecules from non-digital materials (e.g., printed or handwritten documents) has not been possible due to the complex nature of bioactive molecules and impossibility of employing computations. Improving the target prediction accuracy is the most important challenge for computational target prediction. A minimum structure is identified for each group of neighbor molecules in the proposed method. Each group of neighbor molecules represents a distinct structural class of molecules with the same function in relation to the target. The minimum structure is employed as a query to search for molecules that perfectly satisfy the minimum structure of what is guessed crucial for the targeted activity. The proposed method is based on chemical similarity, but only molecules that perfectly satisfy the minimum structure are considered. Structurally related bioactive molecules found with the same minimum structure were considered as neighbor molecules of the query molecule. The known target of the neighbor molecule is used as a reference for predicting the target of the neighbor molecule with an unknown target. A lot of information is needed to identify the minimum structure, because it is necessary to know which part(s) of the bioactive molecule determines the precise target or targets responsible for the observed phenotype. Therefore, the predicted target based on the minimum structure without employing the statistical significance is considered as a reliable prediction. Since only molecules that perfectly (and not partly) satisfy the minimum structure are considered, the minimum structure can be used without similarity calculations in non-digital materials and with similarity calculations (perfect similarity) in machine-readable materials. Nine tools (PASS online, PPB, SEA, TargetHunter, PharmMapper, ChemProt, HitPick, SuperPred, and SPiDER), which can be used for computational target prediction, are compared with the proposed method for 550 target predictions. The proposed method, SEA, PPB, and PASS online, showed the best quality and quantity for the accurate predictions.
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Affiliation(s)
- Abed Forouzesh
- Iranian Research Institute of Plant Protection, Agricultural Research Education and Extension Organization (AREEO), Tehran, Iran
| | - Sadegh Samadi Foroushani
- Iranian Research Institute of Plant Protection, Agricultural Research Education and Extension Organization (AREEO), Tehran, Iran
| | - Fatemeh Forouzesh
- Department of Medicine, Tehran Medical Branch, Islamic Azad University, Tehran, Iran
| | - Eskandar Zand
- Iranian Research Institute of Plant Protection, Agricultural Research Education and Extension Organization (AREEO), Tehran, Iran
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22
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Yang X, Wang Y, Byrne R, Schneider G, Yang S. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chem Rev 2019; 119:10520-10594. [PMID: 31294972 DOI: 10.1021/acs.chemrev.8b00728] [Citation(s) in RCA: 343] [Impact Index Per Article: 68.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.
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Affiliation(s)
- Xin Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
| | - Yifei Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
| | - Ryan Byrne
- ETH Zurich , Department of Chemistry and Applied Biosciences , Vladimir-Prelog-Weg 4 , CH-8093 Zurich , Switzerland
| | - Gisbert Schneider
- ETH Zurich , Department of Chemistry and Applied Biosciences , Vladimir-Prelog-Weg 4 , CH-8093 Zurich , Switzerland
| | - Shengyong Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
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23
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Jordan EJ, Patil K, Suresh K, Park JH, Mosse YP, Lemmon MA, Radhakrishnan R. Computational algorithms for in silico profiling of activating mutations in cancer. Cell Mol Life Sci 2019; 76:2663-2679. [PMID: 30982079 PMCID: PMC6589134 DOI: 10.1007/s00018-019-03097-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Revised: 04/01/2019] [Accepted: 04/08/2019] [Indexed: 12/17/2022]
Abstract
Methods to catalog and computationally assess the mutational landscape of proteins in human cancers are desirable. One approach is to adapt evolutionary or data-driven methods developed for predicting whether a single-nucleotide polymorphism (SNP) is deleterious to protein structure and function. In cases where understanding the mechanism of protein activation and regulation is desired, an alternative approach is to employ structure-based computational approaches to predict the effects of point mutations. Through a case study of mutations in kinase domains of three proteins, namely, the anaplastic lymphoma kinase (ALK) in pediatric neuroblastoma patients, serine/threonine-protein kinase B-Raf (BRAF) in melanoma patients, and erythroblastic oncogene B 2 (ErbB2 or HER2) in breast cancer patients, we compare the two approaches above. We find that the structure-based method is most appropriate for developing a binary classification of several different mutations, especially infrequently occurring ones, concerning the activation status of the given target protein. This approach is especially useful if the effects of mutations on the interactions of inhibitors with the target proteins are being sought. However, many patients will present with mutations spread across different target proteins, making structure-based models computationally demanding to implement and execute. In this situation, data-driven methods-including those based on machine learning techniques and evolutionary methods-are most appropriate for recognizing and illuminate mutational patterns. We show, however, that, in the present status of the field, the two methods have very different accuracies and confidence values, and hence, the optimal choice of their deployment is context-dependent.
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Affiliation(s)
- E Joseph Jordan
- Graduate Group in Biochemistry and Molecular Biophysics, University of Pennsylvania, Philadelphia, PA, USA
| | - Keshav Patil
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Krishna Suresh
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Jin H Park
- Department of Pharmacology, Yale University, New Haven, CT, USA
- Cancer Biology Institute, Yale University, West Haven, CT, USA
| | - Yael P Mosse
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Mark A Lemmon
- Department of Pharmacology, Yale University, New Haven, CT, USA
- Cancer Biology Institute, Yale University, West Haven, CT, USA
| | - Ravi Radhakrishnan
- Graduate Group in Biochemistry and Molecular Biophysics, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
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24
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Wu G, Zhao T, Kang D, Zhang J, Song Y, Namasivayam V, Kongsted J, Pannecouque C, De Clercq E, Poongavanam V, Liu X, Zhan P. Overview of Recent Strategic Advances in Medicinal Chemistry. J Med Chem 2019; 62:9375-9414. [PMID: 31050421 DOI: 10.1021/acs.jmedchem.9b00359] [Citation(s) in RCA: 95] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Introducing novel strategies, concepts, and technologies that speed up drug discovery and the drug development cycle is of great importance both in the highly competitive pharmaceutical industry as well as in academia. This Perspective aims to present a "big-picture" overview of recent strategic innovations in medicinal chemistry and drug discovery.
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Affiliation(s)
- Gaochan Wu
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences , Shandong University , 44 West Culture Road , 250012 Ji'nan , Shandong , P. R. China
| | - Tong Zhao
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences , Shandong University , 44 West Culture Road , 250012 Ji'nan , Shandong , P. R. China
| | - Dongwei Kang
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences , Shandong University , 44 West Culture Road , 250012 Ji'nan , Shandong , P. R. China
| | - Jian Zhang
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences , Shandong University , 44 West Culture Road , 250012 Ji'nan , Shandong , P. R. China
| | - Yuning Song
- Department of Clinical Pharmacy , Qilu Hospital of Shandong University , 250012 Ji'nan , China
| | - Vigneshwaran Namasivayam
- Pharmaceutical Institute, Pharmaceutical Chemistry II , University of Bonn , 53121 Bonn , Germany
| | - Jacob Kongsted
- Department of Physics, Chemistry, and Pharmacy , University of Southern Denmark , DK-5230 Odense M , Denmark
| | - Christophe Pannecouque
- Rega Institute for Medical Research, Laboratory of Virology and Chemotherapy , K.U. Leuven , Herestraat 49 Postbus 1043 (09.A097) , B-3000 Leuven , Belgium
| | - Erik De Clercq
- Rega Institute for Medical Research, Laboratory of Virology and Chemotherapy , K.U. Leuven , Herestraat 49 Postbus 1043 (09.A097) , B-3000 Leuven , Belgium
| | - Vasanthanathan Poongavanam
- Department of Physics, Chemistry, and Pharmacy , University of Southern Denmark , DK-5230 Odense M , Denmark
| | - Xinyong Liu
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences , Shandong University , 44 West Culture Road , 250012 Ji'nan , Shandong , P. R. China
| | - Peng Zhan
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences , Shandong University , 44 West Culture Road , 250012 Ji'nan , Shandong , P. R. China
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25
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Wang T, Liu XH, Guan J, Ge S, Wu MB, Lin JP, Yang LR. Advancement of multi-target drug discoveries and promising applications in the field of Alzheimer's disease. Eur J Med Chem 2019; 169:200-223. [PMID: 30884327 DOI: 10.1016/j.ejmech.2019.02.076] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Revised: 02/12/2019] [Accepted: 02/28/2019] [Indexed: 12/22/2022]
Abstract
Complex diseases (e.g., Alzheimer's disease) or infectious diseases are usually caused by complicated and varied factors, including environmental and genetic factors. Multi-target (polypharmacology) drugs have been suggested and have emerged as powerful and promising alternative paradigms in modern medicinal chemistry for the development of versatile chemotherapeutic agents to solve these medical challenges. The multifunctional agents capable of modulating multiple biological targets simultaneously display great advantages of higher efficacy, improved safety profile, and simpler administration compared to single-targeted agents. Therefore, multifunctional agents would certainly open novel avenues to rationally design the next generation of more effective but less toxic therapeutic agents. Herein, the authors review the recent progress made in the discovery and design processes of selective multi-targeted agents, especially the successful application of multi-target drugs for the treatment of Alzheimer's disease.
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Affiliation(s)
- Tao Wang
- School of Biological Science, Jining Medical University, Jining, China; Key Laboratory of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, 310027, China.
| | - Xiao-Huan Liu
- School of Biological Science, Jining Medical University, Jining, China
| | - Jing Guan
- School of Biological Science, Jining Medical University, Jining, China
| | - Shun Ge
- School of Biological Science, Jining Medical University, Jining, China.
| | - Mian-Bin Wu
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, 310027, China; Zhejiang Key Laboratory of Antifungal Drugs, Taizhou, 318000, China
| | - Jian-Ping Lin
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Li-Rong Yang
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, 310027, China
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26
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Sohrabi SM, Mohammadi M, Tabatabaiepour SN, Tabatabaiepour SZ, Hosseini-Nave H, Soltani MF, Alizadeh H, Hadizadeh M. A SystematicIn SilicoAnalysis of theLegionellaceaeFamily for Identification of Novel Drug Target Candidates. Microb Drug Resist 2019; 25:157-166. [DOI: 10.1089/mdr.2017.0328] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
| | - Mohsen Mohammadi
- Department of Pharmaceutical Biotechnology, Faculty of Pharmacy, Lorestan University of Medical Sciences, Khorramabad, Iran
| | | | | | - Hossein Hosseini-Nave
- Department of Microbiology and Virology, School of Medicine, Kerman University of Medical Sciences, Kerman, Iran
| | - Mohammad Fazel Soltani
- Molecular Genetics and Genetic Engineering, Department of Crop Production and Plant Breeding, School of Agriculture, Razi University, Kermanshah, Iran
| | - Hosniyeh Alizadeh
- Endocrinology and Metabolism Research Center, Institute of Basic and Clinical Physiology Sciences, Kerman University of Medical Sciences, Kerman, Iran
| | - Morteza Hadizadeh
- Physiology Research Center, Institute of Basic and Clinical Physiology Sciences, Kerman University of Medical Sciences, Kerman, Iran
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27
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Pizzorno A, Terrier O, Nicolas de Lamballerie C, Julien T, Padey B, Traversier A, Roche M, Hamelin ME, Rhéaume C, Croze S, Escuret V, Poissy J, Lina B, Legras-Lachuer C, Textoris J, Boivin G, Rosa-Calatrava M. Repurposing of Drugs as Novel Influenza Inhibitors From Clinical Gene Expression Infection Signatures. Front Immunol 2019; 10:60. [PMID: 30761132 PMCID: PMC6361841 DOI: 10.3389/fimmu.2019.00060] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Accepted: 01/10/2019] [Indexed: 11/13/2022] Open
Abstract
Influenza virus infections remain a major and recurrent public health burden. The intrinsic ever-evolving nature of this virus, the suboptimal efficacy of current influenza inactivated vaccines, as well as the emergence of resistance against a limited antiviral arsenal, highlight the critical need for novel therapeutic approaches. In this context, the aim of this study was to develop and validate an innovative strategy for drug repurposing as host-targeted inhibitors of influenza viruses and the rapid evaluation of the most promising candidates in Phase II clinical trials. We exploited in vivo global transcriptomic signatures of infection directly obtained from a patient cohort to determine a shortlist of already marketed drugs with newly identified, host-targeted inhibitory properties against influenza virus. The antiviral potential of selected repurposing candidates was further evaluated in vitro, in vivo, and ex vivo. Our strategy allowed the selection of a shortlist of 35 high potential candidates out of a rationalized computational screening of 1,309 FDA-approved bioactive molecules, 31 of which were validated for their significant in vitro antiviral activity. Our in vivo and ex vivo results highlight diltiazem, a calcium channel blocker currently used in the treatment of hypertension, as a promising option for the treatment of influenza infections. Additionally, transcriptomic signature analysis further revealed the so far undescribed capacity of diltiazem to modulate the expression of specific genes related to the host antiviral response and cholesterol metabolism. Finally, combination treatment with diltiazem and virus-targeted oseltamivir neuraminidase inhibitor further increased antiviral efficacy, prompting rapid authorization for the initiation of a Phase II clinical trial. This original, host-targeted, drug repurposing strategy constitutes an effective and highly reactive process for the rapid identification of novel anti-infectious drugs, with potential major implications for the management of antimicrobial resistance and the rapid response to future epidemic or pandemic (re)emerging diseases for which we are still disarmed.
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Affiliation(s)
- Andrés Pizzorno
- Virologie et Pathologie Humaine—VirPath Team, Centre International de Recherche en Infectiologie, INSERM U1111, CNRS UMR5308, ENS Lyon, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France
- Research Center in Infectious Diseases of the CHU de Quebec and Laval University, Quebec City, QC, Canada
| | - Olivier Terrier
- Virologie et Pathologie Humaine—VirPath Team, Centre International de Recherche en Infectiologie, INSERM U1111, CNRS UMR5308, ENS Lyon, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France
| | - Claire Nicolas de Lamballerie
- Virologie et Pathologie Humaine—VirPath Team, Centre International de Recherche en Infectiologie, INSERM U1111, CNRS UMR5308, ENS Lyon, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France
- Viroscan3D SAS, Lyon, France
| | - Thomas Julien
- Virologie et Pathologie Humaine—VirPath Team, Centre International de Recherche en Infectiologie, INSERM U1111, CNRS UMR5308, ENS Lyon, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France
- VirNext, Faculté de Médecine RTH Laennec, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France
| | - Blandine Padey
- Virologie et Pathologie Humaine—VirPath Team, Centre International de Recherche en Infectiologie, INSERM U1111, CNRS UMR5308, ENS Lyon, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France
- VirNext, Faculté de Médecine RTH Laennec, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France
| | - Aurélien Traversier
- Virologie et Pathologie Humaine—VirPath Team, Centre International de Recherche en Infectiologie, INSERM U1111, CNRS UMR5308, ENS Lyon, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France
| | | | - Marie-Eve Hamelin
- Research Center in Infectious Diseases of the CHU de Quebec and Laval University, Quebec City, QC, Canada
| | - Chantal Rhéaume
- Research Center in Infectious Diseases of the CHU de Quebec and Laval University, Quebec City, QC, Canada
| | - Séverine Croze
- ProfileXpert, SFR-Est, CNRS UMR-S3453, INSERM US7, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France
| | - Vanessa Escuret
- Virologie et Pathologie Humaine—VirPath Team, Centre International de Recherche en Infectiologie, INSERM U1111, CNRS UMR5308, ENS Lyon, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France
- Laboratoire de Virologie, Centre National de Référence des virus Influenza Sud, Institut des Agents Infectieux, Groupement Hospitalier Nord, Hospices Civils de Lyon, Lyon, France
| | - Julien Poissy
- Pôle de Réanimation, Hôpital Roger Salengro, Centre Hospitalier Régional et Universitaire de Lille, Université de Lille 2, Lille, France
| | - Bruno Lina
- Virologie et Pathologie Humaine—VirPath Team, Centre International de Recherche en Infectiologie, INSERM U1111, CNRS UMR5308, ENS Lyon, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France
- Laboratoire de Virologie, Centre National de Référence des virus Influenza Sud, Institut des Agents Infectieux, Groupement Hospitalier Nord, Hospices Civils de Lyon, Lyon, France
| | - Catherine Legras-Lachuer
- Viroscan3D SAS, Lyon, France
- Ecologie Microbienne, UMR CNRS 5557, USC INRA 1364, Université Claude Bernard Lyon 1, Université de Lyon, Villeurbanne, France
| | - Julien Textoris
- Service d'Anesthésie et de Réanimation, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
- Pathophysiology of Injury-Induced Immunosuppression (PI3), EA 7426 Hospices Civils de Lyon, bioMérieux, Université Claude Bernard Lyon 1, Hôpital Edouard Herriot, Lyon, France
| | - Guy Boivin
- Research Center in Infectious Diseases of the CHU de Quebec and Laval University, Quebec City, QC, Canada
| | - Manuel Rosa-Calatrava
- Virologie et Pathologie Humaine—VirPath Team, Centre International de Recherche en Infectiologie, INSERM U1111, CNRS UMR5308, ENS Lyon, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France
- VirNext, Faculté de Médecine RTH Laennec, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France
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28
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Li Y, Zhu H, Yang J, Ke K, Zhu Y, Chen L, Qu Y, Suo R, Chen X, Zhu Y. Discovering Proangiogenic Drugs in Ischemic Stroke Based on the Relationship between Protein Domain and Drug Substructure. ACS Chem Neurosci 2019; 10:507-517. [PMID: 30346717 DOI: 10.1021/acschemneuro.8b00381] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
As an important protective mechanism against cerebral ischemia, angiogenesis has become a topic of interest in the treatment of ischemic stroke with the challenge that few drugs promote angiogenesis. Previous studies of the identification of drug-target interactions mainly focused on the overall structures of drugs and proteins, which limited the discovery of novel structure drugs. In this article, we proposed a new strategy for discovering proangiogenic drugs based on the assumption that drug-protein interaction is mediated by substructure and domain. First, we identified substructure-domain relationships according to the known drug-protein interactions and established the drug-substructure-domain-protein relationships of genes that are proangiogenic in brain tissue and expressed significantly during ischemic stroke. Then we quantified the intensity of interaction between each drug and each protein. Finally, we obtained 540 interactive relationships between 238 drugs and 54 genes, establishing a drug-gene network with two patterns of independent and complex drug-gene interactions. Both of the patterns facilitated finding not only drugs with the same overall structure but also drugs with a different overall structure based on the same or a similar protein spectrum. In addition, we analyzed the mechanism of action of each predicted drug and extracted drugs with similar mechanisms. In vitro, our results showed that azelnidipine, azilsartan, lercanidipine, nafcillin, and vortioxetine enhanced bEnd.3 cell proliferation, migration, tube formation, and the expression of angiogenic marker PCNA. Azelnidipine, azilsartan, lercanidipine, and nafcillin increased the level of expression of proangiogenic factor VEGF. Unlike previous studies focusing on the overall structures of drugs, our research highlighted local structural similarity, which has great potential in the search for more proangiogenic drugs with novel structures.
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Affiliation(s)
- Yunong Li
- Department of Neurology, Second Affiliated Hospital of Harbin Medical University, Harbin 150001, Heilongjiang, China
| | - Haixia Zhu
- Department of Pharmacology, Harbin Medical University, Harbin 150001, Heilongjiang, China
| | - Jingbo Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150001, Heilongjiang, China
| | - Kehui Ke
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150001, Heilongjiang, China
| | - Yanmei Zhu
- Department of Neurology, Second Affiliated Hospital of Harbin Medical University, Harbin 150001, Heilongjiang, China
| | - Li Chen
- Department of Neurology, Second Affiliated Hospital of Harbin Medical University, Harbin 150001, Heilongjiang, China
| | - Youyang Qu
- Department of Neurology, Second Affiliated Hospital of Harbin Medical University, Harbin 150001, Heilongjiang, China
| | - Rui Suo
- Department of Neurology, Second Affiliated Hospital of Harbin Medical University, Harbin 150001, Heilongjiang, China
| | - Xiujie Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150001, Heilongjiang, China
| | - Yulan Zhu
- Department of Neurology, Second Affiliated Hospital of Harbin Medical University, Harbin 150001, Heilongjiang, China
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29
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Abstract
Drug repurposing is a powerful technique which has been recently employed in both industry and academia to discover and validate previously approved drugs for new indications. It provides the quickest possible transition from bench to bedside. In essence, computational strategies are appealing because they putatively nominate the most encouraging candidate for a given indication. A wide range of computational methods exist for repositioning. In this chapter we present the guidelines for performing integrated drug repurposing strategy by combining disease-disease association and molecular simulation analysis.
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30
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Ehrt C, Brinkjost T, Koch O. A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs). PLoS Comput Biol 2018; 14:e1006483. [PMID: 30408032 PMCID: PMC6224041 DOI: 10.1371/journal.pcbi.1006483] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Accepted: 09/02/2018] [Indexed: 11/24/2022] Open
Abstract
The automated comparison of protein-ligand binding sites provides useful insights into yet unexplored site similarities. Various stages of computational and chemical biology research can benefit from this knowledge. The search for putative off-targets and the establishment of polypharmacological effects by comparing binding sites led to promising results for numerous projects. Although many cavity comparison methods are available, a comprehensive analysis to guide the choice of a tool for a specific application is wanting. Moreover, the broad variety of binding site modeling approaches, comparison algorithms, and scoring metrics impedes this choice. Herein, we aim to elucidate strengths and weaknesses of binding site comparison methodologies. A detailed benchmark study is the only possibility to rationalize the selection of appropriate tools for different scenarios. Specific evaluation data sets were developed to shed light on multiple aspects of binding site comparison. An assembly of all applied benchmark sets (ProSPECCTs–Protein Site Pairs for the Evaluation of Cavity Comparison Tools) is made available for the evaluation and optimization of further and still emerging methods. The results indicate the importance of such analyses to facilitate the choice of a methodology that complies with the requirements of a specific scientific challenge. Binding site similarities are useful in the context of promiscuity prediction, drug repurposing, the analysis of protein-ligand and protein-protein complexes, function prediction, and further fields of general interest in chemical biology and biochemistry. Many years of research have led to the development of a multitude of methods for binding site analysis and comparison. On the one hand, their availability supports research. On the other hand, the huge number of methods hampers the efficient selection of a specific tool. Our research is dedicated to the analysis of different cavity comparison tools. We use several binding site data sets to establish guidelines which can be applied to ensure a successful application of comparison methods by circumventing potential pitfalls.
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Affiliation(s)
- Christiane Ehrt
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Dortmund, Germany
| | - Tobias Brinkjost
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Dortmund, Germany
- Department of Computer Science, TU Dortmund University, Dortmund, Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Dortmund, Germany
- * E-mail: ,
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31
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Wang C, Kurgan L. Review and comparative assessment of similarity-based methods for prediction of drug–protein interactions in the druggable human proteome. Brief Bioinform 2018; 20:2066-2087. [DOI: 10.1093/bib/bby069] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 06/26/2018] [Accepted: 07/10/2018] [Indexed: 12/18/2022] Open
Abstract
AbstractDrug–protein interactions (DPIs) underlie the desired therapeutic actions and the adverse side effects of a significant majority of drugs. Computational prediction of DPIs facilitates research in drug discovery, characterization and repurposing. Similarity-based methods that do not require knowledge of protein structures are particularly suitable for druggable genome-wide predictions of DPIs. We review 35 high-impact similarity-based predictors that were published in the past decade. We group them based on three types of similarities and their combinations that they use. We discuss and compare key aspects of these methods including source databases, internal databases and their predictive models. Using our novel benchmark database, we perform comparative empirical analysis of predictive performance of seven types of representative predictors that utilize each type of similarity individually and all possible combinations of similarities. We assess predictive quality at the database-wide DPI level and we are the first to also include evaluation over individual drugs. Our comprehensive analysis shows that predictors that use more similarity types outperform methods that employ fewer similarities, and that the model combining all three types of similarities secures area under the receiver operating characteristic curve of 0.93. We offer a comprehensive analysis of sensitivity of predictive performance to intrinsic and extrinsic characteristics of the considered predictors. We find that predictive performance is sensitive to low levels of similarities between sequences of the drug targets and several extrinsic properties of the input drug structures, drug profiles and drug targets. The benchmark database and a webserver for the seven predictors are freely available at http://biomine.cs.vcu.edu/servers/CONNECTOR/.
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Affiliation(s)
- Chen Wang
- Computer Science Department, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Lukasz Kurgan
- Computer Science Department, Virginia Commonwealth University, Richmond, VA 23284, USA
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32
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Naderi M, Govindaraj RG, Brylinski M. eModel-BDB: a database of comparative structure models of drug-target interactions from the Binding Database. Gigascience 2018; 7:5057873. [PMID: 30052959 PMCID: PMC6131211 DOI: 10.1093/gigascience/giy091] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Accepted: 07/16/2018] [Indexed: 01/14/2023] Open
Abstract
Background The structural information on proteins in their ligand-bound conformational state is invaluable for protein function studies and rational drug design. Compared to the number of available sequences, not only is the repertoire of the experimentally determined structures of holo-proteins limited, these structures do not always include pharmacologically relevant compounds at their binding sites. In addition, binding affinity databases provide vast quantities of information on interactions between drug-like molecules and their targets, however, often lacking structural data. On that account, there is a need for computational methods to complement existing repositories by constructing the atomic-level models of drug-protein assemblies that will not be determined experimentally in the near future. Results We created eModel-BDB, a database of 200,005 comparative models of drug-bound proteins based on 1,391,403 interaction data obtained from the Binding Database and the PDB library of 31 January 2017. Complex models in eModel-BDB were generated with a collection of the state-of-the-art techniques, including protein meta-threading, template-based structure modeling, refinement and binding site detection, and ligand similarity-based docking. In addition to a rigorous quality control maintained during dataset generation, a subset of weakly homologous models was selected for the retrospective validation against experimental structural data recently deposited to the Protein Data Bank. Validation results indicate that eModel-BDB contains models that are accurate not only at the global protein structure level but also with respect to the atomic details of bound ligands. Conclusions Freely available eModel-BDB can be used to support structure-based drug discovery and repositioning, drug target identification, and protein structure determination.
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Affiliation(s)
- Misagh Naderi
- Department of Biological Sciences, Louisiana State University, 202 Life Sciences Bldg, Baton Rouge, LA 70803, USA
| | - Rajiv Gandhi Govindaraj
- Department of Biological Sciences, Louisiana State University, 202 Life Sciences Bldg, Baton Rouge, LA 70803, USA
| | - Michal Brylinski
- Department of Biological Sciences, Louisiana State University, 202 Life Sciences Bldg, Baton Rouge, LA 70803, USA.,Center for Computation & Technology, Louisiana State University, 2054 Digital Media Center, Baton Rouge, LA 70803, USA
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33
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Pizzorno A, Dubois J, Machado D, Cartet G, Traversier A, Julien T, Lina B, Bourdon JC, Rosa-Calatrava M, Terrier O. Influenza A viruses alter the stability and antiviral contribution of host E3-ubiquitin ligase Mdm2 during the time-course of infection. Sci Rep 2018; 8:3746. [PMID: 29487367 PMCID: PMC5829072 DOI: 10.1038/s41598-018-22139-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Accepted: 02/19/2018] [Indexed: 11/09/2022] Open
Abstract
The interplay between influenza A viruses (IAV) and the p53 pathway has been reported in several studies, highlighting the antiviral contribution of p53. Here, we investigated the impact of IAV on the E3-ubiquitin ligase Mdm2, a major regulator of p53, and observed that IAV targets Mdm2, notably via its non-structural protein (NS1), therefore altering Mdm2 stability, p53/Mdm2 interaction and regulatory loop during the time-course of infection. This study also highlights a new antiviral facet of Mdm2 possibly increasing the list of its many p53-independent functions. Altogether, our work contributes to better understand the mechanisms underlining the complex interactions between IAV and the p53 pathway, for which both NS1 and Mdm2 arise as key players.
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Affiliation(s)
- Andrés Pizzorno
- Virologie et Pathologie Humaine-VirPath team, Centre International de Recherche en Infectiologie (CIRI), INSERM U1111, ENS Lyon, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, CNRS UMR5308, France
| | - Julia Dubois
- Virologie et Pathologie Humaine-VirPath team, Centre International de Recherche en Infectiologie (CIRI), INSERM U1111, ENS Lyon, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, CNRS UMR5308, France
| | - Daniela Machado
- Virologie et Pathologie Humaine-VirPath team, Centre International de Recherche en Infectiologie (CIRI), INSERM U1111, ENS Lyon, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, CNRS UMR5308, France
- Laboratoire des Pathogènes Emergents, Fondation Mérieux. CIRI, UCBL1- INSERM U1111, ENS Lyon, CNRS UMR5308, Lyon, France
| | - Gaëlle Cartet
- Virologie et Pathologie Humaine-VirPath team, Centre International de Recherche en Infectiologie (CIRI), INSERM U1111, ENS Lyon, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, CNRS UMR5308, France
| | - Aurelien Traversier
- Virologie et Pathologie Humaine-VirPath team, Centre International de Recherche en Infectiologie (CIRI), INSERM U1111, ENS Lyon, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, CNRS UMR5308, France
| | - Thomas Julien
- Virologie et Pathologie Humaine-VirPath team, Centre International de Recherche en Infectiologie (CIRI), INSERM U1111, ENS Lyon, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, CNRS UMR5308, France
| | - Bruno Lina
- Virologie et Pathologie Humaine-VirPath team, Centre International de Recherche en Infectiologie (CIRI), INSERM U1111, ENS Lyon, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, CNRS UMR5308, France
- Laboratoire de Virologie, Centre National de Référence des virus Influenza, Institut des Agents Infectieux, Groupement Hospitalier Nord, Hospices Civils de Lyon, Lyon, France
| | - Jean-Christophe Bourdon
- Division of Cancer Research, University of Dundee, Ninewells Hospital and Medical School, Dundee, United Kingdom
| | - Manuel Rosa-Calatrava
- Virologie et Pathologie Humaine-VirPath team, Centre International de Recherche en Infectiologie (CIRI), INSERM U1111, ENS Lyon, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, CNRS UMR5308, France
| | - Olivier Terrier
- Virologie et Pathologie Humaine-VirPath team, Centre International de Recherche en Infectiologie (CIRI), INSERM U1111, ENS Lyon, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, CNRS UMR5308, France.
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34
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Talevi A. Drug repositioning: current approaches and their implications in the precision medicine era. EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT 2018. [DOI: 10.1080/23808993.2018.1424535] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Alan Talevi
- Laboratory of Research and Development of Bioactive Compounds – Medicinal Chemistry, Department of Biological Sciences, Faculty of Exact Sciences, University of La Plata, La Plata, Argentina
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35
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Raškevičius V, Mikalayeva V, Antanavičiūtė I, Ceslevičienė I, Skeberdis VA, Kairys V, Bordel S. Genome scale metabolic models as tools for drug design and personalized medicine. PLoS One 2018; 13:e0190636. [PMID: 29304175 PMCID: PMC5755790 DOI: 10.1371/journal.pone.0190636] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Accepted: 12/18/2017] [Indexed: 01/03/2023] Open
Abstract
In this work we aim to show how Genome Scale Metabolic Models (GSMMs) can be used as tools for drug design. By comparing the chemical structures of human metabolites (obtained using their KEGG indexes) and the compounds contained in the DrugBank database, we have observed that compounds showing Tanimoto scores higher than 0.9 with a metabolite, are 29.5 times more likely to bind the enzymes metabolizing the considered metabolite, than ligands chosen randomly. By using RNA-seq data to constrain a human GSMM it is possible to obtain an estimation of its distribution of metabolic fluxes and to quantify the effects of restraining the rate of chosen metabolic reactions (for example using a drug that inhibits the enzymes catalyzing the mentioned reactions). This method allowed us to predict the differential effects of lipoamide analogs on the proliferation of MCF7 (a breast cancer cell line) and ASM (airway smooth muscle) cells respectively. These differential effects were confirmed experimentally, which provides a proof of concept of how human GSMMs could be used to find therapeutic windows against cancer. By using RNA-seq data of 34 different cancer cell lines and 26 healthy tissues, we assessed the putative anticancer effects of the compounds in DrugBank which are structurally similar to human metabolites. Among other results it was predicted that the mevalonate pathway might constitute a good therapeutic window against cancer proliferation, due to the fact that most cancer cell lines do not express the cholesterol transporter NPC1L1 and the lipoprotein lipase LPL, which makes them rely on the mevalonate pathway to obtain cholesterol.
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Affiliation(s)
- Vytautas Raškevičius
- Institute of Cardiology, Lithuanian University of Health Sciences, Kaunas, Lithuania
- Department of Bioinformatics, Institute of Biotechnology, Vilnius University, Vilnius, Lithuania
- * E-mail:
| | - Valeryia Mikalayeva
- Institute of Cardiology, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Ieva Antanavičiūtė
- Institute of Cardiology, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Ieva Ceslevičienė
- Institute of Cardiology, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | | | - Visvaldas Kairys
- Department of Bioinformatics, Institute of Biotechnology, Vilnius University, Vilnius, Lithuania
| | - Sergio Bordel
- Institute of Cardiology, Lithuanian University of Health Sciences, Kaunas, Lithuania
- Department Chemical Engineering and Environmental Technology, University of Valladolid, Valladolid, Spain
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36
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Kumar AP, Nguyen MN, Verma C, Lukman S. Structural analysis of protein tyrosine phosphatase 1B reveals potentially druggable allosteric binding sites. Proteins 2018; 86:301-321. [PMID: 29235148 DOI: 10.1002/prot.25440] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Revised: 11/16/2017] [Accepted: 12/10/2017] [Indexed: 12/11/2022]
Abstract
Catalytic proteins such as human protein tyrosine phosphatase 1B (PTP1B), with conserved and highly polar active sites, warrant the discovery of druggable nonactive sites, such as allosteric sites, and potentially, therapeutic small molecules that can bind to these sites. Catalyzing the dephosphorylation of numerous substrates, PTP1B is physiologically important in intracellular signal transduction pathways in diverse cell types and tissues. Aberrant PTP1B is associated with obesity, diabetes, cancers, and neurodegenerative disorders. Utilizing clustering methods (based on root mean square deviation, principal component analysis, nonnegative matrix factorization, and independent component analysis), we have examined multiple PTP1B structures. Using the resulting representative structures in different conformational states, we determined consensus clustroids and used them to identify both known and novel binding sites, some of which are potentially allosteric. We report several lead compounds that could potentially bind to the novel PTP1B binding sites and can be further optimized. Considering the possibility for drug repurposing, we discovered homologous binding sites in other proteins, with ligands that could potentially bind to the novel PTP1B binding sites.
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Affiliation(s)
- Ammu Prasanna Kumar
- Department of Chemistry, College of Arts and Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Minh N Nguyen
- Bioinformatics Institute, Agency for Science, Technology and Research, Singapore
| | - Chandra Verma
- Bioinformatics Institute, Agency for Science, Technology and Research, Singapore.,Department of Biological Sciences, National University of Singapore, Singapore.,School of Biological Sciences, Nanyang Technological University, Singapore
| | - Suryani Lukman
- Department of Chemistry, College of Arts and Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
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37
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Ravikumar B, Aittokallio T. Improving the efficacy-safety balance of polypharmacology in multi-target drug discovery. Expert Opin Drug Discov 2017; 13:179-192. [DOI: 10.1080/17460441.2018.1413089] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Affiliation(s)
- Balaguru Ravikumar
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
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38
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Mello JDFRE, Gomes RA, Vital-Fujii DG, Ferreira GM, Trossini GHG. Fragment-based drug discovery as alternative strategy to the drug development for neglected diseases. Chem Biol Drug Des 2017; 90:1067-1078. [PMID: 28547936 DOI: 10.1111/cbdd.13030] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2017] [Revised: 04/12/2017] [Accepted: 05/08/2017] [Indexed: 12/24/2022]
Abstract
Neglected diseases (NDs) affect large populations and almost whole continents, representing 12% of the global health burden. In contrast, the treatment available today is limited and sometimes ineffective. Under this scenery, the Fragment-Based Drug Discovery emerged as one of the most promising alternatives to the traditional methods of drug development. This method allows achieving new lead compounds with smaller size of fragment libraries. Even with the wide Fragment-Based Drug Discovery success resulting in new effective therapeutic agents against different diseases, until this moment few studies have been applied this approach for NDs area. In this article, we discuss the basic Fragment-Based Drug Discovery process, brief successful ideas of general applications and show a landscape of its use in NDs, encouraging the implementation of this strategy as an interesting way to optimize the development of new drugs to NDs.
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Affiliation(s)
- Juliana da Fonseca Rezende E Mello
- Litec, Laboratório de Integração Entre Técnicas Computacionais e Experimentais no Planejamento de Fármacos, Departamento de Farmácia, Faculdade de Ciências Farmacêuticas, Universidade de São Paulo, São Paulo, Brazil
| | - Renan Augusto Gomes
- Litec, Laboratório de Integração Entre Técnicas Computacionais e Experimentais no Planejamento de Fármacos, Departamento de Farmácia, Faculdade de Ciências Farmacêuticas, Universidade de São Paulo, São Paulo, Brazil
| | - Drielli Gomes Vital-Fujii
- Litec, Laboratório de Integração Entre Técnicas Computacionais e Experimentais no Planejamento de Fármacos, Departamento de Farmácia, Faculdade de Ciências Farmacêuticas, Universidade de São Paulo, São Paulo, Brazil
| | - Glaucio Monteiro Ferreira
- Litec, Laboratório de Integração Entre Técnicas Computacionais e Experimentais no Planejamento de Fármacos, Departamento de Farmácia, Faculdade de Ciências Farmacêuticas, Universidade de São Paulo, São Paulo, Brazil.,Programa de Pós-graduação em Toxicologia e Análises Toxicológicas, Faculdade de Ciências Farmacêuticas, Universidade de São Paulo, São Paulo, Brazil
| | - Gustavo Henrique Goulart Trossini
- Litec, Laboratório de Integração Entre Técnicas Computacionais e Experimentais no Planejamento de Fármacos, Departamento de Farmácia, Faculdade de Ciências Farmacêuticas, Universidade de São Paulo, São Paulo, Brazil.,Programa de Pós-graduação em Toxicologia e Análises Toxicológicas, Faculdade de Ciências Farmacêuticas, Universidade de São Paulo, São Paulo, Brazil
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39
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Duran-Frigola M, Siragusa L, Ruppin E, Barril X, Cruciani G, Aloy P. Detecting similar binding pockets to enable systems polypharmacology. PLoS Comput Biol 2017; 13:e1005522. [PMID: 28662117 PMCID: PMC5490940 DOI: 10.1371/journal.pcbi.1005522] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2016] [Accepted: 04/15/2017] [Indexed: 01/19/2023] Open
Abstract
In the era of systems biology, multi-target pharmacological strategies hold promise for tackling disease-related networks. In this regard, drug promiscuity may be leveraged to interfere with multiple receptors: the so-called polypharmacology of drugs can be anticipated by analyzing the similarity of binding sites across the proteome. Here, we perform a pairwise comparison of 90,000 putative binding pockets detected in 3,700 proteins, and find that 23,000 pairs of proteins have at least one similar cavity that could, in principle, accommodate similar ligands. By inspecting these pairs, we demonstrate how the detection of similar binding sites expands the space of opportunities for the rational design of drug polypharmacology. Finally, we illustrate how to leverage these opportunities in protein-protein interaction networks related to several therapeutic classes and tumor types, and in a genome-scale metabolic model of leukemia. Traditionally, the fact that most drugs are promiscuous binders has been a major concern in pharmacology, due to the occurrence of undesired off-target clinical events. In the recent years, however, the realization that many diseases are the result of complex biological processes has encouraged rethinking of drug promiscuity as a promising feature, since it is sometimes necessary to interfere with multiple receptors in order to overcome the robustness of disease-related networks. One way to identify groups of proteins that could be targeted simultaneously is to look for similar binding sites. We have massively done so for all human proteins with a known high-resolution three-dimensional structure, unveiling a vast space of ‘polypharmacology’ opportunities. Of these, we know, a great majority is not of therapeutic interest. To pinpoint promising multi-target combinations, we advocate for the use of computational tools that are able to rapidly simulate the effect of drug-target interactions on biological networks.
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Affiliation(s)
- Miquel Duran-Frigola
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | | | - Eytan Ruppin
- Department of Computer Science & Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, United States of America
- School of Computer Sciences, Tel Aviv University, Tel Aviv, Israel
- Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Xavier Barril
- Departament de Fisicoquímica, Facultat de Farmàcia, Universitat de Barcelona, Barcelona, Catalonia, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain
| | - Gabriele Cruciani
- Molecular Discovery Limited, London, United Kingdom
- Department of Chemistry, Biology and Biotechnology, University of Perugia, Perugia, Italy
| | - Patrick Aloy
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain
- * E-mail:
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40
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Pagano N, Teriete P, Mattmann ME, Yang L, Snyder BA, Cai Z, Heil ML, Cosford NDP. An integrated chemical biology approach reveals the mechanism of action of HIV replication inhibitors. Bioorg Med Chem 2017; 25:6248-6265. [PMID: 28442262 DOI: 10.1016/j.bmc.2017.03.061] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2016] [Revised: 03/25/2017] [Accepted: 03/29/2017] [Indexed: 10/19/2022]
Abstract
Continuous flow (microfluidic) chemistry was employed to prepare a small focused library of dihydropyrimidinone (DHPM) derivatives. Compounds in this class have been reported to exhibit activity against the human immunodeficiency virus (HIV), but their molecular target had not been identified. We tested the initial set of DHPMs in phenotypic assays providing a hit (1i) that inhibited the replication of the human immunodeficiency virus HIV in cells. Flow chemistry-driven optimization of 1i led to the identification of HIV replication inhibitors such as 1l with cellular potency comparable with the clinical drug nevirapine (NVP). Mechanism of action (MOA) studies using cellular and biochemical assays coupled with 3D fingerprinting and in silico modeling demonstrated that these drug-like probe compounds exert their effects by inhibiting the viral reverse transcriptase polymerase (RT). This led to the design and synthesis of the novel DHPM 1at that inhibits the replication of drug resistant strains of HIV. Our work demonstrates that combining flow chemistry-driven analogue refinement with phenotypic assays, in silico modeling and MOA studies is a highly effective strategy for hit-to-lead optimization applicable to the discovery of future therapeutic agents.
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Affiliation(s)
- Nicholas Pagano
- Cancer Metabolism & Signaling Networks Program, Sanford Burnham Prebys Medical Discovery Institute, 10901 N. Torrey Pines Rd., La Jolla, CA 92037, United States
| | - Peter Teriete
- Cancer Metabolism & Signaling Networks Program, Sanford Burnham Prebys Medical Discovery Institute, 10901 N. Torrey Pines Rd., La Jolla, CA 92037, United States
| | - Margrith E Mattmann
- Cancer Metabolism & Signaling Networks Program, Sanford Burnham Prebys Medical Discovery Institute, 10901 N. Torrey Pines Rd., La Jolla, CA 92037, United States
| | - Li Yang
- Cancer Metabolism & Signaling Networks Program, Sanford Burnham Prebys Medical Discovery Institute, 10901 N. Torrey Pines Rd., La Jolla, CA 92037, United States
| | - Beth A Snyder
- Southern Research Institute, Drug Development Division, 431 Aviation Way, Frederick, MD 21701, United States
| | - Zhaohui Cai
- Southern Research Institute, Drug Development Division, 431 Aviation Way, Frederick, MD 21701, United States
| | - Marintha L Heil
- Southern Research Institute, Drug Development Division, 431 Aviation Way, Frederick, MD 21701, United States
| | - Nicholas D P Cosford
- Cancer Metabolism & Signaling Networks Program, Sanford Burnham Prebys Medical Discovery Institute, 10901 N. Torrey Pines Rd., La Jolla, CA 92037, United States.
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41
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A structure- and chemical genomics-based approach for repositioning of drugs against VCP/p97 ATPase. Sci Rep 2017; 7:44912. [PMID: 28322292 PMCID: PMC5359624 DOI: 10.1038/srep44912] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2016] [Accepted: 02/14/2017] [Indexed: 12/31/2022] Open
Abstract
Valosin-containing protein (VCP/p97) ATPase (a.k.a. Cdc48) is a key member of the ER-associated protein degradation (ERAD) pathway. ERAD and VCP/p97 have been implicated in a multitude of human diseases, such as neurodegenerative diseases and cancer. Inhibition of VCP/p97 induces proteotoxic ER stress and cell death in cancer cells, making it an attractive target for cancer treatment. However, no drugs exist against this protein in the market. Repositioning of drugs towards new indications is an attractive alternative to the de novo drug development due to the potential for significantly shorter time to clinical translation. Here, we employed an integrative strategy for the repositioning of drugs as novel inhibitors of the VCP/p97 ATPase. We integrated structure-based virtual screening with the chemical genomics analysis of drug molecular signatures, and identified several candidate inhibitors of VCP/p97 ATPase. Importantly, experimental validation with cell-based and in vitro ATPase assays confirmed three (ebastine, astemizole and clotrimazole) out of seven tested candidates (~40% true hit rate) as direct inhibitors of VCP/p97 and ERAD. This study introduces an effective integrative strategy for drug repositioning, and identified new drugs against the VCP/p97/ERAD pathway in human diseases.
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42
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Lukman S, Nguyen MN, Sim K, Teo JCM. Discovery of Rab1 binding sites using an ensemble of clustering methods. Proteins 2017; 85:859-871. [PMID: 28120477 DOI: 10.1002/prot.25254] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2016] [Revised: 12/28/2016] [Accepted: 01/19/2017] [Indexed: 12/29/2022]
Abstract
Targeting non-native-ligand binding sites for potential investigative and therapeutic applications is an attractive strategy in proteins that share common native ligands, as in Rab1 protein. Rab1 is a subfamily member of Rab proteins, which are members of Ras GTPase superfamily. All Ras GTPase superfamily members bind to native ligands GTP and GDP, that switch on and off the proteins, respectively. Rab1 is physiologically essential for autophagy and transport between endoplasmic reticulum and Golgi apparatus. Pathologically, Rab1 is implicated in human cancers, a neurodegenerative disease, cardiomyopathy, and bacteria-caused infectious diseases. We have performed structural analyses on Rab1 protein using a unique ensemble of clustering methods, including multi-step principal component analysis, non-negative matrix factorization, and independent component analysis, to better identify representative Rab1 proteins than the application of a single clustering method alone does. We then used the identified representative Rab1 structures, resolved in multiple ligand states, to map their known and novel binding sites. We report here at least a novel binding site on Rab1, involving Rab1-specific residues that could be further explored for the rational design and development of investigative probes and/or therapeutic small molecules against the Rab1 protein. Proteins 2017; 85:859-871. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Suryani Lukman
- Khalifa University, Abu Dhabi Campus, PO Box, 127788, Abu Dhabi, United Arab Emirates
| | - Minh N Nguyen
- Bioinformatics Institute, Agency for Science, Technology and Research, 30 Biopolis Street, #07-01 Matrix, Singapore, 138671, Singapore
| | - Kelvin Sim
- OneAnalytix Pte Ltd, Onn Wah Building #04-01, 11 Changi South Lane Singapore, 486154, Singapore
| | - Jeremy C M Teo
- Khalifa University, Abu Dhabi Campus, PO Box, 127788, Abu Dhabi, United Arab Emirates
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43
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ProBiS tools (algorithm, database, and web servers) for predicting and modeling of biologically interesting proteins. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2017; 128:24-32. [PMID: 28212856 DOI: 10.1016/j.pbiomolbio.2017.02.005] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2016] [Revised: 12/14/2016] [Accepted: 02/07/2017] [Indexed: 01/30/2023]
Abstract
ProBiS (Protein Binding Sites) Tools consist of algorithm, database, and web servers for prediction of binding sites and protein ligands based on the detection of structurally similar binding sites in the Protein Data Bank. In this article, we review the operations that ProBiS Tools perform, provide comments on the evolution of the tools, and give some implementation details. We review some of its applications to biologically interesting proteins. ProBiS Tools are freely available at http://probis.cmm.ki.si and http://probis.nih.gov.
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44
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Hao M, Bryant SH, Wang Y. Predicting drug-target interactions by dual-network integrated logistic matrix factorization. Sci Rep 2017; 7:40376. [PMID: 28079135 PMCID: PMC5227688 DOI: 10.1038/srep40376] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Accepted: 12/05/2016] [Indexed: 12/20/2022] Open
Abstract
In this work, we propose a dual-network integrated logistic matrix factorization (DNILMF) algorithm to predict potential drug-target interactions (DTI). The prediction procedure consists of four steps: (1) inferring new drug/target profiles and constructing profile kernel matrix; (2) diffusing drug profile kernel matrix with drug structure kernel matrix; (3) diffusing target profile kernel matrix with target sequence kernel matrix; and (4) building DNILMF model and smoothing new drug/target predictions based on their neighbors. We compare our algorithm with the state-of-the-art method based on the benchmark dataset. Results indicate that the DNILMF algorithm outperforms the previously reported approaches in terms of AUPR (area under precision-recall curve) and AUC (area under curve of receiver operating characteristic) based on the 5 trials of 10-fold cross-validation. We conclude that the performance improvement depends on not only the proposed objective function, but also the used nonlinear diffusion technique which is important but under studied in the DTI prediction field. In addition, we also compile a new DTI dataset for increasing the diversity of currently available benchmark datasets. The top prediction results for the new dataset are confirmed by experimental studies or supported by other computational research.
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Affiliation(s)
- Ming Hao
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Stephen H Bryant
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Yanli Wang
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
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45
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Karuppasamy R, Verma K, Sequeira VM, Basavanna LN, Veerappapillai S. An Integrative Drug Repurposing Pipeline: Switching Viral Drugs to Breast Cancer. J Cell Biochem 2017; 118:1412-1422. [PMID: 27859674 DOI: 10.1002/jcb.25799] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Accepted: 11/15/2016] [Indexed: 11/07/2022]
Abstract
The growing incidence rate of breast cancer, coupled with cellular chemotherapeutic resistance, has made this disease one of the most prevalent cancers among women worldwide. Despite the recent efforts to understand the underlying cause of the resistance due to mutation, there are no feasible tactics to overcome this bottleneck. This issue could be addressed by the concept of polypharmacology-disguising drugs present in the pharmacopeia for novel purposes (drug repurposing). Of note, we have proposed a multi-modal computational drug-repositioning stratagem to predict drugs possessing anti-proliferative effect. Our results suggest that Ombitasvir, a Hepatitis C NS5B polymerase inhibitor, could be "repurposed" for the control and prevention of beta-tubulin-driven breast cancers. J. Cell. Biochem. 118: 1412-1422, 2017. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Ramanathan Karuppasamy
- Department of Biotechnology, School of Bio Sciences and Technology, VIT University, Vellore, 632014, Tamil Nadu, India
| | - Kanika Verma
- Department of Biotechnology, School of Bio Sciences and Technology, VIT University, Vellore, 632014, Tamil Nadu, India
| | - Velin Marita Sequeira
- Department of Biotechnology, School of Bio Sciences and Technology, VIT University, Vellore, 632014, Tamil Nadu, India
| | - Lokapriya Nandan Basavanna
- Department of Biotechnology, School of Bio Sciences and Technology, VIT University, Vellore, 632014, Tamil Nadu, India
| | - Shanthi Veerappapillai
- Department of Biotechnology, School of Bio Sciences and Technology, VIT University, Vellore, 632014, Tamil Nadu, India
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46
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Xin M, Fan J, Liu M, Jiang Z. Exploration and analysis of drug modes of action through feature integration. MOLECULAR BIOSYSTEMS 2017; 13:425-431. [DOI: 10.1039/c6mb00635c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Identifying drug modes of action (MoA) is of paramount importance for having a good grasp of drug indications in clinical tests.
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Affiliation(s)
- Mingyuan Xin
- Shanghai Key Laboratory of Regulatory Biology
- Institute of Biomedical Sciences and School of Life Sciences
- East China Normal University
- Shanghai 200241
- China
| | - Jun Fan
- Shanghai Key Laboratory of Multidimensional Information Processing
- Department of Computer Science and Technology
- East China Normal University
- Shanghai 200241
- China
| | - Mingyao Liu
- Shanghai Key Laboratory of Regulatory Biology
- Institute of Biomedical Sciences and School of Life Sciences
- East China Normal University
- Shanghai 200241
- China
| | - Zhenran Jiang
- Shanghai Key Laboratory of Multidimensional Information Processing
- Department of Computer Science and Technology
- East China Normal University
- Shanghai 200241
- China
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47
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Yao H, Liu J, Xu S, Zhu Z, Xu J. The structural modification of natural products for novel drug discovery. Expert Opin Drug Discov 2016; 12:121-140. [DOI: 10.1080/17460441.2016.1272757] [Citation(s) in RCA: 96] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Hong Yao
- State Key Laboratory of Natural Medicines and Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing, P. R. China
| | - Junkai Liu
- State Key Laboratory of Natural Medicines and Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing, P. R. China
| | - Shengtao Xu
- State Key Laboratory of Natural Medicines and Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing, P. R. China
| | - Zheying Zhu
- Division of Molecular Therapeutics & Formulation, School of Pharmacy, The University of Nottingham, Nottingham, UK
| | - Jinyi Xu
- State Key Laboratory of Natural Medicines and Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing, P. R. China
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48
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Li Y, Guo B, Xu Z, Li B, Cai T, Zhang X, Yu Y, Wang H, Shi J, Zhu W. Repositioning organohalogen drugs: a case study for identification of potent B-Raf V600E inhibitors via docking and bioassay. Sci Rep 2016; 6:31074. [PMID: 27501852 PMCID: PMC4977465 DOI: 10.1038/srep31074] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2016] [Accepted: 07/14/2016] [Indexed: 11/09/2022] Open
Abstract
Drug repositioning has been attracting increasingly attention for its advantages of reducing costs and risks. Statistics showed that around one quarter of the marketed drugs are organohalogens. However, no study has been reported, to the best of our knowledge, to aim at efficiently repositioning organohalogen drugs, which may be attributed to the lack of accurate halogen bonding scoring function. Here, we present a study to show that two organohalogen drugs were successfully repositioned as potent B-Raf V600E inhibitors via molecular docking with halogen bonding scoring function, namely D(3)DOCKxb developed in our lab, and bioassay. After virtual screening by D(3)DOCKxb against the database CMC (Comprehensive Medicinal Chemistry), 3 organohalogen drugs that were predicted to form strong halogen bonding with B-Raf V600E were purchased and tested with ELISA-based assay. In the end, 2 of them, rafoxanide and closantel, were identified as potent inhibitors with IC50 values of 0.07 μM and 1.90 μM, respectively, which are comparable to that of vemurafenib (IC50: 0.17 μM), a marketed drug targeting B-Raf V600E. Single point mutagenesis experiments confirmed the conformations predicted by D(3)DOCKxb. And comparison experiment revealed that halogen bonding scoring function is essential for repositioning those drugs with heavy halogen atoms in their molecular structures.
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Affiliation(s)
- Yisu Li
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
- Nano Science and Technology Institute, University of Science and Technology of China, Suzhou, Jiangsu, 215123, China
| | - Binbin Guo
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Zhijian Xu
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Bo Li
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Tingting Cai
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Xinben Zhang
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Yuqi Yu
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Heyao Wang
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Jiye Shi
- UCB Biopharma SPRL, Chemin du Foriest, Braine-l’Alleud, Belgium
| | - Weiliang Zhu
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
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Ehrt C, Brinkjost T, Koch O. Impact of Binding Site Comparisons on Medicinal Chemistry and Rational Molecular Design. J Med Chem 2016; 59:4121-51. [PMID: 27046190 DOI: 10.1021/acs.jmedchem.6b00078] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Modern rational drug design not only deals with the search for ligands binding to interesting and promising validated targets but also aims to identify the function and ligands of yet uncharacterized proteins having impact on different diseases. Additionally, it contributes to the design of inhibitors with distinct selectivity patterns and the prediction of possible off-target effects. The identification of similarities between binding sites of various proteins is a useful approach to cope with those challenges. The main scope of this perspective is to describe applications of different protein binding site comparison approaches to outline their applicability and impact on molecular design. The article deals with various substantial application domains and provides some outstanding examples to show how various binding site comparison methods can be applied to promote in silico drug design workflows. In addition, we will also briefly introduce the fundamental principles of different protein binding site comparison methods.
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Affiliation(s)
- Christiane Ehrt
- Faculty of Chemistry and Chemical Biology, TU Dortmund University , Otto-Hahn-Straße 6, 44227 Dortmund, Germany
| | - Tobias Brinkjost
- Faculty of Chemistry and Chemical Biology, TU Dortmund University , Otto-Hahn-Straße 6, 44227 Dortmund, Germany.,Department of Computer Science, TU Dortmund University , Otto-Hahn-Straße 14, 44224 Dortmund, Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology, TU Dortmund University , Otto-Hahn-Straße 6, 44227 Dortmund, Germany
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50
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Berenstein AJ, Magariños MP, Chernomoretz A, Agüero F. A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases. PLoS Negl Trop Dis 2016; 10:e0004300. [PMID: 26735851 PMCID: PMC4703370 DOI: 10.1371/journal.pntd.0004300] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2015] [Accepted: 11/21/2015] [Indexed: 12/16/2022] Open
Abstract
Drug development for neglected diseases has been historically hampered due to lack of market incentives. The advent of public domain resources containing chemical information from high throughput screenings is changing the landscape of drug discovery for these diseases. In this work we took advantage of data from extensively studied organisms like human, mouse, E. coli and yeast, among others, to develop a novel integrative network model to prioritize and identify candidate drug targets in neglected pathogen proteomes, and bioactive drug-like molecules. We modeled genomic (proteins) and chemical (bioactive compounds) data as a multilayer weighted network graph that takes advantage of bioactivity data across 221 species, chemical similarities between 1.7 105 compounds and several functional relations among 1.67 105 proteins. These relations comprised orthology, sharing of protein domains, and shared participation in defined biochemical pathways. We showcase the application of this network graph to the problem of prioritization of new candidate targets, based on the information available in the graph for known compound-target associations. We validated this strategy by performing a cross validation procedure for known mouse and Trypanosoma cruzi targets and showed that our approach outperforms classic alignment-based approaches. Moreover, our model provides additional flexibility as two different network definitions could be considered, finding in both cases qualitatively different but sensible candidate targets. We also showcase the application of the network to suggest targets for orphan compounds that are active against Plasmodium falciparum in high-throughput screens. In this case our approach provided a reduced prioritization list of target proteins for the query molecules and showed the ability to propose new testable hypotheses for each compound. Moreover, we found that some predictions highlighted by our network model were supported by independent experimental validations as found post-facto in the literature. Neglected tropical diseases are human infectious diseases that are often associated with poverty. Historically, lack of interest from the pharmaceutical industry resulted in the lack of good drugs to combat the majority of the pathogens that cause these diseases. Recently, the availability of open chemical information has increased with the advent of public domain chemical resources and the release of data from high throughput screening assays. Our aim in this work was to make use of data from extensively studied organisms like human, mouse, E. coli and yeast, among others, to prioritize and identify candidate drug targets in neglected pathogen proteomes, and drug-like bioactive molecules to foster drug development against neglected diseases. Our approach to the problem relied on applying bioinformatics and computational biology strategies to model large datasets spanning complete proteomes and extensive chemical information from publicly available sources. As a result, we were able to prioritize drug targets and identify potential targets for orphan bioactive drugs.
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Affiliation(s)
- Ariel José Berenstein
- Laboratorio de Bioinformática, Fundación Instituto Leloir, Buenos Aires, Argentina
- Departamento de Física, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - María Paula Magariños
- Laboratorio de Genómica y Bioinformática, Instituto de Investigaciones Biotecnológicas–Instituto Tecnológico de Chascomús, Universidad de San Martín–CONICET, Sede San Martín, San Martín, Buenos Aires, Argentina
| | - Ariel Chernomoretz
- Laboratorio de Bioinformática, Fundación Instituto Leloir, Buenos Aires, Argentina
- Departamento de Física, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Fernán Agüero
- Laboratorio de Genómica y Bioinformática, Instituto de Investigaciones Biotecnológicas–Instituto Tecnológico de Chascomús, Universidad de San Martín–CONICET, Sede San Martín, San Martín, Buenos Aires, Argentina
- * E-mail: ,
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