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Moshawih S, Bu ZH, Goh HP, Kifli N, Lee LH, Goh KW, Ming LC. Consensus holistic virtual screening for drug discovery: a novel machine learning model approach. J Cheminform 2024; 16:62. [PMID: 38807196 DOI: 10.1186/s13321-024-00855-8] [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: 12/03/2023] [Accepted: 05/10/2024] [Indexed: 05/30/2024] Open
Abstract
In drug discovery, virtual screening is crucial for identifying potential hit compounds. This study aims to present a novel pipeline that employs machine learning models that amalgamates various conventional screening methods. A diverse array of protein targets was selected, and their corresponding datasets were subjected to active/decoy distribution analysis prior to scoring using four distinct methods: QSAR, Pharmacophore, docking, and 2D shape similarity, which were ultimately integrated into a single consensus score. The fine-tuned machine learning models were ranked using the novel formula "w_new", consensus scores were calculated, and an enrichment study was performed for each target. Distinctively, consensus scoring outperformed other methods in specific protein targets such as PPARG and DPP4, achieving AUC values of 0.90 and 0.84, respectively. Remarkably, this approach consistently prioritized compounds with higher experimental PIC50 values compared to all other screening methodologies. Moreover, the models demonstrated a range of moderate to high performance in terms of R2 values during external validation. In conclusion, this novel workflow consistently delivered superior results, emphasizing the significance of a holistic approach in drug discovery, where both quantitative metrics and active enrichment play pivotal roles in identifying the best virtual screening methodology.Scientific contributionWe presented a novel consensus scoring workflow in virtual screening, merging diverse methods for enhanced compound selection. We also introduced 'w_new', a groundbreaking metric that intricately refines machine learning model rankings by weighing various model-specific parameters, revolutionizing their efficacy in drug discovery in addition to other domains.
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Affiliation(s)
- Said Moshawih
- PAPRSB Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam.
- Faculty of Data Science and Information Technology, INTI International University, Nilai, Malaysia.
| | - Zhen Hui Bu
- Faculty of Computing and Engineering, Quest International University, Ipoh, Malaysia
| | - Hui Poh Goh
- PAPRSB Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Nurolaini Kifli
- PAPRSB Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Lam Hong Lee
- Faculty of Computing and Engineering, Quest International University, Ipoh, Malaysia
| | - Khang Wen Goh
- Faculty of Data Science and Information Technology, INTI International University, Nilai, Malaysia
| | - Long Chiau Ming
- PAPRSB Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
- School of Medical and Life Sciences, Sunway University, Sunway City, Malaysia
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2
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Ibraheim MH, Maher I, Khater I. In Silico Repurposing of a Novel Inhibitor (drug) of EGFR and VEGFR-2 Kinases of Cancer by Pharmacokinetics, Toxicity, Molecular Docking, and Molecular Dynamics Simulation. Appl Biochem Biotechnol 2024:10.1007/s12010-024-04958-8. [PMID: 38782800 DOI: 10.1007/s12010-024-04958-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/16/2024] [Indexed: 05/25/2024]
Abstract
Vascular endothelial growth factor is an angiogenic that promotes the development and metastasis of tumors (VEGF). The epidermal growth factor receptor, or EGFR, controls the division, growth, and death of cells via several signaling pathways. It has been found that most of the tyrosine kinase EGFR/VEGFR-2 inhibited by drugs that the FDA has approved are so far. The main objective of the present study was to identify an efficacious and selective dual inhibitor of VEGFR-2/EGFR for the treatment of cancer. Out of the 400 ligands tested against the kinases, 12 compounds demonstrated the best docking scores through molecular docking for the two kinases. Of these, only compound SCHEMBL2435814 inhibited the kinases with the highest score values when compared to a reference, vandetanib, as a dual inhibitor of EGFR/VEGFR-2 kinases through interaction with the identified active sites pocket. Following drug-likeness score toxicity and pharmacokinetic testing, the two compounds, SCHEMBL2435814 and vandetanib, were analyzed to determine how the two kinases interacted with each other. The results of calculations of π-cation interactions, hydrogen bonds, and hydrophobic interactions demonstrated a strong interaction between the two kinases and SCHEMBL2435814. Eventually, molecular dynamic modeling was used to assess the stability of complexes. This demonstrated many characteristics, including RMSF, RMSD, SASA, RG, and H-bond analysis, which demonstrated that SCHEMBL2435814 with the two kinases was more stable than vandetanib over a 100ns simulation period. By suppressing EGFR/VEGFR-2, chemical SCHEMBL2435814 may be able to postpone the signaling pathway of proteins that are essential to the advancement of cancer.
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Affiliation(s)
- Mona H Ibraheim
- Physics Department, Faculty of Science, Zagazig University, P.O.44519, Zagazig, Egypt
| | - Ibrahim Maher
- Physics Department, Faculty of Science, Zagazig University, P.O.44519, Zagazig, Egypt.
| | - Ibrahim Khater
- Biophysics Department, Faculty of Science, Cairo University, Giza, Egypt
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3
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Loukas AT, Papadourakis M, Panagiotopoulos V, Zarmpala A, Chontzopoulou E, Christodoulou S, Katsila T, Zoumpoulakis P, Matsoukas MT. Natural Compounds for Bone Remodeling: A Computational and Experimental Approach Targeting Bone Metabolism-Related Proteins. Int J Mol Sci 2024; 25:5047. [PMID: 38732267 PMCID: PMC11084538 DOI: 10.3390/ijms25095047] [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: 03/15/2024] [Revised: 04/25/2024] [Accepted: 04/26/2024] [Indexed: 05/13/2024] Open
Abstract
Osteoporosis, characterized by reduced bone density and increased fracture risk, affects over 200 million people worldwide, predominantly older adults and postmenopausal women. The disruption of the balance between bone-forming osteoblasts and bone-resorbing osteoclasts underlies osteoporosis pathophysiology. Standard treatment includes lifestyle modifications, calcium and vitamin D supplementation and specific drugs that either inhibit osteoclasts or stimulate osteoblasts. However, these treatments have limitations, including side effects and compliance issues. Natural products have emerged as potential osteoporosis therapeutics, but their mechanisms of action remain poorly understood. In this study, we investigate the efficacy of natural compounds in modulating molecular targets relevant to osteoporosis, focusing on the Mitogen-Activated Protein Kinase (MAPK) pathway and the gut microbiome's influence on bone homeostasis. Using an in silico and in vitro methodology, we have identified quercetin as a promising candidate in modulating MAPK activity, offering a potential therapeutic perspective for osteoporosis treatment.
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Affiliation(s)
- Alexandros-Timotheos Loukas
- Department of Food Science and Technology, University of West Attica, Ag. Spyridonos, 12243 Egaleo, Greece; (A.-T.L.); (P.Z.)
- Cloudpharm Private Company, Kifissias Avenue 44, 15125 Marousi, Greece; (V.P.); (A.Z.); (E.C.); (S.C.)
| | - Michail Papadourakis
- Cloudpharm Private Company, Kifissias Avenue 44, 15125 Marousi, Greece; (V.P.); (A.Z.); (E.C.); (S.C.)
| | - Vasilis Panagiotopoulos
- Cloudpharm Private Company, Kifissias Avenue 44, 15125 Marousi, Greece; (V.P.); (A.Z.); (E.C.); (S.C.)
- Department of Biomedical Engineering, University of West Attica, Ag. Spyridonos, 12243 Egaleo, Greece
| | - Apostolia Zarmpala
- Cloudpharm Private Company, Kifissias Avenue 44, 15125 Marousi, Greece; (V.P.); (A.Z.); (E.C.); (S.C.)
| | - Eleni Chontzopoulou
- Cloudpharm Private Company, Kifissias Avenue 44, 15125 Marousi, Greece; (V.P.); (A.Z.); (E.C.); (S.C.)
| | - Stephanos Christodoulou
- Cloudpharm Private Company, Kifissias Avenue 44, 15125 Marousi, Greece; (V.P.); (A.Z.); (E.C.); (S.C.)
| | - Theodora Katsila
- Institute of Chemical Biology, National Hellenic Research Foundation, 11635 Athens, Greece;
| | - Panagiotis Zoumpoulakis
- Department of Food Science and Technology, University of West Attica, Ag. Spyridonos, 12243 Egaleo, Greece; (A.-T.L.); (P.Z.)
- Institute of Chemical Biology, National Hellenic Research Foundation, 11635 Athens, Greece;
| | - Minos-Timotheos Matsoukas
- Cloudpharm Private Company, Kifissias Avenue 44, 15125 Marousi, Greece; (V.P.); (A.Z.); (E.C.); (S.C.)
- Department of Biomedical Engineering, University of West Attica, Ag. Spyridonos, 12243 Egaleo, Greece
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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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Lorenc A, Badura A, Karolak M, Pałkowski Ł, Kubik Ł, Buciński A. Antimicrobial Activity Classification of Imidazolium Derivatives Predicted by Artificial Neural Networks. Pharm Res 2024; 41:891-898. [PMID: 38632156 PMCID: PMC11116175 DOI: 10.1007/s11095-024-03699-x] [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: 11/14/2023] [Accepted: 04/09/2024] [Indexed: 04/19/2024]
Abstract
PURPOSE This study assesses the Multilayer Perceptron (MLP) neural network, complemented by other Machine Learning techniques (CART, PCA), in predicting the antimicrobial activity of 140 newly designed imidazolium chlorides against Klebsiella pneumoniae before synthesis. Emphasis is on leveraging molecular properties for predictive analysis. METHODS Classification and regression decision trees (CART) identified the top 200 predictive molecular descriptors. Principal Component Analysis (PCA) reduced these descriptors to 5 components, retaining 99.57% of raw data information. Antimicrobial activity, categorized as high or low, was based on experimentally proven minimal inhibitory concentration (MIC), with a cut-point at MIC = 0.856 mol/L. A 12-fold cross-validation trained the MLP (architecture 5-12-2 with 5 Principal Components). RESULTS The MLP exhibited commendable performance, achieving almost 90% correct classifications across learning, validation, and test sets, outperforming models without PCA dimension reduction. Key metrics, including accuracy (0.907), sensitivity (0.905), specificity (0.909), and precision (0.891), were notably high. These results highlight the MLP model's efficacy with PCA as a high-quality classifier for determining antimicrobial activity. CONCLUSIONS The study concludes that the MLP neural network, along with CART and PCA, is a robust tool for predicting the antimicrobial activity class of imidazolium chlorides against Klebsiella pneumoniae. CART and PCA, used in this study, allowed input variable reduction without significant information loss. High classification accuracy and associated metrics affirm the method's potential utility in pre-synthesis assessments, offering valuable insights for antimicrobial compound design.
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Affiliation(s)
- Andżelika Lorenc
- Department of Biopharmacy, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, dr A. Jurasza 2, 85-089, Bydgoszcz, Poland.
| | - Anna Badura
- Department of Biopharmacy, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, dr A. Jurasza 2, 85-089, Bydgoszcz, Poland
| | - Maciej Karolak
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, dr A. Jurasza 2, 85-089, Bydgoszcz, Poland
| | - Łukasz Pałkowski
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, dr A. Jurasza 2, 85-089, Bydgoszcz, Poland
| | - Łukasz Kubik
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Gen. J. Hallera 107, 80-416, Gdańsk, Poland
| | - Adam Buciński
- Department of Biopharmacy, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, dr A. Jurasza 2, 85-089, Bydgoszcz, Poland
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Almoyad MAA, Wahab S, Ansari MN, Ahmad W, Hani U, Chandra S. Predictive insights into plant-based compounds as fibroblast growth factor receptor 1 inhibitors: a combined molecular docking and dynamics simulation study. J Biomol Struct Dyn 2024:1-10. [PMID: 38669200 DOI: 10.1080/07391102.2024.2335297] [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: 09/25/2023] [Accepted: 03/20/2024] [Indexed: 04/28/2024]
Abstract
The discovery of novel therapeutic agents with potent anticancer activity remains a critical challenge in drug development. Natural products, particularly bioactive phytoconstituents derived from plants, have emerged as promising sources for anticancer drug discovery. In this study, we used virtual screening techniques to explore the potential of bioactive phytoconstituents as inhibitors of fibroblast growth factor receptor 1 (FGFR1), a key signaling protein implicated in cancer progression. We used virtual screening techniques to analyze phytoconstituents extracted from the IMPPAT 2.0 database. Our primary objective was to discover promising inhibitors of FGFR1. To ensure the selection of promising candidates, we initially filtered the molecules based on their physicochemical properties. Subsequently, we performed binding affinity calculations, PAINS, ADMET, and PASS filters to identify nontoxic and highly effective hits. Through this screening process, one phytocompound, namely Mundulone, emerged as a potential lead. This compound demonstrated an appreciable affinity for FGFR1 and exhibited specific interactions with the ATP-binding site residues. To gain further insights into the conformational dynamics of Mundulone and the reference FGFR1 inhibitor, Lenvatinib, we conducted time-evolution analyses employing 200 ns molecular dynamics simulations (MDS) and essential dynamics. These analyses provided valuable information regarding the dynamic behavior and stability of the compounds in complexes with FGFR1. Overall, the findings indicate that Mundulone exhibits promising binding affinity, specific interactions, and favorable drug profiles, making it a promising lead candidate. Further experimental analysis will be necessary to confirm its effectiveness and safety profiles for therapeutic advancement in the cancer field.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Mohammad Ali Abdullah Almoyad
- Department of Basic Medical Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia
| | - Shadma Wahab
- Department of Pharmacognosy, College of Pharmacy, King Khalid University, Abha, Saudi Arabia
| | - Mohammed Nazam Ansari
- Department of Pharmacology & Toxicology, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Saudi Arabia
| | - Wasim Ahmad
- Department of Pharmacy, Mohammed Al-Mana College for Medical Sciences, Dammam, Saudi Arabia
| | - Umme Hani
- Department of Pharmaceutics, College of Pharmacy, King Khalid University, Abha, Saudi Arabia
| | - Subhash Chandra
- Department of Botany, Soban Singh Jeena University, Almora, India
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Moon DO. Deciphering the Role of BCAR3 in Cancer Progression: Gene Regulation, Signal Transduction, and Therapeutic Implications. Cancers (Basel) 2024; 16:1674. [PMID: 38730626 PMCID: PMC11083344 DOI: 10.3390/cancers16091674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 04/24/2024] [Accepted: 04/24/2024] [Indexed: 05/13/2024] Open
Abstract
This review comprehensively explores the gene BCAR3, detailing its regulation at the gene, mRNA, and protein structure levels, and delineating its multifunctional roles in cellular signaling within cancer contexts. The discussion covers BCAR3's involvement in integrin signaling and its impact on cancer cell migration, its capability to induce anti-estrogen resistance, and its significant functions in cell cycle regulation. Further highlighted is BCAR3's modulation of immune responses within the tumor microenvironment, a novel area of interest that holds potential for innovative cancer therapies. Looking forward, this review outlines essential future research directions focusing on transcription factor binding studies, isoform-specific expression profiling, therapeutic targeting of BCAR3, and its role in immune cell function. Each segment builds towards a holistic understanding of BCAR3's operational mechanisms, presenting a critical evaluation of its therapeutic potential in oncology. This synthesis aims to not only extend current knowledge but also catalyze further research that could pivotally influence the development of targeted cancer treatments.
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Affiliation(s)
- Dong Oh Moon
- Department of Biology Education, Daegu University, 201 Daegudae-ro, Gyeongsan-si 38453, Gyeongsangbuk-do, Republic of Korea
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Meewan I, Panmanee J, Petchyam N, Lertvilai P. HBCVTr: an end-to-end transformer with a deep neural network hybrid model for anti-HBV and HCV activity predictor from SMILES. Sci Rep 2024; 14:9262. [PMID: 38649402 PMCID: PMC11035669 DOI: 10.1038/s41598-024-59933-4] [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: 01/09/2024] [Accepted: 04/16/2024] [Indexed: 04/25/2024] Open
Abstract
Hepatitis B and C viruses (HBV and HCV) are significant causes of chronic liver diseases, with approximately 350 million infections globally. To accelerate the finding of effective treatment options, we introduce HBCVTr, a novel ligand-based drug design (LBDD) method for predicting the inhibitory activity of small molecules against HBV and HCV. HBCVTr employs a hybrid model consisting of double encoders of transformers and a deep neural network to learn the relationship between small molecules' simplified molecular-input line-entry system (SMILES) and their antiviral activity against HBV or HCV. The prediction accuracy of HBCVTr has surpassed baseline machine learning models and existing methods, with R-squared values of 0.641 and 0.721 for the HBV and HCV test sets, respectively. The trained models were successfully applied to virtual screening against 10 million compounds within 240 h, leading to the discovery of the top novel inhibitor candidates, including IJN04 for HBV and IJN12 and IJN19 for HCV. Molecular docking and dynamics simulations identified IJN04, IJN12, and IJN19 target proteins as the HBV core antigen, HCV NS5B RNA-dependent RNA polymerase, and HCV NS3/4A serine protease, respectively. Overall, HBCVTr offers a new and rapid drug discovery and development screening method targeting HBV and HCV.
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Affiliation(s)
- Ittipat Meewan
- Center for Advanced Therapeutics, Institute of Molecular Biosciences, Mahidol University, Nakhon Pathom, 73170, Thailand.
| | - Jiraporn Panmanee
- Research Center for Neuroscience, Institute of Molecular Biosciences, Mahidol University, Nakhon Pathom, 73170, Thailand
| | - Nopphon Petchyam
- Center for Advanced Therapeutics, Institute of Molecular Biosciences, Mahidol University, Nakhon Pathom, 73170, Thailand
| | - Pichaya Lertvilai
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, 92037, USA
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Kim H, Lee K, Kim C, Lim J, Kim WY. DFRscore: Deep Learning-Based Scoring of Synthetic Complexity with Drug-Focused Retrosynthetic Analysis for High-Throughput Virtual Screening. J Chem Inf Model 2024; 64:2432-2444. [PMID: 37651152 DOI: 10.1021/acs.jcim.3c01134] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Recently emerging generative AI models enable us to produce a vast number of compounds for potential applications. While they can provide novel molecular structures, the synthetic feasibility of the generated molecules is often questioned. To address this issue, a few recent studies have attempted to use deep learning models to estimate the synthetic accessibility of many molecules rapidly. However, retrosynthetic analysis tools used to train the models rely on reaction templates automatically extracted from a large reaction database that are not domain-specific and may exhibit low chemical correctness. To overcome this limitation, we introduce DFRscore (Drug-Focused Retrosynthetic score), a deep learning-based approach for a more practical assessment of synthetic accessibility in drug discovery. The DFRscore model is trained exclusively on drug-focused reactions, providing a predicted number of minimally required synthetic steps for each compound. This approach enables practitioners to filter out compounds that do not meet their desired level of synthetic accessibility at an early stage of high-throughput virtual screening for accelerated drug discovery. The proposed strategy can be easily adapted to other domains by adjusting the synthesis planning setup of the reaction templates and starting materials.
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Affiliation(s)
- Hyeongwoo Kim
- Department of Chemistry, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Kyunghoon Lee
- Department of Chemistry, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Chansu Kim
- Department of Chemistry, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Jaechang Lim
- HITS Incorporation, 124 Teheran-ro, Gangnam-gu, Seoul 06234, Republic of Korea
| | - Woo Youn Kim
- Department of Chemistry, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
- HITS Incorporation, 124 Teheran-ro, Gangnam-gu, Seoul 06234, Republic of Korea
- AI Institute, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
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Banerjee S, Mahesh Y, Prabhu D, Sekar K, Sen P. Identification of potent anti-fibrinolytic compounds against plasminogen and tissue-type plasminogen activator employing in silico approaches. J Biomol Struct Dyn 2024; 42:3204-3222. [PMID: 37216286 DOI: 10.1080/07391102.2023.2213343] [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: 10/27/2022] [Accepted: 05/03/2023] [Indexed: 05/24/2023]
Abstract
The zymogen protease Plasminogen (Plg) and its active form plasmin (Plm) carry out important functions in the blood clot disintegration (breakdown of fibrin fibers) process. Inhibition of plasmin effectively reduces fibrinolysis to circumvent heavy bleeding. Currently, available Plm inhibitor tranexamic acid (TXA) used for treating severe hemorrhages is associated with an increased incidence of seizures which in turn were traced to gamma-aminobutyric acid antagonistic activity (GABAa) in addition to having multiple side effects. Fibrinolysis can be suppressed by targeting the three important protein domains: the kringle-2 domain of tissue plasminogen activator, the kringle-1 domain of plasminogen, and the serine protease domain of plasminogen. In the present study, one million molecules were screened from the ZINC database. These ligands were docked to their respective protein targets using Autodock Vina, Schrödinger Glide, and ParDOCK/BAPPL+. Thereafter, the drug-likeness properties of the ligands were evaluated using Discovery Studio 3.5. Subsequently, we subjected the protein-ligand complexes to molecular dynamics simulation of 200 ns in GROMACS. The identified ligands P76(ZINC09970930), C97(ZINC14888376), and U97(ZINC11839443) for each protein target are found to impart higher stability and greater compactness to the protein-ligand complexes. Principal component analysis (PCA) implicates, that the identified ligands occupy smaller phase space, form stable clusters, and provide greater rigidity to the protein-ligand complexes. Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) analysis reveals that P76, C97, and U97 exhibit better binding free energy (ΔG) when compared to that of the standard ligands. Thus, our findings can be useful for the development of promising anti-fibrinolytic agents.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Suparna Banerjee
- School of Biological Sciences, Indian Association for the Cultivation of Science, Kolkata, West Bengal, India
| | - Yeshwanth Mahesh
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, Karnataka, India
| | - Dhamodharan Prabhu
- Center for Drug Discovery, Department of Biotechnology, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India
| | - Kanagaraj Sekar
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, Karnataka, India
| | - Prosenjit Sen
- School of Biological Sciences, Indian Association for the Cultivation of Science, Kolkata, West Bengal, India
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Nandi S, Bhaduri S, Das D, Ghosh P, Mandal M, Mitra P. Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence. Mol Pharm 2024; 21:1563-1590. [PMID: 38466810 DOI: 10.1021/acs.molpharmaceut.3c01161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.
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Affiliation(s)
- Suvendu Nandi
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Soumyadeep Bhaduri
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Debraj Das
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Priya Ghosh
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Mahitosh Mandal
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Pralay Mitra
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
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12
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Eurtivong C, Zimmer C, Schirmeister T, Butkinaree C, Saruengkhanphasit R, Niwetmarin W, Ruchirawat S, Bhambra AS. A structure-based virtual high-throughput screening, molecular docking, molecular dynamics and MM/PBSA study identified novel putative drug-like dual inhibitors of trypanosomal cruzain and rhodesain cysteine proteases. Mol Divers 2024; 28:531-551. [PMID: 36617352 DOI: 10.1007/s11030-023-10600-2] [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: 09/05/2022] [Accepted: 01/02/2023] [Indexed: 01/09/2023]
Abstract
Virtual screening a collection of ~ 25,000 ChemBridge molecule collection identified two nitrogenous heterocyclic molecules, 12 and 15, with potential dual inhibitory properties against trypanosomal cruzain and rhodesain cysteine proteases. Similarity search in DrugBank found the two virtual hits with novel chemical structures with unreported anti-trypanosomal activities. Investigations into the binding mechanism by molecular dynamics simulations for 100 ns revealed the molecules were able to occupy the binding sites and stabilise the protease complexes. Binding affinities calculated using the MM/PBSA method for the last 20 ns showed that the virtual hits have comparable binding affinities to other known inhibitors from literature suggesting both molecules as promising scaffolds with dual cruzain and rhodesain inhibition properties, i.e. 12 has predicted ΔGbind values of - 38.1 and - 38.2 kcal/mol to cruzain and rhodesain, respectively, and 15 has predicted ΔGbind values of - 34.4 and - 25.8 kcal/mol to rhodesain. Per residue binding free energy decomposition studies and visual inspection at 100 ns snapshots revealed hydrogen bonding and non-polar attractions with important amino acid residues that contributed to the ΔGbind values. The interactions are similar to those previously reported in the literature. The overall ADMET predictions for the two molecules were favourable for drug development with acceptable pharmacokinetic profiles and adequate oral bioavailability.
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Affiliation(s)
- Chatchakorn Eurtivong
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Mahidol Univeristy, 447 Sri-Ayutthaya Road, Ratchathewi, Bangkok, 10400, Thailand.
- Program in Chemical Sciences, Chulabhorn Graduate Institute, Chulabhorn Royal Academy, 906 Kamphaeng Phet 6, Talat Bang Khen, Lak Si, Bangkok, 10210, Thailand.
- Center of Excellence On Environmental Health and Toxicology (EHT), OPS, Ministry of Higher Education, Science, Research and Innovation, Bangkok, Thailand.
| | - Collin Zimmer
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University, Mainz, Germany
| | - Tanja Schirmeister
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University, Mainz, Germany
| | - Chutikarn Butkinaree
- National Omics Center, National Science and Technology Development Agency, Khlong Luang, 12120, Pathum Thani, Thailand
| | - Rungroj Saruengkhanphasit
- Program in Chemical Sciences, Chulabhorn Graduate Institute, Chulabhorn Royal Academy, 906 Kamphaeng Phet 6, Talat Bang Khen, Lak Si, Bangkok, 10210, Thailand
- Center of Excellence On Environmental Health and Toxicology (EHT), OPS, Ministry of Higher Education, Science, Research and Innovation, Bangkok, Thailand
| | - Worawat Niwetmarin
- Program in Chemical Sciences, Chulabhorn Graduate Institute, Chulabhorn Royal Academy, 906 Kamphaeng Phet 6, Talat Bang Khen, Lak Si, Bangkok, 10210, Thailand
- Center of Excellence On Environmental Health and Toxicology (EHT), OPS, Ministry of Higher Education, Science, Research and Innovation, Bangkok, Thailand
| | - Somsak Ruchirawat
- Program in Chemical Sciences, Chulabhorn Graduate Institute, Chulabhorn Royal Academy, 906 Kamphaeng Phet 6, Talat Bang Khen, Lak Si, Bangkok, 10210, Thailand
- Center of Excellence On Environmental Health and Toxicology (EHT), OPS, Ministry of Higher Education, Science, Research and Innovation, Bangkok, Thailand
- Laboratory of Medicinal Chemistry, Chulabhorn Research Institute, Bangkok, 10210, Thailand
| | - Avninder S Bhambra
- Leicester School of Allied Health Sciences, Faculty of Health and Life Sciences, de Montfort University, Leicester, UK
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13
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Huang L, Xu T, Yu Y, Zhao P, Chen X, Han J, Xie Z, Li H, Zhong W, Wong KC, Zhang H. A dual diffusion model enables 3D molecule generation and lead optimization based on target pockets. Nat Commun 2024; 15:2657. [PMID: 38531837 DOI: 10.1038/s41467-024-46569-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 03/01/2024] [Indexed: 03/28/2024] Open
Abstract
Structure-based generative chemistry is essential in computer-aided drug discovery by exploring a vast chemical space to design ligands with high binding affinity for targets. However, traditional in silico methods are limited by computational inefficiency, while machine learning approaches face bottlenecks due to auto-regressive sampling. To address these concerns, we have developed a conditional deep generative model, PMDM, for 3D molecule generation fitting specified targets. PMDM consists of a conditional equivariant diffusion model with both local and global molecular dynamics, enabling PMDM to consider the conditioned protein information to generate molecules efficiently. The comprehensive experiments indicate that PMDM outperforms baseline models across multiple evaluation metrics. To evaluate the applications of PMDM under real drug design scenarios, we conduct lead compound optimization for SARS-CoV-2 main protease (Mpro) and Cyclin-dependent Kinase 2 (CDK2), respectively. The selected lead optimization molecules are synthesized and evaluated for their in-vitro activities against CDK2, displaying improved CDK2 activity.
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Affiliation(s)
- Lei Huang
- City University of Hong Kong, Hong Kong, SAR, China
- Tencent AI Lab, Shenzhen, China
| | | | - Yang Yu
- Tencent AI Lab, Shenzhen, China
| | | | | | - Jing Han
- Regor Therapeutics Group, Shanghai, China
| | - Zhi Xie
- Regor Therapeutics Group, Shanghai, China
| | - Hailong Li
- Regor Therapeutics Group, Shanghai, China.
| | | | - Ka-Chun Wong
- City University of Hong Kong, Hong Kong, SAR, China.
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14
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Choksi H, Carbone J, Paradis NJ, Bennett L, Bui-Linh C, Wu C. Novel Inhibitors to MmpL3 Transporter of Mycobacterium tuberculosis by Structure-Based High-Throughput Virtual Screening and Molecular Dynamics Simulations. ACS OMEGA 2024; 9:13782-13796. [PMID: 38559933 PMCID: PMC10976370 DOI: 10.1021/acsomega.3c08401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 02/07/2024] [Accepted: 02/12/2024] [Indexed: 04/04/2024]
Abstract
Tuberculosis (TB)-causing bacterium Mycobacterium tuberculosis (Mtb) utilizes mycolic acids for building the mycobacterial cell wall, which is critical in providing defense against external factors and resisting antibiotic action. MmpL3 is a secondary resistance nodulation division transporter that facilitates the coupled transport of mycolic acid precursor into the periplasm using the proton motive force, thus making it an attractive drug target for TB infection. In 2019, X-ray crystal structures of MmpL3 from M. smegmatis were solved with a promising inhibitor SQ109, which showed promise against drug-resistant TB in Phase II clinical trials. Still, there is a pressing need to discover more effective MmpL3 inhibitors to counteract rising antibiotic resistance. In this study, structure-based high-throughput virtual screening combined with molecular dynamics (MD) simulations identified potential novel MmpL3 inhibitors. Approximately 17 million compounds from the ZINC15 database were screened against the SQ109 binding site on the MmpL3 protein using drug property filters and glide XP docking scores. From this, the top nine compounds and the MmpL3-SQ109 crystal complex structure each underwent 2 × 200 ns MD simulations to probe the inhibitor binding energetics to MmpL3. Four of the nine compounds exhibited stable binding properties and favorable drug properties, suggesting these four compounds could be potential novel inhibitors of MmpL3 for M. tuberculosis.
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Affiliation(s)
| | | | - Nicholas J. Paradis
- Department of Molecular &
Cellular Biosciences, College of Science and Mathematics, Rowan University, Glassboro, New Jersey 08028, United States
| | - Lucas Bennett
- Department of Molecular &
Cellular Biosciences, College of Science and Mathematics, Rowan University, Glassboro, New Jersey 08028, United States
| | - Candice Bui-Linh
- Department of Molecular &
Cellular Biosciences, College of Science and Mathematics, Rowan University, Glassboro, New Jersey 08028, United States
| | - Chun Wu
- Department of Molecular &
Cellular Biosciences, College of Science and Mathematics, Rowan University, Glassboro, New Jersey 08028, United States
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15
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Majoumo-Mbe F, Sangbong NA, Tadjong Tcho A, Namba-Nzanguim CT, Simoben CV, Eni DB, Alhaji Isa M, Poli ANR, Cassel J, Salvino JM, Montaner LJ, Tietjen I, Ntie-Kang F. 5-chloro-3-(2-(2,4-dinitrophenyl) hydrazono)indolin-2-one: synthesis, characterization, biochemical and computational screening against SARS-CoV-2. CHEMICKE ZVESTI 2024; 78:3431-3441. [PMID: 38685970 PMCID: PMC11055700 DOI: 10.1007/s11696-023-03274-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Accepted: 12/04/2023] [Indexed: 05/02/2024]
Abstract
Chemical prototypes with broad-spectrum antiviral activity are important toward developing new therapies that can act on both existing and emerging viruses. Binding of the SARS-CoV-2 spike protein to the host angiotensin-converting enzyme 2 (ACE2) receptor is required for cellular entry of SARS-CoV-2. Toward identifying new chemical leads that can disrupt this interaction, including in the presence of SARS-CoV-2 adaptive mutations found in variants like omicron that can circumvent vaccine, immune, and therapeutic antibody responses, we synthesized 5-chloro-3-(2-(2,4-dinitrophenyl)hydrazono)indolin-2-one (H2L) from the condensation reaction of 5-chloroisatin and 2,4-dinitrophenylhydrazine in good yield. H2L was characterised by elemental and spectral (IR, electronic, Mass) analyses. The NMR spectrum of H2L indicated a keto-enol tautomerism, with the keto form being more abundant in solution. H2L was found to selectively interfere with binding of the SARS-CoV-2 spike receptor-binding domain (RBD) to the host angiotensin-converting enzyme 2 receptor with a 50% inhibitory concentration (IC50) of 0.26 μM, compared to an unrelated PD-1/PD-L1 ligand-receptor-binding pair with an IC50 of 2.06 μM in vitro (Selectivity index = 7.9). Molecular docking studies revealed that the synthesized ligand preferentially binds within the ACE2 receptor-binding site in a region distinct from where spike mutations in SARS-CoV-2 variants occur. Consistent with these models, H2L was able to disrupt ACE2 interactions with the RBDs from beta, delta, lambda, and omicron variants with similar activities. These studies indicate that H2L-derived compounds are potential inhibitors of multiple SARS-CoV-2 variants, including those capable of circumventing vaccine and immune responses. Supplementary Information The online version contains supplementary material available at 10.1007/s11696-023-03274-5.
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Affiliation(s)
- Felicite Majoumo-Mbe
- Department of Chemistry, Faculty of Science, University of Buea, P. O. Box 63, Buea, Cameroon
| | - Neba Abongwa Sangbong
- Department of Chemistry, Faculty of Science, University of Buea, P. O. Box 63, Buea, Cameroon
| | - Alain Tadjong Tcho
- Department of Chemistry, Faculty of Science, University of Buea, P. O. Box 63, Buea, Cameroon
| | - Cyril T. Namba-Nzanguim
- Department of Chemistry, Faculty of Science, University of Buea, P. O. Box 63, Buea, Cameroon
- Center for Drug Discovery, Faculty of Science, University of Buea, P. O. Box 63, Buea, Cameroon
| | - Conrad V. Simoben
- Center for Drug Discovery, Faculty of Science, University of Buea, P. O. Box 63, Buea, Cameroon
| | - Donatus B. Eni
- Department of Chemistry, Faculty of Science, University of Buea, P. O. Box 63, Buea, Cameroon
- Center for Drug Discovery, Faculty of Science, University of Buea, P. O. Box 63, Buea, Cameroon
| | - Mustafa Alhaji Isa
- Department of Microbiology, Faculty of Sciences, University of Maiduguri, PMB 1069, Maiduguri, Borno State Nigeria
| | | | - Joel Cassel
- The Wistar Institute, 3601 Spruce Street, Philadelphia, PA 19104 USA
| | - Joseph M. Salvino
- The Wistar Institute, 3601 Spruce Street, Philadelphia, PA 19104 USA
| | - Luis J. Montaner
- The Wistar Institute, 3601 Spruce Street, Philadelphia, PA 19104 USA
| | - Ian Tietjen
- The Wistar Institute, 3601 Spruce Street, Philadelphia, PA 19104 USA
| | - Fidele Ntie-Kang
- Department of Chemistry, Faculty of Science, University of Buea, P. O. Box 63, Buea, Cameroon
- Center for Drug Discovery, Faculty of Science, University of Buea, P. O. Box 63, Buea, Cameroon
- Institute of Pharmacy, Martin-Luther University Halle-Wittenberg, Kurt-Mothes-Strasse 3, 06120 Halle (Saale), Germany
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16
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Tu G, Fu T, Zheng G, Xu B, Gou R, Luo D, Wang P, Xue W. Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives. J Chem Inf Model 2024; 64:1433-1455. [PMID: 38294194 DOI: 10.1021/acs.jcim.3c01736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.
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Affiliation(s)
- Gao Tu
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Tingting Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | | | - Binbin Xu
- Chengdu Sintanovo Biotechnology Co., Ltd., Chengdu 610200, China
| | - Rongpei Gou
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Ding Luo
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Panpan Wang
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China
| | - Weiwei Xue
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
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17
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Cebi E, Lee J, Subramani VK, Bak N, Oh C, Kim KK. Cryo-electron microscopy-based drug design. Front Mol Biosci 2024; 11:1342179. [PMID: 38501110 PMCID: PMC10945328 DOI: 10.3389/fmolb.2024.1342179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 01/31/2024] [Indexed: 03/20/2024] Open
Abstract
Structure-based drug design (SBDD) has gained popularity owing to its ability to develop more potent drugs compared to conventional drug-discovery methods. The success of SBDD relies heavily on obtaining the three-dimensional structures of drug targets. X-ray crystallography is the primary method used for solving structures and aiding the SBDD workflow; however, it is not suitable for all targets. With the resolution revolution, enabling routine high-resolution reconstruction of structures, cryogenic electron microscopy (cryo-EM) has emerged as a promising alternative and has attracted increasing attention in SBDD. Cryo-EM offers various advantages over X-ray crystallography and can potentially replace X-ray crystallography in SBDD. To fully utilize cryo-EM in drug discovery, understanding the strengths and weaknesses of this technique and noting the key advancements in the field are crucial. This review provides an overview of the general workflow of cryo-EM in SBDD and highlights technical innovations that enable its application in drug design. Furthermore, the most recent achievements in the cryo-EM methodology for drug discovery are discussed, demonstrating the potential of this technique for advancing drug development. By understanding the capabilities and advancements of cryo-EM, researchers can leverage the benefits of designing more effective drugs. This review concludes with a discussion of the future perspectives of cryo-EM-based SBDD, emphasizing the role of this technique in driving innovations in drug discovery and development. The integration of cryo-EM into the drug design process holds great promise for accelerating the discovery of new and improved therapeutic agents to combat various diseases.
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Affiliation(s)
| | | | | | | | - Changsuk Oh
- Department of Precision Medicine, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea
| | - Kyeong Kyu Kim
- Department of Precision Medicine, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea
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18
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Chen X, Gao M, Xia Y, Wang X, Qin J, He H, Liu W, Zhang X, Peng S, Zeng Z, Su Y, Zhang X. Phase separation of Nur77 mediates XS561-induced apoptosis by promoting the formation of Nur77/Bcl-2 condensates. Acta Pharm Sin B 2024; 14:1204-1221. [PMID: 38486987 PMCID: PMC10935061 DOI: 10.1016/j.apsb.2023.11.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/15/2023] [Accepted: 10/24/2023] [Indexed: 03/17/2024] Open
Abstract
The orphan nuclear receptor Nur77 is a critical regulator of the survival and death of tumor cells. The pro-death effect of Nur77 can be regulated by its interaction with Bcl-2, resulting in conversion of Bcl-2 from a survival to killer. As Bcl-2 is overexpressed in various cancers preventing them from apoptosis and promoting their resistance to chemotherapy, targeting the apoptotic pathway of Nur77/Bcl-2 may lead to new cancer therapeutics. Here, we report our identification of XS561 as a novel Nur77 ligand that induces apoptosis of tumor cells by activating the Nur77/Bcl-2 pathway. In vitro and animal studies revealed an apoptotic effect of XS561 in a range of tumor cell lines including MDA-MB-231 triple-negative breast cancer (TNBC) and MCF-7/LCC2 tamoxifen-resistant breast cancer (TAMR) in a Nur77-dependent manner. Mechanistic studies showed XS561 potently induced the translocation of Nur77 from the nucleus to mitochondria, resulting in mitochondria-related apoptosis. Interestingly, XS561-induced accumulation of Nur77 at mitochondria was associated with XS561 induction of Nur77 phase separation and the formation of Nur77/Bcl-2 condensates. Together, our studies identify XS561 as a new activator of the Nur77/Bcl-2 apoptotic pathway and reveal a role of phase separation in mediating the apoptotic effect of Nur77 at mitochondria.
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Affiliation(s)
- Xiaohui Chen
- School of Pharmaceutical Science, Fujian Provincial Key Laboratory of Innovative Drug Target Research, Xiamen University, Xiamen 361002, China
- Department of Clinical Laboratory, the Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai 519000, China
| | - Meichun Gao
- School of Pharmaceutical Science, Fujian Provincial Key Laboratory of Innovative Drug Target Research, Xiamen University, Xiamen 361002, China
| | - Yongzhen Xia
- School of Pharmaceutical Science, Fujian Provincial Key Laboratory of Innovative Drug Target Research, Xiamen University, Xiamen 361002, China
| | - Xin Wang
- School of Pharmaceutical Science, Fujian Provincial Key Laboratory of Innovative Drug Target Research, Xiamen University, Xiamen 361002, China
| | - Jingbo Qin
- School of Pharmaceutical Science, Fujian Provincial Key Laboratory of Innovative Drug Target Research, Xiamen University, Xiamen 361002, China
| | - Hongying He
- School of Pharmaceutical Science, Fujian Provincial Key Laboratory of Innovative Drug Target Research, Xiamen University, Xiamen 361002, China
| | - Weirong Liu
- School of Pharmaceutical Science, Fujian Provincial Key Laboratory of Innovative Drug Target Research, Xiamen University, Xiamen 361002, China
| | - Xiaowei Zhang
- School of Pharmaceutical Science, Fujian Provincial Key Laboratory of Innovative Drug Target Research, Xiamen University, Xiamen 361002, China
| | - Shuangzhou Peng
- School of Pharmaceutical Science, Fujian Provincial Key Laboratory of Innovative Drug Target Research, Xiamen University, Xiamen 361002, China
| | - Zhiping Zeng
- School of Pharmaceutical Science, Fujian Provincial Key Laboratory of Innovative Drug Target Research, Xiamen University, Xiamen 361002, China
| | - Ying Su
- School of Pharmaceutical Science, Fujian Provincial Key Laboratory of Innovative Drug Target Research, Xiamen University, Xiamen 361002, China
- NucMito Pharmaceuticals Co., Ltd., Xiamen 361000, China
| | - Xiaokun Zhang
- School of Pharmaceutical Science, Fujian Provincial Key Laboratory of Innovative Drug Target Research, Xiamen University, Xiamen 361002, China
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19
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Buttenschoen M, Morris GM, Deane CM. PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences. Chem Sci 2024; 15:3130-3139. [PMID: 38425520 PMCID: PMC10901501 DOI: 10.1039/d3sc04185a] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 11/17/2023] [Indexed: 03/02/2024] Open
Abstract
The last few years have seen the development of numerous deep learning-based protein-ligand docking methods. They offer huge promise in terms of speed and accuracy. However, despite claims of state-of-the-art performance in terms of crystallographic root-mean-square deviation (RMSD), upon closer inspection, it has become apparent that they often produce physically implausible molecular structures. It is therefore not sufficient to evaluate these methods solely by RMSD to a native binding mode. It is vital, particularly for deep learning-based methods, that they are also evaluated on steric and energetic criteria. We present PoseBusters, a Python package that performs a series of standard quality checks using the well-established cheminformatics toolkit RDKit. The PoseBusters test suite validates chemical and geometric consistency of a ligand including its stereochemistry, and the physical plausibility of intra- and intermolecular measurements such as the planarity of aromatic rings, standard bond lengths, and protein-ligand clashes. Only methods that both pass these checks and predict native-like binding modes should be classed as having "state-of-the-art" performance. We use PoseBusters to compare five deep learning-based docking methods (DeepDock, DiffDock, EquiBind, TankBind, and Uni-Mol) and two well-established standard docking methods (AutoDock Vina and CCDC Gold) with and without an additional post-prediction energy minimisation step using a molecular mechanics force field. We show that both in terms of physical plausibility and the ability to generalise to examples that are distinct from the training data, no deep learning-based method yet outperforms classical docking tools. In addition, we find that molecular mechanics force fields contain docking-relevant physics missing from deep-learning methods. PoseBusters allows practitioners to assess docking and molecular generation methods and may inspire new inductive biases still required to improve deep learning-based methods, which will help drive the development of more accurate and more realistic predictions.
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20
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Le TN, Dinh TT, Mai-Hoang TD, Razzazi-Fazeli E, Tran-Van H. Serine protease inhibitor 3 (Serpin3) from Penaeus vannamei selectively interacts with Vibrio parahaemolyticus PirA vp. JOURNAL OF FISH DISEASES 2024:e13935. [PMID: 38403934 DOI: 10.1111/jfd.13935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 02/08/2024] [Accepted: 02/14/2024] [Indexed: 02/27/2024]
Abstract
Acute Hepatopancreatic Necrosis Disease (AHPND) represents a significant challenge in the field of shrimp aquaculture. This disease is primarily caused by Vibrio parahaemolyticus strains harbouring the pVA1 plasmid encoding the PirAvp and PirBvp toxins. To combat this epidemic and mitigate its devastating consequences, it is crucial to identify and characterize the receptors responsible for the binding of these pathogenic toxins. Our studied discovered that Penaeus vannamei's Serine protease inhibitor 3 (PvSerpin3) derived from shrimp hepatopancreatic tissues could bind to recombinant PirAvp , confirming its role as a novel PirAvp -binding protein (PA BP). Through comprehensive computational methods, we revealed two truncated PirAvp -binding proteins derived from PvSerpin3 called Serpin3(13) and Serpin3(22), which had higher affinity to PirAvp than the full-length PvSerpin3. The PA BP genes were amplified from a cDNA library that was reversed from total RNA extracted from shrimp, cloned and expressed in Escherichia coli. Three PA BP inclusion bodies were refolded to obtain the soluble form, and the recovery efficacy was found to be 100% for Serpin3 and Serpin3(13), while Serpin3(22) had a recovery efficacy of roundly 50%. Co-Immunoprecipitation (co-IP) and dot blot assays substantiated the interaction of these recombinant PA BPs with both recombinant PirAvp and VPAHPND (XN89)-producing natural toxins.
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Affiliation(s)
- Thanh-Nguyen Le
- Laboratory of Biosensors, Department of Molecular and Environmental Biotechnology, Faculty of Biology and Biotechnology, University of Science, Ho Chi Minh, Vietnam
- Laboratory of Molecular Biotechnology, University of Science, Ho Chi Minh, Vietnam
- Vietnam National University, Ho Chi Minh, Vietnam
| | - Thuan-Thien Dinh
- Laboratory of Biosensors, Department of Molecular and Environmental Biotechnology, Faculty of Biology and Biotechnology, University of Science, Ho Chi Minh, Vietnam
- Laboratory of Molecular Biotechnology, University of Science, Ho Chi Minh, Vietnam
- Vietnam National University, Ho Chi Minh, Vietnam
| | - Thuy-Dung Mai-Hoang
- Laboratory of Biosensors, Department of Molecular and Environmental Biotechnology, Faculty of Biology and Biotechnology, University of Science, Ho Chi Minh, Vietnam
- Laboratory of Molecular Biotechnology, University of Science, Ho Chi Minh, Vietnam
- Vietnam National University, Ho Chi Minh, Vietnam
| | - Ebrahim Razzazi-Fazeli
- VetCore Facility for Research, Proteomics Facility, Veterinary Medicine University, Vienna, Austria
| | - Hieu Tran-Van
- Laboratory of Biosensors, Department of Molecular and Environmental Biotechnology, Faculty of Biology and Biotechnology, University of Science, Ho Chi Minh, Vietnam
- Laboratory of Molecular Biotechnology, University of Science, Ho Chi Minh, Vietnam
- Vietnam National University, Ho Chi Minh, Vietnam
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21
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Tandi M, Tripathi N, Gaur A, Gopal B, Sundriyal S. Curation and cheminformatics analysis of a Ugi-reaction derived library (URDL) of synthetically tractable small molecules for virtual screening application. Mol Divers 2024; 28:37-50. [PMID: 36574164 DOI: 10.1007/s11030-022-10588-1] [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: 10/11/2022] [Accepted: 12/17/2022] [Indexed: 12/28/2022]
Abstract
Virtual screening (VS) is an important approach in drug discovery and relies on the availability of a virtual library of synthetically tractable molecules. Ugi reaction (UR) represents an important multi-component reaction (MCR) that reliably produces a peptidomimetic scaffold. Recent literature shows that a tactically assembled Ugi adduct can be subjected to further chemical modifications to yield a variety of rings and scaffolds, thus, renewing the interest in this old reaction. Given the reliability and efficiency of UR, we collated an UR derived library (URDL) of small molecules (total = 5773) for VS. The synthesis of the majority of URDL molecules may be carried out in 1-2 pots in a time and cost-effective manner. The detailed analysis of the average property and chemical space of URDL was also carried out using the open-source Datawarrior program. The comparison with FDA-approved oral drugs and inhibitors of protein-protein interactions (iPPIs) suggests URDL molecules are 'clean', drug-like, and conform to a structurally distinct space from the other two categories. The average physicochemical properties of compounds in the URDL library lie closer to iPPI molecules than oral drugs thus suggesting that the URDL resource can be applied to discover novel iPPI molecules. The URDL molecules consist of diverse ring systems, many of which have not been exploited yet for drug design. Thus, URDL represents a small virtual library of drug-like molecules with unexplored chemical space designed for VS. The structures of all molecules of URDL, oral drugs, and iPPI compounds are being made freely accessible as supplementary information for broader application.
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Affiliation(s)
- Mukesh Tandi
- Department of Pharmacy, Birla Institute of Technology and Science Pilani, Pilani Campus, Rajasthan, 333031, India
| | - Nancy Tripathi
- Department of Pharmacy, Birla Institute of Technology and Science Pilani, Pilani Campus, Rajasthan, 333031, India
| | - Animesh Gaur
- Department of Pharmacy, Birla Institute of Technology and Science Pilani, Pilani Campus, Rajasthan, 333031, India
| | | | - Sandeep Sundriyal
- Department of Pharmacy, Birla Institute of Technology and Science Pilani, Pilani Campus, Rajasthan, 333031, India.
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22
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Sharma T, Mondal T, Khan S, Churqui MP, Nyström K, Thombare K, Baig MH, Dong JJ. Identifying novel inhibitors targeting Exportin-1 for the potential treatment of COVID-19. Arch Microbiol 2024; 206:69. [PMID: 38240823 DOI: 10.1007/s00203-023-03761-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 11/09/2023] [Accepted: 11/19/2023] [Indexed: 01/23/2024]
Abstract
The nuclear export protein 1 (XPO1) mediates the nucleocytoplasmic transport of proteins and ribonucleic acids (RNAs) and plays a prominent role in maintaining cellular homeostasis. XPO1 has emerged as a promising therapeutic approach to interfere with the lifecycle of many viruses. In our earlier study, we proved the inhibition of XPO1 as a therapeutic strategy for managing SARS-COV-2 and its variants. In this study, we have utilized pharmacophore-assisted computational methods to identify prominent XPO1 inhibitors. After several layers of screening, a few molecules were shortlisted for further experimental validation on the in vitro SARS-CoV-2 cell infection model. It was observed that these compounds reduced spike positivity, suggesting inhibition of SARS-COV-2 infection. The outcome of this study could be considered further for developing novel antiviral therapeutic strategies against SARS-CoV-2.
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Affiliation(s)
- Tanuj Sharma
- Department of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Gangnam-gu, Seoul, 06273, Republic of Korea
| | - Tanmoy Mondal
- Department of Laboratory Medicine, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
- Department of Clinical Chemistry, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Sajid Khan
- Department of Biochemistry, Aligarh Muslim University, Aligarh, India
| | - Marianela Patzi Churqui
- Department of Infectious Diseases, Sahlgrenska Academy, University of Gothenburg, 41345, Gothenburg, Sweden
| | - Kristina Nyström
- Department of Infectious Diseases, Sahlgrenska Academy, University of Gothenburg, 41345, Gothenburg, Sweden
| | - Ketan Thombare
- Department of Laboratory Medicine, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
- Department of Clinical Chemistry, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Mohammad Hassan Baig
- Department of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Gangnam-gu, Seoul, 06273, Republic of Korea.
| | - Jae-June Dong
- Department of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Gangnam-gu, Seoul, 06273, Republic of Korea.
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Balius TE, Tan YS, Chakrabarti M. DOCK 6: Incorporating hierarchical traversal through precomputed ligand conformations to enable large-scale docking. J Comput Chem 2024; 45:47-63. [PMID: 37743732 DOI: 10.1002/jcc.27218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 08/17/2023] [Indexed: 09/26/2023]
Abstract
To allow DOCK 6 access to unprecedented chemical space for screening billions of small molecules, we have implemented features from DOCK 3.7 into DOCK 6, including a search routine that traverses precomputed ligand conformations stored in a hierarchical database. We tested them on the DUDE-Z and SB2012 test sets. The hierarchical database search routine is 16 times faster than anchor-and-grow. However, the ability of hierarchical database search to reproduce the experimental pose is 16% worse than that of anchor-and-grow. The enrichment performance is on average similar, but DOCK 3.7 has better enrichment than DOCK 6, and DOCK 6 is on average 1.7 times slower. However, with post-docking torsion minimization, DOCK 6 surpasses DOCK 3.7. A large-scale virtual screen is performed with DOCK 6 on 23 million fragment molecules. We use current features in DOCK 6 to complement hierarchical database calculations, including torsion minimization, which is not available in DOCK 3.7.
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Affiliation(s)
- Trent E Balius
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, Maryland, USA
| | - Y Stanley Tan
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, Maryland, USA
| | - Mayukh Chakrabarti
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, Maryland, USA
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24
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Senthil R, Archunan G, Vithya D, Saravanan KM. Hexadecanoic acid analogs as potential CviR-mediated quorum sensing inhibitors in Chromobacterium violaceum: an in silico study. J Biomol Struct Dyn 2024:1-10. [PMID: 38165661 DOI: 10.1080/07391102.2023.2299945] [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: 10/06/2023] [Accepted: 12/20/2023] [Indexed: 01/04/2024]
Abstract
Chromobacterium violaceum is a Gram-negative, rod-shaped and opportunistic human pathogen. C. violaceum is resistant to various antibiotics due to the production of quorum sensing (QS)-controlled virulence factor and biofilm formation. Hence, we need to find alternative strategies to overcome the antimicrobial resistance and biofilm formation in Gram-negative bacteria. QS is a mechanism in which bacteria's ability to regulate the virulence factors and biofilm formations leads to disease progression. Previously, hexadecanoic acid was identified as a CviR-mediated quorum-sensing inhibitor. In this study, we aimed to discover potential analogs of hexadecanoic acid as a CviR-mediated quorum-sensing inhibitor against C. violaceum by using ADME/T prediction, density functional theory, molecular docking, molecular dynamics and free energy binding calculations. ADME/T properties predicted for analogs were acceptable for human oral absorption and feasibility. The highest occupied molecular orbitals and lowest unoccupied molecular orbitals gap energies predicted and found oleic acid with -0.3748 energies. Docosatrienoic acid exhibited the highest binding affinity -8.15 Kcal/mol and strong and stable interactions with the amino acid residues on the active site of the CviR protein. These compounds on MD simulations for 100 ns show strong hydrogen-bonding interactions with the protein and remain stable inside the active site. Our results suggest hexadecanoic acid analogs could serve as anti-QS and anti-biofilm molecules for treating C. violaceum infections. However, further validation and investigation of these inhibitors against CviR are needed to claim their candidacy for clinical trials.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Renganathan Senthil
- Department of Bioinformatics, School of Lifesciences, Vel's Institute of Science, Technology and Advanced Studies, Pallavaram, Chennai, Tamil Nadu, India
- Lysine Biotech Private Limited, Taramani, Chennai, Tamil Nadu, India
| | - Govindaraju Archunan
- Dean-Research, Maruthupandiyar College (Affiliated to Bharathidasan University), Thanjavur, Tamil Nadu, India
| | - Dharmaraj Vithya
- Department of Biotechnology, Dhanalakshmi Srinivasan College of Arts and Science for Women (Affiliated to Bharathidasan University), Perambalur, Tamil Nadu, India
| | - Konda Mani Saravanan
- Department of Biotechnology, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India
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25
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Park J, Choi W, Kim J, Kim HW, Lee JY, Lee J, Kim B. Quantitative Analysis and Molecular Docking Simulation of Flavonols from Eruca sativa Mill. and Their Effect on Skin Barrier Function. Curr Issues Mol Biol 2024; 46:398-408. [PMID: 38248327 PMCID: PMC10814921 DOI: 10.3390/cimb46010025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 12/22/2023] [Accepted: 12/28/2023] [Indexed: 01/23/2024] Open
Abstract
Eruca sativa is a commonly used edible plant in Italian cuisine. E. sativa 70% ethanol extract (ES) was fractionated with five organic solvents, including n-hexane (EHex), chloroform (ECHCl3), ethyl acetate (EEA), n-butyl alcohol (EBuOH), and water (EDW). Ethyl acetate fraction (EEA) had the highest antioxidant activity, which was correlated with the total polyphenol and flavonoid content. ES and EEA acted as PPAR-α ligands by PPAR-α competitive binding assay. EEA significantly increased cornified envelope formation as a keratinocyte terminal differentiation marker in HaCaT cells. Further, it significantly reduced nitric oxide and pro-inflammatory cytokines (IL-6 and TNF-α) in lipopolysaccharide-stimulated RAW 264.7 cells. The main flavonol forms detected in high amounts from EEA are mono-and di-glycoside of each aglycone. The main flavonol form of EEA is the mono-glycoside of each aglycone detected, and the most abundant flavonol mono-glycoside is kaempferol 3-glucoside 7.4%, followed by quercetin-3-glucoside 2.3% and isorhamnetin 3-glucoside 1.4%. Flavonol mono-glycosides were shown to be a potent PPAR-α ligand using molecular docking simulation and showed the inhibition of nitric oxide. These results suggest that the flavonol composition of E. sativa is suitable for use in improving skin barrier function and inflammation in skin disorders, such as atopic dermatitis.
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Affiliation(s)
- Jihye Park
- Department of Chemistry, The Graduate School, Mokwon University, Daejeon 35349, Republic of Korea; (J.P.); (J.K.); (H.W.K.)
| | - Wonchul Choi
- Interdisciplinary Program in Biocosmetics, Sungkyunkwan University, Suwon 16419, Republic of Korea;
| | - Jayoung Kim
- Department of Chemistry, The Graduate School, Mokwon University, Daejeon 35349, Republic of Korea; (J.P.); (J.K.); (H.W.K.)
| | - Hye Won Kim
- Department of Chemistry, The Graduate School, Mokwon University, Daejeon 35349, Republic of Korea; (J.P.); (J.K.); (H.W.K.)
| | - Jee-Young Lee
- Structure Based Drug Design Laboratory, New Drug Development Center, Daegu Gyeongbuk Medical Innovation Foundation (K-Medi Hub), Daegu 41061, Republic of Korea;
| | - Jongsung Lee
- Interdisciplinary Program in Biocosmetics, Sungkyunkwan University, Suwon 16419, Republic of Korea;
| | - Bora Kim
- Department of Chemistry, The Graduate School, Mokwon University, Daejeon 35349, Republic of Korea; (J.P.); (J.K.); (H.W.K.)
- Department of Cosmetics Engineering, Mokwon University, Daejeon 35349, Republic of Korea
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26
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Sharma V, Singh A, Chauhan S, Sharma PK, Chaudhary S, Sharma A, Porwal O, Fuloria NK. Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer. Curr Drug Deliv 2024; 21:870-886. [PMID: 37670704 DOI: 10.2174/1567201821666230905090621] [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: 10/31/2022] [Revised: 03/08/2023] [Accepted: 03/24/2023] [Indexed: 09/07/2023]
Abstract
Drug discovery and development (DDD) is a highly complex process that necessitates precise monitoring and extensive data analysis at each stage. Furthermore, the DDD process is both timeconsuming and costly. To tackle these concerns, artificial intelligence (AI) technology can be used, which facilitates rapid and precise analysis of extensive datasets within a limited timeframe. The pathophysiology of cancer disease is complicated and requires extensive research for novel drug discovery and development. The first stage in the process of drug discovery and development involves identifying targets. Cell structure and molecular functioning are complex due to the vast number of molecules that function constantly, performing various roles. Furthermore, scientists are continually discovering novel cellular mechanisms and molecules, expanding the range of potential targets. Accurately identifying the correct target is a crucial step in the preparation of a treatment strategy. Various forms of AI, such as machine learning, neural-based learning, deep learning, and network-based learning, are currently being utilised in applications, online services, and databases. These technologies facilitate the identification and validation of targets, ultimately contributing to the success of projects. This review focuses on the different types and subcategories of AI databases utilised in the field of drug discovery and target identification for cancer.
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Affiliation(s)
- Vishal Sharma
- Department of Pharmacy, Galgotias University, Greater Noida, Uttar Pradesh, 201310, India
| | - Amit Singh
- Department of Pharmacy, Galgotias University, Greater Noida, Uttar Pradesh, 201310, India
| | - Sanjana Chauhan
- Department of Pharmacy, Galgotias University, Greater Noida, Uttar Pradesh, 201310, India
| | - Pramod Kumar Sharma
- Department of Pharmacy, Galgotias University, Greater Noida, Uttar Pradesh, 201310, India
| | - Shubham Chaudhary
- Department of Pharmacy, Galgotias University, Greater Noida, Uttar Pradesh, 201310, India
| | - Astha Sharma
- Department of Pharmacy, Galgotias University, Greater Noida, Uttar Pradesh, 201310, India
| | - Omji Porwal
- Department of Pharmacognosy, Faculty of Pharmacy, Tishk International University, Erbil 44001, Iraq
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27
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Akpınar R, Yıldırım Baştemur G, Bıçak B, Sanli NO, Mertoğlu Kamalı E, Pekmez M, Kecel Gündüz S, Perçin Özkorucuklu S. Phytochemical profiling, in vitro biological activities, and in silico (molecular docking and absorption, distribution, metabolism, excretion, toxicity) studies of Polygonum cognatum Meissn. J Sep Sci 2024; 47:e2300750. [PMID: 38066395 DOI: 10.1002/jssc.202300750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/23/2023] [Accepted: 11/27/2023] [Indexed: 01/19/2024]
Abstract
Polygonum cognatum Meissn, a perennial herbaceous belonging to the Polygonaceae family, is an aromatic plant. High-performance liquid chromatography/diode array detector method was developed and validated for the phytochemical analysis of the plant. Also, various methods were used to investigate the antioxidant, antimicrobial, and cytotoxic activities of the methanolic extracts. Antioxidant activities were researched by 2,2'-diphenyl-1-picrylhydrazyl and cupric reducing antioxidant capacity methods. Among the tested standard microbial strains, Candida albicans was found to be more sensitive with a 24.60 ± 0.55 mm inhibition zone according to the diffusion tests. In the microdilution tests, the minimum inhibitory concentration and minimum bactericidal/fungicidal concentration values were 4.75 and ≥ 4.75 mg/mL, respectively, for all tested pathogens. Human colon carcinoma cells were used to investigate cytotoxicity by using 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyl tetrazolium bromide analysis (IC50 = 2891 μg/mL for Plant A, IC50 = 3291 μg/mL for Plant B). Molecular docking and absorption, distribution, metabolism, excretion, and toxicity analysis were used to explain inhibition mechanisms of major phenolic compounds of plants against Tankyrase 1, Tankyrase 2 enzymes, and deoxyribonucleic acid gyrase subunit B and found compatible with experimental results.
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Affiliation(s)
- Reyhan Akpınar
- Programme of Molecular Biotechnology and Genetics, Institute of Science, Istanbul University, Istanbul, Turkey
| | - Gizem Yıldırım Baştemur
- Department of Molecular Biology and Genetics, Faculty of Science, Istanbul University, Istanbul, Turkey
| | - Bilge Bıçak
- Department of Physics, Faculty of Science, Istanbul University, Istanbul, Turkey
| | - Nazmiye Ozlem Sanli
- Department of Biology, Faculty of Science, Istanbul University, Istanbul, Turkey
| | - Elif Mertoğlu Kamalı
- Department of Molecular Biology and Genetics, Faculty of Science, Istanbul University, Istanbul, Turkey
| | - Murat Pekmez
- Department of Molecular Biology and Genetics, Faculty of Science, Istanbul University, Istanbul, Turkey
| | - Serda Kecel Gündüz
- Department of Physics, Faculty of Science, Istanbul University, Istanbul, Turkey
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28
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Waseem M, Das S, Mondal D, Jain M, Thakur JK, Subbarao N. Identification of novel inhibitors against Med15a KIX domain of Candida glabrata. Int J Biol Macromol 2023; 253:126720. [PMID: 37678676 DOI: 10.1016/j.ijbiomac.2023.126720] [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: 05/19/2023] [Revised: 08/20/2023] [Accepted: 09/03/2023] [Indexed: 09/09/2023]
Abstract
Candida glabrata, the second most common cause of invasive fungal infections, exhibits multi-drug resistance to commonly used antifungal drugs. To counter this resistance, there is a critical need for novel antifungals. This study identifies small molecule inhibitors that target a three-helix bundle KIX domain in the Med15a Mediator subunit of Candida glabrata (CgMed15a KIX). This domain plays a crucial role by interacting with the Pleiotropic Drug Resistance transcription factor Pdr1, a key regulator of the multidrug resistance pathway in Candida glabrata. We performed high throughput computational screening of large chemical datasets against the binding sites of the CgMed15a KIX domain to identify novel inhibitors. We selected six potential candidates with high affinity and confirmed their binding with the CgMed15a KIX domain. A phytochemical compound, Chebulinic acid binds to the CgMed15a KIX domain with a KD value of 0.339 μM and shows significant inhibitory effects on the growth of Candida glabrata. Molecular dynamics simulation studies further revealed the structural stability of the CgMed15a KIX-Chebulinic acid complex. Thus, in conclusion, this study highlights Chebulinic acid as a novel potential antifungal compound against Candida glabrata.
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Affiliation(s)
- Mohd Waseem
- School of computational and integrative sciences, Jawaharlal Nehru University, New Delhi 110067, India
| | - Shubhashis Das
- Plant Mediator Lab, National Institute of Plant Genome Research, New Delhi 110067, India
| | - Debarati Mondal
- Plant Transcription Regulation Group, International Centre for Genetic Engineering and Biotechnology, New Delhi 110067, India
| | - Monika Jain
- Plant Transcription Regulation Group, International Centre for Genetic Engineering and Biotechnology, New Delhi 110067, India
| | - Jitendra K Thakur
- Plant Mediator Lab, National Institute of Plant Genome Research, New Delhi 110067, India; Plant Transcription Regulation Group, International Centre for Genetic Engineering and Biotechnology, New Delhi 110067, India.
| | - Naidu Subbarao
- School of computational and integrative sciences, Jawaharlal Nehru University, New Delhi 110067, India.
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29
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Bhowmick S, Roy K, Saha A. Structure-guided screening of protein-protein interaction for the identification of Myc-Max heterodimer complex modulators. J Biomol Struct Dyn 2023:1-19. [PMID: 38109131 DOI: 10.1080/07391102.2023.2294174] [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: 08/07/2023] [Accepted: 12/01/2023] [Indexed: 12/19/2023]
Abstract
De-regulation of oncogenic myelocytomatosis (c-Myc or Myc) transcription factor is one of the most common molecular anomalies encountered in human cancers, and it is typically linked to many aggressive malignancies including breast, lung, cervix, colon glioblastomas, and other haematological organs. The Myc belongs to the basic helix-loop-helix zipper protein family (bHLH-ZIP), and its dimerization with another principal interactor protein partner Myc-associated factor X (Max) is essentially required for cellular transformation, cell growth and proliferation, and transcriptional activation. Intermolecular interactions have been evaluated between hetero-dimer Myc-Max protein, which identified protein-protein interaction (PPI) specific modulators using highly précised molecular docking study followed by long-range interaction stability analyzed through molecular dynamic (MD) simulation. Moreover, ADME profile analyses have been estimated for screened hit compounds. MM-GBSA-based binding free energy (ΔG) estimations have been performed for all screened hit compounds obtained from multi-step molecular docking-based virtual screening technique. According to the employed various rigorous multi-chemometric techniques, four identified inhibitors/modulators appear to have a considerable number of intermolecular contacts with hotspot residues in the hetero-dimer interface region of the Myc-Max PPI complex. However, identified hit compounds might need further structural optimization or extensive biophysical analyses for better understanding of the molecular mechanism for exhibiting the Myc-Max PPI interface binding stability.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Shovonlal Bhowmick
- Department of Chemical Technology, University of Calcutta, Kolkata, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - Achintya Saha
- Department of Chemical Technology, University of Calcutta, Kolkata, India
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30
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Lim EQ, Ahemad N, Yap MKK. High-throughput virtual screening, pharmacophore modelling and antagonist effects of small molecule inhibitors against cytotoxin-induced cytotoxicity. J Biomol Struct Dyn 2023:1-15. [PMID: 38100546 DOI: 10.1080/07391102.2023.2293275] [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: 05/05/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023]
Abstract
Cobra venom cytotoxins (CTX) cause dermonecrosis in envenomed patients who suffered from limb amputations due to the limitation of serotherapy-based antivenoms. This study aimed to identify small molecule inhibitors against CTX. A structure-based high-throughput virtual screening (HTVS) was conducted based on a conserved CTX, using the Natural Product Activity and Species Source (NPASS) screening library. The hits were valerenic acid, 1-oxo-2H-isoquinoline-4-carboxylic acid, acenaphthene, and 5-bromopyrrole-2-carboxamide, which interacted with contemporary antivenom binding site A and functional loops I-III of CTX, respectively, in molecular docking studies. Furthermore, molecular dynamic simulations were performed along with analysis of ligand fitness through their pharmacophore and pharmacokinetics properties. The antagonist effects of these hits on CTX-induced cytotoxicity were examined in human keratinocytes (HaCaT). Despite having a low binding affinity (KD = 14.45 × 10-4 M), acenaphthene demonstrated a significant increase of cell viability at 6 h and 24 h in experimental envenomed HaCaT. It also demonstrated the highest neutralization potency against CTX with a median effective concentration (EC50) of 0.05 mL/mg. Acenaphthene interacted with the functional loop II, which is the crucial cytotoxic site of CTX. It has an aromatic ring as its primary pharmacophoric feature, commonly used for rational drug design. In conclusion, acenaphthene could be a promising lead compound as a small molecule inhibitor.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- En Qi Lim
- School of Science, Monash University Malaysia, Bandar Sunway, Selangor, Malaysia
| | - Nafees Ahemad
- School of Pharmacy, Monash University Malaysia, Bandar Sunway, Selangor, Malaysia
| | - Michelle Khai Khun Yap
- School of Science, Monash University Malaysia, Bandar Sunway, Selangor, Malaysia
- Tropical Medicine and Biology Multidisciplinary Platform, Monash University Malaysia, Bandar Sunway, Selangor, Malaysia
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31
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Karthick K, Abinaya M, Shankar T, Swarnalatha K. In Vitro and In Silico Screening of Benzimidazole-Based Ruthenium(II) Complexes as Potent ALK Inhibitor for Cancer Prevention. Appl Biochem Biotechnol 2023; 195:7397-7413. [PMID: 37000352 DOI: 10.1007/s12010-023-04435-8] [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] [Accepted: 03/15/2023] [Indexed: 04/01/2023]
Abstract
Two new heteroleptic Ru(II) polypyridyl complexes, [Ru(bpy)2(B)]Cl2 (RBB) (bpy = 2,2'-bipyridine and B = 4,4'-bis(benzimidazolyl)-2,2'-bipyridine) and [Ru(phen)2(B)]Cl2 (RPB), were synthesized, and the structural features were confirmed by the analytical and spectral tools such as FT-IR, 1H-NMR, and UV-Visible spectroscopy. We have explored the possibility of improving the selectivity of cytotoxic Ru(II) complex and their preliminary biological evaluation against MCF-7 and MG-63 cell lines and clinical pathogens. The results of the antimicrobial screening show that the ligand and complexes have a range of abilities against the species of bacteria and fungi that were tested. The anti-inflammatory activity of the compounds was found to be in the range of 30-75%. Molecular docking study was performed for these ligand and complexes to evaluate and analyze the anti-lymphoma cancer activity. Molecular docking score and the rank revealed the bonding affinity towards the site of interaction of the oncoprotein anaplastic lymphoma kinase (ALK).
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Affiliation(s)
- Kamaraj Karthick
- Photochemistry Research Laboratory, Department of Chemistry, Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, 627012, Tamil Nadu, India
| | - Muthukumar Abinaya
- Photochemistry Research Laboratory, Department of Chemistry, Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, 627012, Tamil Nadu, India
| | - Thangaraj Shankar
- Photochemistry Research Laboratory, Department of Chemistry, Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, 627012, Tamil Nadu, India
| | - Kalaiyar Swarnalatha
- Photochemistry Research Laboratory, Department of Chemistry, Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, 627012, Tamil Nadu, India.
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32
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Dong T, Yang Z, Zhou J, Chen CYC. Equivariant Flexible Modeling of the Protein-Ligand Binding Pose with Geometric Deep Learning. J Chem Theory Comput 2023; 19:8446-8459. [PMID: 37938978 DOI: 10.1021/acs.jctc.3c00273] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
Flexible modeling of the protein-ligand complex structure is a fundamental challenge for in silico drug development. Recent studies have improved commonly used docking tools by incorporating extra-deep learning-based steps. However, such strategies limit their accuracy and efficiency because they retain massive sampling pressure and lack consideration for flexible biomolecular changes. In this study, we propose FlexPose, a geometric graph network capable of direct flexible modeling of complex structures in Euclidean space without the following conventional sampling and scoring strategies. Our model adopts two key designs: scalar-vector dual feature representation and SE(3)-equivariant network, to manage dynamic structural changes, as well as two strategies: conformation-aware pretraining and weakly supervised learning, to boost model generalizability in unseen chemical space. Benefiting from these paradigms, our model dramatically outperforms all tested popular docking tools and recently advanced deep learning methods, especially in tasks involving protein conformation changes. We further investigate the impact of protein and ligand similarity on the model performance with two conformation-aware strategies. Moreover, FlexPose provides an affinity estimation and model confidence for postanalysis.
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Affiliation(s)
- Tiejun Dong
- Intelligent Medical Research Center, School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, Guangdong 510275, China
| | - Ziduo Yang
- Intelligent Medical Research Center, School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, Guangdong 510275, China
| | - Jun Zhou
- Intelligent Medical Research Center, School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, Guangdong 510275, China
| | - Calvin Yu-Chian Chen
- Intelligent Medical Research Center, School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, Guangdong 510275, China
- AI for Science (AI4S)-Preferred Program, Peking University Shenzhen Graduate School, Shenzhen, Guangdong 518055, China
- School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen, Guangdong 518055, China
- Department of Medical Research, China Medical University Hospital, Taichung 40447, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
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Xia S, Chen E, Zhang Y. Integrated Molecular Modeling and Machine Learning for Drug Design. J Chem Theory Comput 2023; 19:7478-7495. [PMID: 37883810 PMCID: PMC10653122 DOI: 10.1021/acs.jctc.3c00814] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/10/2023] [Accepted: 10/11/2023] [Indexed: 10/28/2023]
Abstract
Modern therapeutic development often involves several stages that are interconnected, and multiple iterations are usually required to bring a new drug to the market. Computational approaches have increasingly become an indispensable part of helping reduce the time and cost of the research and development of new drugs. In this Perspective, we summarize our recent efforts on integrating molecular modeling and machine learning to develop computational tools for modulator design, including a pocket-guided rational design approach based on AlphaSpace to target protein-protein interactions, delta machine learning scoring functions for protein-ligand docking as well as virtual screening, and state-of-the-art deep learning models to predict calculated and experimental molecular properties based on molecular mechanics optimized geometries. Meanwhile, we discuss remaining challenges and promising directions for further development and use a retrospective example of FDA approved kinase inhibitor Erlotinib to demonstrate the use of these newly developed computational tools.
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Affiliation(s)
- Song Xia
- Department
of Chemistry, New York University, New York, New York 10003, United States
| | - Eric Chen
- Department
of Chemistry, New York University, New York, New York 10003, United States
| | - Yingkai Zhang
- Department
of Chemistry, New York University, New York, New York 10003, United States
- Simons
Center for Computational Physical Chemistry at New York University, New York, New York 10003, United States
- NYU-ECNU
Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
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Grimm LM, Setiadi J, Tkachenko B, Schreiner PR, Gilson MK, Biedermann F. The temperature-dependence of host-guest binding thermodynamics: experimental and simulation studies. Chem Sci 2023; 14:11818-11829. [PMID: 37920355 PMCID: PMC10619620 DOI: 10.1039/d3sc01975f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 09/24/2023] [Indexed: 11/04/2023] Open
Abstract
The thermodynamic parameters of host-guest binding can be used to describe, understand, and predict molecular recognition events in aqueous systems. However, interpreting binding thermodynamics remains challenging, even for these relatively simple molecules, as they are determined by both direct and solvent-mediated host-guest interactions. In this contribution, we focus on the contributions of water to binding by studying binding thermodynamics, both experimentally and computationally, for a series of nearly rigid, electrically neutral host-guest systems and report the temperature-dependent thermodynamic binding contributions ΔGb(T), ΔHb(T), ΔSb(T), and ΔCp,b. Combining isothermal titration calorimetry (ITC) measurements with molecular dynamics (MD) simulations, we provide insight into the binding forces at play for the macrocyclic hosts cucurbit[n]uril (CBn, n = 7-8) and β-cyclodextrin (β-CD) with a range of guest molecules. We find consistently negative changes in heat capacity on binding (ΔCp,b) for all systems studied herein - as well as for literature host-guest systems - indicating increased enthalpic driving forces for binding at higher temperatures. We ascribe these trends to solvation effects, as the solvent properties of water deteriorate as temperature rises. Unlike the entropic and enthalpic contributions to binding, with their differing signs and magnitudes for the classical and non-classical hydrophobic effect, heat capacity changes appear to be a unifying and more general feature of host-guest complex formation in water. This work has implications for understanding protein-ligand interactions and other complex systems in aqueous environments.
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Affiliation(s)
- Laura M Grimm
- Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT) Hermann-von-Helmholtz Platz 1 76344 Eggenstein-Leopoldshafen Germany
| | - Jeffry Setiadi
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego 9255 Pharmacy Lane La Jolla CA 92093 USA
| | - Boryslav Tkachenko
- Institute of Organic Chemistry, Justus Liebig University Giessen Heinrich-Buff-Ring 17 35392 Giessen Germany
| | - Peter R Schreiner
- Institute of Organic Chemistry, Justus Liebig University Giessen Heinrich-Buff-Ring 17 35392 Giessen Germany
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego 9255 Pharmacy Lane La Jolla CA 92093 USA
| | - Frank Biedermann
- Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT) Hermann-von-Helmholtz Platz 1 76344 Eggenstein-Leopoldshafen Germany
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35
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Monari L, Galentino K, Cecchini M. ChemFlow_py: a flexible toolkit for docking and rescoring. J Comput Aided Mol Des 2023; 37:565-572. [PMID: 37620503 DOI: 10.1007/s10822-023-00527-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 07/26/2023] [Indexed: 08/26/2023]
Abstract
The design of accurate virtual screening tools is an open challenge in drug discovery. Several structure-based methods have been developed at different levels of approximation. Among them, molecular docking is an established technique with high efficiency, but typically low accuracy. Moreover, docking performances are known to be target-dependent, which makes the choice of the docking program and corresponding scoring function critical when approaching a new protein target. To compare the performances of different docking protocols, we developed ChemFlow_py, an automated tool to perform docking and rescoring. Using four protein systems extracted from DUD-E with 100 known active compounds and 3000 decoys per target, we compared the performances of several rescoring strategies including consensus scoring. We found that the average docking results can be improved by consensus ranking, which emphasizes the relevance of consensus scoring when little or no chemical information is available for a given target. ChemFlow_py is a free toolkit to optimize the performances of virtual high-throughput screening (vHTS). The software is publicly available at https://github.com/IFMlab/ChemFlow_py .
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Affiliation(s)
- Luca Monari
- Institut de Chimie de Strasbourg, UMR7177, CNRS, Université de Strasbourg, 67083, Strasbourg, Cedex, France
| | - Katia Galentino
- Institut de Chimie de Strasbourg, UMR7177, CNRS, Université de Strasbourg, 67083, Strasbourg, Cedex, France
| | - Marco Cecchini
- Institut de Chimie de Strasbourg, UMR7177, CNRS, Université de Strasbourg, 67083, Strasbourg, Cedex, France.
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Shaikh S, Ali S, Lim JH, Ahmad K, Han KS, Lee EJ, Choi I. Virtual Insights into Natural Compounds as Potential 5α-Reductase Type II Inhibitors: A Structure-Based Screening and Molecular Dynamics Simulation Study. Life (Basel) 2023; 13:2152. [PMID: 38004292 PMCID: PMC10671996 DOI: 10.3390/life13112152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 10/30/2023] [Accepted: 10/30/2023] [Indexed: 11/26/2023] Open
Abstract
Androgenic alopecia (AGA) is a dermatological disease with psychosocial consequences for those who experience hair loss. AGA is linked to an increase in androgen levels caused by an excess of dihydrotestosterone in blood capillaries produced from testosterone by 5α-reductase type II (5αR2), which is expressed in scalp hair follicles; 5αR2 activity and dihydrotestosterone levels are elevated in balding scalps. The diverse health benefits of flavonoids have been widely reported in epidemiological studies, and research interest continues to increase. In this study, a virtual screening approach was used to identify compounds that interact with active site residues of 5αR2 by screening a library containing 241 flavonoid compounds. Here, we report two potent flavonoid compounds, eriocitrin and silymarin, that interacted strongly with 5αR2, with binding energies of -12.1 and -11.7 kcal/mol, respectively, which were more significant than those of the control, finasteride (-11.2 kcal/mol). Molecular dynamic simulations (200 ns) were used to optimize the interactions between compounds and 5αR2 and revealed that the interaction of eriocitrin and silymarin with 5αR2 was stable. The study shows that eriocitrin and silymarin provide developmental bases for novel 5αR2 inhibitors for the management of AGA.
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Affiliation(s)
- Sibhghatulla Shaikh
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan 38541, Republic of Korea; (S.S.); (S.A.); (J.H.L.); (K.A.); (E.J.L.)
- Research Institute of Cell Culture, Yeungnam University, Gyeongsan 38541, Republic of Korea
| | - Shahid Ali
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan 38541, Republic of Korea; (S.S.); (S.A.); (J.H.L.); (K.A.); (E.J.L.)
- Research Institute of Cell Culture, Yeungnam University, Gyeongsan 38541, Republic of Korea
| | - Jeong Ho Lim
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan 38541, Republic of Korea; (S.S.); (S.A.); (J.H.L.); (K.A.); (E.J.L.)
- Research Institute of Cell Culture, Yeungnam University, Gyeongsan 38541, Republic of Korea
| | - Khurshid Ahmad
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan 38541, Republic of Korea; (S.S.); (S.A.); (J.H.L.); (K.A.); (E.J.L.)
- Research Institute of Cell Culture, Yeungnam University, Gyeongsan 38541, Republic of Korea
| | - Ki Soo Han
- Neo Cremar Co., Ltd., Seoul 05702, Republic of Korea;
| | - Eun Ju Lee
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan 38541, Republic of Korea; (S.S.); (S.A.); (J.H.L.); (K.A.); (E.J.L.)
- Research Institute of Cell Culture, Yeungnam University, Gyeongsan 38541, Republic of Korea
| | - Inho Choi
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan 38541, Republic of Korea; (S.S.); (S.A.); (J.H.L.); (K.A.); (E.J.L.)
- Research Institute of Cell Culture, Yeungnam University, Gyeongsan 38541, Republic of Korea
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37
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Chen L, Gu R, Li Y, Liu H, Han W, Yan Y, Chen Y, Zhang Y, Jiang Y. Epigenetic target identification strategy based on multi-feature learning. J Biomol Struct Dyn 2023:1-17. [PMID: 37827992 DOI: 10.1080/07391102.2023.2259511] [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: 01/16/2023] [Accepted: 06/20/2023] [Indexed: 10/14/2023]
Abstract
The identification of potential epigenetic targets for a known bioactive compound is essential and promising as more and more epigenetic drugs are used in cancer clinical treatment and the availability of chemogenomic data related to epigenetics increases. In this study, we introduce a novel epigenetic target identification strategy (ETI-Strategy) that integrates a multi-task graph convolutional neural network prior model and a protein-ligand interaction classification discriminating model using large-scale bioactivity data for a panel of 55 epigenetic targets. Our approach utilizes machine learning techniques to achieve an AUC value of 0.934 for the prior model and 0.830 for the discriminating model, outperforming inverse docking in predicting protein-ligand interactions. When comparing with other open-source target identification tools, it was found that only our tool was able to accurately predict all the targets corresponding to each compound. This further demonstrates the ability of our strategy to take full advantage of molecular-level information as well as protein-level information in molecular activity prediction. Our work highlights the contribution of machine learning in the identification of potential epigenetic targets and offers a novel approach for epigenetic drug discovery and development.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Lingfeng Chen
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Rui Gu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Yuanyuan Li
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Haichun Liu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Weijie Han
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Yingchao Yan
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Yadong Chen
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Yanmin Zhang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Yulei Jiang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
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Yaraguppi DA, Bagewadi ZK, Patil NR, Mantri N. Iturin: A Promising Cyclic Lipopeptide with Diverse Applications. Biomolecules 2023; 13:1515. [PMID: 37892197 PMCID: PMC10604914 DOI: 10.3390/biom13101515] [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: 09/11/2023] [Revised: 10/10/2023] [Accepted: 10/11/2023] [Indexed: 10/29/2023] Open
Abstract
This comprehensive review examines iturin, a cyclic lipopeptide originating from Bacillus subtilis and related bacteria. These compounds are structurally diverse and possess potent inhibitory effects against plant disease-causing bacteria and fungi. Notably, Iturin A exhibits strong antifungal properties and low toxicity, making it valuable for bio-pesticides and mycosis treatment. Emerging research reveals additional capabilities, including anticancer and hemolytic features. Iturin finds applications across industries. In food, iturin as a biosurfactant serves beyond surface tension reduction, enhancing emulsions and texture. Biosurfactants are significant in soil remediation, agriculture, wound healing, and sustainability. They also show promise in Microbial Enhanced Oil Recovery (MEOR) in the petroleum industry. The pharmaceutical and cosmetic industries recognize iturin's diverse properties, such as antibacterial, antifungal, antiviral, anticancer, and anti-obesity effects. Cosmetic applications span emulsification, anti-wrinkle, and antibacterial use. Understanding iturin's structure, synthesis, and applications gains importance as biosurfactant and lipopeptide research advances. This review focuses on emphasizing iturin's structural characteristics, production methods, biological effects, and applications across industries. It probes iturin's antibacterial, antifungal potential, antiviral efficacy, and cancer treatment capabilities. It explores diverse applications in food, petroleum, pharmaceuticals, and cosmetics, considering recent developments, challenges, and prospects.
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Affiliation(s)
- Deepak A. Yaraguppi
- Department of Biotechnology, KLE Technological University, Hubballi 580031, Karnataka, India;
| | - Zabin K. Bagewadi
- Department of Biotechnology, KLE Technological University, Hubballi 580031, Karnataka, India;
| | - Ninganagouda R. Patil
- Department of Physics, B. V Bhoomaraddi College of Engineering and Technology, Hubballi 580031, Karnataka, India;
| | - Nitin Mantri
- The Pangenomics Lab, School of Science, RMIT University, Bundoora, VIC 3083, Australia
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia
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Liu T, Zhang B, Gao Y, Zhang X, Tong J, Li Z. Identification of ACHE as the hub gene targeting solasonine associated with non-small cell lung cancer (NSCLC) using integrated bioinformatics analysis. PeerJ 2023; 11:e16195. [PMID: 37842037 PMCID: PMC10573390 DOI: 10.7717/peerj.16195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 09/06/2023] [Indexed: 10/17/2023] Open
Abstract
Background Solasonine, as a major biological component of Solanum nigrum L., has demonstrated anticancer effects against several malignancies. However, little is understood regarding its biological target and mechanism in non-small cell lung cancer (NSCLC). Methods We conducted an analysis on transcriptomic data to identify differentially expressed genes (DEGs), and employed an artificial intelligence (AI) strategy to predict the target protein for solasonine. Subsequently, genetic dependency analysis and molecular docking were performed, with Acetylcholinesterase (ACHE) selected as a pivotal marker for solasonine. We then employed a range of bioinformatic approaches to explore the relationship between ACHE and solasonine. Furthermore, we investigated the impact of solasonine on A549 cells, a human lung cancer cell line. Cell inhibition of A549 cells following solasonine treatment was analyzed using the CCK8 assay. Additionally, we assessed the protein expression of ACHE, as well as markers associated with apoptosis and inflammation, using western blotting. To investigate their functions, we employed a plasmid-based ACHE overexpression system. Finally, we performed dynamics simulations to simulate the interaction mode between solasonine and ACHE. Results The results of the genetic dependency analysis revealed that ACHE could be identified as the pivotal target with the highest docking affinity. The cell experiments yielded significant findings, as evidenced by the negative regulatory effect of solasonine treatment on tumor cells, as demonstrated by the CCK8 assay. Western blotting analysis revealed that solasonine treatment resulted in the downregulation of the Bcl-2/Bax ratio and upregulation of cleaved caspase-3 protein expression levels. Moreover, we observed that ACHE overexpression promoted the expression of the Bcl-2/Bax ratio and decreased cleaved caspase-3 expression in the OE-ACHE group. Notably, solasonine treatment rescued the Bcl-2/Bax ratio and cleaved caspase-3 expression in OE-ACHE cells compared to OE-ACHE cells without solasonine treatment, suggesting that solasonine induces apoptosis. Besides, solasonine exhibited its anti-inflammatory effects by inhibiting P38 MAPK. This was supported by the decline in protein levels of IL-1β and TNF-α, as well as the phosphorylated forms of JNK and P38 MAPK. The results from the molecular docking and dynamics simulations further confirmed the potent binding affinity and effective inhibitory action between solasonine and ACHE. Conclusions The findings of the current investigation show that solasonine exerts its pro-apoptosis and anti-inflammatory effects by suppressing the expression of ACHE.
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Affiliation(s)
- Tong Liu
- Anhui University of Chinese Medicine, Hefei, Anhui, China
- Key Laboratory of Xin’An Medicine, Ministry of Education, Hefei, Anhui, China
| | - Boke Zhang
- The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China
| | - Yating Gao
- The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China
| | - Xingxing Zhang
- The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China
| | - Jiabing Tong
- Anhui University of Chinese Medicine, Hefei, Anhui, China
- The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China
- Key Laboratory of Anhui Provincial Department of Education, Hefei, Anhui, China
- Center for Xin’an Medicine and Modernization of Traditional Chinese Medicine, Institute of Health and Medicine, Hefei Comprehensive National Science Center, Hefei, Anhui, China
| | - Zegeng Li
- Anhui University of Chinese Medicine, Hefei, Anhui, China
- The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China
- Key Laboratory of Anhui Provincial Department of Education, Hefei, Anhui, China
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Wu K, Karapetyan E, Schloss J, Vadgama J, Wu Y. Advancements in small molecule drug design: A structural perspective. Drug Discov Today 2023; 28:103730. [PMID: 37536390 PMCID: PMC10543554 DOI: 10.1016/j.drudis.2023.103730] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 07/19/2023] [Accepted: 07/27/2023] [Indexed: 08/05/2023]
Abstract
In this review, we outline recent advancements in small molecule drug design from a structural perspective. We compare protein structure prediction methods and explore the role of the ligand binding pocket in structure-based drug design. We examine various structural features used to optimize drug candidates, including functional groups, stereochemistry, and molecular weight. Computational tools such as molecular docking and virtual screening are discussed for predicting and optimizing drug candidate structures. We present examples of drug candidates designed based on their molecular structure and discuss future directions in the field. By effectively integrating structural information with other valuable data sources, we can improve the drug discovery process, leading to the identification of novel therapeutics with improved efficacy, specificity, and safety profiles.
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Affiliation(s)
- Ke Wu
- Division of Cancer Research and Training, Department of Internal Medicine, Charles R. Drew University of Medicine and Science, David Geffen UCLA School of Medicine and UCLA Jonsson Comprehensive Cancer Center, Los Angeles, CA 90095, USA
| | - Eduard Karapetyan
- Division of Cancer Research and Training, Department of Internal Medicine, Charles R. Drew University of Medicine and Science, David Geffen UCLA School of Medicine and UCLA Jonsson Comprehensive Cancer Center, Los Angeles, CA 90095, USA
| | - John Schloss
- Division of Cancer Research and Training, Department of Internal Medicine, Charles R. Drew University of Medicine and Science, David Geffen UCLA School of Medicine and UCLA Jonsson Comprehensive Cancer Center, Los Angeles, CA 90095, USA; School of Pharmacy, American University of Health Sciences, Signal Hill, CA 90755, USA
| | - Jaydutt Vadgama
- Division of Cancer Research and Training, Department of Internal Medicine, Charles R. Drew University of Medicine and Science, David Geffen UCLA School of Medicine and UCLA Jonsson Comprehensive Cancer Center, Los Angeles, CA 90095, USA; School of Pharmacy, American University of Health Sciences, Signal Hill, CA 90755, USA.
| | - Yong Wu
- Division of Cancer Research and Training, Department of Internal Medicine, Charles R. Drew University of Medicine and Science, David Geffen UCLA School of Medicine and UCLA Jonsson Comprehensive Cancer Center, Los Angeles, CA 90095, USA.
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41
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Secker C, Fackeldey K, Weber M, Ray S, Gorgulla C, Schütte C. Novel multi-objective affinity approach allows to identify pH-specific μ-opioid receptor agonists. J Cheminform 2023; 15:85. [PMID: 37726792 PMCID: PMC10510211 DOI: 10.1186/s13321-023-00746-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 08/21/2023] [Indexed: 09/21/2023] Open
Abstract
Opioids are essential pharmaceuticals due to their analgesic properties, however, lethal side effects, addiction, and opioid tolerance are extremely challenging. The development of novel molecules targeting the [Formula: see text]-opioid receptor (MOR) in inflamed, but not in healthy tissue, could significantly reduce these unwanted effects. Finding such novel molecules can be achieved by maximizing the binding affinity to the MOR at acidic pH while minimizing it at neutral pH, thus combining two conflicting objectives. Here, this multi-objective optimal affinity approach is presented, together with a virtual drug discovery pipeline for its practical implementation. When applied to finding pH-specific drug candidates, it combines protonation state-dependent structure and ligand preparation with high-throughput virtual screening. We employ this pipeline to characterize a set of MOR agonists identifying a morphine-like opioid derivative with higher predicted binding affinities to the MOR at low pH compared to neutral pH. Our results also confirm existing experimental evidence that NFEPP, a previously described fentanyl derivative with reduced side effects, and recently reported [Formula: see text]-fluorofentanyls and -morphines show an increased specificity for the MOR at acidic pH when compared to fentanyl and morphine. We further applied our approach to screen a >50K ligand library identifying novel molecules with pH-specific predicted binding affinities to the MOR. The presented differential docking pipeline can be applied to perform multi-objective affinity optimization to identify safer and more specific drug candidates at large scale.
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Affiliation(s)
- Christopher Secker
- Zuse Institute Berlin, Berlin, Germany.
- Max Delbrück Center for Molecular Medicine, Berlin, Germany.
| | - Konstantin Fackeldey
- Zuse Institute Berlin, Berlin, Germany
- Institute of Mathematics, Technical University Berlin, Berlin, Germany
| | | | | | - Christoph Gorgulla
- Zuse Institute Berlin, Berlin, Germany
- Department of Physics, Harvard University, Cambridge, MA, 02138, USA
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Christof Schütte
- Zuse Institute Berlin, Berlin, Germany
- Mathematics Institute, Freie Universität Berlin, Berlin, Germany
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42
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Kaur R, Suresh PK. Chemoresistance Mechanisms in Non-Small Cell Lung Cancer-Opportunities for Drug Repurposing. Appl Biochem Biotechnol 2023:10.1007/s12010-023-04595-7. [PMID: 37721630 DOI: 10.1007/s12010-023-04595-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/26/2023] [Indexed: 09/19/2023]
Abstract
Globally, lung cancer contributes significantly to the public health burden-associated mortality. As this form of cancer is insidious in nature, there is an inevitable diagnostic delay leading to chronic tumor development. Non-small cell lung cancer (NSCLC) constitutes 80-85% of all lung cancer cases, making this neoplasia form a prevalent subset of lung carcinoma. One of the most vital aspects for proper diagnosis, prognosis, and adequate therapy is the precise classification of non-small cell lung cancer based on biomarker expression profiling. This form of biomarker profiling has provided opportunities for improvements in patient stratification, mechanistic insights, and probable druggable targets. However, numerous patients have exhibited numerous toxic side effects, tumor relapse, and development of therapy-based chemoresistance. As a result of these exacting situations, there is a dire need for efficient and effective new cancer therapeutics. De novo drug development approach is a costly and tedious endeavor, with an increased attrition rate, attributed, in part, to toxicity-related issues. Drug repurposing, on the other hand, when combined with computer-assisted systems biology approach, provides alternatives to the discovery of new, efficacious, and safe drugs. Therefore, in this review, we focus on a comparison of the conventional therapy-based chemoresistance mechanisms with the repurposed anti-cancer drugs from three different classes-anti-parasitic, anti-depressants, and anti-psychotics for cancer treatment with a primary focus on NSCLC therapeutics. Certainly, amalgamating these novel therapeutic approaches with that of the conventional drug regimen in NSCLC-affected patients will possibly complement/synergize the existing therapeutic modalities. This approach has tremendous translational significance, since it can combat drug resistance and cytotoxicity-based side effects and provides a relatively new strategy for possible application in therapy of individuals with NSCLC.
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Affiliation(s)
- Rajdeep Kaur
- Department of Bio-Medical Sciences, School of Biosciences and Technology, VIT University, Vellore, 632014, Tamil Nadu, India
| | - P K Suresh
- Department of Bio-Medical Sciences, School of Biosciences and Technology, VIT University, Vellore, 632014, Tamil Nadu, India.
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Alsrhani A, Farhana A, Khan YS, Ashraf GM, Shahwan M, Shamsi A. Phytoconstituents as potential therapeutic agents against COVID-19: a computational study on inhibition of SARS-CoV-2 main protease. J Biomol Struct Dyn 2023:1-12. [PMID: 37713337 DOI: 10.1080/07391102.2023.2257328] [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: 07/19/2023] [Accepted: 09/04/2023] [Indexed: 09/17/2023]
Abstract
The Coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) has become a global health crisis, and the urgent need for effective treatments is evident. One potential target for COVID-19 therapeutics is the main protease (Mpro) of SARS‑CoV‑2, an essential enzyme for viral replication. Natural compounds have been explored as a source of potential inhibitors for Mpro due to their safety and availability. In this study, we employed a computational approach to screen a library of phytoconstituents and identified potential Mpro inhibitors based on their binding affinities and molecular interactions. The top-ranking compounds were further validated through molecular dynamics simulations (MDS) and free energy calculations. As a result of the above procedures, we identified two phytoconstituents, Khelmarin B and Neogitogenin, with appreciable binding affinity and specificity towards the Mpro binding pocket. Our results suggest that Khelmarin B and Neogitogenin could potentially serve as Mpro inhibitors and have the potential to be developed as COVID-19 therapeutics. Further experimental studies are required to confirm the efficacy and safety of these compounds.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Abdullah Alsrhani
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Jouf University, Sakaka, Saudi Arabia
| | - Aisha Farhana
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Jouf University, Sakaka, Saudi Arabia
| | - Yusuf Saleem Khan
- Department of Anatomy, College of Medicine, Jouf University, Sakaka, Saudi Arabia
| | - Ghulam Md Ashraf
- Department of Medical Laboratory Sciences, College of Health Sciences, and Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - Moyad Shahwan
- Center of Medical and Bio-Allied Health Sciences Research (CMBHSR), Ajman University, Ajman, United Arab Emirates
| | - Anas Shamsi
- Center of Medical and Bio-Allied Health Sciences Research (CMBHSR), Ajman University, Ajman, United Arab Emirates
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Shamsian S, Sokouti B, Dastmalchi S. Benchmarking different docking protocols for predicting the binding poses of ligands complexed with cyclooxygenase enzymes and screening chemical libraries. BIOIMPACTS : BI 2023; 14:29955. [PMID: 38505677 PMCID: PMC10945300 DOI: 10.34172/bi.2023.29955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 08/09/2023] [Accepted: 08/23/2023] [Indexed: 03/21/2024]
Abstract
Introduction Non-steroidal anti-inflammatory drugs (NSAIDs) constitute an important class of pharmaceuticals acting on cyclooxygenase COX-1 and COX-2 enzymes. Due to their numerous severe side effects, it is necessary to search for new selective, safe, and effective anti-inflammatory drugs. In silico design of novel therapeutics plays an important role in nowadays drug discovery pipelines. In most cases, the design strategies require the use of molecular docking calculations. The docking procedure may require case-specific condition for a successful result. Additionally, many different docking programs are available, which highlights the importance of identifying the most proper docking method and condition for a given problem. Methods In the current work, the performances of five popular molecular docking programs, namely, GOLD, AutoDock, FlexX, Molegro Virtual Docker (MVD) and Glide to predict the binding mode of co- crystallized inhibitors in the structures of known complexes available for cyclooxygenases were evaluated. Furthermore, the best performers, Glide, AutoDock, GOLD and FlexX, were further evaluated in docking-based virtual screening of libraries consisted of active ligands and decoy molecules for cyclooxygenase enzymes and the obtained docking scores were assessed by receiver operating characteristics (ROC) analysis. Results The results of docking experiments indicated that Glide program outperformed other docking programs by correctly predicting the binding poses (RMSD less than 2 Å) of all studied co-crystallized ligands of COX-1 and COX-2 enzymes (i.e., the performance was 100%). However, the performances of the other studied docking methods for correctly predicting the binding poses of the ligands were between 59% to 82%. Virtual screening results treated by ROC analysis revealed that all tested methods are useful tools for classification and enrichment of molecules targeting COX enzymes. The obtained AUCs range between 0.61-0.92 with enrichment factors of 8 - 40 folds. Conclusion The obtained results support the importance of choosing appropriate docking method for predicting ligand-receptor binding modes, and provide specific information about docking calculations on COXs ligands.
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Affiliation(s)
- Sara Shamsian
- Student Research Committee, Tabriz University of Medical Sciences, Tabriz, 5165665931, Iran
- Department of Medicinal Chemistry, School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, 5166414766, Iran
| | - Babak Sokouti
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, 5165665813, Iran
| | - Siavoush Dastmalchi
- Department of Medicinal Chemistry, School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, 5166414766, Iran
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, 5165665813, Iran
- Faculty of Pharmacy, Near East University, POBOX:99138, Nicosia, North Cyprus, Mersin 10, Turkey
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Hossain MR, Alam R, Chung HJ, Eva TA, Kabir MF, Mamurat H, Hong ST, Hafiz MA, Hossen SMM. In Vivo, In Vitro and In Silico Study of Cucurbita moschata Flower Extract: A Promising Source of Natural Analgesic, Anti-Inflammatory, and Antibacterial Agents. Molecules 2023; 28:6573. [PMID: 37764349 PMCID: PMC10536299 DOI: 10.3390/molecules28186573] [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: 05/25/2023] [Revised: 09/02/2023] [Accepted: 09/03/2023] [Indexed: 09/29/2023] Open
Abstract
For thousands of years, medicinal plants have played a pivotal role in maintaining human health and improving the quality of human life. This study was designed to analyze the analgesic, anti-inflammatory, and antibacterial potentials of a hydro-methanolic extract of Cucurbita moschata flowers, along with qualitative and quantitative phytochemical screening. The anti-inflammatory effect was tested using the in vitro membrane stabilizing method for human red blood cells (HRBC), the analgesic effect was tested using the in vivo acetic acid-induced writing method, and the antibacterial effect was tested using the disc diffusion method. In silico ADME/T and molecular docking studies were performed to assess the potential of the stated phytochemicals against Cyclooxygenase-II enzyme. Phytochemical screening confirmed the presence of flavonoids, alkaloids, glycosides, tannins, and carbohydrates. The flower extract demonstrated the maximum protection of human red blood cells at 1000 µg/mL, with a 65.73% reduction in hemolysis in a hypotonic solution. The extract also showed significant (p < 0.05) and dose-dependent analgesic effects at oral doses of 200 and 400 mg/kg on the tested animals. Furthermore, the flower extract exhibited potent antibacterial activity due to the disc diffusion method, which was compared with standard ciprofloxacin. In silico testing revealed that 42 phytochemicals exhibited notable pharmacokinetic properties and passed drug likeness screening tests. Among the six best-selected compounds, 3,4-dihydro-2H-pyran-2-yl)methanamine showed the highest binding affinity (-10.1) with significant non-bonding interactions with the target enzyme. In conclusion, the hydro-methanolic extract of Cucurbita moschata was found to be rich in various phytochemicals that may be associated with therapeutic potential, and this study supports the traditional use of Cucurbita moschata flowers in the management of inflammation and painful conditions.
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Affiliation(s)
- Md. Rabiul Hossain
- Department of Pharmacy, University of Science and Technology, Foy’s Lake, Chittagong 4202, Bangladesh; (M.R.H.); (H.M.)
| | - Rashedul Alam
- Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC 29425, USA;
| | - Hea-Jong Chung
- Gwanju Center, Korea Basic Science Institute, Gwanju 61715, Republic of Korea
| | - Taslima Akter Eva
- Department of Pharmacy, University of Chittagong, Chittagong 4331, Bangladesh;
| | | | - Husnum Mamurat
- Department of Pharmacy, University of Science and Technology, Foy’s Lake, Chittagong 4202, Bangladesh; (M.R.H.); (H.M.)
| | - Seong-Tshool Hong
- Department of Biomedical Sciences, Institute for Medical Science, Jeonbuk National University Medical School, Jeonju 54907, Republic of Korea;
| | - Md. Al Hafiz
- Department of Pharmacy, East West University, Dhaka 1212, Bangladesh;
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Alnammi M, Liu S, Ericksen SS, Ananiev GE, Voter AF, Guo S, Keck JL, Hoffmann FM, Wildman SA, Gitter A. Evaluating Scalable Supervised Learning for Synthesize-on-Demand Chemical Libraries. J Chem Inf Model 2023; 63:5513-5528. [PMID: 37625010 PMCID: PMC10538940 DOI: 10.1021/acs.jcim.3c00912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Indexed: 08/27/2023]
Abstract
Traditional small-molecule drug discovery is a time-consuming and costly endeavor. High-throughput chemical screening can only assess a tiny fraction of drug-like chemical space. The strong predictive power of modern machine-learning methods for virtual chemical screening enables training models on known active and inactive compounds and extrapolating to much larger chemical libraries. However, there has been limited experimental validation of these methods in practical applications on large commercially available or synthesize-on-demand chemical libraries. Through a prospective evaluation with the bacterial protein-protein interaction PriA-SSB, we demonstrate that ligand-based virtual screening can identify many active compounds in large commercial libraries. We use cross-validation to compare different types of supervised learning models and select a random forest (RF) classifier as the best model for this target. When predicting the activity of more than 8 million compounds from Aldrich Market Select, the RF substantially outperforms a naïve baseline based on chemical structure similarity. 48% of the RF's 701 selected compounds are active. The RF model easily scales to score one billion compounds from the synthesize-on-demand Enamine REAL database. We tested 68 chemically diverse top predictions from Enamine REAL and observed 31 hits (46%), including one with an IC50 value of 1.3 μM.
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Affiliation(s)
- Moayad Alnammi
- Department
of Computer Sciences, University of Wisconsin−Madison, Madison, Wisconsin 53706, United States
- Morgridge
Institute for Research, Madison, Wisconsin 53715, United States
- Department
of Information and Computer Science, King
Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
| | - Shengchao Liu
- Department
of Computer Sciences, University of Wisconsin−Madison, Madison, Wisconsin 53706, United States
- Morgridge
Institute for Research, Madison, Wisconsin 53715, United States
| | - Spencer S. Ericksen
- Small
Molecule Screening Facility, University
of Wisconsin−Madison, Madison, Wisconsin 53792, United States
| | - Gene E. Ananiev
- Small
Molecule Screening Facility, University
of Wisconsin−Madison, Madison, Wisconsin 53792, United States
| | - Andrew F. Voter
- Department
of Biomolecular Chemistry, University of
Wisconsin−Madison, Madison, Wisconsin 53706, United States
| | - Song Guo
- Small
Molecule Screening Facility, University
of Wisconsin−Madison, Madison, Wisconsin 53792, United States
| | - James L. Keck
- Department
of Biomolecular Chemistry, University of
Wisconsin−Madison, Madison, Wisconsin 53706, United States
| | - F. Michael Hoffmann
- Small
Molecule Screening Facility, University
of Wisconsin−Madison, Madison, Wisconsin 53792, United States
- McArdle Laboratory
for Cancer Research, University of Wisconsin−Madison, Madison, Wisconsin 53705, United States
| | - Scott A. Wildman
- Small
Molecule Screening Facility, University
of Wisconsin−Madison, Madison, Wisconsin 53792, United States
| | - Anthony Gitter
- Department
of Computer Sciences, University of Wisconsin−Madison, Madison, Wisconsin 53706, United States
- Morgridge
Institute for Research, Madison, Wisconsin 53715, United States
- Department
of Biostatistics and Medical Informatics, University of Wisconsin−Madison, Madison, Wisconsin 53792, United States
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Das AP, Nandekar P, Mathur P, Agarwal SM. A systematic pipeline of protein structure selection for computer-aided drug discovery: A case study on T790M/L858R mutant EGFR structures. Protein Sci 2023; 32:e4740. [PMID: 37515373 PMCID: PMC10443354 DOI: 10.1002/pro.4740] [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: 02/22/2023] [Revised: 07/24/2023] [Accepted: 07/26/2023] [Indexed: 07/30/2023]
Abstract
Virtual screening (VS) is a routine method to evaluate chemical libraries for lead identification. Therefore, the selection of appropriate protein structures for VS is an essential prerequisite to identify true actives during docking. But the presence of several crystal structures of the same protein makes it difficult to select one or few structures rationally for screening. Therefore, a computational prioritization protocol has been developed for shortlisting crystal structures that identify true active molecules with better efficiency. As identification of small-molecule inhibitors is an important clinical requirement for the T790M/L858R (TMLR) EGFR mutant, it has been selected as a case study. The approach involves cross-docking of 21 co-crystal ligands with all the structures of the same protein to select structures that dock non-native ligands with lower RMSD. The cross docking performance was then correlated with ligand similarity and binding-site conformational similarity. Eventually, structures were shortlisted by integrating cross-docking performance, and ligand and binding-site similarity. Thereafter, binding pose metadynamics was employed to identify structures having stable co-crystal ligands in their respective binding pockets. Finally, different enrichment metrics like BEDROC, RIE, AUAC, and EF1% were evaluated leading to the identification of five TMLR structures (5HCX, 5CAN, 5CAP, 5CAS, and 5CAO). These structures docked a number of non-native ligands with low RMSD, contain structurally dissimilar ligands, have conformationally dissimilar binding sites, harbor stable co-crystal ligands, and also identify true actives early. The present approach can be implemented for shortlisting protein targets of any other important therapeutic kinases.
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Affiliation(s)
- Agneesh Pratim Das
- Bioinformatics Division, ICMR—National Institute of Cancer Prevention and ResearchNoidaUttar PradeshIndia
- Amity Institute of BiotechnologyAmity University Uttar PradeshNoidaUttar PradeshIndia
| | | | - Puniti Mathur
- Amity Institute of BiotechnologyAmity University Uttar PradeshNoidaUttar PradeshIndia
| | - Subhash M. Agarwal
- Bioinformatics Division, ICMR—National Institute of Cancer Prevention and ResearchNoidaUttar PradeshIndia
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Boopathi V, Nahar J, Murugesan M, Subramaniyam S, Kong BM, Choi SK, Lee CS, Ling L, Yang DU, Yang DC, Mathiyalagan R, Chan Kang S. In silico and in vitro inhibition of host-based viral entry targets and cytokine storm in COVID-19 by ginsenoside compound K. Heliyon 2023; 9:e19341. [PMID: 37809955 PMCID: PMC10558348 DOI: 10.1016/j.heliyon.2023.e19341] [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: 11/02/2022] [Revised: 08/07/2023] [Accepted: 08/18/2023] [Indexed: 10/10/2023] Open
Abstract
SARS-CoV-2 is a novel coronavirus that emerged as an epidemic, causing a respiratory disease with multiple severe symptoms and deadly consequences. ACE-2 and TMPRSS2 play crucial and synergistic roles in the membrane fusion and viral entry of SARS-CoV-2 (COVID-19). The spike (S) protein of SARS-CoV-2 binds to the ACE-2 receptor for viral entry, while TMPRSS2 proteolytically cleaves the S protein into S1 and S2 subunits, promoting membrane fusion. Therefore, ACE-2 and TMPRSS2 are potential drug targets for treating COVID-19, and their inhibition is a promising strategy for treatment and prevention. This study proposes that ginsenoside compound K (G-CK), a triterpenoid saponin abundant in Panax Ginseng, a dietary and medicinal herb highly consumed in Korea and China, effectively binds to and inhibits ACE-2 and TMPRSS2 expression. We initially conducted an in-silico evaluation where G-CK showed a high affinity for the binding sites of the two target proteins of SARS-CoV-2. Additionally, we evaluated the stability of G-CK using molecular dynamics (MD) simulations for 100 ns, followed by MM-PBSA calculations. The MD simulations and free energy calculations revealed that G-CK has stable and favorable energies, leading to strong binding with the targets. Furthermore, G-CK suppressed ACE2 and TMPRSS2 mRNA expression in A549, Caco-2, and MCF7 cells at a concentration of 12.5 μg/mL and in LPS-induced RAW 264.7 cells at a concentration of 6.5 μg/mL, without significant cytotoxicity.ACE2 and TMPRSS2 expression were significantly lower in A549 and RAW 264.7 cells following G-CK treatment. These findings suggest that G-CK may evolve as a promising therapeutic against COVID-19.
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Affiliation(s)
- Vinothini Boopathi
- Graduate School of Biotechnology, College of Life Sciences, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, South Korea
| | - Jinnatun Nahar
- Graduate School of Biotechnology, College of Life Sciences, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, South Korea
| | - Mohanapriya Murugesan
- Graduate School of Biotechnology, College of Life Sciences, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, South Korea
| | | | - Byoung Man Kong
- Department of Oriental Medicinal Biotechnology, College of Life Science, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, South Korea
| | - Sung-Keun Choi
- Daedong Korea Ginseng Co., Ltd, 86, Gunbuk-ro, Gunbuk-myeon, Geumsan-gun, Chungcheongnam-do 32718 Republic of Korea
| | - Chang-Soon Lee
- Daedong Korea Ginseng Co., Ltd, 86, Gunbuk-ro, Gunbuk-myeon, Geumsan-gun, Chungcheongnam-do 32718 Republic of Korea
| | - Li Ling
- Department of Oriental Medicinal Biotechnology, College of Life Science, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, South Korea
| | - Dong Uk Yang
- Graduate School of Biotechnology, College of Life Sciences, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, South Korea
| | - Deok Chun Yang
- Graduate School of Biotechnology, College of Life Sciences, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, South Korea
- Department of Oriental Medicinal Biotechnology, College of Life Science, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, South Korea
| | - Ramya Mathiyalagan
- Graduate School of Biotechnology, College of Life Sciences, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, South Korea
| | - Se Chan Kang
- Graduate School of Biotechnology, College of Life Sciences, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, South Korea
- Department of Oriental Medicinal Biotechnology, College of Life Science, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, South Korea
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Alabbas AB. Identification of promising methionine aminopeptidase enzyme inhibitors: A combine study of comprehensive virtual screening and dynamics simulation study. Saudi Pharm J 2023; 31:101745. [PMID: 37638221 PMCID: PMC10448168 DOI: 10.1016/j.jsps.2023.101745] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 08/05/2023] [Indexed: 08/29/2023] Open
Abstract
Methionine aminopeptidase (MetAP) enzymes play a critical role in bacterial cell survival by cleaving formyl-methionine initiators at N-terminal of nascent protein, a process which is vital in proper protein folding. This makes MetAP an attractive and novel antibacterial target to unveil promising antibiotics. In this study, the crystal structure of R. prowazekii MetAP was used in structure-based virtual screening of drug libraries such as Asinex antibacterial library and Comprehensive Marine Natural Products Database (CMNPD) to identify promising lead molecules against the enzyme. This shortlisted three drug molecules; BDE-25098678, BDE-30686468 and BDD_25351157 as most potent leads that showed strong binding to the MetAP enzyme. The static docked conformation of the compounds to the MetAP was reevaluated in molecular dynamics simulation studies. The analysis observed the docked complexes as stable structure with no major local or global deviations noticed. These findings suggest the formation of strong intermolecular docked complexes, which showed stable dynamics and atomic level interactions network. The binding free energy analysis predicted net MMGBSA energy of complexes as: BDE-25098678 (-73.41 kcal/mol), BDE-30686468 (-59.93 kcal/mol), and BDD_25351157 (-75.39 kcal/mol). In case of MMPBSA, the complexes net binding energy was as; BDE-25098678 (-77.47 kcal/mol), BDE-30686468 (-69.47 kcal/mol), and BDD_25351157 (-75.6 kcal/mol). Further, the compounds were predicted to follow the famous Lipinski rule of five and have non-toxic, non-carcinogenic and non-mutagenic profile. The screened compounds might be used in experimental test to highlight the real anti- R. prowazekii MetAP activity.
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Affiliation(s)
- Alhumaidi B. Alabbas
- Department of Pharmaceutical Chemistry, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
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50
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Ai C, Yang H, Ding Y, Tang J, Guo F. Low Rank Matrix Factorization Algorithm Based on Multi-Graph Regularization for Detecting Drug-Disease Association. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3033-3043. [PMID: 37159322 DOI: 10.1109/tcbb.2023.3274587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Detecting potential associations between drugs and diseases plays an indispensable role in drug development, which has also become a research hotspot in recent years. Compared with traditional methods, some computational approaches have the advantages of fast speed and low cost, which greatly accelerate the progress of predicting the drug-disease association. In this study, we propose a novel similarity-based method of low-rank matrix decomposition based on multi-graph regularization. On the basis of low-rank matrix factorization with L2 regularization, the multi-graph regularization constraint is constructed by combining a variety of similarity matrices from drugs and diseases respectively. In the experiments, we analyze the difference in the combination of different similarities, resulting that combining all the similarity information on drug space is unnecessary, and only a part of the similarity information can achieve the desired performance. Then our method is compared with other existing models on three data sets (Fdataset, Cdataset and LRSSLdataset) and have a good advantage in the evaluation measurement of AUPR. Besides, a case study experiment is conducted and showing that the superior ability for predicting the potential disease-related drugs of our model. Finally, we compare our model with some methods on six real world datasets, and our model has a good performance in detecting real world data.
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