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Han X, Guo K. Pharmacophore-based virtual screening of commercial databases against β-secretase 1 for drug development against Alzheimer's disease. Front Chem 2024; 12:1412349. [PMID: 39045333 PMCID: PMC11263123 DOI: 10.3389/fchem.2024.1412349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Accepted: 06/11/2024] [Indexed: 07/25/2024] Open
Abstract
β-secretase 1, one of the most important proteins, is an aspartate protease. This membrane-associated protein is used for treating Alzheimer's disease (AD). Several inhibitors have been pursued against β-secretase 1, but they still have not resulted effectively. Virtual screening based on pharmacophores has been shown to be useful for lead optimization and hit identification in the preliminary phase of developing a new drug. Here, we screen the commercially available databases to find the hits against β-secretase 1 for drug discovery against AD. Virtual screening for 200,000 compounds was done using the database from the Vitas-M Laboratory. The phase screen score was utilized to assess the screened hits. Molecular docking was performed on compounds with phase scores >1.9. According to the study, the 66H ligand of the crystal structure has the maximum performance against β-secretase 1. The redocking of the co-crystal ligand showed that the docked ligand was seamlessly united with the crystal structure. The reference complex had three hydrogen bonds with Asp93, Asp289, and Gly291; one van der Waals interaction with Gly74; and three hydrophobic interactions. After equilibration, the RMSD of the reference compound sustained a value of ∼1.5 Å until 30 ns and then boosted to 2.5 Å. On comparison, the RMSD of the S1 complex steadily increased to ∼2.5 Å at 15 ns, displayed slight aberrations at approximately ∼2.5-3 Å until 80 ns, and then achieved steadiness toward the end of the simulation. The arrangements of proteins stayed condensed during the mockup when bonded to these complexes as stable Rg values showed. Furthermore, the MM/GBSA technique was employed to analyze both compounds' total binding free energies (ΔGtotal). Our research study provides a new understanding of using 66H as anti-β-secretase 1 for drug development against AD.
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Affiliation(s)
- Xu Han
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Kaibo Guo
- Department of Oncology, Affiliated Hangzhou First People’s Hospital, School of Medicine, Westlake University, Hangzhou, China
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2
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Kumar N, Acharya V. Advances in machine intelligence-driven virtual screening approaches for big-data. Med Res Rev 2024; 44:939-974. [PMID: 38129992 DOI: 10.1002/med.21995] [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: 09/12/2022] [Revised: 07/15/2023] [Accepted: 10/29/2023] [Indexed: 12/23/2023]
Abstract
Virtual screening (VS) is an integral and ever-evolving domain of drug discovery framework. The VS is traditionally classified into ligand-based (LB) and structure-based (SB) approaches. Machine intelligence or artificial intelligence has wide applications in the drug discovery domain to reduce time and resource consumption. In combination with machine intelligence algorithms, VS has emerged into revolutionarily progressive technology that learns within robust decision orders for data curation and hit molecule screening from large VS libraries in minutes or hours. The exponential growth of chemical and biological data has evolved as "big-data" in the public domain demands modern and advanced machine intelligence-driven VS approaches to screen hit molecules from ultra-large VS libraries. VS has evolved from an individual approach (LB and SB) to integrated LB and SB techniques to explore various ligand and target protein aspects for the enhanced rate of appropriate hit molecule prediction. Current trends demand advanced and intelligent solutions to handle enormous data in drug discovery domain for screening and optimizing hits or lead with fewer or no false positive hits. Following the big-data drift and tremendous growth in computational architecture, we presented this review. Here, the article categorized and emphasized individual VS techniques, detailed literature presented for machine learning implementation, modern machine intelligence approaches, and limitations and deliberated the future prospects.
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Affiliation(s)
- Neeraj Kumar
- Artificial Intelligence for Computational Biology Lab (AICoB), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India
- Academy of Scientific and Innovative Research, Ghaziabad, India
| | - Vishal Acharya
- Artificial Intelligence for Computational Biology Lab (AICoB), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India
- Academy of Scientific and Innovative Research, Ghaziabad, India
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3
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Zhang Y, Tong Y, Xia X, Wu Q, Su Y. A domain-label-guided translation model for molecular optimization. Methods 2024; 224:71-78. [PMID: 38395182 DOI: 10.1016/j.ymeth.2024.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 02/11/2024] [Accepted: 02/17/2024] [Indexed: 02/25/2024] Open
Abstract
Molecular optimization, which aims to improve molecular properties by modifying complex molecular structures, is a crucial and challenging task in drug discovery. In recent years, translation models provide a promising way to transform low-property molecules to high-property molecules, which enables molecular optimization to achieve remarkable progress. However, most existing models require matched molecular pairs, which are prone to be limited by the datasets. Although some models do not require matched molecular pairs, their performance is usually sacrificed due to the lack of useful supervising information. To address this issue, a domain-label-guided translation model is proposed in this paper, namely DLTM. In the model, the domain label information of molecules is exploited as a control condition to obtain different embedding representations, enabling the model to generate diverse molecules. Besides, the model adopts a classifier network to identify the property categories of transformed molecules, guiding the model to generate molecules with desired properties. The performance of DLTM is verified on two optimization tasks, namely the quantitative estimation of drug-likeness and penalized logP. Experimental results show that the proposed DLTM is superior to the compared baseline models.
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Affiliation(s)
- Yajie Zhang
- School of Computer Science and Technology, Anhui University, Hefei, 230601, China.
| | - Yongqi Tong
- School of Computer Science and Technology, Anhui University, Hefei, 230601, China.
| | - Xin Xia
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China; School of Artificial Intelligence, Anhui University, Hefei, 230601, China.
| | - Qingwen Wu
- Affiliated Hospital of Jining Medical University, Jining, 272007, China.
| | - Yansen Su
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China; School of Artificial Intelligence, Anhui University, Hefei, 230601, China.
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4
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Wang X, Ge Q, Zeng Q, Zou K, Bao Z, Ying J, Wu Z, Jin H, Chen J, Xu T. Dnmt3b ablation affects fracture repair process by regulating apoptosis. BMC Musculoskelet Disord 2024; 25:180. [PMID: 38413962 PMCID: PMC10900613 DOI: 10.1186/s12891-024-07283-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 02/14/2024] [Indexed: 02/29/2024] Open
Abstract
PURPOSE Previous studies have shown that DNA methyltransferase 3b (Dnmt3b) is the only Dnmt responsive to fracture repair and Dnmt3b ablation in Prx1-positive stem cells and chondrocyte cells both delayed fracture repair. Our study aims to explore the influence of Dnmt3b ablation in Gli1-positive stem cells in fracture healing mice and the underlying mechanism. METHODS We generated Gli1-CreERT2; Dnmt3bflox/flox (Dnmt3bGli1ER) mice to operated tibia fracture. Fracture callus tissues of Dnmt3bGli1ER mice and control mice were collected and analyzed by X-ray, micro-CT, biomechanical testing, histopathology and TUNEL assay. RESULTS The cartilaginous callus significantly decrease in ablation of Dnmt3b in Gli1-positive stem cells during fracture repair. The chondrogenic and osteogenic indicators (Sox9 and Runx2) in the fracture healing tissues in Dnmt3bGli1ER mice much less than control mice. Dnmt3bGli1ER mice led to delayed bone callus remodeling and decreased biomechanical properties of the newly formed bone during fracture repair. Both the expressions of Caspase-3 and Caspase-8 were upregulated in Dnmt3bGli1ER mice as well as the expressions of BCL-2. CONCLUSIONS Our study provides an evidence that Dnmt3b ablation Gli1-positive stem cells can affect fracture healing and lead to poor fracture healing by regulating apoptosis to decrease chondrocyte hypertrophic maturation.
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Affiliation(s)
- Xu Wang
- Institute of Orthopedics and Traumatology, the First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang Province, China
- The First College of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, China
| | - Qinwen Ge
- Institute of Orthopedics and Traumatology, the First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang Province, China
- The First College of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, China
| | - Qinghe Zeng
- Institute of Orthopedics and Traumatology, the First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang Province, China
- The First College of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, China
| | - Kaiao Zou
- Institute of Orthopedics and Traumatology, the First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang Province, China
- The First College of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, China
| | - Zhengsheng Bao
- Institute of Orthopedics and Traumatology, the First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang Province, China
- The Second College of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, China
| | - Jun Ying
- Institute of Orthopedics and Traumatology, the First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang Province, China
- The First College of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, China
| | - Zhen Wu
- Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang Province, China
| | - Hongting Jin
- Institute of Orthopedics and Traumatology, the First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang Province, China.
| | - Jiali Chen
- Institute of Orthopedics and Traumatology, the First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang Province, China.
| | - Taotao Xu
- Institute of Orthopedics and Traumatology, the First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang Province, China.
- The First College of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, China.
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Siddiqui NF, Vishwakarma P, Thakur S, Jadhav HR. Bioactivity predictions and virtual screening using machine learning predictive model. J Biomol Struct Dyn 2024:1-20. [PMID: 38217308 DOI: 10.1080/07391102.2023.2300132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/23/2023] [Indexed: 01/15/2024]
Abstract
Recently, there has been significant attention on machine learning algorithms for predictive modeling. Prediction models for enzyme inhibitors are limited, and it is essential to account for chemical biases while developing them. The lack of repeatability in available models and chemical bias issues constrain drug discovery and development. A new prediction model for enzyme inhibitors has been developed, and the model efficacy was checked using Dipeptidyl peptidase 4 (DPP-4) inhibitors. A Python script was prepared and can be provided for personal use upon request. Among various machine learning algorithms, it was found that Random Forest offers the best accuracy. Two models were compared, one with diverse training and test data and the other with a random split. It was concluded that machine learning predictive models based on the Murcko scaffold can address chemical bias concerns. In-silico screening of the Drug Bank database identified two molecules against DPP-4, which are previously proven hit molecules. The approach was further validated through molecular docking studies and molecular dynamics simulations, demonstrating the credibility and relevance of the developed model for future investigations and potential translation into clinical applications.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Noor Fatima Siddiqui
- Department of Pharmacy, Pharmaceutical Chemistry Research Laboratory, Birla Institute of Technology and Science Pilani, Pilani, RJ, India
| | - Pinky Vishwakarma
- Department of Pharmacy, Pharmaceutical Chemistry Research Laboratory, Birla Institute of Technology and Science Pilani, Pilani, RJ, India
| | - Shikha Thakur
- Department of Pharmacy, Pharmaceutical Chemistry Research Laboratory, Birla Institute of Technology and Science Pilani, Pilani, RJ, India
| | - Hemant R Jadhav
- Department of Pharmacy, Pharmaceutical Chemistry Research Laboratory, Birla Institute of Technology and Science Pilani, Pilani, RJ, India
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AL-Zobaidy MJ, Akeel Al-Hussaniy H, S. Al-tameemi Z. In silico comparison between the mutated and wild-type androgen receptors and their influence on the selection of optimum androgenic receptor blockers for the treatment of prostate cancer. F1000Res 2024; 11:516. [PMID: 38779468 PMCID: PMC11109556 DOI: 10.12688/f1000research.110072.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/05/2023] [Indexed: 05/25/2024] Open
Abstract
Background: Prostate cancer is a disease that occurs in men aged more than 50 years. In Iraq, 8.89 men per 100,000 population suffer from prostate cancer, with the incidence being 14,016 cases and mortality being 6,367 cases. Despite advances in treatment against prostate cancer, it can become resistant to drugs. Therefore, the aim of current study was to search and identify binding sites for the repositioning of drugs by computational methods (docking). Methods: Based on the protein structure of the wild androgen receptor, the analysis parameters (22x22x22 on the X, Y, and Z axes) were established. Results: The interactions of the natural ligands with androgen receptor were 10.0 (testosterone) and 10.8 (dihydrotestosterone) while mutated androgen receptor (T877A) had a low affinity with testosterone and dihydrotestosterone (-5.3 and -6.7, respectively). In the interactions of both receptors with the reported inhibitors (antagonists), a decrease with Bicalutamide (-8.3 and -4.3, respectively) and an increase in affinity with Flutamide and Nilutamide (-7.7 and 8.6, wild AR; -8.7 and -9.3 AR T877A) were observed. As for Enzalutamide and Apalutamide (second-generation antagonists), the change was minimal between wild androgen receptor and T877A (-7.6 and -7.7; -7.3 and -7.3, respectively). The change in the affinity of the ligands with androgen receptor and androgen receptor T877A shows how a mutation alters the bonds between these molecules. Conclusion: The identification of key sites and potent inhibitors against abnormal androgen receptor functions will enrich prostate cancer treatments.
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Affiliation(s)
| | - Hany Akeel Al-Hussaniy
- Department of Pharmacy, Bilad Alrafidain University College, Diyala Junction, Baqubah, Diyala, Iraq
- Dr Hany Akeel Institute, Iraqi Medical Research Center, Baghdad, Iraq
| | - Zahraa S. Al-tameemi
- Department of Pharmacy, Bilad Alrafidain University College, Diyala Junction, Baqubah, Diyala, Iraq
- Dr Hany Akeel Institute, Iraqi Medical Research Center, Baghdad, Iraq
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7
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Huo D, Sun Z, Wang M, Yan A. Ligand and structure based hierarchical virtual screening cascade for finding novel epidermal growth factor receptor inhibitors. Chem Biol Drug Des 2024; 103:e14375. [PMID: 37849030 DOI: 10.1111/cbdd.14375] [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: 05/07/2023] [Revised: 09/11/2023] [Accepted: 10/02/2023] [Indexed: 10/19/2023]
Abstract
The epidermal growth factor receptor (EGFR) tyrosine kinase plays an important role in tumor formation and growth by mediating cell growth and other physiological processes. Therefore, EGFR is a promising target for the treatment of cancer. In this work, we combined ligand-based and structure-based virtual screening methods to identify novel EGFR inhibitors from a library of more than 103 thousand compounds. We first obtained hundreds of compounds with similar physiochemical properties through 3D molecular shape and electrostatic similarity screening with potent inhibitors AEE788 and Afatinib as queries. Next, we identified compounds with strong binding affinities to the EGFR pocket through molecular docking, which makes good use of the structure information of the receptor. After molecular scaffold analysis, our bioassay confirmed 13 compounds with EGFR inhibitory activity and three compounds had IC50 values below 1000 nM. In addition, we collected 5371 EGFR inhibitors from online databases, and clustered them into 7 groups by K-means method using their ECFP4 fingerprints as input. Each cluster had typical molecular fragments and corresponding activity characteristics, which could guide the design of EGFR inhibitors, and we concluded that the fragments from some of the hits are indicated in the highly active scaffolds.
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Affiliation(s)
- Donghui Huo
- State Key Laboratory of Chemical Resource Engineering, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, China
- Dalian (Fushun) Research Institute of Petroleum and Petrochemicals, China Petroleum & Chemical Corporation (SINOPEC), Dalian, China
| | - Zhiqi Sun
- State Key Laboratory of Chemical Resource Engineering, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, China
| | - Maolin Wang
- Clinical Research Center, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China
- Department of Physiology, School of Basic Medical Sciences, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, China
| | - Aixia Yan
- State Key Laboratory of Chemical Resource Engineering, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, China
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8
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Tanabe M, Sakate R, Nakabayashi J, Tsumura K, Ohira S, Iwato K, Kimura T. A novel in silico scaffold-hopping method for drug repositioning in rare and intractable diseases. Sci Rep 2023; 13:19358. [PMID: 37938624 PMCID: PMC10632405 DOI: 10.1038/s41598-023-46648-1] [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: 06/16/2023] [Accepted: 11/03/2023] [Indexed: 11/09/2023] Open
Abstract
In the field of rare and intractable diseases, new drug development is difficult and drug repositioning (DR) is a key method to improve this situation. In this study, we present a new method for finding DR candidates utilizing virtual screening, which integrates amino acid interaction mapping into scaffold-hopping (AI-AAM). At first, we used a spleen associated tyrosine kinase inhibitor as a reference to evaluate the technique, and succeeded in scaffold-hopping maintaining the pharmacological activity. Then we applied this method to five drugs and obtained 144 compounds with diverse structures. Among these, 31 compounds were known to target the same proteins as their reference compounds and 113 compounds were known to target different proteins. We found that AI-AAM dominantly selected functionally similar compounds; thus, these selected compounds may represent improved alternatives to their reference compounds. Moreover, the latter compounds were presumed to bind to the targets of their references as well. This new "compound-target" information provided DR candidates that could be utilized for future drug development.
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Affiliation(s)
- Mao Tanabe
- Laboratory of Rare Disease Information and Resource Library, Center for Intractable Diseases and ImmunoGenomics Research, National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Ibaraki, Osaka, Japan
| | - Ryuichi Sakate
- Laboratory of Rare Disease Information and Resource Library, Center for Intractable Diseases and ImmunoGenomics Research, National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Ibaraki, Osaka, Japan
| | - Jun Nakabayashi
- Analysis Technology Center, FUJIFILM Corporation, 210 Nakanuma, Minami-ashigara, Kanagawa, Japan
| | - Kyosuke Tsumura
- Analysis Technology Center, FUJIFILM Corporation, 210 Nakanuma, Minami-ashigara, Kanagawa, Japan
| | - Shino Ohira
- Analysis Technology Center, FUJIFILM Corporation, 210 Nakanuma, Minami-ashigara, Kanagawa, Japan
| | - Kaoru Iwato
- Analysis Technology Center, FUJIFILM Corporation, 210 Nakanuma, Minami-ashigara, Kanagawa, Japan
| | - Tomonori Kimura
- Reverse Translational Research Project, National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Ibaraki-City, Osaka, Japan.
- KAGAMI Project, National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Ibaraki, Osaka, Japan.
- Department of Nephrology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
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9
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Yao J, Tao Y, Hu Z, Li J, Xue Z, Zhang Y, Lei Y. Optimization of small molecule degraders and antagonists for targeting estrogen receptor based on breast cancer: current status and future. Front Pharmacol 2023; 14:1225951. [PMID: 37808197 PMCID: PMC10551544 DOI: 10.3389/fphar.2023.1225951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Accepted: 09/05/2023] [Indexed: 10/10/2023] Open
Abstract
The estrogen receptor (ER) is a classical receptor protein that plays a crucial role in mediating multiple signaling pathways in various target organs. It has been shown that ER-targeting therapies inhibit breast cancer cell proliferation, enhance neuronal protection, and promote osteoclast formation. Several drugs have been designed to specifically target ER in ER-positive (ER+) breast cancer, including selective estrogen receptor modulators (SERM) such as Tamoxifen. However, the emergence of drug resistance in ER+ breast cancer and the potential side effects on the endometrium which has high ER expression has posed significant challenges in clinical practice. Recently, novel ER-targeted drugs, namely, selective estrogen receptor degrader (SERD) and selective estrogen receptor covalent antagonist (SERCA) have shown promise in addressing these concerns. This paper provides a comprehensive review of the structural functions of ER and highlights recent advancements in SERD and SERCA-related small molecule drugs, especially focusing on their structural optimization strategies and future optimization directions. Additionally, the therapeutic potential and challenges of novel SERDs and SERCAs in breast cancer and other ER-related diseases have been discussed.
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Affiliation(s)
- Jiaqi Yao
- General Practice Ward/International Medical Center, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- College of Life Sciences, Sichuan University, Chengdu, China
| | - Yiran Tao
- West China-California Research Center for Predictive Intervention Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Zelin Hu
- General Practice Ward/International Medical Center, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- College of Life Sciences, Sichuan University, Chengdu, China
| | - Junjie Li
- Precision Medicine Key Laboratory of Sichuan Province and Precision Medicine Research Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Ziyi Xue
- Department of Statistics, College of Liberal Arts and Sciences, University of Illinois Urbana-Champaign, Champaign, IL, United States
| | - Ya Zhang
- West China-California Research Center for Predictive Intervention Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yi Lei
- General Practice Ward/International Medical Center, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
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Oyedele AQK, Adelusi TI, Ogunlana AT, Ayoola MA, Adeyemi RO, Babalola MO, Ayorinde JB, Isong JA, Ajasa TO, Boyenle ID. Promising disruptors of p53-MDM2 dimerization from some medicinal plant phytochemicals: a molecular modeling study. J Biomol Struct Dyn 2023; 41:5817-5826. [PMID: 35822492 DOI: 10.1080/07391102.2022.2097313] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 06/28/2022] [Indexed: 01/18/2023]
Abstract
Cancer is a major global health issue that has a high mortality rate. p53, which functions as a tumor suppressor, is critical in preventing tumor development by regulating the cell cycle and inducing apoptosis in damaged cells. However, the tumor suppressor function of p53 is effectively inhibited by its direct interaction with the hydrophobic cleft of MDM2 protein via multiple mechanisms As a result, restoring p53 activity by blocking the p53-MDM2 protein-protein interaction has been proposed as a compelling therapeutic strategy for cancer treatment. The use of molecular docking and phytochemical screening procedures are appraised to inhibit MDM2's hydrophobic cleft and disrupt the p53-MDM2 interaction. For this purpose, a library of 51 bioactive compounds from 10 medicinal plants was compiled and subjected to structure-based virtual screening. Out of these, only 3 compounds (Atalantoflavone, Cudraxanthone 1, and Ursolic acid) emerged as promising inhibitors of MDM2-p53 based on their binding affinities (-9.1 kcal/mol, -8.8 kcal/mol, and -8.8 kcal/mol respectively) when compared to the standard (-8.8 kcal/mol). Moreover, these compounds showed better pharmacokinetic and drug-like profiling than the standard inhibitor (Chromonotriazolopyrimidine 1). Finally, the 100 ns MD simulation analysis confirmed no significant perturbation in the conformational dynamics of the simulated binary complexes when compared to the standard. In particular, Ursolic acid was found to satisfy the molecular enumeration the most compared to the other inhibitors. Our overall molecular modeling finding shows why these compounds may emerge as potent arsenals for cancer therapeutics. Nonetheless, extensive experimental and clinical research is needed to augment their use in clinics.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Abdul-Quddus Kehinde Oyedele
- Computational Biology, Drug Discovery Laboratory, Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomosho, Nigeria
- Department of Biochemistry and Nutrition, Nigerian Institute of Medical Research (NIMR), Lagos, Nigeria
| | - Temitope Isaac Adelusi
- Computational Biology, Drug Discovery Laboratory, Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomosho, Nigeria
| | - Abdeen Tunde Ogunlana
- Computational Biology, Drug Discovery Laboratory, Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomosho, Nigeria
| | - Mojeed Ashiru Ayoola
- Department of Chemical Sciences, Biochemistry Unit, College of Natural and Applied Science, Fountain University, Osogbo, Nigeria
| | - Rofiat Oluwabusola Adeyemi
- Computational Biology, Drug Discovery Laboratory, Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomosho, Nigeria
| | | | - James Babatunde Ayorinde
- Department of Biochemistry and Nutrition, Nigerian Institute of Medical Research (NIMR), Lagos, Nigeria
| | - Josiah Ayoola Isong
- Department of Biochemistry and Nutrition, Nigerian Institute of Medical Research (NIMR), Lagos, Nigeria
| | - Toheeb Olakunle Ajasa
- Computational Biology, Drug Discovery Laboratory, Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomosho, Nigeria
| | - Ibrahim Damilare Boyenle
- Computational Biology, Drug Discovery Laboratory, Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomosho, Nigeria
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Mendes GO, Pita SSDR, Carvalho PBD, Silva MPD, Taranto AG, Leite FHA. Molecular Multi-Target Approach for Human Acetylcholinesterase, Butyrylcholinesterase and β-Secretase 1: Next Generation for Alzheimer's Disease Treatment. Pharmaceuticals (Basel) 2023; 16:880. [PMID: 37375827 DOI: 10.3390/ph16060880] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 05/26/2023] [Accepted: 05/29/2023] [Indexed: 06/29/2023] Open
Abstract
Alzheimer's Disease (AD) is a neurodegenerative condition characterized by progressive memory loss and other affected cognitive functions. Pharmacological therapy of AD relies on inhibitors of the enzymes acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), offering only a palliative effect and being incapable of stopping or reversing the neurodegenerative process. However, recent studies have shown that inhibiting the enzyme β-secretase 1 (BACE-1) may be able to stop neurodegeneration, making it a promising target. Considering these three enzymatic targets, it becomes feasible to apply computational techniques to guide the identification and planning of molecules capable of binding to all of them. After virtually screening 2119 molecules from a library, 13 hybrids were built and further screened by triple pharmacophoric model, molecular docking, and molecular dynamics (t = 200 ns). The selected hybrid G meets all stereo-electronic requirements to bind to AChE, BChE, and BACE-1 and offers a promising structure for future synthesis, enzymatic testing, and validation.
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Affiliation(s)
- Géssica Oliveira Mendes
- Laboratory of Molecular Modeling, Department of Health, State University of Feira de Santana, Salvador 44036-900, BA, Brazil
- Postgraduate Program in Pharmaceutical Sciences, State University of Feira de Santana, Salvador 44036-900, BA, Brazil
| | - Samuel Silva da Rocha Pita
- Postgraduate Program in Pharmaceutical Sciences, State University of Feira de Santana, Salvador 44036-900, BA, Brazil
- Laboratory of Bioinformatics and Molecular Modeling (LaBiMM), Pharmacy College, Federal University of Bahia (UFBA), Salvador 40170-110, BA, Brazil
| | | | - Michel Pires da Silva
- Laboratory of Bioinformatics and Drug Design, Department of Bioengineering, Federal University of Sao Joao del-Rei, São João del-Rei 36301-1601, MG, Brazil
- Federal Center for Technological Education of Minas Gerais, Department of Informatics, Management and Design, R. Álvares de Azevedo, 400, Bela Vista, Divinópolis 35503-822, MG, Brazil
| | - Alex Gutterres Taranto
- Laboratory of Bioinformatics and Drug Design, Department of Bioengineering, Federal University of Sao Joao del-Rei, São João del-Rei 36301-1601, MG, Brazil
| | - Franco Henrique Andrade Leite
- Laboratory of Molecular Modeling, Department of Health, State University of Feira de Santana, Salvador 44036-900, BA, Brazil
- Postgraduate Program in Pharmaceutical Sciences, State University of Feira de Santana, Salvador 44036-900, BA, Brazil
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12
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Yu Y, Cai C, Wang J, Bo Z, Zhu Z, Zheng H. Uni-Dock: GPU-Accelerated Docking Enables Ultralarge Virtual Screening. J Chem Theory Comput 2023. [PMID: 37125970 DOI: 10.1021/acs.jctc.2c01145] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Molecular docking, a structure-based virtual screening method, is a reliable tool to enrich potential bioactive molecules from molecular databases. With the rapid expansion of compound library sizes, the speed of existing molecular docking programs becomes less than adequate to meet the demand for screening ultralarge libraries containing tens of millions or billions of molecules. Here, we propose Uni-Dock, a GPU-accelerated molecular docking program that supports various scoring functions including vina, vinardo, and ad4. Uni-Dock achieves more than 1000-fold speedup with high accuracy compared with the AutoDock Vina running in single CPU core, outperforming reported GPU-accelerated docking programs including AutoDock-GPU and Vina-GPU based on head-to-head experiments. Uni-Dock docks molecules in batches simultaneously using concurrent threads of each molecule. The data flow between GPU and CPU is optimized to eliminate CPU hotspots and maximize GPU utility. Additionally, Uni-Dock also supports hydrogen bond biased docking for all scoring functions and can be migrated to multiple GPUs of different architectures and manufacturers. We analyzed the improved performance of Uni-Dock on the CASF-2016 and DUD-E datasets and recommend three combinations of hyperparameters corresponding to different docking scenarios. To demonstrate Uni-Dock's capability on routinely screening ultralarge libraries, we performed hierarchical virtual screening experiments with Uni-Dock on the Enamine Diverse REAL druglike set containing 38.2 million molecules to a popular target KRAS G12D in 12 h using 100 NVIDIA V100 GPUs. To the best of our knowledge, Uni-Dock should be the fastest GPU-accelerated docking program to date.
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Affiliation(s)
- Yuejiang Yu
- Beijing DP Technology Co., Ltd., Beijing 100080, China
- School of EECS, Peking University, Beijing 100871, China
| | - Chun Cai
- Beijing DP Technology Co., Ltd., Beijing 100080, China
| | - Jiayue Wang
- Beijing DP Technology Co., Ltd., Beijing 100080, China
| | - Zonghua Bo
- Beijing DP Technology Co., Ltd., Beijing 100080, China
| | - Zhengdan Zhu
- Beijing DP Technology Co., Ltd., Beijing 100080, China
| | - Hang Zheng
- Beijing DP Technology Co., Ltd., Beijing 100080, China
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13
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Ashiru MA, Ogunyemi SO, Temionu OR, Ajibare AC, Cicero-Mfon NC, Ihekuna OA, Jagun MO, Abdulmumin L, Adisa QK, Asibor YE, Okorie CJ, Lawal MO, Babalola MO, Abdulrasaq IT, Salau LB, Olatunji IO, Bankole MA, Daud AB, Adeyemi AO. Identification of EGFR inhibitors as potential agents for cancer therapy: pharmacophore-based modeling, molecular docking, and molecular dynamics investigations. J Mol Model 2023; 29:128. [PMID: 37016200 DOI: 10.1007/s00894-023-05531-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Accepted: 03/24/2023] [Indexed: 04/06/2023]
Abstract
CONTEXT As a member of a large family of proteins that together regulate various aspects of cell growth and development, the epidermal growth factor receptor (EGFR) is a validated target for the development of new drugs. Herein, we compiled a library of 62 compounds from the PubChem database with similar pharmacophores as osimertinib, which to our knowledge represents the only drug capable of overcoming EGFR-T790M-mutated NSCLC until date. Subsequently, we launched a docking-based virtual screening campaign against the EGFR kinase with the compiled chemical entities. The virtual screen identified 3 hit candidates (CID_126667097, CID_137660592, and CID_137659061) with lower binding energy/higher affinity (- 8.7 kcal/mol, - 8.6 kcal/mol, and - 8.5 kcal/mol, respectively) than the standard osimertinib (- 8.4 kcal/mol). Molecular dynamics metrics such as RMSD, RMSF, ROG, and intermolecular H-bond were used to substantiate the stability of the promising drug candidates at the binding pocket of EGFR after 100,000 ps production run. Overall, our molecular modeling study portrayed CID_126667097, CID_137660592, and CID_137659061 as lead-like drug candidates that may be further developed for the treatment of EGFR-associated cancer disease. METHODS Molecular docking was conducted with Autodock Vina. A total of 62 compounds were compiled for the docking screen, which were then downloaded in SMILE format and converted to Protein Data Bank (PDB) format using the Openbabel online server. Finally, Gromacs 2022.3 was used to perform MD simulation to substantiate the stability of the hit candidates.
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Affiliation(s)
- Mojeed Ayoola Ashiru
- Department of Chemical Sciences, Biochemistry Unit, College of Natural and Applied Sciences, Fountain University, Osogbo, Osun State, Nigeria
| | - Sherif Olabisi Ogunyemi
- Faculty of Basic Medical Sciences, Department of Biochemistry, College of Medicine, University of Lagos, Lagos, Lagos State, Nigeria
| | - Oluwakemi Rita Temionu
- Department of Medical Laboratory Technology, Lagos State College of Health Technology, Lagos, Lagos State, Nigeria
| | - Abosede Christina Ajibare
- Faculty of Basic Medical Sciences, Department of Biochemistry, College of Medicine, University of Lagos, Lagos, Lagos State, Nigeria
| | - Nsikak Cicerella Cicero-Mfon
- Faculty of Basic Medical Sciences, Department of Biochemistry, College of Medicine, University of Lagos, Lagos, Lagos State, Nigeria
| | - Ogechi Augustina Ihekuna
- Faculty of Basic Medical Sciences, Department of Biochemistry, College of Medicine, University of Lagos, Lagos, Lagos State, Nigeria
| | - Monsurat Olatoun Jagun
- Department of Medical Laboratory Technology, Lagos State College of Health Technology, Lagos, Lagos State, Nigeria
| | - Latifatu Abdulmumin
- Faculty of Basic Medical Sciences, Department of Biochemistry, College of Medicine, University of Lagos, Lagos, Lagos State, Nigeria
| | - Quadri Kolawole Adisa
- Department of Chemical Sciences, Biochemistry Unit, College of Natural and Applied Sciences, Fountain University, Osogbo, Osun State, Nigeria
| | - Yemisi Elizabeth Asibor
- Faculty of Science, Department of Pure & Applied Chemistry, Osun State University, Osogbo, Osun State, Nigeria
| | - Chika Joseph Okorie
- Faculty of Physical Sciences, Department of Pure & Industrial Chemistry, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria
| | - Mariam Omowumi Lawal
- Faculty of Basic Medical Sciences, Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria
| | - Musa Oladayo Babalola
- Faculty of Basic Medical Sciences, Department of Biochemistry, College of Medicine, University of Lagos, Lagos, Lagos State, Nigeria
| | - Ibrahim Tope Abdulrasaq
- Faculty of Basic Medical Sciences, Department of Biochemistry, College of Medicine, University of Lagos, Lagos, Lagos State, Nigeria
| | - Latifat Bukola Salau
- Faculty of Basic Medical Science, Department of Physiology, University of Lagos, Lagos, Nigeria
| | - Idayat Omotolani Olatunji
- Faculty of Basic Medical Sciences, Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria
| | - Memunat Alake Bankole
- Faculty of Basic Medical Sciences, Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria
| | - Abibat Bisola Daud
- Faculty of Basic Medical Sciences, Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria
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14
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Pivotal role for S-nitrosylation of DNA methyltransferase 3B in epigenetic regulation of tumorigenesis. Nat Commun 2023; 14:621. [PMID: 36739439 PMCID: PMC9899281 DOI: 10.1038/s41467-023-36232-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 01/19/2023] [Indexed: 02/06/2023] Open
Abstract
DNA methyltransferases (DNMTs) catalyze methylation at the C5 position of cytosine with S-adenosyl-L-methionine. Methylation regulates gene expression, serving a variety of physiological and pathophysiological roles. The chemical mechanisms regulating DNMT enzymatic activity, however, are not fully elucidated. Here, we show that protein S-nitrosylation of a cysteine residue in DNMT3B attenuates DNMT3B enzymatic activity and consequent aberrant upregulation of gene expression. These genes include Cyclin D2 (Ccnd2), which is required for neoplastic cell proliferation in some tumor types. In cell-based and in vivo cancer models, only DNMT3B enzymatic activity, and not DNMT1 or DNMT3A, affects Ccnd2 expression. Using structure-based virtual screening, we discovered chemical compounds that specifically inhibit S-nitrosylation without directly affecting DNMT3B enzymatic activity. The lead compound, designated DBIC, inhibits S-nitrosylation of DNMT3B at low concentrations (IC50 ≤ 100 nM). Treatment with DBIC prevents nitric oxide (NO)-induced conversion of human colonic adenoma to adenocarcinoma in vitro. Additionally, in vivo treatment with DBIC strongly attenuates tumor development in a mouse model of carcinogenesis triggered by inflammation-induced generation of NO. Our results demonstrate that de novo DNA methylation mediated by DNMT3B is regulated by NO, and DBIC protects against tumor formation by preventing aberrant S-nitrosylation of DNMT3B.
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15
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Chang Y, Hawkins BA, Du JJ, Groundwater PW, Hibbs DE, Lai F. A Guide to In Silico Drug Design. Pharmaceutics 2022; 15:pharmaceutics15010049. [PMID: 36678678 PMCID: PMC9867171 DOI: 10.3390/pharmaceutics15010049] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/16/2022] [Accepted: 12/17/2022] [Indexed: 12/28/2022] Open
Abstract
The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.
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Affiliation(s)
- Yiqun Chang
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Bryson A. Hawkins
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Jonathan J. Du
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Paul W. Groundwater
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - David E. Hibbs
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Felcia Lai
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
- Correspondence:
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16
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Lin Y, Zhang Y, Wang D, Yang B, Shen YQ. Computer especially AI-assisted drug virtual screening and design in traditional Chinese medicine. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2022; 107:154481. [PMID: 36215788 DOI: 10.1016/j.phymed.2022.154481] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 09/14/2022] [Accepted: 09/27/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Traditional Chinese medicine (TCM), as a significant part of the global pharmaceutical science, the abundant molecular compounds it contains is a valuable potential source of designing and screening new drugs. However, due to the un-estimated quantity of the natural molecular compounds and diversity of the related problems drug discovery such as precise screening of molecular compounds or the evaluation of efficacy, physicochemical properties and pharmacokinetics, it is arduous for researchers to design or screen applicable compounds through old methods. With the rapid development of computer technology recently, especially artificial intelligence (AI), its innovation in the field of virtual screening contributes to an increasing efficiency and accuracy in the process of discovering new drugs. PURPOSE This study systematically reviewed the application of computational approaches and artificial intelligence in drug virtual filtering and devising of TCM and presented the potential perspective of computer-aided TCM development. STUDY DESIGN We made a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Then screening the most typical articles for our research. METHODS The systematic review was performed by following the PRISMA guidelines. The databases PubMed, EMBASE, Web of Science, CNKI were used to search for publications that focused on computer-aided drug virtual screening and design in TCM. RESULT Totally, 42 corresponding articles were included in literature reviewing. Aforementioned studies were of great significance to the treatment and cost control of many challenging diseases such as COVID-19, diabetes, Alzheimer's Disease (AD), etc. Computational approaches and AI were widely used in virtual screening in the process of TCM advancing, which include structure-based virtual screening (SBVS) and ligand-based virtual screening (LBVS). Besides, computational technologies were also extensively applied in absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction of candidate drugs and new drug design in crucial course of drug discovery. CONCLUSIONS The applications of computer and AI play an important role in the drug virtual screening and design in the field of TCM, with huge application prospects.
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Affiliation(s)
- Yumeng Lin
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - You Zhang
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Dongyang Wang
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Bowen Yang
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Ying-Qiang Shen
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
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17
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Menon D, Ranganathan R. A Generative Approach to Materials Discovery, Design, and Optimization. ACS OMEGA 2022; 7:25958-25973. [PMID: 35936396 PMCID: PMC9352221 DOI: 10.1021/acsomega.2c03264] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 07/11/2022] [Indexed: 05/25/2023]
Abstract
Despite its potential to transform society, materials research suffers from a major drawback: its long research timeline. Recently, machine-learning techniques have emerged as a viable solution to this drawback and have shown accuracies comparable to other computational techniques like density functional theory (DFT) at a fraction of the computational time. One particular class of machine-learning models, known as "generative models", is of particular interest owing to its ability to approximate high-dimensional probability distribution functions, which in turn can be used to generate novel data such as molecular structures by sampling these approximated probability distribution functions. This review article aims to provide an in-depth understanding of the underlying mathematical principles of popular generative models such as recurrent neural networks, variational autoencoders, and generative adversarial networks and discuss their state-of-the-art applications in the domains of biomaterials and organic drug-like materials, energy materials, and structural materials. Here, we discuss a broad range of applications of these models spanning from the discovery of drugs that treat cancer to finding the first room-temperature superconductor and from the discovery and optimization of battery and photovoltaic materials to the optimization of high-entropy alloys. We conclude by presenting a brief outlook of the major challenges that lie ahead for the mainstream usage of these models for materials research.
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18
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Zhong C, Ai J, Yang Y, Ma F, Sun W. Small Molecular Drug Screening Based on Clinical Therapeutic Effect. Molecules 2022; 27:molecules27154807. [PMID: 35956770 PMCID: PMC9369618 DOI: 10.3390/molecules27154807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 07/22/2022] [Accepted: 07/25/2022] [Indexed: 11/16/2022] Open
Abstract
Virtual screening can significantly save experimental time and costs for early drug discovery. Drug multi-classification can speed up virtual screening and quickly predict the most likely class for a drug. In this study, 1019 drug molecules with actual therapeutic effects are collected from multiple databases and documents, and molecular sets are grouped according to therapeutic effect and mechanism of action. Molecular descriptors and molecular fingerprints are obtained through SMILES to quantify molecular structures. After using the Kennard–Stone method to divide the data set, a better combination can be obtained by comparing the combined results of five classification algorithms and a fusion method. Furthermore, for a specific data set, the model with the best performance is used to predict the validation data set. The test set shows that prediction accuracy can reach 0.862 and kappa coefficient can reach 0.808. The highest classification accuracy of the validation set is 0.873. The more reliable molecular set has been found, which could be used to predict potential attributes of unknown drug compounds and even to discover new use for old drugs. We hope this research can provide a reference for virtual screening of multiple classes of drugs at the same time in the future.
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Affiliation(s)
| | | | | | | | - Wei Sun
- Correspondence: ; Tel.: +86-10-64445826
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19
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Taldaev A, Terekhov R, Nikitin I, Zhevlakova A, Selivanova I. Insights into the Pharmacological Effects of Flavonoids: The Systematic Review of Computer Modeling. Int J Mol Sci 2022; 23:6023. [PMID: 35682702 PMCID: PMC9181432 DOI: 10.3390/ijms23116023] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 05/21/2022] [Accepted: 05/23/2022] [Indexed: 12/13/2022] Open
Abstract
Computer modeling is a method that is widely used in scientific investigations to predict the biological activity, toxicity, pharmacokinetics, and synthesis strategy of compounds based on the structure of the molecule. This work is a systematic review of articles performed in accordance with the recommendations of PRISMA and contains information on computer modeling of the interaction of classical flavonoids with different biological targets. The review of used computational approaches is presented. Furthermore, the affinities of flavonoids to different targets that are associated with the infection, cardiovascular, and oncological diseases are discussed. Additionally, the methodology of bias risks in molecular docking research based on principles of evidentiary medicine was suggested and discussed. Based on this data, the most active groups of flavonoids and lead compounds for different targets were determined. It was concluded that flavonoids are a promising object for drug development and further research of pharmacology by in vitro, ex vivo, and in vivo models is required.
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Affiliation(s)
- Amir Taldaev
- Laboratoty of Nanobiotechnology, Institute of Biomedical Chemistry, Pogodinskaya Str. 10/8, 119121 Moscow, Russia
- Department of Chemistry, Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia; (R.T.); (I.N.); (A.Z.); (I.S.)
| | - Roman Terekhov
- Department of Chemistry, Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia; (R.T.); (I.N.); (A.Z.); (I.S.)
| | - Ilya Nikitin
- Department of Chemistry, Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia; (R.T.); (I.N.); (A.Z.); (I.S.)
| | - Anastasiya Zhevlakova
- Department of Chemistry, Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia; (R.T.); (I.N.); (A.Z.); (I.S.)
| | - Irina Selivanova
- Department of Chemistry, Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia; (R.T.); (I.N.); (A.Z.); (I.S.)
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Mansoor SE. How Structural Biology Has Directly Impacted Our Understanding of P2X Receptor Function and Gating. Methods Mol Biol 2022; 2510:1-29. [PMID: 35776317 DOI: 10.1007/978-1-0716-2384-8_1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
P2X receptors are ATP-gated ion channels expressed in a wide variety of eukaryotic cells. They play key roles in diverse processes such as platelet activation, smooth muscle contraction, synaptic transmission, nociception, cell proliferation, and inflammation making this receptor family an important pharmacological target. Structures of P2X receptors solved by X-ray crystallography have been instrumental in helping to define mechanisms of molecular P2X receptor function. In 2009, the first X-ray structure of the P2X4 receptor subtype confirmed a trimeric stoichiometry and revealed the overall architecture of the functional ion channel. Subsequent X-ray structures have provided the molecular details to define the orthosteric ATP binding pocket, the orthosteric antagonist binding pocket, an allosteric antagonist binding pocket, and the pore architecture in each of the major conformational states of the receptor gating cycle. Moreover, the unique gating mechanism by which P2X receptor subtypes desensitize at differing rates, referred to as the helical recoil model of receptor desensitization, was discovered directly from X-ray structures of the P2X3 receptor. However, structures of P2X receptors solved by X-ray crystallography have only been able to provide limited information on the cytoplasmic domain of this receptor family, as this domain was always truncated to varying degrees in order to facilitate crystallization. Because the P2X7 receptor subtype has a significantly larger cytoplasmic domain that has been shown to be necessary for its ability to initiate apoptosis, an absence of structural information on the P2X7 receptor cytoplasmic domain has limited our understanding of its complex signaling pathways as well as its unusual ability to remain open without undergoing desensitization. This absence of cytoplasmic structural information for P2X7 receptors was recently overcome when the first full-length P2X7 receptor structures were solved by single-particle cryogenic electron microscopy. These structures finally provide insight into the large and unique P2X7 receptor cytoplasmic domain and revealed two novel structural elements and several surprising findings: first, a cytoplasmic structural element called the cytoplasmic ballast was identified that contains a dinuclear zinc ion complex and a high affinity guanosine nucleotide binding site and second, a palmitoylated membrane proximal structural element called the C-cys anchor was identified which prevents P2X7 receptor desensitization. This chapter will highlight the major structural and functional aspects of P2X receptors discovered through structural biology, with a key emphasis on the most recent cryogenic electron microscopy structures of the full-length, wild-type P2X7 receptor.
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Affiliation(s)
- Steven E Mansoor
- Department of Chemical Physiology and Biochemistry, Oregon Health & Science University, Portland, OR, USA.
- Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR, USA.
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Identification of lead inhibitors of TMPRSS2 isoform 1 of SARS-CoV-2 target using neural network, random forest, and molecular docking. DATA SCIENCE FOR COVID-19 2022. [PMCID: PMC8988906 DOI: 10.1016/b978-0-323-90769-9.00021-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was responsible for over 4 million confirmed cases of severe acute respiratory syndrome, of which more than 300,000 cases were confirmed to be dead as of May 2020. The virulent endocytotic activities of SARS-CoV-2 have been associated with angiotensin-converting enzyme 2 (ACE2) and transmembrane protease serine 2 (TMPRSS2). Previous studies on the viral activation of TMPRSS2 focused most often than not on the isoform 2 of TMPRSS2, but the isoform 1 (529 residues) has also been shown to be expressed in target cells and contribute to viral activation in host. The inhibition of TMPRSS2 has been reported to grossly reduce the pathogenic effects of SARS-CoV-2 endocytotic activities. In this study therefore, we developed two machine learning models using random forest classifier (RFC) and neural networks (NNs) based on 2251 serine protease inhibitors to screen a database of 21,000,000 virtual compounds. We screened the hit compounds using absorption, distribution, metabolism, and excretion (ADME) properties and finally docked the filtered compounds into the predicted binding site of TMPRSS2 isoform 1 homology model to determine their corresponding binding affinity and plausible molecular interactions. One (ASONN) and four (ASOIRFC1–4) lead compounds were obtained from the ADME-NN and RFC filtered hits, respectively, having better binding affinity and lead-likeness properties than those of camostat; this could be due to extensive hydrogen and hydrophobic interactions.
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22
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Mao Y, Li S, Gong B, Lai L, He G, Li H. ePharmer: An integrated and graphical software for pharmacophore-based virtual screening. J Comput Chem 2021; 42:2181-2195. [PMID: 34410013 DOI: 10.1002/jcc.26743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 05/03/2021] [Accepted: 06/01/2021] [Indexed: 11/09/2022]
Abstract
Pharmacophore-based virtual screening (VS) has emerged as an efficient computer-aided drug design technique when appraising multiple ligands with similar structures or targets with unknown crystal structures. Current pharmacophore modeling and analysis software suffers from inadequate integration of mainstream methods and insufficient user-friendly program interface. In this study, we propose a stand-alone, integrated, graphical software for pharmacophore-based VS, termed ePharmer. Both ligand-based and structure-based pharmacophore generation methods were integrated into a compact architecture. Fine-grained modules were carefully organized into the computing, integration, and visualization layers. Graphical design covered the global user interface and specific user operations including editing, evaluation, and task management. Metabolites prediction analysis with the chosen VS result is provided for preselection of wet experiments. Moreover, the underlying computing units largely adopted the preliminary work of our research team. The presented software is currently in client use and will be released for both professional and nonexpert users. Experimental results verified the favorable computing capability, user convenience, and case performance of the proposed software.
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Affiliation(s)
- Yuxia Mao
- School of Computer Science and Technology, East China Normal University, Shanghai, China
| | - Shiliang Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, China
| | - Bojie Gong
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China
| | - Luhua Lai
- BNLMS, Peking-Tsinghua Center for Life Sciences at College of Chemistry and Molecular Engineering, Peking University, Beijing, China
| | - Gaoqi He
- School of Computer Science and Technology, East China Normal University, Shanghai, China
| | - Honglin Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, China
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Singh YP, Moses JC, Bhardwaj N, Mandal BB. Overcoming the Dependence on Animal Models for Osteoarthritis Therapeutics - The Promises and Prospects of In Vitro Models. Adv Healthc Mater 2021; 10:e2100961. [PMID: 34302436 DOI: 10.1002/adhm.202100961] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 07/10/2021] [Indexed: 12/19/2022]
Abstract
Osteoarthritis (OA) is a musculoskeletal disease characterized by progressive degeneration of osteochondral tissues. Current treatment is restricted to the reduction of pain and loss of function of the joint. To better comprehend the OA pathophysiological conditions, several models are employed, however; there is no consensus on a suitable model. In this review, different in vitro models being developed for possible therapeutic intervention of OA are outlined. Herein, various in vitro OA models starting from 2D model, co-culture model, 3D models, dynamic culture model to advanced technologies-based models such as 3D bioprinting, bioassembly, organoids, and organ-on-chip-based models are discussed with their advantages and disadvantages. Besides, different growth factors, cytokines, and chemicals being utilized for induction of OA condition are reviewed in detail. Furthermore, there is focus on scrutinizing different molecular and possible therapeutic targets for better understanding the mechanisms and OA therapeutics. Finally, the underlying challenges associated with in vitro models are discussed followed by future prospective. Taken together, a comprehensive overview of in vitro OA models, factors to induce OA-like conditions, and intricate molecular targets with the potential to develop personalized osteoarthritis therapeutics in the future with clinical translation is provided.
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Affiliation(s)
- Yogendra Pratap Singh
- Department of Biosciences and Bioengineering Indian Institute of Technology Guwahati Guwahati Assam 781039 India
| | - Joseph Christakiran Moses
- Department of Biosciences and Bioengineering Indian Institute of Technology Guwahati Guwahati Assam 781039 India
| | - Nandana Bhardwaj
- Department of Science and Mathematics Indian Institute of Information Technology Guwahati Bongora Guwahati Assam 781015 India
| | - Biman B. Mandal
- Department of Biosciences and Bioengineering Indian Institute of Technology Guwahati Guwahati Assam 781039 India
- Centre for Nanotechnology Indian Institute of Technology Guwahati Guwahati Assam 781039 India
- School of Health Sciences and Technology Indian Institute of Technology Guwahati Guwahati Assam 781039 India
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24
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Idris MO, Yekeen AA, Alakanse OS, Durojaye OA. Computer-aided screening for potential TMPRSS2 inhibitors: a combination of pharmacophore modeling, molecular docking and molecular dynamics simulation approaches. J Biomol Struct Dyn 2021; 39:5638-5656. [PMID: 32672528 PMCID: PMC7441808 DOI: 10.1080/07391102.2020.1792346] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Accepted: 06/24/2020] [Indexed: 12/13/2022]
Abstract
Transmembrane serine protease 2 (TMPRSS2) has been established as one of the host proteins that facilitate entry of coronaviruses into host cells. One of the approaches often employed towards preventing the entry and proliferation of viruses is computer-aided inhibition studies to identify potent compounds that can inhibit activity of viral targets in the host through binding at the active site. In this study, we developed a pharmacophore model of reportedly potent drugs against severe acute respiratory syndrome coronaviruses 1 and 2 (SARS-CoV-1 and -2). The model was used to screen the ZINC database for commercially available compounds having similar features with the experimentally tested drugs. The top 3000 compounds retrieved were docked into the active sites of a homology-modelled TMPRSS2. Docking scores of the top binders were validated and the top-ranked compounds were subjected to ADME, Lipinski's and medicinal Chemistry property predictions for druglikeness analyses. Two lead compounds, ZINC64606047 and ZINC05296775, were identified having binding affinities higher than those of the reference inhibitors, favorable interactions with TMPRSS2 active site residues and good ADME and medicinal chemistry properties. Molecular dynamics simulation was used to assess the stability and dynamics of the interactions of these compounds with TMPRSS2. Binding free energy and contribution energy evaluations were determined using MMPBSA method. Analyses of the trajectory dynamics collectively established further that the lead compounds bound and interacted stably with active site residues of TMPRSS2. Nonetheless, experimental studies are needed to further assess the potentials of these compounds as possible therapeutics against coronaviruses.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
| | - Abeeb Abiodun Yekeen
- School of Life Sciences, University of Science and Technology of China, Hefei, Anhui, China
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Jiménez-Avalos G, Vargas-Ruiz AP, Delgado-Pease NE, Olivos-Ramirez GE, Sheen P, Fernández-Díaz M, Quiliano M, Zimic M. Comprehensive virtual screening of 4.8 k flavonoids reveals novel insights into allosteric inhibition of SARS-CoV-2 M PRO. Sci Rep 2021; 11:15452. [PMID: 34326429 PMCID: PMC8322093 DOI: 10.1038/s41598-021-94951-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 06/29/2021] [Indexed: 02/07/2023] Open
Abstract
SARS-CoV-2 main protease is a common target for inhibition assays due to its high conservation among coronaviruses. Since flavonoids show antiviral activity, several in silico works have proposed them as potential SARS-CoV-2 main protease inhibitors. Nonetheless, there is reason to doubt certain results given the lack of consideration for flavonoid promiscuity or main protease plasticity, usage of short library sizes, absence of control molecules and/or the limitation of the methodology to a single target site. Here, we report a virtual screening study where dorsilurin E, euchrenone a11, sanggenol O and CHEMBL2171598 are proposed to inhibit main protease through different pathways. Remarkably, novel structural mechanisms were observed after sanggenol O and CHEMBL2171598 bound to experimentally proven allosteric sites. The former drastically affected the active site, while the latter triggered a hinge movement which has been previously reported for an inactive SARS-CoV main protease mutant. The use of a curated database of 4.8 k flavonoids, combining two well-known docking software (AutoDock Vina and AutoDock4.2), molecular dynamics and MMPBSA, guaranteed an adequate analysis and robust interpretation. These criteria can be considered for future screening campaigns against SARS-CoV-2 main protease.
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Affiliation(s)
- Gabriel Jiménez-Avalos
- Laboratorio de Bioinformática, Biología Molecular y Desarrollos Tecnológicos, Facultad de Ciencias y Filosofía, Departamento de Ciencias Celulares y Moleculares, Universidad Peruana Cayetano Heredia (UPCH), 15102, Lima, Peru.
| | - A Paula Vargas-Ruiz
- Laboratorio de Bioinformática, Biología Molecular y Desarrollos Tecnológicos, Facultad de Ciencias y Filosofía, Departamento de Ciencias Celulares y Moleculares, Universidad Peruana Cayetano Heredia (UPCH), 15102, Lima, Peru
| | - Nicolás E Delgado-Pease
- Laboratorio de Bioinformática, Biología Molecular y Desarrollos Tecnológicos, Facultad de Ciencias y Filosofía, Departamento de Ciencias Celulares y Moleculares, Universidad Peruana Cayetano Heredia (UPCH), 15102, Lima, Peru
| | - Gustavo E Olivos-Ramirez
- Laboratorio de Bioinformática, Biología Molecular y Desarrollos Tecnológicos, Facultad de Ciencias y Filosofía, Departamento de Ciencias Celulares y Moleculares, Universidad Peruana Cayetano Heredia (UPCH), 15102, Lima, Peru
| | - Patricia Sheen
- Laboratorio de Bioinformática, Biología Molecular y Desarrollos Tecnológicos, Facultad de Ciencias y Filosofía, Departamento de Ciencias Celulares y Moleculares, Universidad Peruana Cayetano Heredia (UPCH), 15102, Lima, Peru
| | | | - Miguel Quiliano
- Faculty of Health Sciences, Centre for Research and Innovation, Universidad Peruana de Ciencias Aplicadas (UPC), 15023, Lima, Peru
| | - Mirko Zimic
- Laboratorio de Bioinformática, Biología Molecular y Desarrollos Tecnológicos, Facultad de Ciencias y Filosofía, Departamento de Ciencias Celulares y Moleculares, Universidad Peruana Cayetano Heredia (UPCH), 15102, Lima, Peru.
- Farmacológicos Veterinarios - FARVET S.A.C. Chincha, Lima, Peru.
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26
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Zhang H, Mao J, Yang YL, Liu CT, Shen C, Zhang HR, Xie HZ, Ding L. Discovery of novel tubulin inhibitors targeting taxanes site by virtual screening, molecular dynamic simulation, and biological evaluation. J Cell Biochem 2021; 122:1609-1624. [PMID: 34237164 DOI: 10.1002/jcb.30077] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 06/22/2021] [Accepted: 06/24/2021] [Indexed: 02/05/2023]
Abstract
Microtubules play crucial role in process of mitosis and cell proliferation, which have been considered as attractive drug targets for anticancer therapy. The aim of this study was to discover novel and chemically diverse tubulin inhibitors for treatment of cancer. In this investigation, the multilayer virtual screening methods, including common feature pharmacophore model, structure-based pharmacophore model and molecular docking, were developed to screen BioDiversity database with 30,000 compounds. A total of 102 compounds were obtained by the virtual screening, and further filtered by diverse chemical clusters with desired properties and PAINS analysis. Finally, 50 compounds were selected and submitted to the biological evaluation. Among these hits, hits 8 and 30 with novel scaffolds displayed stronger antiproliferative activity on four human tumor cells including Hela, A549, MCF-7, and HepG2. Moreover, the two hits were subsequently submitted to molecular dynamic simulations of 90 ns with the aim of exploring the stability of ligand-protein interactions into the binding pocket, and further probing the mechanism of the interaction between tubulin and hits. The molecular dynamic simulation results revealed there had stronger interactions between tubulin and hits in equilibrium state. Therefore, the hits 8 and 30 have been well characterized as lead compounds for developing new tubulin inhibitors with potential anticancer activity.
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Affiliation(s)
- Hui Zhang
- Department of Pharmaceutical Engineering, College of Life Science, Northwest Normal University, Lanzhou, Gansu, China.,State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jun Mao
- Department of Pharmaceutical Engineering, College of Life Science, Northwest Normal University, Lanzhou, Gansu, China
| | - Yan-Li Yang
- Department of Pharmaceutical Engineering, College of Life Science, Northwest Normal University, Lanzhou, Gansu, China
| | - Chun-Tao Liu
- Department of Pharmaceutical Engineering, College of Life Science, Northwest Normal University, Lanzhou, Gansu, China
| | - Chen Shen
- Department of Pharmaceutical Engineering, College of Life Science, Northwest Normal University, Lanzhou, Gansu, China
| | - Hong-Rui Zhang
- Department of Pharmaceutical Engineering, College of Life Science, Northwest Normal University, Lanzhou, Gansu, China
| | - Huan-Zhang Xie
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.,Department of Marine Drug R&D Center, Institute of Oceanography, MinJiang University, Fuzhou, Fujian, China
| | - Lan Ding
- Department of Pharmaceutical Engineering, College of Life Science, Northwest Normal University, Lanzhou, Gansu, China
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27
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Dos Santos Nascimento IJ, de Aquino TM, da Silva-Júnior EF. Drug Repurposing: A Strategy for Discovering Inhibitors against Emerging Viral Infections. Curr Med Chem 2021; 28:2887-2942. [PMID: 32787752 DOI: 10.2174/0929867327666200812215852] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 07/21/2020] [Accepted: 07/22/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Viral diseases are responsible for several deaths around the world. Over the past few years, the world has seen several outbreaks caused by viral diseases that, for a long time, seemed to possess no risk. These are diseases that have been forgotten for a long time and, until nowadays, there are no approved drugs or vaccines, leading the pharmaceutical industry and several research groups to run out of time in the search for new pharmacological treatments or prevention methods. In this context, drug repurposing proves to be a fast and economically viable technique, considering the fact that it uses drugs that have a well-established safety profile. Thus, in this review, we present the main advances in drug repurposing and their benefit for searching new treatments against emerging viral diseases. METHODS We conducted a search in the bibliographic databases (Science Direct, Bentham Science, PubMed, Springer, ACS Publisher, Wiley, and NIH's COVID-19 Portfolio) using the keywords "drug repurposing", "emerging viral infections" and each of the diseases reported here (CoV; ZIKV; DENV; CHIKV; EBOV and MARV) as an inclusion/exclusion criterion. A subjective analysis was performed regarding the quality of the works for inclusion in this manuscript. Thus, the selected works were those that presented drugs repositioned against the emerging viral diseases presented here by means of computational, high-throughput screening or phenotype-based strategies, with no time limit and of relevant scientific value. RESULTS 291 papers were selected, 24 of which were CHIKV; 52 for ZIKV; 43 for DENV; 35 for EBOV; 10 for MARV; and 56 for CoV and the rest (72 papers) related to the drugs repurposing and emerging viral diseases. Among CoV-related articles, most were published in 2020 (31 papers), updating the current topic. Besides, between the years 2003 - 2005, 10 articles were created, and from 2011 - 2015, there were 7 articles, portraying the outbreaks that occurred at that time. For ZIKV, similar to CoV, most publications were during the period of outbreaks between the years 2016 - 2017 (23 articles). Similarly, most CHIKV (13 papers) and DENV (14 papers) publications occur at the same time interval. For EBOV (13 papers) and MARV (4 papers), they were between the years 2015 - 2016. Through this review, several drugs were highlighted that can be evolved in vivo and clinical trials as possible used against these pathogens showed that remdesivir represent potential treatments against CoV. Furthermore, ribavirin may also be a potential treatment against CHIKV; sofosbuvir against ZIKV; celgosivir against DENV, and favipiravir against EBOV and MARV, representing new hopes against these pathogens. CONCLUSION The conclusions of this review manuscript show the potential of the drug repurposing strategy in the discovery of new pharmaceutical products, as from this approach, drugs could be used against emerging viral diseases. Thus, this strategy deserves more attention among research groups and is a promising approach to the discovery of new drugs against emerging viral diseases and also other diseases.
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Off-Target-Based Design of Selective HIV-1 PROTEASE Inhibitors. Int J Mol Sci 2021; 22:ijms22116070. [PMID: 34199858 PMCID: PMC8200130 DOI: 10.3390/ijms22116070] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 05/28/2021] [Accepted: 06/02/2021] [Indexed: 11/17/2022] Open
Abstract
The approval of the first HIV-1 protease inhibitors (HIV-1 PRIs) marked a fundamental step in the control of AIDS, and this class of agents still represents the mainstay therapy for this illness. Despite the undisputed benefits, the necessary lifelong treatment led to numerous severe side-effects (metabolic syndrome, hepatotoxicity, diabetes, etc.). The HIV-1 PRIs are capable of interacting with "secondary" targets (off-targets) characterized by different biological activities from that of HIV-1 protease. In this scenario, the in-silico techniques undoubtedly contributed to the design of new small molecules with well-fitting selectivity against the main target, analyzing possible undesirable interactions that are already in the early stages of the research process. The present work is focused on a new mixed-hierarchical, ligand-structure-based protocol, which is centered on an on/off-target approach, to identify the new selective inhibitors of HIV-1 PR. The use of the well-established, ligand-based tools available in the DRUDIT web platform, in combination with a conventional, structure-based molecular docking process, permitted to fast screen a large database of active molecules and to select a set of structure with optimal on/off-target profiles. Therefore, the method exposed herein, could represent a reliable help in the research of new selective targeted small molecules, permitting to design new agents without undesirable interactions.
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29
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Qin T, Zhu Z, Wang XS, Xia J, Wu S. Computational representations of protein-ligand interfaces for structure-based virtual screening. Expert Opin Drug Discov 2021; 16:1175-1192. [PMID: 34011222 DOI: 10.1080/17460441.2021.1929921] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Introduction: Structure-based virtual screening (SBVS) is an essential strategy for hit identification. SBVS primarily uses molecular docking, which exploits the protein-ligand binding mode and associated affinity score for compound ranking. Previous studies have shown that computational representation of protein-ligand interfaces and the later establishment of machine learning models are efficacious in improving the accuracy of SBVS.Areas covered: The authors review the computational methods for representing protein-ligand interfaces, which include the traditional ones that use deliberately designed fingerprints and descriptors and the more recent methods that automatically extract features with deep learning. The effects of these methods on the performance of machine learning models are briefly discussed. Additionally, case studies that applied various computational representations to machine learning are cited with remarks.Expert opinion: It has become a trend to extract binding features automatically by deep learning, which uses a completely end-to-end representation. However, there is still plenty of scope for improvement . The interpretability of deep-learning models, the organization of data management, the quantity and quality of available data, and the optimization of hyperparameters could impact the accuracy of feature extraction. In addition, other important structural factors such as water molecules and protein flexibility should be considered.
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Affiliation(s)
- Tong Qin
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zihao Zhu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiang Simon Wang
- Artificial Intelligence and Drug Discovery Core Laboratory for District of Columbia Center for AIDS Research (DC CFAR), Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, U.S.A
| | - Jie Xia
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Song Wu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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30
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Chen W, Chen Q, Kumar A, Jiang X, Zhang KYJ, Yang Q. Structure-based virtual screening of highly potent inhibitors of the nematode chitinase CeCht1. J Enzyme Inhib Med Chem 2021; 36:1198-1204. [PMID: 34074203 PMCID: PMC8174485 DOI: 10.1080/14756366.2021.1931862] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
Abstract
Nematode chitinases play vital roles in various physiological processes, including egg hatching, larva moulting, and reproduction. Small-molecule inhibitors of nematode chitinases have potential applications for controlling nematode pests. On the basis of the crystal structure of CeCht1, a representative chitinase indispensable to the eggshell chitin degradation of the model nematode Caenorhabditis elegans, we have discovered a series of novel inhibitors bearing a (R)-3,4-diphenyl-4,5-dihydropyrrolo[3,4-c]pyrazol-6(2H)-one scaffold by hierarchical virtual screening. The crystal structures of CeCht1 complexed with two of these inhibitors clearly elucidated their interactions with the enzyme active site. Based on the inhibitory mechanism, several analogues with improved inhibitory activities were identified, among which the compound PP28 exhibited the most potent activity with a Ki value of 0.18 μM. This work provides the structural basis for the development of novel nematode chitinase inhibitors.
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Affiliation(s)
- Wei Chen
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Qi Chen
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Ashutosh Kumar
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, Yokohama, Japan
| | - Xi Jiang
- Guangdong Laboratory for Lingnan Modern Agriculture (Shenzhen Branch), Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Kam Y J Zhang
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, Yokohama, Japan
| | - Qing Yang
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China.,Guangdong Laboratory for Lingnan Modern Agriculture (Shenzhen Branch), Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.,School of Bioengineering, Dalian University of Technology, Dalian, China
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31
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Lloyd MD, Yevglevskis M, Nathubhai A, James TD, Threadgill MD, Woodman TJ. Racemases and epimerases operating through a 1,1-proton transfer mechanism: reactivity, mechanism and inhibition. Chem Soc Rev 2021; 50:5952-5984. [PMID: 34027955 PMCID: PMC8142540 DOI: 10.1039/d0cs00540a] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Indexed: 12/12/2022]
Abstract
Racemases and epimerases catalyse changes in the stereochemical configurations of chiral centres and are of interest as model enzymes and as biotechnological tools. They also occupy pivotal positions within metabolic pathways and, hence, many of them are important drug targets. This review summarises the catalytic mechanisms of PLP-dependent, enolase family and cofactor-independent racemases and epimerases operating by a deprotonation/reprotonation (1,1-proton transfer) mechanism and methods for measuring their catalytic activity. Strategies for inhibiting these enzymes are reviewed, as are specific examples of inhibitors. Rational design of inhibitors based on substrates has been extensively explored but there is considerable scope for development of transition-state mimics and covalent inhibitors and for the identification of inhibitors by high-throughput, fragment and virtual screening approaches. The increasing availability of enzyme structures obtained using X-ray crystallography will facilitate development of inhibitors by rational design and fragment screening, whilst protein models will facilitate development of transition-state mimics.
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Affiliation(s)
- Matthew D Lloyd
- Drug & Target Discovery, Department of Pharmacy & Pharmacology, University of Bath, Claverton Down, Bath BA2 7AY, UK.
| | - Maksims Yevglevskis
- Drug & Target Discovery, Department of Pharmacy & Pharmacology, University of Bath, Claverton Down, Bath BA2 7AY, UK. and CatSci Ltd., CBTC2, Capital Business Park, Wentloog, Cardiff CF3 2PX, UK
| | - Amit Nathubhai
- Drug & Target Discovery, Department of Pharmacy & Pharmacology, University of Bath, Claverton Down, Bath BA2 7AY, UK. and University of Sunderland, School of Pharmacy & Pharmaceutical Sciences, Sciences Complex, Sunderland SR1 3SD, UK
| | - Tony D James
- Department of Chemistry, University of Bath, Claverton Down, Bath BA2 7AY, UK and School of Chemistry and Chemical Engineering, Henan Normal University, Xinxiang 453007, People's Republic of China
| | - Michael D Threadgill
- Drug & Target Discovery, Department of Pharmacy & Pharmacology, University of Bath, Claverton Down, Bath BA2 7AY, UK. and Institute of Biological, Environmental & Rural Sciences, Aberystwyth University, Aberystwyth SY23 3BY, UK
| | - Timothy J Woodman
- Drug & Target Discovery, Department of Pharmacy & Pharmacology, University of Bath, Claverton Down, Bath BA2 7AY, UK.
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Varela‐Rial A, Majewski M, De Fabritiis G. Structure based virtual screening: Fast and slow. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1544] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Alejandro Varela‐Rial
- Acellera Labs Barcelona Spain
- Computational Science Laboratory Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB) Barcelona Spain
| | - Maciej Majewski
- Computational Science Laboratory Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB) Barcelona Spain
| | - Gianni De Fabritiis
- Computational Science Laboratory Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB) Barcelona Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA) Barcelona Spain
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33
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Chukwuemeka PO, Umar HI, Iwaloye O, Oretade OM, Olowosoke CB, Elabiyi MO, Igbe FO, Oretade OJ, Eigbe JO, Adeojo FJ. Targeting p53-MDM2 interactions to identify small molecule inhibitors for cancer therapy: beyond "Failure to rescue". J Biomol Struct Dyn 2021; 40:9158-9176. [PMID: 33988074 DOI: 10.1080/07391102.2021.1924267] [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] [Indexed: 12/14/2022]
Abstract
At present, disrupting p53-MDM2 interactions through small molecule ligands is a promising approach to safe treatment and management of human cancer. Tumor cells unlike the normal cells, are rapidly evolving affecting the efficacy of many approved anti-cancer agents due to drug resistance. Therefore, identifying a potential anticancer compound is crucial. Pharmacophore based virtual screening, followed by molecular docking, ADMET evaluation, and molecular dynamics studies against MDM2 protein was investigated to identify potential ligands that may act as inhibitors. The model (AHRR_1) with survival score (4.176) was selected among the top ranked generated Pharmacophore hypothesis. Validation of the model hypothesis by an external dataset of actives and inactive compounds produced significant validation attributes including; AUC = 0.85, BEDROC = 0.56 at α = 20.0, RIE = 8.18, AUAC = 0.88, and EF of 6.2 at the top 2% of the dataset. The model was use for screening the ZINC database, and the top 1375 hits satisfying the model hypothesis were subjected to molecular docking studies to understand the molecular and structural basis of selectivity of compounds for MDM2 protein. A sub-set of 25 compounds with binding energy lower than the reference inhibitors were evaluated for pharmacokinetic properties. Four compounds (ZINC02639178, ZINC06752762, ZINC38933175, and ZINC77969611) showed the most desired pharmacokinetic profile. Lastly, investigation of the dynamic behaviour of leads-protein complexes through MD simulation showed similar RMSD, RMSF, and H-bond occupancy profile compared to a reference inhibitor, suggesting stability throughout the simulation time. However, ZINC02639178 was found to satisfy the molecular enumeration the most compared to the other three leads. It may emerge as potential treatment option after extensive experimental studies. Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Prosper Obed Chukwuemeka
- Department of Biotechnology, School of Sciences (SOS), Federal University of Technology Akure, Akure, Nigeria
| | - Haruna Isiyaku Umar
- Department of Biochemistry, School of Sciences (SOS), Federal University of Technology Akure, Akure, Nigeria
| | - Opeyemi Iwaloye
- Bioinformatics and Molecular biology unit, Department of Biochemistry, School of Sciences (SOS), Federal University of Technology Akure, Akure, Nigeria
| | - Oluwaseyi Matthew Oretade
- Department of Biotechnology, School of Sciences (SOS), Federal University of Technology Akure, Akure, Nigeria
| | | | - Michael Omoniyi Elabiyi
- Department of Microbiology, School of Sciences (SOS), Federal University of Technology Akure, Akure, Nigeria
| | - Festus Omotere Igbe
- Department of Biochemistry, School of Sciences (SOS), Federal University of Technology Akure, Akure, Nigeria
| | - Oyeyemi Janet Oretade
- Department of Physiology, College of Health Science (CHS), Osun State University, Osogbo, Nigeria
| | - Joy Oseme Eigbe
- Department of Biomedical Technology, School of Health and Health Technology (SHHT), Federal University of Technology Akure, Akure, Nigeria
| | - Funmilayo Janet Adeojo
- Department of Biotechnology, School of Sciences (SOS), Federal University of Technology Akure, Akure, Nigeria
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34
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Santana K, do Nascimento LD, Lima e Lima A, Damasceno V, Nahum C, Braga RC, Lameira J. Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products. Front Chem 2021; 9:662688. [PMID: 33996755 PMCID: PMC8117418 DOI: 10.3389/fchem.2021.662688] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 02/25/2021] [Indexed: 12/22/2022] Open
Abstract
Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.
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Affiliation(s)
- Kauê Santana
- Instituto de Biodiversidade, Universidade Federal do Oeste do Pará, Santarém, Brazil
| | | | - Anderson Lima e Lima
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | - Vinícius Damasceno
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | - Claudio Nahum
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | | | - Jerônimo Lameira
- Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Brazil
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35
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Kimber TB, Chen Y, Volkamer A. Deep Learning in Virtual Screening: Recent Applications and Developments. Int J Mol Sci 2021; 22:4435. [PMID: 33922714 PMCID: PMC8123040 DOI: 10.3390/ijms22094435] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 04/13/2021] [Accepted: 04/14/2021] [Indexed: 01/03/2023] Open
Abstract
Drug discovery is a cost and time-intensive process that is often assisted by computational methods, such as virtual screening, to speed up and guide the design of new compounds. For many years, machine learning methods have been successfully applied in the context of computer-aided drug discovery. Recently, thanks to the rise of novel technologies as well as the increasing amount of available chemical and bioactivity data, deep learning has gained a tremendous impact in rational active compound discovery. Herein, recent applications and developments of machine learning, with a focus on deep learning, in virtual screening for active compound design are reviewed. This includes introducing different compound and protein encodings, deep learning techniques as well as frequently used bioactivity and benchmark data sets for model training and testing. Finally, the present state-of-the-art, including the current challenges and emerging problems, are examined and discussed.
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Affiliation(s)
| | | | - Andrea Volkamer
- In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany; (T.B.K.); (Y.C.)
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36
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Berenger F, Kumar A, Zhang KYJ, Yamanishi Y. Lean-Docking: Exploiting Ligands' Predicted Docking Scores to Accelerate Molecular Docking. J Chem Inf Model 2021; 61:2341-2352. [PMID: 33861591 DOI: 10.1021/acs.jcim.0c01452] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
In structure-based virtual screening (SBVS), a binding site on a protein structure is used to search for ligands with favorable nonbonded interactions. Because it is computationally difficult, docking is time-consuming and any docking user will eventually encounter a chemical library that is too big to dock. This problem might arise because there is not enough computing power or because preparing and storing so many three-dimensional (3D) ligands requires too much space. In this study, however, we show that quality regressors can be trained to predict docking scores from molecular fingerprints. Although typical docking has a screening rate of less than one ligand per second on one CPU core, our regressors can predict about 5800 docking scores per second. This approach allows us to focus docking on the portion of a database that is predicted to have docking scores below a user-chosen threshold. Herein, usage examples are shown, where only 25% of a ligand database is docked, without any significant virtual screening performance loss. We call this method "lean-docking". To validate lean-docking, a massive docking campaign using several state-of-the-art docking software packages was undertaken on an unbiased data set, with only wet-lab tested active and inactive molecules. Although regressors allow the screening of a larger chemical space, even at a constant docking power, it is also clear that significant progress in the virtual screening power of docking scores is desirable.
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Affiliation(s)
- Francois Berenger
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka 820-8502, Japan
| | - Ashutosh Kumar
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Kam Y J Zhang
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka 820-8502, Japan
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Sahin K, Orhan MD, Avsar T, Durdagi S. Hybrid In Silico and TR-FRET-Guided Discovery of Novel BCL-2 Inhibitors. ACS Pharmacol Transl Sci 2021; 4:1111-1123. [PMID: 34151203 DOI: 10.1021/acsptsci.0c00210] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Indexed: 12/31/2022]
Abstract
B-Cell lymphoma 2 (BCL-2) regulates cell death in humans. In this study, combined multiscale in silico approaches and in vitro studies were employed. A small-molecule library that includes more than 210 000 compounds was used. The predicted therapeutic activity value (TAV) of the compounds in this library was computed with the binary cancer quantitative structure-activity relationships (QSAR) model. The molecules with a high calculated TAV were used in 26 individual toxicity QSAR models. As a result of this screening protocol, 288 nontoxic molecules with high predicted TAV were identified. These selected hits were then screened against the BCL-2 target protein using hybrid docking and molecular dynamics (MD) simulations. The interaction energies of identified compounds were compared with two known BCL-2 inhibitors. Then, the short MD simulations were carried out by initiating the best docking poses of 288 molecules. Average MM/GBSA energies were computed, and long MD simulations were employed to selected hits. The same calculations were also applied for two known BCL-2 inhibitors. Moreover, a five-site (AHRRR) structure-based pharmacophore model was constructed, and this model was used in the screening of the same database. On the basis of hybrid data-driven ligand identification study, final hits were selected and used in in vitro studies. Based on results of the time-resolved fluorescence resonance energy transfer (TR-FRET) analysis, further filtration was carried out for the U87-MG cell line tests. MTT cell proliferation assay analysis results showed that selected three potent compounds were significantly effective on glioma cells.
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Affiliation(s)
- Kader Sahin
- Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahcesehir University, Istanbul 34353, Turkey
| | - Muge Didem Orhan
- Neuroscience Program, Health Sciences Institute, Bahcesehir University, Istanbul 34353, Turkey.,Neuroscience Laboratory, Health Sciences Institute, Bahcesehir University, Istanbul 34353, Turkey
| | - Timucin Avsar
- Neuroscience Program, Health Sciences Institute, Bahcesehir University, Istanbul 34353, Turkey.,Neuroscience Laboratory, Health Sciences Institute, Bahcesehir University, Istanbul 34353, Turkey.,Department of Medical Biology, School of Medicine, Bahcesehir University, Istanbul 34353, Turkey
| | - Serdar Durdagi
- Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahcesehir University, Istanbul 34353, Turkey.,Neuroscience Program, Health Sciences Institute, Bahcesehir University, Istanbul 34353, Turkey.,Neuroscience Laboratory, Health Sciences Institute, Bahcesehir University, Istanbul 34353, Turkey.,Virtual Drug Screening and Development Laboratory, School of Medicine, Bahcesehir University, Istanbul 34353, Turkey
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Chen Q, Chen W, Kumar A, Jiang X, Janezic M, Zhang KYJ, Yang Q. Crystal Structure and Structure-Based Discovery of Inhibitors of the Nematode Chitinase CeCht1. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2021; 69:3519-3526. [PMID: 33691404 DOI: 10.1021/acs.jafc.1c00162] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Nematode chitinases play crucial roles in various processes of the nematode lifecycle, including hatching, molting, and reproduction. Small-molecule inhibitors of nematode chitinases have shown promise for controlling nematode pests. However, the lack of structural information makes it a challenge to develop nematicides targeting nematode chitinases. Here, we report the first crystal structure of a representative nematode chitinase, that of CeCht1 from the model nematode Caenorhabditis elegans, to a 1.7 Å resolution. CeCht1 is a highly conserved chitinase among nematodes, and structural comparison with other chitinases revealed that CeCht1 has a classical TIM-barrel fold with some subtle structural differences in the substrate-binding cleft. Benefiting from the obtained crystal structure, we identified a series of novel inhibitors by hierarchical virtual screening. Analysis of the structure-activity relationships of these compounds provided insight into their interactions with the enzyme active site, which may inform future work in improving the potencies of their inhibitory activities. This work gives an insight into the structural features of nematode chitinases and provides a solid basis for the development of inhibitors.
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Affiliation(s)
- Qi Chen
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Wei Chen
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Ashutosh Kumar
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, Yokohama, Kanagawa 230-0045, Japan
| | - Xi Jiang
- Guangdong Laboratory for Lingnan Modern Agriculture (Shenzhen Branch), Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Matej Janezic
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, Yokohama, Kanagawa 230-0045, Japan
| | - Kam Y J Zhang
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, Yokohama, Kanagawa 230-0045, Japan
| | - Qing Yang
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China
- Guangdong Laboratory for Lingnan Modern Agriculture (Shenzhen Branch), Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
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Mehta P, Miszta P, Filipek S. Molecular Modeling of Histamine Receptors-Recent Advances in Drug Discovery. Molecules 2021; 26:1778. [PMID: 33810008 PMCID: PMC8004658 DOI: 10.3390/molecules26061778] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 03/17/2021] [Accepted: 03/19/2021] [Indexed: 12/11/2022] Open
Abstract
The recent developments of fast reliable docking, virtual screening and other algorithms gave rise to discovery of many novel ligands of histamine receptors that could be used for treatment of allergic inflammatory disorders, central nervous system pathologies, pain, cancer and obesity. Furthermore, the pharmacological profiles of ligands clearly indicate that these receptors may be considered as targets not only for selective but also for multi-target drugs that could be used for treatment of complex disorders such as Alzheimer's disease. Therefore, analysis of protein-ligand recognition in the binding site of histamine receptors and also other molecular targets has become a valuable tool in drug design toolkit. This review covers the period 2014-2020 in the field of theoretical investigations of histamine receptors mostly based on molecular modeling as well as the experimental characterization of novel ligands of these receptors.
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Affiliation(s)
| | | | - Sławomir Filipek
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, 02-093 Warsaw, Poland or (P.M.); (P.M.)
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40
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Shiroma Y, Fujita G, Yamamoto T, Takahashi RU, Kumar A, Zhang KYJ, Ito A, Osada H, Yoshida M, Tahara H. Identification of a Selective RelA Inhibitor Based on DSE-FRET Screening Methods. Int J Mol Sci 2020; 21:ijms21239150. [PMID: 33266352 PMCID: PMC7734590 DOI: 10.3390/ijms21239150] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 11/27/2020] [Accepted: 11/28/2020] [Indexed: 01/04/2023] Open
Abstract
Nuclear factor-κB (NF-κB) is an important transcription factor involved in various biological functions, including tumorigenesis. Hence, NF-κB has attracted attention as a target factor for cancer treatment, leading to the development of several inhibitors. However, existing NF-κB inhibitors do not discriminate between its subunits, namely, RelA, RelB, cRel, p50, and p52. Conventional methods used to evaluate interactions between transcription factors and DNA, such as electrophoretic mobility shift assay and luciferase assays, are unsuitable for high-throughput screening (HTS) and cannot distinguish NF-κB subunits. We developed a HTS method named DNA strand exchange fluorescence resonance energy transfer (DSE-FRET). This assay is suitable for HTS and can discriminate a NF-κB subunit. Using DSE-FRET, we searched for RelA-specific inhibitors and verified RelA inhibition for 32,955 compounds. The compound A55 (2-(3-carbamoyl-6-hydroxy-4-methyl-2-oxopyridin-1(2H)-yl) acetic acid) selectively inhibited RelA–DNA binding. We propose that A55 is a seed compound for RelA-specific inhibition and could be used in clinical applications.
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Affiliation(s)
- Yoshitomo Shiroma
- Department of Cellular and Molecular Biology, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3, Kasumi, Minami-ku, Hiroshima 734-8553, Japan; (Y.S.); (G.F.); (T.Y.); (R.-uT.)
| | - Go Fujita
- Department of Cellular and Molecular Biology, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3, Kasumi, Minami-ku, Hiroshima 734-8553, Japan; (Y.S.); (G.F.); (T.Y.); (R.-uT.)
| | - Takuya Yamamoto
- Department of Cellular and Molecular Biology, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3, Kasumi, Minami-ku, Hiroshima 734-8553, Japan; (Y.S.); (G.F.); (T.Y.); (R.-uT.)
| | - Ryou-u Takahashi
- Department of Cellular and Molecular Biology, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3, Kasumi, Minami-ku, Hiroshima 734-8553, Japan; (Y.S.); (G.F.); (T.Y.); (R.-uT.)
| | - Ashutosh Kumar
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan; (A.K.); (K.Y.J.Z.)
| | - Kam Y. J. Zhang
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan; (A.K.); (K.Y.J.Z.)
| | - Akihiro Ito
- Laboratory of Cell Signaling, School of Life Sciences, Tokyo University of Pharmacy and Life Sciences, 1432-1, Horinouchi, Hachioji, Tokyo 192-0392, Japan.;
- Chemical Genomics Research Group, RIKEN Center for Sustainable Resource Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan;
| | - Hiroyuki Osada
- Chemical Biology Research Group, RIKEN Center for Sustainable Resource Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan;
| | - Minoru Yoshida
- Chemical Genomics Research Group, RIKEN Center for Sustainable Resource Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan;
- Seed Compounds Exploratory Unit for Drug Discovery Platform, RIKEN Center for Sustainable Resource Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Hidetoshi Tahara
- Department of Cellular and Molecular Biology, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3, Kasumi, Minami-ku, Hiroshima 734-8553, Japan; (Y.S.); (G.F.); (T.Y.); (R.-uT.)
- The Research Center for Drug Development and Biomarker Discovery, Hiroshima University, 1-2-3, Kasumi, Minami-ku, Hiroshima 734-8553, Japan
- Correspondence: ; Tel.: +81-082-257-5290 (ext. 5290)
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Grimm M, Liu Y, Yang X, Bu C, Xiao Z, Cao Y. LigMate: A Multifeature Integration Algorithm for Ligand-Similarity-Based Virtual Screening. J Chem Inf Model 2020; 60:6044-6053. [DOI: 10.1021/acs.jcim.9b01210] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Maximilian Grimm
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China
| | - Yang Liu
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China
| | - Xiaocong Yang
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China
| | - Chunya Bu
- College of Biological Science and Engineering, Beijing University of Agriculture, Beijing 102206, China
| | - Zhixiong Xiao
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China
| | - Yang Cao
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China
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42
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Arya R, Paliwal S, Gupta SP, Sharma S, Madan K, Mishra A, Verma K, Chauhan N. In-silico Studies and Biological Activity of Potential BACE-1 Inhibitors. Comb Chem High Throughput Screen 2020; 24:729-736. [PMID: 32957879 DOI: 10.2174/1386207323999200918151331] [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: 02/09/2020] [Revised: 08/08/2020] [Accepted: 08/12/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Alzheimer's disease is a neurological condition causing cognitive inability and dementia. The pathological lesions and neuronal damage in the brain are caused by self-aggregated fragments of mutated Amyloidal precursor protein (APP). OBJECTIVE The controlled APP processing by inhibition of secretase is the strategy to reduce Aβ load to treat Alzheimer's disease. METHODS A QSAR study was performed on 55 Pyrrolidine based ligands as BACE-1 inhibitors with an activity magnitude greater than 4 of compounds. RESULTS In the advent of designing new BACE-1 inhibitors, the pharmacophore model with correlation (r = 0.90) and root mean square deviation (RMSD) of 0.87 was developed and validated. Further, the hits retrieved by the in-silico approach were evaluated by docking interactions. CONCLUSION Two structurally diverse compounds exhibited Asp32 and Thr232 binding with the BACE-1 receptor. The aryl-substituted carbamate compound exhibited the highest fit value and docking score. The biological activity evaluation by in-vitro assay was found to be >0.1μM.
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Affiliation(s)
- Richa Arya
- Banasthali Vidyapith, Banasthali-304022 (Raj.), India
| | | | - Satya P Gupta
- Department of Pharmaceutical Technology, Meerut Institute of Engineering and Technology, Meerut-250005, India
| | | | - Kirtika Madan
- Banasthali Vidyapith, Banasthali-304022 (Raj.), India
| | - Achal Mishra
- Faculty of Pharmaceutical Sciences, Shri Shankaracharya Tech. Campus. Bhilai, India
| | - Kanika Verma
- Banasthali Vidyapith, Banasthali-304022 (Raj.), India
| | - Neha Chauhan
- Banasthali Vidyapith, Banasthali-304022 (Raj.), India
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de Almeida MMA, Pieropan F, de Mattos Oliveira L, Dos Santos Junior MC, David JM, David JP, da Silva VDA, Dos Santos Souza C, Costa SL, Butt AM. The flavonoid agathisflavone modulates the microglial neuroinflammatory response and enhances remyelination. Pharmacol Res 2020; 159:104997. [PMID: 32534098 PMCID: PMC7482432 DOI: 10.1016/j.phrs.2020.104997] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Revised: 05/27/2020] [Accepted: 06/03/2020] [Indexed: 12/20/2022]
Abstract
Myelin loss is the hallmark of the demyelinating disease multiple sclerosis (MS) and plays a significant role in multiple neurodegenerative diseases. A common factor in all neuropathologies is the central role of microglia, the intrinsic immune cells of the central nervous system (CNS). Microglia are activated in pathology and can have both pro- and anti-inflammatory functions. Here, we examined the effects of the flavonoid agathisflavone on microglia and remyelination in the cerebellar slice model following lysolecithin induced demyelination. Notably, agathisflavone enhances remyelination and alters microglial activation state, as determined by their morphology and cytokine profile. Furthermore, these effects of agathisflavone on remyelination and microglial activation were inhibited by blockade of estrogen receptor α. Thus, our results identify agathisflavone as a novel compound that may act via ER to regulate microglial activation and enhance remyelination and repair.
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Affiliation(s)
- Monique Marylin Alves de Almeida
- Department of Biochemistry and Biophysics, Institute of Health Sciences, Federal University of Bahia, Brazil; School of Pharmacy and Biomedical Sciences, University of Portsmouth, United Kingdom
| | - Francesca Pieropan
- School of Pharmacy and Biomedical Sciences, University of Portsmouth, United Kingdom
| | | | | | - Jorge Mauricio David
- Department of General and Inorganic Chemistry, Institute of Chemistry, Federal University of Bahia, Brazil
| | - Juceni Pereira David
- Department of Medication, Faculty of Pharmacy, Federal University of Bahia, Brazil
| | - Victor Diógenes A da Silva
- Department of Biochemistry and Biophysics, Institute of Health Sciences, Federal University of Bahia, Brazil
| | - Cleide Dos Santos Souza
- Department of Biochemistry and Biophysics, Institute of Health Sciences, Federal University of Bahia, Brazil; Sheffield Institute for Translational Neuroscience, University of Sheffield, United Kingdom
| | - Silvia Lima Costa
- Department of Biochemistry and Biophysics, Institute of Health Sciences, Federal University of Bahia, Brazil.
| | - Arthur Morgan Butt
- School of Pharmacy and Biomedical Sciences, University of Portsmouth, United Kingdom.
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Jastrzębski S, Szymczak M, Pocha A, Mordalski S, Tabor J, Bojarski AJ, Podlewska S. Emulating Docking Results Using a Deep Neural Network: A New Perspective for Virtual Screening. J Chem Inf Model 2020; 60:4246-4262. [DOI: 10.1021/acs.jcim.9b01202] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Stanisław Jastrzębski
- Faculty of Mathematics and Computer Science, Jagiellonian University, 6 Łojasiewicza Street, 30-348 Kraków, Poland
| | - Maciej Szymczak
- Faculty of Mathematics and Computer Science, Jagiellonian University, 6 Łojasiewicza Street, 30-348 Kraków, Poland
| | - Agnieszka Pocha
- Faculty of Mathematics and Computer Science, Jagiellonian University, 6 Łojasiewicza Street, 30-348 Kraków, Poland
| | - Stefan Mordalski
- Department of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, 2100 Copenhagen, Denmark
- Maj Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343 Kraków, Poland
| | - Jacek Tabor
- Faculty of Mathematics and Computer Science, Jagiellonian University, 6 Łojasiewicza Street, 30-348 Kraków, Poland
| | - Andrzej J. Bojarski
- Maj Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343 Kraków, Poland
| | - Sabina Podlewska
- Maj Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343 Kraków, Poland
- Department of Technology and Biotechnology of Drugs, Jagiellonian University Medical College, 9 Medyczna Street, 30-688 Kraków, Poland
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Lans I, Anoz-Carbonell E, Palacio-Rodríguez K, Aínsa JA, Medina M, Cossio P. In silico discovery and biological validation of ligands of FAD synthase, a promising new antimicrobial target. PLoS Comput Biol 2020; 16:e1007898. [PMID: 32797038 PMCID: PMC7449411 DOI: 10.1371/journal.pcbi.1007898] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 08/26/2020] [Accepted: 07/09/2020] [Indexed: 01/06/2023] Open
Abstract
New treatments for diseases caused by antimicrobial-resistant microorganisms can be developed by identifying unexplored therapeutic targets and by designing efficient drug screening protocols. In this study, we have screened a library of compounds to find ligands for the flavin-adenine dinucleotide synthase (FADS) -a potential target for drug design against tuberculosis and pneumonia- by implementing a new and efficient virtual screening protocol. The protocol has been developed for the in silico search of ligands of unexplored therapeutic targets, for which limited information about ligands or ligand-receptor structures is available. It implements an integrative funnel-like strategy with filtering layers that increase in computational accuracy. The protocol starts with a pharmacophore-based virtual screening strategy that uses ligand-free receptor conformations from molecular dynamics (MD) simulations. Then, it performs a molecular docking stage using several docking programs and an exponential consensus ranking strategy. The last filter, samples the conformations of compounds bound to the target using MD simulations. The MD conformations are scored using several traditional scoring functions in combination with a newly-proposed score that takes into account the fluctuations of the molecule with a Morse-based potential. The protocol was optimized and validated using a compound library with known ligands of the Corynebacterium ammoniagenes FADS. Then, it was used to find new FADS ligands from a compound library of 14,000 molecules. A small set of 17 in silico filtered molecules were tested experimentally. We identified five inhibitors of the activity of the flavin adenylyl transferase module of the FADS, and some of them were able to inhibit growth of three bacterial species: C. ammoniagenes, Mycobacterium tuberculosis, and Streptococcus pneumoniae, where the last two are human pathogens. Overall, the results show that the integrative VS protocol is a cost-effective solution for the discovery of ligands of unexplored therapeutic targets.
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Affiliation(s)
- Isaias Lans
- Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia UdeA, Medellin, Colombia
| | - Ernesto Anoz-Carbonell
- Departamento de Bioquímica y Biología Molecular y Celular, Facultad de Ciencias, Universidad de Zaragoza, Spain
- Instituto de Biocomputación y Física de Sistemas Complejos (Unidades Asociadas BIFI-IQFR y CBsC-CSIC), Universidad de Zaragoza, Spain
- Grupo de Genética de Micobacterias, Departamento de Microbiología, Pediatría, Radiología y Salud Pública. Facultad de Medicina, Universidad de Zaragoza, Zaragoza, Spain
| | - Karen Palacio-Rodríguez
- Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia UdeA, Medellin, Colombia
| | - José Antonio Aínsa
- Instituto de Biocomputación y Física de Sistemas Complejos (Unidades Asociadas BIFI-IQFR y CBsC-CSIC), Universidad de Zaragoza, Spain
- Grupo de Genética de Micobacterias, Departamento de Microbiología, Pediatría, Radiología y Salud Pública. Facultad de Medicina, Universidad de Zaragoza, Zaragoza, Spain
- CIBER Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Spain
| | - Milagros Medina
- Departamento de Bioquímica y Biología Molecular y Celular, Facultad de Ciencias, Universidad de Zaragoza, Spain
- Instituto de Biocomputación y Física de Sistemas Complejos (Unidades Asociadas BIFI-IQFR y CBsC-CSIC), Universidad de Zaragoza, Spain
| | - Pilar Cossio
- Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia UdeA, Medellin, Colombia
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Frankfurt, Germany
- * E-mail:
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46
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Fresnais L, Ballester PJ. The impact of compound library size on the performance of scoring functions for structure-based virtual screening. Brief Bioinform 2020; 22:5855396. [PMID: 32568385 DOI: 10.1093/bib/bbaa095] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 04/20/2020] [Accepted: 04/28/2020] [Indexed: 12/20/2022] Open
Abstract
Larger training datasets have been shown to improve the accuracy of machine learning (ML)-based scoring functions (SFs) for structure-based virtual screening (SBVS). In addition, massive test sets for SBVS, known as ultra-large compound libraries, have been demonstrated to enable the fast discovery of selective drug leads with low-nanomolar potency. This proof-of-concept was carried out on two targets using a single docking tool along with its SF. It is thus unclear whether this high level of performance would generalise to other targets, docking tools and SFs. We found that screening a larger compound library results in more potent actives being identified in all six additional targets using a different docking tool along with its classical SF. Furthermore, we established that a way to improve the potency of the retrieved molecules further is to rank them with more accurate ML-based SFs (we found this to be true in four of the six targets; the difference was not significant in the remaining two targets). A 3-fold increase in average hit rate across targets was also achieved by the ML-based SFs. Lastly, we observed that classical and ML-based SFs often find different actives, which supports using both types of SFs on those targets.
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Yan XC, Sanders JM, Gao YD, Tudor M, Haidle AM, Klein DJ, Converso A, Lesburg CA, Zang Y, Wood HB. Augmenting Hit Identification by Virtual Screening Techniques in Small Molecule Drug Discovery. J Chem Inf Model 2020; 60:4144-4152. [PMID: 32309939 DOI: 10.1021/acs.jcim.0c00113] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Two orthogonal approaches for hit identification in drug discovery are large-scale in vitro and in silico screening. In recent years, due to the emergence of new targets and a rapid increase in the size of the readily synthesizable chemical space, there is a growing emphasis on the integration of the two techniques to improve the hit finding efficiency. Here, we highlight three examples of drug discovery projects at Merck & Co., Inc., Kenilworth, NJ, USA in which different virtual screening (VS) techniques, each specifically tailored to leverage knowledge available for the target, were utilized to augment the selection of high-quality chemical matter for in vitro assays and to enhance the diversity and tractability of hits. Central to success is a fully integrated workflow combining in silico and experimental expertise at every stage of the hit identification process. We advocate that workflows encompassing VS as part of an integrated hit finding plan should be widely adopted to accelerate hit identification and foster cross-functional collaborations in modern drug discovery.
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48
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Singh N, Chaput L, Villoutreix BO. Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace. Brief Bioinform 2020; 22:1790-1818. [PMID: 32187356 PMCID: PMC7986591 DOI: 10.1093/bib/bbaa034] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.
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Affiliation(s)
- Natesh Singh
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
| | - Ludovic Chaput
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
| | - Bruno O Villoutreix
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
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49
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Jiang X, Kumar A, Motomura Y, Liu T, Zhou Y, Moro K, Zhang KYJ, Yang Q. A Series of Compounds Bearing a Dipyrido-Pyrimidine Scaffold Acting as Novel Human and Insect Pest Chitinase Inhibitors. J Med Chem 2020; 63:987-1001. [PMID: 31928006 DOI: 10.1021/acs.jmedchem.9b01154] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Chitinases not only play vital roles in the human innate immune system but are also essential for the development of pathogenic fungi and pests. Chitinase inhibitors are efficient tools to investigate the elusive role of human chitinases and to control pathogens and pests. Via hierarchical virtual screening, we have discovered a series of chitinase inhibitors with a novel scaffold that have high inhibitory activities and selectivities against human and insect chitinases. The most potent human chitotriosidase inhibitor, compound 40, exhibited a Ki of 49 nM, and the most potent inhibitor of the insect pest chitinase OfChi-h, compound 53, exhibited a Ki of 9 nM. The binding of these two most potent inhibitors was confirmed by X-ray crystallography. In a murine model of bleomycin-induced pulmonary fibrosis, compound 40 was found to suppress the chitotriosidase activity by 60%, leading to a significant increase in inflammatory cells and suggesting that chitotriosidase played a protective role.
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Affiliation(s)
- Xi Jiang
- School of Bioengineering , Dalian University of Technology , 2 Linggong Road , Dalian 116024 , China
| | - Ashutosh Kumar
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research , RIKEN , 1-7-22 Suehiro , Tsurumi, Yokohama , Kanagawa 230-0045 , Japan
| | - Yasutaka Motomura
- Laboratory for Innate Immune Systems, Center for Integrative Medical Sciences , RIKEN , 1-7-22 Suehiro , Tsurumi, Yokohama , Kanagawa 230-0045 , Japan.,Laboratory for Innate Immune Systems, Department of Microbiology and Immunology, Graduate School of Medicine , Osaka University , 2-2 Yamadaoka , Suita-shi, Osaka 565-0871 , Japan
| | - Tian Liu
- School of Bioengineering , Dalian University of Technology , 2 Linggong Road , Dalian 116024 , China
| | - Yong Zhou
- School of Software , Dalian University of Technology , 2 Linggong Road , Dalian 116024 , China
| | - Kazuyo Moro
- Laboratory for Innate Immune Systems, Center for Integrative Medical Sciences , RIKEN , 1-7-22 Suehiro , Tsurumi, Yokohama , Kanagawa 230-0045 , Japan.,Laboratory for Innate Immune Systems, Department of Microbiology and Immunology, Graduate School of Medicine , Osaka University , 2-2 Yamadaoka , Suita-shi, Osaka 565-0871 , Japan
| | - Kam Y J Zhang
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research , RIKEN , 1-7-22 Suehiro , Tsurumi, Yokohama , Kanagawa 230-0045 , Japan
| | - Qing Yang
- School of Bioengineering , Dalian University of Technology , 2 Linggong Road , Dalian 116024 , China.,State Key Laboratory for Biology of Plant Diseases and Insect Pests , Institute of Plant Protection and Shenzhen Agricultural Genome Research Institute, Chinese Academy of Agricultural Sciences , 2 West Yuanmingyuan Road , Beijing 100193 , China
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50
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Yadav DK, Kumar S, Teli MK, Kim MH. Ligand-based pharmacophore modeling and docking studies on vitamin D receptor inhibitors. J Cell Biochem 2020; 121:3570-3583. [PMID: 31904142 DOI: 10.1002/jcb.29640] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Accepted: 12/09/2019] [Indexed: 12/21/2022]
Abstract
In recent years, pharmacophore modeling and molecular docking approaches have been extensively used to characterize the structural requirements and explore the conformational space of a ligand in the binding pocket of the selected target protein. Herein, we report a pharmacophore modeling and molecular docking of 45 compounds comprising of the indole scaffold as vitamin D receptor (VDR) inhibitors. Based on the selected best hypothesis (DRRRR.61), an atom-based three-dimensional quantitative structure-activity relationships model was developed to rationalize the structural requirement of biological activity modulating components. The developed model predicted the binding affinity for the training set and test set with R2 (training) = 0.8869 and R2 (test) = 0.8139, respectively. Furthermore, molecular docking and dynamics simulation were performed to understand the underpinning of binding interaction and stability of selected VDR inhibitors in the binding pocket. In conclusion, the results presented here, in the form of functional and structural data, agreed well with the proposed pharmacophores and provide further insights into the development of novel VDR inhibitors with better activity.
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Affiliation(s)
- Dharmendra Kumar Yadav
- Gachon Institute of Pharmaceutical Science and Department of Pharmacy, College of Pharmacy, Gachon University, Incheon, Republic of Korea
| | - Surendra Kumar
- Gachon Institute of Pharmaceutical Science and Department of Pharmacy, College of Pharmacy, Gachon University, Incheon, Republic of Korea
| | - Mahesh Kumar Teli
- Gachon Institute of Pharmaceutical Science and Department of Pharmacy, College of Pharmacy, Gachon University, Incheon, Republic of Korea
| | - Mi-Hyun Kim
- Gachon Institute of Pharmaceutical Science and Department of Pharmacy, College of Pharmacy, Gachon University, Incheon, Republic of Korea
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