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Napolitano G, Has C, Schwerk A, Yuan JH, Ullrich C. Potential of Artificial Intelligence to Accelerate Drug Development for Rare Diseases. Pharmaceut Med 2024; 38:79-86. [PMID: 38315404 DOI: 10.1007/s40290-023-00504-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/16/2023] [Indexed: 02/07/2024]
Abstract
The growth in breadth and depth of artificial intelligence (AI) applications has been fast, running hand in hand with the increasing amount of digital data available. Here, we comment on the application of AI in the field of drug development, with a strong focus on the specific achievements and challenges posed by rare diseases. Data paucity and high costs make drug development for rare diseases especially hard. AI can enable otherwise inaccessible approaches based on the large-scale integration of heterogeneous datasets and knowledge bases, guided by expert biological understanding. Obstacles still exist for the routine use of AI in the usually conservative pharmaceutical domain, which can easily become disillusioned. It is crucial to acknowledge that AI is a powerful, supportive tool that can assist but not replace human expertise in the various phases and aspects of drug discovery and development.
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Affiliation(s)
| | - Canan Has
- Centogene GmbH, Alboinstraße 36-42, 12103, Berlin, Germany
| | - Anne Schwerk
- Beriln Institute of Health Center for Regenerative Therapies (BCRT), Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Jui-Hung Yuan
- Beriln Institute of Health Center for Regenerative Therapies (BCRT), Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Carsten Ullrich
- Beriln Institute of Health Center for Regenerative Therapies (BCRT), Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Berlin, Germany
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Cheohen CFDAR, Esteves MEA, da Fonseca TS, Leal CM, Assis FDLF, Campos MF, Rebelo RS, Allonso D, Leitão GG, da Silva ML, Leitão SG. In silico screening of phenylethanoid glycosides, a class of pharmacologically active compounds as natural inhibitors of SARS-CoV-2 proteases. Comput Struct Biotechnol J 2023; 21:1461-1472. [PMID: 36817956 PMCID: PMC9920770 DOI: 10.1016/j.csbj.2023.02.020] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 02/09/2023] [Accepted: 02/09/2023] [Indexed: 02/13/2023] Open
Abstract
Since the advent of Covid-19, several natural products have been investigated regarding their in silico interactions with SARS-CoV-2 proteases - 3CLpro and PLpro, two of the most important pharmacological targets for antiviral development. Phenylethanoid glycosides (PG) are a class of natural products present in important medicinal plants and a drug containing this group of active ingredients has been successfully used in the treatment of Covid-19 in China. Thus, a dataset with 567 derivatives of this class was built from reviews published between 1994 and 2020, and their interaction against both SARS-CoV-2 proteases was investigated. The virtual screening was performed by filtering the PGs through the evaluation of scores based on the AutoDock Vina, GOLD/ChemPLP, and GOLD/GoldScore evaluation functions. The bRO5 pharmacokinetic parameters of the PGs ranked in the previous step were analyzed and their interaction with key amino acid residues of the 3CLpro and PLpro enzymes was evaluated. Ninety-eight compounds were identified by computational approaches against PLpro and 80 PGs against 3CLpro. Of these, four interacted with key catalytic residues of PLpro, which is an indicative of inhibitory activity, and three compounds interacted with catalytic key residues of 3CLpro. Of these, five PGs occur in plants of the Traditional Chinese Medicine (TCM), while two are components of plants/formulations currently used in the Covid-19 protocols in China. The data presented here show the potential of PGs as selective inhibitors of SARS-CoV-2 3CLpro and PLpro.
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Affiliation(s)
- Caio Felipe de Araujo Ribas Cheohen
- Programa de Pós-graduação Multicêntrico em Ciências Fisiológicas, Centro de Ciências da Saúde, Instituto de Biodiversidade e Sustentabilidade NUPEM, Universidade Federal do Rio de Janeiro, Macaé, RJ 27965045, Brazil
| | - Maria Eduarda Alves Esteves
- Programa de Pós-graduação em Biologia Computacional e Sistemas, Instituto Oswaldo Cruz, Manguinhos, Rio de Janeiro, RJ 21041361, Brazil
| | - Thamirys Silva da Fonseca
- Faculdade de Farmácia, Centro de Ciências da Saúde, Bl. A 2º andar, Ilha do Fundão, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 21941902, Brazil,Programa de Pós-graduação em Biotecnologia Vegetal e Bioprocessos, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 21941902, Brazil
| | - Carla Monteiro Leal
- Programa de Pós-graduação em Biotecnologia Vegetal e Bioprocessos, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 21941902, Brazil
| | - Fernanda de Lemos Fernandes Assis
- Faculdade de Farmácia, Centro de Ciências da Saúde, Bl. A 2º andar, Ilha do Fundão, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 21941902, Brazil
| | - Mariana Freire Campos
- Faculdade de Farmácia, Centro de Ciências da Saúde, Bl. A 2º andar, Ilha do Fundão, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 21941902, Brazil,Programa de Pós-graduação em Biotecnologia Vegetal e Bioprocessos, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 21941902, Brazil
| | - Raianne Soares Rebelo
- Faculdade de Farmácia, Centro de Ciências da Saúde, Bl. A 2º andar, Ilha do Fundão, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 21941902, Brazil
| | - Diego Allonso
- Faculdade de Farmácia, Centro de Ciências da Saúde, Bl. A 2º andar, Ilha do Fundão, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 21941902, Brazil
| | - Gilda Guimarães Leitão
- Instituto de Pesquisas de Produtos Naturais, Centro de Ciências da Saúde, Bl. H, Ilha do Fundão, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 21941902, Brazil
| | - Manuela Leal da Silva
- Programa de Pós-graduação Multicêntrico em Ciências Fisiológicas, Centro de Ciências da Saúde, Instituto de Biodiversidade e Sustentabilidade NUPEM, Universidade Federal do Rio de Janeiro, Macaé, RJ 27965045, Brazil,Programa de Pós-graduação em Biologia Computacional e Sistemas, Instituto Oswaldo Cruz, Manguinhos, Rio de Janeiro, RJ 21041361, Brazil,Corresponding author at: Programa de Pós-graduação Multicêntrico em Ciências Fisiológicas, Centro de Ciências da Saúde, Instituto de Biodiversidade e Sustentabilidade NUPEM, Universidade Federal do Rio de Janeiro, Macaé, RJ 27965045, Brazil.
| | - Suzana Guimarães Leitão
- Faculdade de Farmácia, Centro de Ciências da Saúde, Bl. A 2º andar, Ilha do Fundão, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 21941902, Brazil,Programa de Pós-graduação em Biotecnologia Vegetal e Bioprocessos, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 21941902, Brazil,Corresponding author at: Faculdade de Farmácia, Centro de Ciências da Saúde, Bl. A 2º andar, Ilha do Fundão, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 21941902, Brazil.
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Procoxacin bidirectionally inhibits osteoblastic and osteoclastic activity in bone and suppresses bone metastasis of prostate cancer. JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH : CR 2023; 42:45. [PMID: 36759880 PMCID: PMC9909988 DOI: 10.1186/s13046-023-02610-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 01/21/2023] [Indexed: 02/11/2023]
Abstract
BACKGROUND Bone is the most common site of metastasis of prostate cancer (PCa). PCa invasion leads to a disruption of osteogenic-osteolytic balance and causes abnormal bone formation. The interaction between PCa and bone stromal cells, especially osteoblasts (OB), is considered essential for the disease progression. However, drugs that effectively block the cancer-bone interaction and regulate the osteogenic-osteolytic balance remain undiscovered. METHODS A reporter gene system was constructed to screen compounds that could inhibit PCa-induced OB activation from 631 compounds. Then, the pharmacological effects of a candidate drug, Procoxacin (Pro), on OBs, osteoclasts (OCs) and cancer-bone interaction were studied in cellular models. Intratibial inoculation, micro-CT and histological analysis were used to explore the effect of Pro on osteogenic and osteolytic metastatic lesions. Bioinformatic analysis and experiments including qPCR, western blotting and ELISA assay were used to identify the effector molecules of Pro in the cancer-bone microenvironment. Virtual screening, molecular docking, surface plasmon resonance assay and RNA knockdown were utilized to identify the drug target of Pro. Experiments including co-IP, western blotting and immunofluorescence were performed to reveal the role of Pro binding to its target. Intracardiac inoculation metastasis model and survival analysis were used to investigate the therapeutic effect of Pro on metastatic cancer. RESULTS Luciferase reporter gene consisted of Runx2 binding sequence, OSE2, and Alp promotor could sensitively reflect the intensity of PCa-OB interaction. Pro best matched the screening criteria among 631 compounds in drug screening. Further study demonstrated that Pro effectively inhibited the PCa-induced osteoblastic changes without killing OBs or PCa cells and directly killed OCs or suppressed osteoclastic functions at very low concentrations. Mechanism study revealed that Pro broke the feedback loop of TGF-β/C-Raf/MAPK pathway by sandwiching into 14-3-3ζ/C-Raf complex and prevented its disassociation. Pro treatment alleviated both osteogenic and osteolytic lesions in PCa-involved bones and reduced the number of metastases of PCa in vivo. CONCLUSIONS In summary, our study provides a drug screening strategy based on the cancer-host microenvironment and demonstrates that Pro effectively inhibits both osteoblastic and osteoclastic lesions in PCa-involved bones, which makes it a promising therapeutic agent for PCa bone metastasis.
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Zhao C, Tan T, Zhang E, Wang T, Gong H, Jia Q, Liu T, Yang X, Zhao J, Wu Z, Wei H, Xiao J, Yang C. A chronicle review of new techniques that facilitate the understanding and development of optimal individualized therapeutic strategies for chordoma. Front Oncol 2022; 12:1029670. [PMID: 36465398 PMCID: PMC9708744 DOI: 10.3389/fonc.2022.1029670] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 10/19/2022] [Indexed: 09/01/2023] Open
Abstract
Chordoma is a rare malignant bone tumor that mainly occurs in the sacrum and the clivus/skull base. Surgical resection is the treatment of choice for chordoma, but the local recurrence rate is high with unsatisfactory prognosis. Compared with other common tumors, there is not much research and individualized treatment for chordoma, partly due to the rarity of the disease and the lack of appropriate disease models, which delay the discovery of therapeutic strategies. Recent advances in modern techniques have enabled gaining a better understanding of a number of rare diseases, including chordoma. Since the beginning of the 21st century, various chordoma cell lines and animal models have been reported, which have partially revealed the intrinsic mechanisms of tumor initiation and progression with the use of next-generation sequencing (NGS) techniques. In this study, we performed a systematic overview of the chordoma models and related sequencing studies in a chronological manner, from the first patient-derived chordoma cell line (U-CH1) to diverse preclinical models such as the patient-derived organoid-based xenograft (PDX) and patient-derived organoid (PDO) models. The use of modern sequencing techniques has discovered mutations and expression signatures that are considered potential treatment targets, such as the expression of Brachyury and overactivated receptor tyrosine kinases (RTKs). Moreover, computational and bioinformatics techniques have made drug repositioning/repurposing and individualized high-throughput drug screening available. These advantages facilitate the research and development of comprehensive and personalized treatment strategies for indicated patients and will dramatically improve their prognoses in the near feature.
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Affiliation(s)
- Chenglong Zhao
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Tao Tan
- Department of Orthopedics, 905 Hospital of People’s Liberation Army Navy, Shanghai, China
| | - E. Zhang
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Ting Wang
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Haiyi Gong
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Qi Jia
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Tielong Liu
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Xinghai Yang
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Jian Zhao
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Zhipeng Wu
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Haifeng Wei
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Jianru Xiao
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Cheng Yang
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
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Shi W, Singha M, Pu L, Srivastava G, Ramanujam J, Brylinski M. GraphSite: Ligand Binding Site Classification with Deep Graph Learning. Biomolecules 2022; 12:biom12081053. [PMID: 36008947 PMCID: PMC9405584 DOI: 10.3390/biom12081053] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 07/18/2022] [Accepted: 07/20/2022] [Indexed: 12/10/2022] Open
Abstract
The binding of small organic molecules to protein targets is fundamental to a wide array of cellular functions. It is also routinely exploited to develop new therapeutic strategies against a variety of diseases. On that account, the ability to effectively detect and classify ligand binding sites in proteins is of paramount importance to modern structure-based drug discovery. These complex and non-trivial tasks require sophisticated algorithms from the field of artificial intelligence to achieve a high prediction accuracy. In this communication, we describe GraphSite, a deep learning-based method utilizing a graph representation of local protein structures and a state-of-the-art graph neural network to classify ligand binding sites. Using neural weighted message passing layers to effectively capture the structural, physicochemical, and evolutionary characteristics of binding pockets mitigates model overfitting and improves the classification accuracy. Indeed, comprehensive cross-validation benchmarks against a large dataset of binding pockets belonging to 14 diverse functional classes demonstrate that GraphSite yields the class-weighted F1-score of 81.7%, outperforming other approaches such as molecular docking and binding site matching. Further, it also generalizes well to unseen data with the F1-score of 70.7%, which is the expected performance in real-world applications. We also discuss new directions to improve and extend GraphSite in the future.
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Affiliation(s)
- Wentao Shi
- Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803, USA; (W.S.); (J.R.)
| | - Manali Singha
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA; (M.S.); (G.S.)
| | - Limeng Pu
- Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA;
| | - Gopal Srivastava
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA; (M.S.); (G.S.)
| | - Jagannathan Ramanujam
- Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803, USA; (W.S.); (J.R.)
- Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA;
| | - Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA; (M.S.); (G.S.)
- Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA;
- Correspondence: ; Tel.: +1-(225)-578-2791; Fax: +1-(225)-578-2597
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Schuler J, Falls Z, Mangione W, Hudson ML, Bruggemann L, Samudrala R. Evaluating the performance of drug-repurposing technologies. Drug Discov Today 2022; 27:49-64. [PMID: 34400352 PMCID: PMC10014214 DOI: 10.1016/j.drudis.2021.08.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 06/20/2021] [Accepted: 08/08/2021] [Indexed: 01/22/2023]
Abstract
Drug-repurposing technologies are growing in number and maturing. However, comparisons to each other and to reality are hindered because of a lack of consensus with respect to performance evaluation. Such comparability is necessary to determine scientific merit and to ensure that only meaningful predictions from repurposing technologies carry through to further validation and eventual patient use. Here, we review and compare performance evaluation measures for these technologies using version 2 of our shotgun repurposing Computational Analysis of Novel Drug Opportunities (CANDO) platform to illustrate their benefits, drawbacks, and limitations. Understanding and using different performance evaluation metrics ensures robust cross-platform comparability, enabling us to continue to strive toward optimal repurposing by decreasing the time and cost of drug discovery and development.
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Affiliation(s)
- James Schuler
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.
| | - Zackary Falls
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - William Mangione
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Matthew L Hudson
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Liana Bruggemann
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Ram Samudrala
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.
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Martínez-deMiguel C, Segura-Bedmar I, Chacón-Solano E, Guerrero-Aspizua S. The RareDis corpus: A corpus annotated with rare diseases, their signs and symptoms. J Biomed Inform 2021; 125:103961. [PMID: 34879250 DOI: 10.1016/j.jbi.2021.103961] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 11/08/2021] [Accepted: 11/22/2021] [Indexed: 11/26/2022]
Abstract
Rare diseases affect a small number of people compared to the general population. However, more than 6,000 different rare diseases exist and, in total, they affect more than 300 million people worldwide. Rare diseases share as part of their main problem, the delay in diagnosis and the sparse information available for researchers, clinicians, and patients. Finding a diagnostic can be a very long and frustrating experience for patients and their families. The average diagnostic delay is between 6-8 years. Many of these diseases result in different manifestations among patients, which hampers even more their detection and the correct treatment choice. Therefore, there is an urgent need to increase the scientific and medical knowledge about rare diseases. Natural Language Processing (NLP) can help to extract relevant information about rare diseases to facilitate their diagnosis and treatments, but most NLP techniques require manually annotated corpora. Therefore, our goal is to create a gold standard corpus annotated with rare diseases and their clinical manifestations. It could be used to train and test NLP approaches and the information extracted through NLP could enrich the knowledge of rare diseases, and thereby, help to reduce the diagnostic delay and improve the treatment of rare diseases. The paper describes the selection of 1,041 texts to be included in the corpus, the annotation process and the annotation guidelines. The entities (disease, rare disease, symptom, sign and anaphor) and the relationships (produces, is a, is acron, is synon, increases risk of, anaphora) were annotated. The RareDis corpus contains more than 5,000 rare diseases and almost 6,000 clinical manifestations are annotated. Moreover, the Inter Annotator Agreement evaluation shows a relatively high agreement (F1-measure equal to 83.5% under exact match criteria for the entities and equal to 81.3% for the relations). Based on these results, this corpus is of high quality, supposing a significant step for the field since there is a scarcity of available corpus annotated with rare diseases. This could open the door to further NLP applications, which would facilitate the diagnosis and treatment of these rare diseases and, therefore, would improve dramatically the quality of life of these patients.
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Affiliation(s)
- Claudia Martínez-deMiguel
- Tissue Engineering and Regenerative Medicine group, Department of Bioengineering, Universidad Carlos III de Madrid, Avenidad de la Universidad, 30, Leganés 28911, Madrid, Spain
| | - Isabel Segura-Bedmar
- Human Language and Accesibility Technologies, Computer Science Department, Avenidad de la Universidad 30, Leganés 28911, Madrid, Spain.
| | - Esteban Chacón-Solano
- Tissue Engineering and Regenerative Medicine group, Department of Bioengineering, Universidad Carlos III de Madrid, Avenidad de la Universidad, 30, Leganés 28911, Madrid, Spain; Hospital Fundación Jiménez Díaz e, Instituto de Investigación, FJD, Av. de los Reyes Católicos 2, Madrid 28040, Madrid, Spain; Epithelial Biomedicine Division, CIEMAT, Madrid 28040, Madrid, Spain
| | - Sara Guerrero-Aspizua
- Tissue Engineering and Regenerative Medicine group, Department of Bioengineering, Universidad Carlos III de Madrid, Avenidad de la Universidad, 30, Leganés 28911, Madrid, Spain; Hospital Fundación Jiménez Díaz e, Instituto de Investigación, FJD, Av. de los Reyes Católicos 2, Madrid 28040, Madrid, Spain; Epithelial Biomedicine Division, CIEMAT, Madrid 28040, Madrid, Spain; Centre for Biomedical Network Research on Rare Diseases (CIBERER), C/Monforte de Lemos 3-5, Madrid 28029, Madrid, Spain
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Nguyen PL, Bui BP, Duong MTH, Lee K, Ahn HC, Cho J. Suppression of LPS-Induced Inflammation and Cell Migration by Azelastine through Inhibition of JNK/NF-κB Pathway in BV2 Microglial Cells. Int J Mol Sci 2021; 22:ijms22169061. [PMID: 34445767 PMCID: PMC8396433 DOI: 10.3390/ijms22169061] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 08/19/2021] [Accepted: 08/20/2021] [Indexed: 02/06/2023] Open
Abstract
The c-Jun N-terminal kinases (JNKs) are implicated in many neuropathological conditions, including neurodegenerative diseases. To explore potential JNK3 inhibitors from the U.S. Food and Drug Administration-approved drug library, we performed structure-based virtual screening and identified azelastine (Aze) as one of the candidates. NMR spectroscopy indicated its direct binding to the ATP-binding site of JNK3, validating our observations. Although the antihistamine effect of Aze is well documented, the involvement of the JNK pathway in its action remains to be elucidated. This study investigated the effects of Aze on lipopolysaccharide (LPS)-induced JNK phosphorylation, pro-inflammatory mediators, and cell migration in BV2 microglial cells. Aze was found to inhibit the LPS-induced phosphorylation of JNK and c-Jun. It also inhibited the LPS-induced production of pro-inflammatory mediators, including interleukin-6, tumor necrosis factor-α, and nitric oxide. Wound healing and transwell migration assays indicated that Aze attenuated LPS-induced BV2 cell migration. Furthermore, Aze inhibited LPS-induced IκB phosphorylation, thereby suppressing nuclear translocation of NF-κB. Collectively, our data demonstrate that Aze exerts anti-inflammatory and anti-migratory effects through inhibition of the JNK/NF-κB pathway in BV2 cells. Based on our findings, Aze may be a potential candidate for drug repurposing to mitigate neuroinflammation in various neurodegenerative disorders, including Alzheimer’s and Parkinson’s diseases.
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Del Castillo-Santaella T, Hernández-Morante JJ, Suárez-Olmos J, Maldonado-Valderrama J, Peña-García J, Martínez-Cortés C, Pérez-Sánchez H. Identification of the thistle milk component Silibinin(A) and Glutathione-disulphide as potential inhibitors of the pancreatic lipase: Potential implications on weight loss. J Funct Foods 2021. [DOI: 10.1016/j.jff.2021.104479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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Ibezim A, Onah E, Dim EN, Ntie-Kang F. A computational multi-targeting approach for drug repositioning for psoriasis treatment. BMC Complement Med Ther 2021; 21:193. [PMID: 34225727 PMCID: PMC8258956 DOI: 10.1186/s12906-021-03359-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 06/14/2021] [Indexed: 12/26/2022] Open
Abstract
Background Psoriasis is an autoimmune inflammatory skin disease that affects 0.5–3% of the world’s population and current treatment options are posed with limitations. The reduced risk of failure in clinical trials for repositioned drug candidates and the time and cost-effectiveness has popularized drug reposition and computational methods in the drug research community. Results The current study attempts to reposition approved drugs for the treatment of psoriasis by docking about 2000 approved drug molecules against fifteen selected and validated anti-psoriatic targets. The docking results showed that a good number of the dataset interacted favorably with the targets as most of them had − 11.00 to − 10.00 kcal/mol binding free energies across the targets. The percentage of the dataset with binding affinity higher than the co-crystallized ligands ranged from 34.76% (JAK-3) to 0.73% (Rac-1). It was observed that 12 out of the 0.73% outperformed all the co-crystallized ligands across the 15 studied proteins. All the 12 drugs identified are currently indicated as either antiviral or anticancer drugs and are of purine and pyrimidine nuclei. This is not surprising given that there is similarity in the mechanism of the mentioned diseases. Conclusion This study, therefore, suggests that; antiviral and anticancer drugs could have anti-psoriatic effects, and molecules with purine and pyrimidine structural architecture are likely templates to consider in developing anti-psoriatic agents.
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Affiliation(s)
- Akachukwu Ibezim
- Department of Pharmaceutical and Medicinal Chemistry, University of Nigeria, Nsukka, Nigeria.
| | - Emmanuel Onah
- Department of Pharmaceutical and Medicinal Chemistry, University of Nigeria, Nsukka, Nigeria
| | - Ebubechukwu N Dim
- Department of Science Laboratory and Technology, University of Nigeria, Nsukka, Nigeria
| | - Fidele Ntie-Kang
- Department of Chemistry, University of Buea, Buea, Cameroon. .,Department of Pharmaceutical Chemistry, Martin-Luther University Halle-Wittenberg, Halle (Saale), Germany.
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Traylor JI, Sheppard HE, Ravikumar V, Breshears J, Raza SM, Lin CY, Patel SR, DeMonte F. Computational Drug Repositioning Identifies Potentially Active Therapies for Chordoma. Neurosurgery 2021; 88:428-436. [PMID: 33017025 PMCID: PMC7803434 DOI: 10.1093/neuros/nyaa398] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2020] [Accepted: 06/28/2020] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Chordomas are aggressive bone tumors that often recur despite maximal resection and adjuvant radiation. To date there are no Food and Drug Administration (FDA)-approved chemotherapies. Computational drug repositioning is an expanding approach to identify pharmacotherapies for clinical trials. OBJECTIVE To identify FDA-approved compounds for repurposing in chordoma. METHODS Previously identified highly differentially expressed genes from chordoma tissue samples at our institution were compared with pharmacogenomic interactions in the Comparative Toxicogenomics Database (CTD) using ksRepo, a drug-repositioning platform. Compounds selected by ksRepo were then validated in CH22 and UM-Chor1 human chordoma cells in Vitro. RESULTS A total of 13 chemical compounds were identified in silico from the CTD, and 6 were selected for preclinical validation in human chordoma cell lines based on their clinical relevance. Of these, 3 identified drugs are FDA-approved chemotherapies for other malignancies (cisplatin, cytarabine, and lucanthone). Cytarabine, a deoxyribonucleic acid polymerase inhibitor approved for the treatment of various leukemias, exhibited a significant concentration-dependent effect against CH22 and UM-Chor1 cells when compared to positive (THZ1) and negative (venetoclax) controls. Tretinoin exhibited a significant concentration-dependent cytotoxic effect in CH22, sacral chordoma-derived cell lines but to a much lesser extent in UM-Chor1, a cell line derived from skull base chordoma. CONCLUSION Cytarabine administration reduces the viability of human chordoma cells. The equally effective reduction in viability seen with tretinoin seems to be cell line dependent. Based on our findings, we recommend the evaluation of cytarabine and tretinoin in an expanded set of human chordoma cell lines and animal models.
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Affiliation(s)
- Jeffrey I Traylor
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Hadley E Sheppard
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas
| | - Visweswaran Ravikumar
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jonathan Breshears
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Shaan M Raza
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Charles Y Lin
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas
- Kronos Bio, Cambridge, Massachusetts
| | - Shreyaskumar R Patel
- Department of Sarcoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Franco DeMonte
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, Texas
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12
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Giorgio E, Pesce E, Pozzi E, Sondo E, Ferrero M, Morerio C, Borrelli G, Della Sala E, Lorenzati M, Cortelli P, Buffo A, Pedemonte N, Brusco A. A high-content drug screening strategy to identify protein level modulators for genetic diseases: A proof-of-principle in autosomal dominant leukodystrophy. Hum Mutat 2020; 42:102-116. [PMID: 33252173 DOI: 10.1002/humu.24147] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 10/09/2020] [Accepted: 11/24/2020] [Indexed: 11/07/2022]
Abstract
In genetic diseases, the most prevalent mechanism of pathogenicity is an altered expression of dosage-sensitive genes. Drugs that restore physiological levels of these genes should be effective in treating the associated conditions. We developed a screening strategy, based on a bicistronic dual-reporter vector, for identifying compounds that modulate protein levels, and used it in a pharmacological screening approach. To provide a proof-of-principle, we chose autosomal dominant leukodystrophy (ADLD), an ultra-rare adult-onset neurodegenerative disorder caused by lamin B1 (LMNB1) overexpression. We used a stable Chinese hamster ovary (CHO) cell line that simultaneously expresses an AcGFP reporter fused to LMNB1 and a Ds-Red normalizer. Using high-content imaging analysis, we screened a library of 717 biologically active compounds and approved drugs, and identified alvespimycin, an HSP90 inhibitor, as a positive hit. We confirmed that alvespimycin can reduce LMNB1 levels by 30%-80% in five different cell lines (fibroblasts, NIH3T3, CHO, COS-7, and rat primary glial cells). In ADLD fibroblasts, alvespimycin reduced cytoplasmic LMNB1 by about 50%. We propose this approach for effectively identifying potential drugs for treating genetic diseases associated with deletions/duplications and paving the way toward Phase II clinical trials.
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Affiliation(s)
- Elisa Giorgio
- Department of Medical Sciences, Medical Genetics Unit, University of Torino, Turin, Italy.,Department of Molecular Medicine, University of Pavia, Pavia, Italy
| | - Emanuela Pesce
- UOC Genetica Medica, IRCCS Istituto Giannina Gaslini, Genova, Italy
| | - Elisa Pozzi
- Department of Medical Sciences, Medical Genetics Unit, University of Torino, Turin, Italy
| | - Elvira Sondo
- UOC Genetica Medica, IRCCS Istituto Giannina Gaslini, Genova, Italy
| | - Marta Ferrero
- Department of Medical Sciences, Medical Genetics Unit, University of Torino, Turin, Italy
| | - Cristina Morerio
- UOC Laboratorio di Genetica Umana, IRCCS Istituto Giannina Gaslini, Genova, Italy
| | - Giusy Borrelli
- Department of Medical Sciences, Medical Genetics Unit, University of Torino, Turin, Italy
| | - Edoardo Della Sala
- Department of Medical Sciences, Medical Genetics Unit, University of Torino, Turin, Italy
| | - Martina Lorenzati
- Department of Neuroscience Rita Levi Montalcini and Neuroscience Institute Cavalieri Ottolenghi, University of Torino, Orbassano, Torino, Italy
| | - Pietro Cortelli
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.,IRCCS Istituto delle Scienze Neurologiche di Bologna, Bellaria Hospital, Bologna, Italy
| | - Annalisa Buffo
- Department of Neuroscience Rita Levi Montalcini and Neuroscience Institute Cavalieri Ottolenghi, University of Torino, Orbassano, Torino, Italy
| | | | - Alfredo Brusco
- Department of Medical Sciences, Medical Genetics Unit, University of Torino, Turin, Italy.,Medical Genetics Unit, Città della Salute e della Scienza University Hospital, Turin, Italy
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13
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Paludo GP, Thompson CE, Miyamoto KN, Guedes RLM, Zaha A, de Vasconcelos ATR, Cancela M, Ferreira HB. Cestode strobilation: prediction of developmental genes and pathways. BMC Genomics 2020; 21:487. [PMID: 32677885 PMCID: PMC7367335 DOI: 10.1186/s12864-020-06878-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 07/02/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Cestoda is a class of endoparasitic worms in the flatworm phylum (Platyhelminthes). During the course of their evolution cestodes have evolved some interesting aspects, such as their increased reproductive capacity. In this sense, they have serial repetition of their reproductive organs in the adult stage, which is often associated with external segmentation in a developmental process called strobilation. However, the molecular basis of strobilation is poorly understood. To assess this issue, an evolutionary comparative study among strobilated and non-strobilated flatworm species was conducted to identify genes and proteins related to the strobilation process. RESULTS We compared the genomic content of 10 parasitic platyhelminth species; five from cestode species, representing strobilated parasitic platyhelminths, and five from trematode species, representing non-strobilated parasitic platyhelminths. This dataset was used to identify 1813 genes with orthologues that are present in all cestode (strobilated) species, but absent from at least one trematode (non-strobilated) species. Development-related genes, along with genes of unknown function (UF), were then selected based on their transcriptional profiles, resulting in a total of 34 genes that were differentially expressed between the larval (pre-strobilation) and adult (strobilated) stages in at least one cestode species. These 34 genes were then assumed to be strobilation related; they included 12 encoding proteins of known function, with 6 related to the Wnt, TGF-β/BMP, or G-protein coupled receptor signaling pathways; and 22 encoding UF proteins. In order to assign function to at least some of the UF genes/proteins, a global gene co-expression analysis was performed for the cestode species Echinococcus multilocularis. This resulted in eight UF genes/proteins being predicted as related to developmental, reproductive, vesicle transport, or signaling processes. CONCLUSIONS Overall, the described in silico data provided evidence of the involvement of 34 genes/proteins and at least 3 developmental pathways in the cestode strobilation process. These results highlight on the molecular mechanisms and evolution of the cestode strobilation process, and point to several interesting proteins as potential developmental markers and/or targets for the development of novel antihelminthic drugs.
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Affiliation(s)
- Gabriela Prado Paludo
- Laboratório de Genômica Estrutural e Funcional, Centro de Biotecnologia (CBiot), Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil
- Programa de Pós-Graduação em Biologia Celular e Molecular, CBiot, UFRGS, Porto Alegre, RS, Brazil
| | - Claudia Elizabeth Thompson
- Programa de Pós-Graduação em Biologia Celular e Molecular, CBiot, UFRGS, Porto Alegre, RS, Brazil
- Departamento de Farmacociências, Universidade Federal de Ciências Médicas de Porto Alegre, Porto Alegre, RS, Brazil
| | - Kendi Nishino Miyamoto
- Programa de Pós-Graduação em Biologia Celular e Molecular, CBiot, UFRGS, Porto Alegre, RS, Brazil
| | - Rafael Lucas Muniz Guedes
- Laboratório Nacional de Computação Científica, Petrópolis, RJ, Brazil
- Present address: Instituto Hermes Pardini, Vespasiano, MG, Brazil
| | - Arnaldo Zaha
- Programa de Pós-Graduação em Biologia Celular e Molecular, CBiot, UFRGS, Porto Alegre, RS, Brazil
| | | | - Martin Cancela
- Laboratório de Genômica Estrutural e Funcional, Centro de Biotecnologia (CBiot), Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil
- Programa de Pós-Graduação em Biologia Celular e Molecular, CBiot, UFRGS, Porto Alegre, RS, Brazil
| | - Henrique Bunselmeyer Ferreira
- Laboratório de Genômica Estrutural e Funcional, Centro de Biotecnologia (CBiot), Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil.
- Programa de Pós-Graduação em Biologia Celular e Molecular, CBiot, UFRGS, Porto Alegre, RS, Brazil.
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14
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Zhu L, Roberts R, Huang R, Zhao J, Xia M, Delavan B, Mikailov M, Tong W, Liu Z. Drug Repositioning for Noonan and LEOPARD Syndromes by Integrating Transcriptomics With a Structure-Based Approach. Front Pharmacol 2020; 11:927. [PMID: 32676024 PMCID: PMC7333460 DOI: 10.3389/fphar.2020.00927] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 06/08/2020] [Indexed: 01/24/2023] Open
Abstract
Noonan and LEOPARD syndromes (NS and LS) belong to a group of related disorders called RASopathies characterized by abnormalities of multiple organs and systems including hypertrophic cardiomyopathy and dysmorphic facial features. There are no approved drugs for these two rare diseases, but it is known that a missense mutation in PTPN11 genes is associated with approximately 50% and 70% of NS and LS cases, respectively. In this study, we implemented a hybrid computational drug repositioning framework by integrating transcriptomic and structure-based approaches to explore potential treatment options for NS and LS. Specifically, disease signatures were derived from the transcriptomic profiles of human induced pluripotent stem cells (iPSCs) from NS and LS patients and reverse correlated to drug transcriptomic signatures from CMap and L1000 projects on the basis that if disease and drug transcriptomic signatures are reversely correlated, the drug has the potential to treat that disease. The compounds that were ranked top based on their transcriptomic profiles were docked to mutated and wild-type 3D structures of PTPN11 by an adjusted Induced Fit Docking (IFD) protocol. In addition, we prioritized repositioned candidates for NS and LS by a consensus ranking strategy. Network analysis and phenotypic anchoring of the transcriptomic data could discriminate the two diseases at the molecular level. Furthermore, the adjusted IFD protocol was able to recapitulate the binding specificity of potential drug candidates to mutated 3D structures, revealing the relevant amino acids. Importantly, a list of potential drug candidates for repositioning was identified including 61 for NS and 43 for LS and was further verified from literature reports and on-going clinical trials. Altogether, this hybrid computational drug repositioning approach has highlighted a number of drug candidates for NS and LS and could be applied to identifying drug candidates for other diseases as well.
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Affiliation(s)
- Liyuan Zhu
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
| | - Ruth Roberts
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States.,Department of Drug Safety, ApconiX, Alderley Edge, United Kingdom.,Department of Biosciences, University of Birmingham, Birmingham, United Kingdom
| | - Ruili Huang
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, United States
| | - Jinghua Zhao
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, United States
| | - Menghang Xia
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, United States
| | - Brian Delavan
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States.,Joint Bioinformatics Graduate Program, University of Arkansas at Little Rock and University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Mike Mikailov
- Office of Science and Engineering Labs, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, United States
| | - Weida Tong
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
| | - Zhichao Liu
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
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15
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Peng Y, Wang M, Xu Y, Wu Z, Wang J, Zhang C, Liu G, Li W, Li J, Tang Y. Drug repositioning by prediction of drug's anatomical therapeutic chemical code via network-based inference approaches. Brief Bioinform 2020; 22:2058-2072. [PMID: 32221552 DOI: 10.1093/bib/bbaa027] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Revised: 02/05/2020] [Accepted: 02/17/2020] [Indexed: 12/17/2022] Open
Abstract
Drug discovery and development is a time-consuming and costly process. Therefore, drug repositioning has become an effective approach to address the issues by identifying new therapeutic or pharmacological actions for existing drugs. The drug's anatomical therapeutic chemical (ATC) code is a hierarchical classification system categorized as five levels according to the organs or systems that drugs act and the pharmacology, therapeutic and chemical properties of drugs. The 2nd-, 3rd- and 4th-level ATC codes reserved the therapeutic and pharmacological information of drugs. With the hypothesis that drugs with similar structures or targets would possess similar ATC codes, we exploited a network-based approach to predict the 2nd-, 3rd- and 4th-level ATC codes by constructing substructure drug-ATC (SD-ATC), target drug-ATC (TD-ATC) and Substructure&Target drug-ATC (STD-ATC) networks. After 10-fold cross validation and two external validations, the STD-ATC models outperformed the SD-ATC and TD-ATC ones. Furthermore, with KR as fingerprint, the STD-ATC model was identified as the optimal model with AUC values at 0.899 ± 0.015, 0.916 and 0.893 for 10-fold cross validation, external validation set 1 and external validation set 2, respectively. To illustrate the predictive capability of the STD-ATC model with KR fingerprint, as a case study, we predicted 25 FDA-approved drugs (22 drugs were actually purchased) to have potential activities on heart failure using that model. Experiments in vitro confirmed that 8 of the 22 old drugs have shown mild to potent cardioprotective activities on both hypoxia model and oxygen-glucose deprivation model, which demonstrated that our STD-ATC prediction model would be an effective tool for drug repositioning.
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Affiliation(s)
- Yayuan Peng
- East China University of Science and Technology, Shanghai, China
| | - Manjiong Wang
- East China University of Science and Technology, Shanghai, China
| | - Yixiang Xu
- East China University of Science and Technology, Shanghai, China
| | - Zengrui Wu
- East China University of Science and Technology, Shanghai, China
| | - Jiye Wang
- East China University of Science and Technology, Shanghai, China
| | - Chao Zhang
- East China University of Science and Technology, Shanghai, China
| | - Guixia Liu
- East China University of Science and Technology, Shanghai, China
| | - Weihua Li
- East China University of Science and Technology, Shanghai, China
| | - Jian Li
- East China University of Science and Technology, Shanghai, China
| | - Yun Tang
- East China University of Science and Technology, Shanghai, China
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16
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Naderi M, Lemoine JM, Govindaraj RG, Kana OZ, Feinstein WP, Brylinski M. Binding site matching in rational drug design: algorithms and applications. Brief Bioinform 2019; 20:2167-2184. [PMID: 30169563 PMCID: PMC6954434 DOI: 10.1093/bib/bby078] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 07/18/2018] [Accepted: 07/29/2018] [Indexed: 01/06/2023] Open
Abstract
Interactions between proteins and small molecules are critical for biological functions. These interactions often occur in small cavities within protein structures, known as ligand-binding pockets. Understanding the physicochemical qualities of binding pockets is essential to improve not only our basic knowledge of biological systems, but also drug development procedures. In order to quantify similarities among pockets in terms of their geometries and chemical properties, either bound ligands can be compared to one another or binding sites can be matched directly. Both perspectives routinely take advantage of computational methods including various techniques to represent and compare small molecules as well as local protein structures. In this review, we survey 12 tools widely used to match pockets. These methods are divided into five categories based on the algorithm implemented to construct binding-site alignments. In addition to the comprehensive analysis of their algorithms, test sets and the performance of each method are described. We also discuss general pharmacological applications of computational pocket matching in drug repurposing, polypharmacology and side effects. Reflecting on the importance of these techniques in drug discovery, in the end, we elaborate on the development of more accurate meta-predictors, the incorporation of protein flexibility and the integration of powerful artificial intelligence technologies such as deep learning.
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Affiliation(s)
- Misagh Naderi
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Jeffrey Mitchell Lemoine
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
- Division of Computer Science and Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
| | | | - Omar Zade Kana
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Wei Pan Feinstein
- High-Performance Computing, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
- Center for Computation & Technology, Louisiana State University, Baton Rouge, LA 70803, USA
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17
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Misselbeck K, Parolo S, Lorenzini F, Savoca V, Leonardelli L, Bora P, Morine MJ, Mione MC, Domenici E, Priami C. A network-based approach to identify deregulated pathways and drug effects in metabolic syndrome. Nat Commun 2019; 10:5215. [PMID: 31740673 PMCID: PMC6861239 DOI: 10.1038/s41467-019-13208-z] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 10/25/2019] [Indexed: 12/11/2022] Open
Abstract
Metabolic syndrome is a pathological condition characterized by obesity, hyperglycemia, hypertension, elevated levels of triglycerides and low levels of high-density lipoprotein cholesterol that increase cardiovascular disease risk and type 2 diabetes. Although numerous predisposing genetic risk factors have been identified, the biological mechanisms underlying this complex phenotype are not fully elucidated. Here we introduce a systems biology approach based on network analysis to investigate deregulated biological processes and subsequently identify drug repurposing candidates. A proximity score describing the interaction between drugs and pathways is defined by combining topological and functional similarities. The results of this computational framework highlight a prominent role of the immune system in metabolic syndrome and suggest a potential use of the BTK inhibitor ibrutinib as a novel pharmacological treatment. An experimental validation using a high fat diet-induced obesity model in zebrafish larvae shows the effectiveness of ibrutinib in lowering the inflammatory load due to macrophage accumulation.
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Affiliation(s)
- Karla Misselbeck
- Fondazione The Microsoft Research University of Trento, Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
- Department of Mathematics, University of Trento, Trento, Italy
| | - Silvia Parolo
- Fondazione The Microsoft Research University of Trento, Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.
| | - Francesca Lorenzini
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Valeria Savoca
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Lorena Leonardelli
- Fondazione The Microsoft Research University of Trento, Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Pranami Bora
- Fondazione The Microsoft Research University of Trento, Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Melissa J Morine
- Fondazione The Microsoft Research University of Trento, Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Maria Caterina Mione
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Enrico Domenici
- Fondazione The Microsoft Research University of Trento, Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy.
| | - Corrado Priami
- Fondazione The Microsoft Research University of Trento, Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.
- Department of Computer Science, University of Pisa, Pisa, Italy.
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18
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Thilakasiri PS, Dmello RS, Nero TL, Parker MW, Ernst M, Chand AL. Repurposing of drugs as STAT3 inhibitors for cancer therapy. Semin Cancer Biol 2019; 68:31-46. [PMID: 31711994 DOI: 10.1016/j.semcancer.2019.09.022] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 09/20/2019] [Accepted: 09/24/2019] [Indexed: 02/06/2023]
Abstract
Drug repurposing is a valuable approach in delivering new cancer therapeutics rapidly into the clinic. Existing safety and patient tolerability data for drugs already in clinical use represent an untapped resource in terms of identifying therapeutic agents for off-label protein targets. The multicellular effects of STAT3 mediated by a range of various upstream signaling pathways make it an attractive therapeutic target with utility in a range of diseases including cancer, and has led to the development of a variety of STAT3 inhibitors. Moreover, heightened STAT3 transcriptional activation in tumor cells and within the cells of the tumor microenvironment contribute to disease progression. Consequently, there are many STAT3 inhibitors in preclinical development or under evaluation in clinical trials for their therapeutic efficacy predominantly in inflammatory diseases and cancer. Despite these advances, many challenges remain in ultimately providing STAT3 inhibitors to patients as cancer treatments, highlighting the need not only for a better understanding of the mechanisms associated with STAT3 activation, but also how various pharmaceutical agents suppress STAT3 activity in various cancers. In this review we discuss the importance of STAT3-dependent functions in cancer, review the status of compounds designed as direct-acting STAT3 inhibitors, and describe some of the strategies for repurposing of drugs as STAT3 inhibitors for cancer therapy.
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Affiliation(s)
- Pathum S Thilakasiri
- Cancer and Inflammation Program, Olivia Newton-John Cancer Research Institute, School of Cancer Medicine, La Trobe University, Heidelberg, Vic., Australia
| | - Rhynelle S Dmello
- Cancer and Inflammation Program, Olivia Newton-John Cancer Research Institute, School of Cancer Medicine, La Trobe University, Heidelberg, Vic., Australia
| | - Tracy L Nero
- ACRF Rational Drug Discovery Centre, St Vincent's Institute, Melbourne, Vic., Australia; Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne, Vic., Australia
| | - Michael W Parker
- ACRF Rational Drug Discovery Centre, St Vincent's Institute, Melbourne, Vic., Australia; Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne, Vic., Australia
| | - Matthias Ernst
- Cancer and Inflammation Program, Olivia Newton-John Cancer Research Institute, School of Cancer Medicine, La Trobe University, Heidelberg, Vic., Australia
| | - Ashwini L Chand
- Cancer and Inflammation Program, Olivia Newton-John Cancer Research Institute, School of Cancer Medicine, La Trobe University, Heidelberg, Vic., Australia.
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19
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Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9214538] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
AutoDock and Vina are two of the most widely used protein–ligand docking programs. The fact that these programs are free and available under an open source license, also makes them a very popular first choice for many users and a common starting point for many virtual screening campaigns, particularly in academia. Here, we evaluated the performance of AutoDock and Vina against an unbiased dataset containing 102 protein targets, 22,432 active compounds and 1,380,513 decoy molecules. In general, the results showed that the overall performance of Vina and AutoDock was comparable in discriminating between actives and decoys. However, the results varied significantly with the type of target. AutoDock was better in discriminating ligands and decoys in more hydrophobic, poorly polar and poorly charged pockets, while Vina tended to give better results for polar and charged binding pockets. For the type of ligand, the tendency was the same for both Vina and AutoDock. Bigger and more flexible ligands still presented a bigger challenge for these docking programs. A set of guidelines was formulated, based on the strengths and weaknesses of both docking program and their limits of validation.
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20
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Pu L, Govindaraj RG, Lemoine JM, Wu HC, Brylinski M. DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network. PLoS Comput Biol 2019; 15:e1006718. [PMID: 30716081 PMCID: PMC6375647 DOI: 10.1371/journal.pcbi.1006718] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 02/14/2019] [Accepted: 12/16/2018] [Indexed: 01/19/2023] Open
Abstract
Comprehensive characterization of ligand-binding sites is invaluable to infer molecular functions of hypothetical proteins, trace evolutionary relationships between proteins, engineer enzymes to achieve a desired substrate specificity, and develop drugs with improved selectivity profiles. These research efforts pose significant challenges owing to the fact that similar pockets are commonly observed across different folds, leading to the high degree of promiscuity of ligand-protein interactions at the system-level. On that account, novel algorithms to accurately classify binding sites are needed. Deep learning is attracting a significant attention due to its successful applications in a wide range of disciplines. In this communication, we present DeepDrug3D, a new approach to characterize and classify binding pockets in proteins with deep learning. It employs a state-of-the-art convolutional neural network in which biomolecular structures are represented as voxels assigned interaction energy-based attributes. The current implementation of DeepDrug3D, trained to detect and classify nucleotide- and heme-binding sites, not only achieves a high accuracy of 95%, but also has the ability to generalize to unseen data as demonstrated for steroid-binding proteins and peptidase enzymes. Interestingly, the analysis of strongly discriminative regions of binding pockets reveals that this high classification accuracy arises from learning the patterns of specific molecular interactions, such as hydrogen bonds, aromatic and hydrophobic contacts. DeepDrug3D is available as an open-source program at https://github.com/pulimeng/DeepDrug3D with the accompanying TOUGH-C1 benchmarking dataset accessible from https://osf.io/enz69/.
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Affiliation(s)
- Limeng Pu
- Division of Electrical & Computer Engineering, Louisiana State University, Baton Rouge, LA, United States of America
| | - Rajiv Gandhi Govindaraj
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, United States of America
| | - Jeffrey Mitchell Lemoine
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, United States of America
- Division of Computer Science and Engineering, Louisiana State University, Baton Rouge, LA, United States of America
| | - Hsiao-Chun Wu
- Division of Electrical & Computer Engineering, Louisiana State University, Baton Rouge, LA, United States of America
| | - Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, United States of America
- Center for Computation & Technology, Louisiana State University, Baton Rouge, LA, United States of America
- * E-mail:
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21
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Hu G, Wang K, Song J, Uversky VN, Kurgan L. Taxonomic Landscape of the Dark Proteomes: Whole-Proteome Scale Interplay Between Structural Darkness, Intrinsic Disorder, and Crystallization Propensity. Proteomics 2018; 18:e1800243. [PMID: 30198635 DOI: 10.1002/pmic.201800243] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Revised: 08/30/2018] [Indexed: 12/14/2022]
Abstract
Growth rate of the protein sequence universe dramatically exceeds the speed of expansion for the protein structure universe, generating an immense dark proteome that includes proteins with unknown structure. A whole-proteome scale analysis of 5.4 million proteins from 987 proteomes in the three domains of life and viruses to systematically dissect an interplay between structural coverage, degree of putative intrinsic disorder, and predicted propensity for structure determination is performed. It has been found that Archaean and Bacterial proteomes have relatively high structural coverage and low amounts of disorder, whereas Eukaryotic and Viral proteomes are characterized by a broad spread of structural coverage and higher disorder levels. The analysis reveals that dark proteomes (i.e., proteomes containing high fractions of proteins with unknown structure) have significantly elevated amounts of intrinsic disorder and are predicted to be difficult to solve structurally. Although the majority of dark proteomes are of viral origin, many dark viral proteomes have at least modest crystallization propensity and only a handful of them are enriched in the intrinsic disorder. The disorder, structural coverage, and propensity are mapped for structural determination onto a novel proteome-level sequence similarity network to analyze the interplay of these characteristics in the taxonomic landscape.
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Affiliation(s)
- Gang Hu
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, P. R. China
| | - Kui Wang
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, P. R. China
| | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.,Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
| | - Vladimir N Uversky
- Department of Molecular Medicine and USF Health Byrd Alzheimer's Research Institute, Morsani College of Medicine, University of South Florida, Tampa, 33612, USA.,Institute for Biological Instrumentation, Russian Academy of Sciences, Pushchino, 142290, Russia
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, 23284, USA
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22
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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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23
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Govindaraj RG, Brylinski M. Comparative assessment of strategies to identify similar ligand-binding pockets in proteins. BMC Bioinformatics 2018. [PMID: 29523085 PMCID: PMC5845264 DOI: 10.1186/s12859-018-2109-2] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
Background Detecting similar ligand-binding sites in globally unrelated proteins has a wide range of applications in modern drug discovery, including drug repurposing, the prediction of side effects, and drug-target interactions. Although a number of techniques to compare binding pockets have been developed, this problem still poses significant challenges. Results We evaluate the performance of three algorithms to calculate similarities between ligand-binding sites, APoc, SiteEngine, and G-LoSA. Our assessment considers not only the capabilities to identify similar pockets and to construct accurate local alignments, but also the dependence of these alignments on the sequence order. We point out certain drawbacks of previously compiled datasets, such as the inclusion of structurally similar proteins, leading to an overestimated performance. To address these issues, a rigorous procedure to prepare unbiased, high-quality benchmarking sets is proposed. Further, we conduct a comparative assessment of techniques directly aligning binding pockets to indirect strategies employing structure-based virtual screening with AutoDock Vina and rDock. Conclusions Thorough benchmarks reveal that G-LoSA offers a fairly robust overall performance, whereas the accuracy of APoc and SiteEngine is satisfactory only against easy datasets. Moreover, combining various algorithms into a meta-predictor improves the performance of existing methods to detect similar binding sites in unrelated proteins by 5–10%. All data reported in this paper are freely available at https://osf.io/6ngbs/.
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Affiliation(s)
| | - Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA. .,Center for Computation & Technology, Louisiana State University, Baton Rouge, LA, USA.
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