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Kumar S, Dubey R, Mishra R, Gupta S, Dwivedi VD, Ray S, Jha NK, Verma D, Tsai LW, Dubey NK. Repurposing of SARS-CoV-2 compounds against Marburg Virus using MD simulation, mm/GBSA, PCA analysis, and free energy landscape. J Biomol Struct Dyn 2024:1-20. [PMID: 38450706 DOI: 10.1080/07391102.2024.2323701] [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: 08/16/2023] [Accepted: 01/22/2024] [Indexed: 03/08/2024]
Abstract
The significant mortality rate associated with Marburg virus infection made it the greatest hazard among infectious diseases. Drug repurposing using in silico methods has been crucial in identifying potential compounds that could prevent viral replication by targeting the virus's primary proteins. This study aimed at repurposing the drugs of SARS-CoV-2 for identifying potential candidates against the matrix protein VP40 of the Marburg virus. Virtual screening was performed where the control compound, Nilotinib, showed a binding score of -9.99 kcal/mol. Based on binding scores, hit compounds 9549298, 11960895, 44545852, 51039094, and 89670174 were selected that had a lower binding score than the control. Subsequent molecular dynamics (MD) simulation revealed that compound 9549298 consistently formed a hydrogen bond with the residue Gln290. This was observed both in molecular docking and MD simulation poses, indicating a strong and significant interaction with the protein. 11960895 had the most stable and consistent RMSD pattern exhibited in 100 ns simulation, while 9549298 had the most identical RMSD plot compared to the control molecule. MM/PBSA analysis showed that the binding free energy (ΔG) of 9549298 and 11960895 was lower than the control, with -30.84 and -38.86 kcal/mol, respectively. It was observed by the PCA (principal component analysis) and FEL (free energy landscape) analysis that compounds 9549298 and 11960895 had lesser conformational variation. Overall, this study proposed 9549298 and 11960895 as potential binders of VP40 MARV that can cause its inhibition, however it inherently lacks experimental validation. Furthermore, the study proposes in-vitro experiments as the next step to validate these computational findings, offering a practical approach to further explore these compounds' potential as antiviral agents.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Sanjay Kumar
- Biological and Bio-computational Lab, Department of Life Science, School of Basic Science and Research, Sharda University, Greater Noida, UP, India
| | - Rajni Dubey
- Division of Cardiology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei City, Taiwan
| | - Richa Mishra
- Department of Computer Engineering, Parul University, Vadodara, Gujarat, India
| | - Saurabh Gupta
- Department of Biotechnology, GLA University, Mathura, Uttar Pradesh, India
| | - Vivek Dhar Dwivedi
- Center for Global Health Research, Saveetha Medical College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India
- Bioinformatics Research Division, Greater Noida, UP, India
| | - Subhasree Ray
- Department of Life Science, School of Basic Science and Research, Sharda University, Greater Noida, India
| | - Niraj Kumar Jha
- School of Bioengineering & Biosciences, Lovely Professional University, Phagwara, India
- Centre of Research Impact and Outreach, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
| | - Devvret Verma
- Department of Biotechnology, Graphic Era (Deemed to Be University), Dehradun, Uttarakhand, India
| | - Lung-Wen Tsai
- Department of Medicine Research, Taipei Medical University Hospital, Taipei, Taiwan
- Department of Information Technology Office, Taipei Medical University Hospital, Taipei, Taiwan
- Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan
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Versini R, Sritharan S, Aykac Fas B, Tubiana T, Aimeur SZ, Henri J, Erard M, Nüsse O, Andreani J, Baaden M, Fuchs P, Galochkina T, Chatzigoulas A, Cournia Z, Santuz H, Sacquin-Mora S, Taly A. A Perspective on the Prospective Use of AI in Protein Structure Prediction. J Chem Inf Model 2024; 64:26-41. [PMID: 38124369 DOI: 10.1021/acs.jcim.3c01361] [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: 12/23/2023]
Abstract
AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable and effective methods for predicting protein structures. This article explores their impact and limitations, focusing on their integration into experimental pipelines and their application in diverse protein classes, including membrane proteins, intrinsically disordered proteins (IDPs), and oligomers. In experimental pipelines, AF2 models help X-ray crystallography in resolving the phase problem, while complementarity with mass spectrometry and NMR data enhances structure determination and protein flexibility prediction. Predicting the structure of membrane proteins remains challenging for both AF2 and RF due to difficulties in capturing conformational ensembles and interactions with the membrane. Improvements in incorporating membrane-specific features and predicting the structural effect of mutations are crucial. For intrinsically disordered proteins, AF2's confidence score (pLDDT) serves as a competitive disorder predictor, but integrative approaches including molecular dynamics (MD) simulations or hydrophobic cluster analyses are advocated for accurate dynamics representation. AF2 and RF show promising results for oligomeric models, outperforming traditional docking methods, with AlphaFold-Multimer showing improved performance. However, some caveats remain in particular for membrane proteins. Real-life examples demonstrate AF2's predictive capabilities in unknown protein structures, but models should be evaluated for their agreement with experimental data. Furthermore, AF2 models can be used complementarily with MD simulations. In this Perspective, we propose a "wish list" for improving deep-learning-based protein folding prediction models, including using experimental data as constraints and modifying models with binding partners or post-translational modifications. Additionally, a meta-tool for ranking and suggesting composite models is suggested, driving future advancements in this rapidly evolving field.
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Affiliation(s)
- Raphaelle Versini
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Sujith Sritharan
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Burcu Aykac Fas
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Thibault Tubiana
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Sana Zineb Aimeur
- Université Paris-Saclay, CNRS, Institut de Chimie Physique, 91405 Orsay, France
| | - Julien Henri
- Sorbonne Université, CNRS, Laboratoire de Biologie, Computationnelle et Quantitative UMR 7238, Institut de Biologie Paris-Seine, 4 Place Jussieu, F-75005 Paris, France
| | - Marie Erard
- Université Paris-Saclay, CNRS, Institut de Chimie Physique, 91405 Orsay, France
| | - Oliver Nüsse
- Université Paris-Saclay, CNRS, Institut de Chimie Physique, 91405 Orsay, France
| | - Jessica Andreani
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Marc Baaden
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Patrick Fuchs
- Sorbonne Université, École Normale Supérieure, PSL University, CNRS, Laboratoire des Biomolécules, LBM, 75005 Paris, France
- Université de Paris, UFR Sciences du Vivant, 75013 Paris, France
| | - Tatiana Galochkina
- Université Paris Cité and Université des Antilles and Université de la Réunion, INSERM, BIGR, F-75014 Paris, France
| | - Alexios Chatzigoulas
- Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
- Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, 15784 Athens, Greece
| | - Zoe Cournia
- Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
- Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, 15784 Athens, Greece
| | - Hubert Santuz
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Sophie Sacquin-Mora
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Antoine Taly
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
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3
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Li M, Lan X, Lu X, Zhang J. A Structure-Based Allosteric Modulator Design Paradigm. HEALTH DATA SCIENCE 2023; 3:0094. [PMID: 38487194 PMCID: PMC10904074 DOI: 10.34133/hds.0094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 10/11/2023] [Indexed: 03/17/2024]
Abstract
Importance: Allosteric drugs bound to topologically distal allosteric sites hold a substantial promise in modulating therapeutic targets deemed undruggable at their orthosteric sites. Traditionally, allosteric modulator discovery has predominantly relied on serendipitous high-throughput screening. Nevertheless, the landscape has undergone a transformative shift due to recent advancements in our understanding of allosteric modulation mechanisms, coupled with a significant increase in the accessibility of allosteric structural data. These factors have extensively promoted the development of various computational methodologies, especially for machine-learning approaches, to guide the rational design of structure-based allosteric modulators. Highlights: We here presented a comprehensive structure-based allosteric modulator design paradigm encompassing 3 critical stages: drug target acquisition, allosteric binding site, and modulator discovery. The recent advances in computational methods in each stage are encapsulated. Furthermore, we delve into analyzing the successes and obstacles encountered in the rational design of allosteric modulators. Conclusion: The structure-based allosteric modulator design paradigm holds immense potential for the rational design of allosteric modulators. We hope that this review would heighten awareness of the use of structure-based computational methodologies in advancing the field of allosteric drug discovery.
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Affiliation(s)
- Mingyu Li
- College of Pharmacy,
Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital,
Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center,
Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xiaobin Lan
- College of Pharmacy,
Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China
- Medicinal Chemistry and Bioinformatics Center,
Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xun Lu
- College of Pharmacy,
Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital,
Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center,
Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jian Zhang
- College of Pharmacy,
Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital,
Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center,
Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
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Panda SK, Sen Gupta PS, Karmakar S, Biswal S, Mahanandia NC, Rana MK. Unmasking an Allosteric Binding Site of the Papain-like Protease in SARS-CoV-2: Molecular Dynamics Simulations of Corticosteroids. J Phys Chem Lett 2023; 14:10278-10284. [PMID: 37942913 DOI: 10.1021/acs.jpclett.3c01980] [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: 11/10/2023]
Abstract
To date, mechanistic insights into many clinical drugs against COVID-19 remain unexplored. Dexamethasone, a corticosteroid, is one of them. While treating the entire corticosteroid database, including vitamins D2 and D3, with cutting-edge computational techniques, several intriguing results are unfolded. From the top-notch candidates, dexamethasone is likely to inhibit the viral main protease (Mpro), with vitamin D3 exhibiting multitarget [Mpro, papain-like protease (PLpro), and nucleocapsid protein (N-pro)] roles and ciclesonide's dynamic flipping disinterring a cryptic allosteric site in the PLpro enzyme. The results rationalize why these drugs improve the health of COVID-19 patients. Understanding an enzyme's secret binding site is essential to understanding how the enzyme works and how to inhibit its function. Ciclesonide's allosteric inhibition could not only jeopardize PLpro's catalytic role in polyprotein processing but also make it less vulnerable to the host body's defense machinery. Hotspot residues in the identified allosteric site could be considered for effective therapeutic designs against PLpro.
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Affiliation(s)
- Saroj Kumar Panda
- Department of Chemical Sciences, Indian Institute of Science Education and Research (IISER), Berhampur 760010, Odisha, India
| | - Parth Sarthi Sen Gupta
- School of Biosciences and Bioengineering, D Y Patil International University (DYPIU), Akurdi, Pune 411044, Maharashtra, India
| | - Shaswata Karmakar
- Department of Chemical Sciences, Indian Institute of Science Education and Research (IISER), Berhampur 760010, Odisha, India
| | - Satyaranjan Biswal
- Department of Chemical Sciences, Indian Institute of Science Education and Research (IISER), Berhampur 760010, Odisha, India
| | - Nimai Charan Mahanandia
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, Pusa 110012, New Delhi, India
| | - Malay Kumar Rana
- Department of Chemical Sciences, Indian Institute of Science Education and Research (IISER), Berhampur 760010, Odisha, India
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Buckbinder L, St. Jean DJ, Tieu T, Ladd B, Hilbert B, Wang W, Alltucker JT, Manimala S, Kryukov GV, Brooijmans N, Dowdell G, Jonsson P, Huff M, Guzman-Perez A, Jackson EL, Goncalves MD, Stuart DD. STX-478, a Mutant-Selective, Allosteric PI3Kα Inhibitor Spares Metabolic Dysfunction and Improves Therapeutic Response in PI3Kα-Mutant Xenografts. Cancer Discov 2023; 13:2432-2447. [PMID: 37623743 PMCID: PMC10618743 DOI: 10.1158/2159-8290.cd-23-0396] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 07/24/2023] [Accepted: 08/23/2023] [Indexed: 08/26/2023]
Abstract
Phosphoinositide 3-kinase α (PIK3CA) is one of the most mutated genes across cancers, especially breast, gynecologic, and head and neck squamous cell carcinoma tumors. Mutations occur throughout the gene, but hotspot mutations in the helical and kinase domains predominate. The therapeutic benefit of isoform-selective PI3Kα inhibition was established with alpelisib, which displays equipotent activity against the wild-type and mutant enzyme. Inhibition of wild-type PI3Kα is associated with severe hyperglycemia and rash, which limits alpelisib use and suggests that selectively targeting mutant PI3Kα could reduce toxicity and improve efficacy. Here we describe STX-478, an allosteric PI3Kα inhibitor that selectively targets prevalent PI3Kα helical- and kinase-domain mutant tumors. STX-478 demonstrated robust efficacy in human tumor xenografts without causing the metabolic dysfunction observed with alpelisib. Combining STX-478 with fulvestrant and/or cyclin-dependent kinase 4/6 inhibitors was well tolerated and provided robust and durable tumor regression in ER+HER2- xenograft tumor models. SIGNIFICANCE These preclinical data demonstrate that the mutant-selective, allosteric PI3Kα inhibitor STX-478 provides robust efficacy while avoiding the metabolic dysfunction associated with the nonselective inhibitor alpelisib. Our results support the ongoing clinical evaluation of STX-478 in PI3Kα-mutated cancers, which is expected to expand the therapeutic window and mitigate counterregulatory insulin release. See related commentary by Kearney and Vasan, p. 2313. This article is featured in Selected Articles from This Issue, p. 2293.
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Affiliation(s)
| | - David J. St. Jean
- Research and Development, Scorpion Therapeutics, Boston, Massachusetts
| | - Trang Tieu
- Research and Development, Scorpion Therapeutics, Boston, Massachusetts
| | - Brendon Ladd
- Research and Development, Scorpion Therapeutics, Boston, Massachusetts
| | - Brendan Hilbert
- Research and Development, Scorpion Therapeutics, Boston, Massachusetts
| | - Weixue Wang
- Research and Development, Scorpion Therapeutics, Boston, Massachusetts
| | | | - Samantha Manimala
- Research and Development, Scorpion Therapeutics, Boston, Massachusetts
| | | | | | - Gregory Dowdell
- Research and Development, Scorpion Therapeutics, Boston, Massachusetts
| | - Philip Jonsson
- Research and Development, Scorpion Therapeutics, Boston, Massachusetts
| | - Michael Huff
- Research and Development, Scorpion Therapeutics, Boston, Massachusetts
| | | | - Erica L. Jackson
- Department of Biology, Scorpion Therapeutics, South San Francisco, California
| | - Marcus D. Goncalves
- Division of Endocrinology, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Darrin D. Stuart
- Research and Development, Scorpion Therapeutics, Boston, Massachusetts
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6
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Cirillo D, Diceglie M, Nazaré M. Isoform-selective targeting of PI3K: time to consider new opportunities? Trends Pharmacol Sci 2023; 44:601-621. [PMID: 37438206 DOI: 10.1016/j.tips.2023.06.002] [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: 05/13/2023] [Revised: 06/06/2023] [Accepted: 06/07/2023] [Indexed: 07/14/2023]
Abstract
Phosphoinositide-3-kinases (PI3Ks) are central to several cellular signaling pathways in human physiology and are potential pharmacological targets for many pathologies including cancer, thrombosis, and pulmonary diseases. Tremendous efforts to develop isoform-selective inhibitors have culminated in the approval of several drugs, validating PI3K as a tractable and therapeutically relevant target. Although successful therapeutic validation has focused on isoform-selective class I orthosteric inhibitors, recent clinical findings have indicated challenges regarding poor drug tolerance owing to sustained on-target inhibition. Hence, additional approaches are warranted to increase the clinical benefits of specific clinical treatment options, which may involve the employment of so far underexploited targeting modalities or the development of inhibitors for currently underexplored PI3K class II isoforms. We review recent key discoveries in the development of isoform-selective inhibitors, focusing particularly on PI3K class II isoforms, and highlight the emerging importance of developing a broader arsenal of pharmacological tools.
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Affiliation(s)
- Davide Cirillo
- Leibniz-Forschungsinstitut für Molekulare Pharmakologie (FMP), Campus Berlin-Buch, Berlin, Germany
| | - Marta Diceglie
- Leibniz-Forschungsinstitut für Molekulare Pharmakologie (FMP), Campus Berlin-Buch, Berlin, Germany
| | - Marc Nazaré
- Leibniz-Forschungsinstitut für Molekulare Pharmakologie (FMP), Campus Berlin-Buch, Berlin, Germany.
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Kotzampasi DM, Premeti K, Papafotika A, Syropoulou V, Christoforidis S, Cournia Z, Leondaritis G. The orchestrated signaling by PI3Kα and PTEN at the membrane interface. Comput Struct Biotechnol J 2022; 20:5607-5621. [PMID: 36284707 PMCID: PMC9578963 DOI: 10.1016/j.csbj.2022.10.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 10/03/2022] [Accepted: 10/03/2022] [Indexed: 11/16/2022] Open
Abstract
The oncogene PI3Kα and the tumor suppressor PTEN represent two antagonistic enzymatic activities that regulate the interconversion of the phosphoinositide lipids PI(4,5)P2 and PI(3,4,5)P3 in membranes. As such, they are defining components of phosphoinositide-based cellular signaling and membrane trafficking pathways that regulate cell survival, growth, and proliferation, and are often deregulated in cancer. In this review, we highlight aspects of PI3Kα and PTEN interplay at the intersection of signaling and membrane trafficking. We also discuss the mechanisms of PI3Kα- and PTEN- membrane interaction and catalytic activation, which are fundamental for our understanding of the structural and allosteric implications on signaling at the membrane interface and may aid current efforts in pharmacological targeting of these proteins.
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Affiliation(s)
- Danai Maria Kotzampasi
- Biomedical Research Foundation, Academy of Athens, Athens 11527, Greece
- Department of Biology, University of Crete, Heraklion 71500, Greece
| | - Kyriaki Premeti
- Laboratory of Pharmacology, Faculty of Medicine, University of Ioannina, Ioannina 45110, Greece
| | - Alexandra Papafotika
- Laboratory of Biological Chemistry, Faculty of Medicine, School of Health Sciences, University of Ioannina, Ioannina 45110, Greece
- Biomedical Research Institute, Foundation for Research and Technology, Ioannina 45110, Greece
| | - Vasiliki Syropoulou
- Laboratory of Pharmacology, Faculty of Medicine, University of Ioannina, Ioannina 45110, Greece
| | - Savvas Christoforidis
- Laboratory of Biological Chemistry, Faculty of Medicine, School of Health Sciences, University of Ioannina, Ioannina 45110, Greece
- Biomedical Research Institute, Foundation for Research and Technology, Ioannina 45110, Greece
| | - Zoe Cournia
- Biomedical Research Foundation, Academy of Athens, Athens 11527, Greece
| | - George Leondaritis
- Laboratory of Pharmacology, Faculty of Medicine, University of Ioannina, Ioannina 45110, Greece
- Institute of Biosciences, University Research Center of Ioannina, Ioannina 45110, Greece
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Jemmy Christy H, Vasudevan S, Sudha S, Kandeel M, Subramanian K, Pugazhvendan SR, Ronald Ross P, Velmurugan. Targeting Streptomyces-Derived Streptenol Derivatives against Gynecological Cancer Target PIK3CA: An In Silico Approach. BIOMED RESEARCH INTERNATIONAL 2022; 2022:6600403. [PMID: 35860806 PMCID: PMC9293527 DOI: 10.1155/2022/6600403] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 06/10/2022] [Accepted: 06/15/2022] [Indexed: 01/21/2023]
Abstract
Streptomyces is amongst the most amenable genera for biotechnological applications, and it is extensively used as a scaffold for drug development. One of the most effective therapeutic applications in the treatment of cancer is targeted therapy. Small molecule therapy is one of them, and it has gotten a lot of attention recently. Streptomyces derived compounds namely streptenols A, C, and F-I and streptazolin were subjected for ADMET property assessment. Our computational studies based on molecular docking effectively displayed the synergistic effect of streptomyces-derived compounds on the gynecological cancer target PIK3CA. These compounds were observed with the highest docking scores as well as promising intermolecular interaction stability throughout the molecular dynamic simulation. Molecular docking and molecular dynamic modeling techniques were utilized to investigate the binding mode stability of drugs using a pharmacophore scaffold, as well as physicochemical and pharmacokinetic aspects linked to alpelisib. With a root mean square fluctuation of the protein backbone of less than 0.7 nm, they demonstrated a steady binding mode in the target binding pocket. They have also prompted hydrogen bonding throughout the simulations, implying that the chemicals have firmly occupied the active site. A comprehensive study showed that streptenol D, streptenol E, streptenol C, streptenol G, streptenol F, and streptenol B can be considered as lead compounds for PIK3CA-based inhibitor design. To warrant the treatment efficacy against cancer, comprehensive computational research based on proposed chemicals must be assessed through in vitro studies.
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Affiliation(s)
- H. Jemmy Christy
- Department of Bioinformatics, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
| | - Swetha Vasudevan
- Department of Bioinformatics, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
| | - S. Sudha
- Department of Biotechnology, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
| | - Mahmoud Kandeel
- Department of Biomedical Sciences, College of Veterinary Medicine, King Faisal University, Al-Ahsa, Saudi Arabia
- Department of Pharmacology, Faculty of Veterinary Medicine, Kafrelshikh University, Kafrelshikh, Egypt
| | - Kumaran Subramanian
- Centre for Drug Discovery and Development, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
| | - S. R. Pugazhvendan
- Department of Zoology, Arignar Anna Government Arts College, Cheyyar, Tamil Nadu, India
- Department of Zoology, Annamalai University, Annamalai Nagar, Cuddalore, Tamil Nadu, India
| | - P. Ronald Ross
- Department of Zoology, Annamalai University, Annamalai Nagar, Cuddalore, Tamil Nadu, India
| | - Velmurugan
- Department of Biology, School of Natural Science, Madda Walabu University, Oromiya Region, Ethiopia
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Drug Discovery of Plausible Lead Natural Compounds That Target the Insulin Signaling Pathway: Bioinformatics Approaches. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2022; 2022:2832889. [PMID: 35356248 PMCID: PMC8958086 DOI: 10.1155/2022/2832889] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 01/16/2022] [Accepted: 02/09/2022] [Indexed: 12/11/2022]
Abstract
The growing smooth talk in the field of natural compounds is due to the ancient and current interest in herbal medicine and their potentially positive effects on health. Dozens of antidiabetic natural compounds were reported and tested in vivo, in silico, and in vitro. The role of these natural compounds, their actions on the insulin signaling pathway, and the stimulation of the glucose transporter-4 (GLUT4) insulin-responsive translocation to the plasma membrane (PM) are all crucial in the treatment of diabetes and insulin resistance. In this review, we collected and summarized a group of available in vivo and in vitro studies which targeted isolated phytochemicals with possible antidiabetic activity. Moreover, the in silico docking of natural compounds with some of the insulin signaling cascade key proteins is also summarized based on the current literature. In this review, hundreds of recent studies on pure natural compounds that alleviate type II diabetes mellitus (type II DM) were revised. We focused on natural compounds that could potentially regulate blood glucose and stimulate GLUT4 translocation through the phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt) pathway. On attempt to point out potential new natural antidiabetic compounds, this review also focuses on natural ingredients that were shown to interact with proteins in the insulin signaling pathway in silico, regardless of their in vitro/in vivo antidiabetic activity. We invite interested researchers to test these compounds as potential novel type II DM drugs and explore their therapeutic mechanisms.
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10
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Chatzigoulas A, Cournia Z. Predicting protein–membrane interfaces of peripheral membrane proteins using ensemble machine learning. Brief Bioinform 2022; 23:6527274. [PMID: 35152294 PMCID: PMC8921665 DOI: 10.1093/bib/bbab518] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 10/23/2021] [Accepted: 11/12/2021] [Indexed: 12/13/2022] Open
Abstract
Abstract
Abnormal protein–membrane attachment is involved in deregulated cellular pathways and in disease. Therefore, the possibility to modulate protein–membrane interactions represents a new promising therapeutic strategy for peripheral membrane proteins that have been considered so far undruggable. A major obstacle in this drug design strategy is that the membrane-binding domains of peripheral membrane proteins are usually unknown. The development of fast and efficient algorithms predicting the protein–membrane interface would shed light into the accessibility of membrane–protein interfaces by drug-like molecules. Herein, we describe an ensemble machine learning methodology and algorithm for predicting membrane-penetrating amino acids. We utilize available experimental data from the literature for training 21 machine learning classifiers and meta-classifiers. Evaluation of the best ensemble classifier model accuracy yields a macro-averaged F1 score = 0.92 and a Matthews correlation coefficient = 0.84 for predicting correctly membrane-penetrating amino acids on unknown proteins of a validation set. The python code for predicting protein–membrane interfaces of peripheral membrane proteins is available at https://github.com/zoecournia/DREAMM.
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Affiliation(s)
- Alexios Chatzigoulas
- Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 11527 Athens, Greece
- Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, 15784 Athens, Greece
| | - Zoe Cournia
- Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 11527 Athens, Greece
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11
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Design, Synthesis, Biological Evaluation, and Molecular Modeling of 2-Difluoromethylbenzimidazole Derivatives as Potential PI3Kα Inhibitors. Molecules 2022; 27:molecules27020387. [PMID: 35056702 PMCID: PMC8777764 DOI: 10.3390/molecules27020387] [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: 12/14/2021] [Revised: 01/01/2022] [Accepted: 01/04/2022] [Indexed: 11/17/2022] Open
Abstract
PI3Kα is one of the potential targets for novel anticancer drugs. In this study, a series of 2-difluoromethylbenzimidazole derivatives were studied based on the combination of molecular modeling techniques 3D-QSAR, molecular docking, and molecular dynamics. The results showed that the best comparative molecular field analysis (CoMFA) model had q2 = 0.797 and r2 = 0.996 and the best comparative molecular similarity indices analysis (CoMSIA) model had q2 = 0.567 and r2 = 0.960. It was indicated that these 3D-QSAR models have good verification and excellent prediction capabilities. The binding mode of the compound 29 and 4YKN was explored using molecular docking and a molecular dynamics simulation. Ultimately, five new PI3Kα inhibitors were designed and screened by these models. Then, two of them (86, 87) were selected to be synthesized and biologically evaluated, with a satisfying result (22.8 nM for 86 and 33.6 nM for 87).
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12
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Chatzigoulas A, Cournia Z. Rational design of allosteric modulators: Challenges and successes. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1529] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Alexios Chatzigoulas
- Biomedical Research Foundation Academy of Athens Athens Greece
- Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece
| | - Zoe Cournia
- Biomedical Research Foundation Academy of Athens Athens Greece
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13
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Zhang M, Jang H, Nussinov R. Structural Features that Distinguish Inactive and Active PI3K Lipid Kinases. J Mol Biol 2020; 432:5849-5859. [PMID: 32918948 PMCID: PMC8916166 DOI: 10.1016/j.jmb.2020.09.002] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 08/10/2020] [Accepted: 09/02/2020] [Indexed: 12/25/2022]
Abstract
PI3K lipid kinases signal through the PI3K/Akt pathway, regulating cell growth and proliferation. While the structural features that distinguish between the active and inactive states of protein kinases are well established, that has not been the case for lipid kinases, and neither was the structural mechanism controlling the switch between the two states. Class I PI3Ks are obligate heterodimers with catalytic and regulatory subunits. Here, we analyze PI3K crystal structures. Structures with the nSH2 (inactive state) are featured by collapsed activation loop (a-loop) and an IN kinase domain helix 11 (kα11). In the active state, the a-loop is extended and kα11 in the OUT conformation. Our analysis suggests that the nSH2 domain in the regulatory subunit regulates activation, catalysis and autoinhibition through the a-loop. Inhibition, activation and catalytic scenarios are shared by class IA PI3Ks; the activation is mimicked by oncogenic mutations and the inhibition offers an allosteric inhibitor strategy.
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Affiliation(s)
- Mingzhen Zhang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD 21702, USA
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD 21702, USA
| | - Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
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14
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Gkeka P, Stoltz G, Barati Farimani A, Belkacemi Z, Ceriotti M, Chodera JD, Dinner AR, Ferguson AL, Maillet JB, Minoux H, Peter C, Pietrucci F, Silveira A, Tkatchenko A, Trstanova Z, Wiewiora R, Lelièvre T. Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems. J Chem Theory Comput 2020; 16:4757-4775. [PMID: 32559068 PMCID: PMC8312194 DOI: 10.1021/acs.jctc.0c00355] [Citation(s) in RCA: 82] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Machine learning encompasses tools and algorithms that are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as well, where machine learning offers promises of extracting valuable information from the enormous amounts of data generated by simulation of complex systems. We provide here a review of our current understanding of goals, benefits, and limitations of machine learning techniques for computational studies on atomistic systems, focusing on the construction of empirical force fields from ab initio databases and the determination of reaction coordinates for free energy computation and enhanced sampling.
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Affiliation(s)
- Paraskevi Gkeka
- Integrated Drug Discovery, Sanofi R&D, 91385 Chilly-Mazarin, France
| | - Gabriel Stoltz
- CERMICS, Ecole des Ponts, Marne-la-Vallée, France
- Matherials Project-Team, Inria Paris, 75012 Paris, France
| | | | - Zineb Belkacemi
- Integrated Drug Discovery, Sanofi R&D, 91385 Chilly-Mazarin, France
- CERMICS, Ecole des Ponts, Marne-la-Vallée, France
| | - Michele Ceriotti
- Laboratory of Computational Science and Modelling, Institute of Materials, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Aaron R Dinner
- Department of Chemistry, The University of Chicago, Chicago, Illinois 60637, United States
| | - Andrew L Ferguson
- Pritzker School of Molecular Engineering, University of Chicago, 5640 South Ellis Avenue, Chicago, Illinois 60637, United States
| | | | - Hervé Minoux
- Integrated Drug Discovery, Sanofi R&D, 94403 Vitry-sur-Seine, France
| | | | - Fabio Pietrucci
- UMR CNRS 7590, MNHN, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, Sorbonne Université, 75005 Paris, France
| | - Ana Silveira
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Alexandre Tkatchenko
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Zofia Trstanova
- School of Mathematics, The University of Edinburgh, Edinburgh EH9 3FD, U.K
| | - Rafal Wiewiora
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Tony Lelièvre
- CERMICS, Ecole des Ponts, Marne-la-Vallée, France
- Matherials Project-Team, Inria Paris, 75012 Paris, France
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15
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Zhang M, Jang H, Nussinov R. PI3K inhibitors: review and new strategies. Chem Sci 2020; 11:5855-5865. [PMID: 32953006 PMCID: PMC7472334 DOI: 10.1039/d0sc01676d] [Citation(s) in RCA: 97] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Accepted: 05/18/2020] [Indexed: 12/16/2022] Open
Abstract
The search is on for effective specific inhibitors for PI3Kα mutants. PI3Kα, a critical lipid kinase, has two subunits, catalytic and inhibitory. PIK3CA, the gene that encodes the p110α catalytic subunit is a highly mutated protein in cancer. Dysregulation of PI3Kα signalling is commonly associated with tumorigenesis and drug resistance. Despite its vast importance, only recently the FDA approved the first drug (alpelisib by Novartis) for breast cancer. A second (GDC0077), classified as PI3Kα isoform-specific, is undergoing clinical trials. Not surprisingly, these ATP-competitive drugs commonly elicit severe concentration-dependent side effects. Here we briefly review PI3Kα mutations, focus on PI3K drug repertoire and propose new, to-date unexplored PI3Kα therapeutic strategies. These include (1) an allosteric and orthosteric inhibitor combination and (2) taking advantage of allosteric rescue mutations to guide drug discovery.
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Affiliation(s)
- Mingzhen Zhang
- Computational Structural Biology Section , Frederick National Laboratory for Cancer Research , National Cancer Institute at Frederick , Frederick , MD 21702 , USA . ; Tel: +1-301-846-5579
| | - Hyunbum Jang
- Computational Structural Biology Section , Frederick National Laboratory for Cancer Research , National Cancer Institute at Frederick , Frederick , MD 21702 , USA . ; Tel: +1-301-846-5579
| | - Ruth Nussinov
- Computational Structural Biology Section , Frederick National Laboratory for Cancer Research , National Cancer Institute at Frederick , Frederick , MD 21702 , USA . ; Tel: +1-301-846-5579
- Department of Human Molecular Genetics and Biochemistry , Sackler School of Medicine , Tel Aviv University , Tel Aviv 69978 , Israel
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16
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Gagic Z, Ruzic D, Djokovic N, Djikic T, Nikolic K. In silico Methods for Design of Kinase Inhibitors as Anticancer Drugs. Front Chem 2020; 7:873. [PMID: 31970149 PMCID: PMC6960140 DOI: 10.3389/fchem.2019.00873] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2019] [Accepted: 12/04/2019] [Indexed: 12/11/2022] Open
Abstract
Rational drug design implies usage of molecular modeling techniques such as pharmacophore modeling, molecular dynamics, virtual screening, and molecular docking to explain the activity of biomolecules, define molecular determinants for interaction with the drug target, and design more efficient drug candidates. Kinases play an essential role in cell function and therefore are extensively studied targets in drug design and discovery. Kinase inhibitors are clinically very important and widely used antineoplastic drugs. In this review, computational methods used in rational drug design of kinase inhibitors are discussed and compared, considering some representative case studies.
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Affiliation(s)
- Zarko Gagic
- Department of Pharmaceutical Chemistry, Faculty of Medicine, University of Banja Luka, Banja Luka, Bosnia and Herzegovina
| | - Dusan Ruzic
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia
| | - Nemanja Djokovic
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia
| | - Teodora Djikic
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia
| | - Katarina Nikolic
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia
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17
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Orr AA, Yang J, Sule N, Chawla R, Hull KG, Zhu M, Romo D, Lele PP, Jayaraman A, Manson MD, Tamamis P. Molecular Mechanism for Attractant Signaling to DHMA by E. coli Tsr. Biophys J 2019; 118:492-504. [PMID: 31839263 DOI: 10.1016/j.bpj.2019.11.3382] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Revised: 11/05/2019] [Accepted: 11/19/2019] [Indexed: 12/20/2022] Open
Abstract
The attractant chemotaxis response of Escherichia coli to norepinephrine requires that it be converted to 3,4-dihydroxymandelic acid (DHMA) by the monoamine oxidase TynA and the aromatic aldehyde dehydrogenase FeaB. DHMA is sensed by the serine chemoreceptor Tsr, and the attractant response requires that at least one subunit of the periplasmic domain of the Tsr homodimer (pTsr) has an intact serine-binding site. DHMA that is generated in vivo by E. coli is expected to be a racemic mixture of the (R) and (S) enantiomers, so it has been unclear whether one or both chiral forms are active. Here, we used a combination of state-of-the-art tools in molecular docking and simulations, including an in-house simulation-based docking protocol, to investigate the binding properties of (R)-DHMA and (S)-DHMA to E. coli pTsr. Our studies computationally predicted that (R)-DHMA should promote a stronger attractant response than (S)-DHMA because of a consistently greater-magnitude piston-like pushdown of the pTsr α-helix 4 toward the membrane upon binding of (R)-DHMA than upon binding of (S)-DHMA. This displacement is caused primarily by interaction of DHMA with Tsr residue Thr156, which has been shown by genetic studies to be critical for the attractant response to L-serine and DHMA. These findings led us to separate the two chiral species and test their effectiveness as chemoattractants. Both the tethered cell and motility migration coefficient assays validated the prediction that (R)-DHMA is a stronger attractant than (S)-DHMA. Our study demonstrates that refined computational docking and simulation studies combined with experiments can be used to investigate situations in which subtle differences between ligands may lead to diverse chemotactic responses.
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Affiliation(s)
- Asuka A Orr
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas
| | - Jingyun Yang
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas
| | - Nitesh Sule
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas
| | - Ravi Chawla
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas
| | - Kenneth G Hull
- Department of Chemistry & Biochemistry and CPRIT Synthesis and Drug-Lead Discovery Laboratory, Baylor University, Waco, Texas
| | - Mingzhao Zhu
- Department of Chemistry & Biochemistry and CPRIT Synthesis and Drug-Lead Discovery Laboratory, Baylor University, Waco, Texas
| | - Daniel Romo
- Department of Chemistry & Biochemistry and CPRIT Synthesis and Drug-Lead Discovery Laboratory, Baylor University, Waco, Texas
| | - Pushkar P Lele
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas
| | - Arul Jayaraman
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas
| | - Michael D Manson
- Department of Biology, Texas A&M University, College Station, Texas.
| | - Phanourios Tamamis
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas.
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18
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Determination of comprehensive in silico determinants as a strategy for identification of novel PI3Kα inhibitors. Struct Chem 2019. [DOI: 10.1007/s11224-019-01303-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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19
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Wodak SJ, Paci E, Dokholyan NV, Berezovsky IN, Horovitz A, Li J, Hilser VJ, Bahar I, Karanicolas J, Stock G, Hamm P, Stote RH, Eberhardt J, Chebaro Y, Dejaegere A, Cecchini M, Changeux JP, Bolhuis PG, Vreede J, Faccioli P, Orioli S, Ravasio R, Yan L, Brito C, Wyart M, Gkeka P, Rivalta I, Palermo G, McCammon JA, Panecka-Hofman J, Wade RC, Di Pizio A, Niv MY, Nussinov R, Tsai CJ, Jang H, Padhorny D, Kozakov D, McLeish T. Allostery in Its Many Disguises: From Theory to Applications. Structure 2019; 27:566-578. [PMID: 30744993 PMCID: PMC6688844 DOI: 10.1016/j.str.2019.01.003] [Citation(s) in RCA: 233] [Impact Index Per Article: 46.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 11/29/2018] [Accepted: 01/02/2019] [Indexed: 12/19/2022]
Abstract
Allosteric regulation plays an important role in many biological processes, such as signal transduction, transcriptional regulation, and metabolism. Allostery is rooted in the fundamental physical properties of macromolecular systems, but its underlying mechanisms are still poorly understood. A collection of contributions to a recent interdisciplinary CECAM (Center Européen de Calcul Atomique et Moléculaire) workshop is used here to provide an overview of the progress and remaining limitations in the understanding of the mechanistic foundations of allostery gained from computational and experimental analyses of real protein systems and model systems. The main conceptual frameworks instrumental in driving the field are discussed. We illustrate the role of these frameworks in illuminating molecular mechanisms and explaining cellular processes, and describe some of their promising practical applications in engineering molecular sensors and informing drug design efforts.
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Affiliation(s)
| | | | - Nikolay V Dokholyan
- Department of Biochemistry & Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Departments of Pharmacology and Biochemistry & Molecular Biology, Penn State Medical Center, Hershey, PA, USA
| | - Igor N Berezovsky
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A(∗)STAR), and Department of Biological Sciences, National University of Singapore, Singapore, Singapore
| | - Amnon Horovitz
- Department of Structural Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Jing Li
- Departments of Biology and T.C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, USA
| | - Vincent J Hilser
- Departments of Biology and T.C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, USA
| | - Ivet Bahar
- School of Medicine, University of Pittsburgh, Pittsburgh, USA
| | | | - Gerhard Stock
- Biomolecular Dynamics, Institute of Physics, Albert Ludwigs University, Freiburg, Germany
| | - Peter Hamm
- Department of Chemistry, University of Zurich, Zurich, Switzerland
| | - Roland H Stote
- Department of Integrative Structural Biology, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Illkirch, France
| | - Jerome Eberhardt
- Department of Integrative Structural Biology, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Illkirch, France
| | - Yassmine Chebaro
- Department of Integrative Structural Biology, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Illkirch, France
| | - Annick Dejaegere
- Department of Integrative Structural Biology, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Illkirch, France
| | - Marco Cecchini
- Institut de Chimie de Strasbourg, UMR7177 CNRS & Université de Strasbourg, Strasbourg, France
| | | | - Peter G Bolhuis
- van 't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Amsterdam, Netherlands
| | - Jocelyne Vreede
- van 't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Amsterdam, Netherlands
| | - Pietro Faccioli
- Physics Department, Università di Trento and INFN-TIFPA, Trento, Italy
| | - Simone Orioli
- Physics Department, Università di Trento and INFN-TIFPA, Trento, Italy
| | - Riccardo Ravasio
- Institute of Physics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Le Yan
- Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA
| | - Carolina Brito
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS 91501-970, Brazil
| | - Matthieu Wyart
- Institute of Physics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Paraskevi Gkeka
- Structure Design and Informatics, Sanofi R&D, Chilly-Mazarin, France
| | - Ivan Rivalta
- École Normale Supérieure de Lyon, Université de Lyon, CNRS, Université Claude Bernard Lyon 1, Lyon, France
| | - Giulia Palermo
- Department of Chemistry and Biochemistry, University of California, San Diego, USA; Department of Bioengineering, University of California Riverside, CA 92507, USA
| | - J Andrew McCammon
- Department of Chemistry and Biochemistry, University of California, San Diego, USA
| | - Joanna Panecka-Hofman
- Division of Biophysics, Institute of Experimental Physics, Faculty of Physics, University of Warsaw, Warsaw, Poland
| | - Rebecca C Wade
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS) and Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, Heidelberg, Germany; Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
| | - Antonella Di Pizio
- Leibniz-Institute for Food Systems Biology, Technical University of Munich, Munich, Germany
| | - Masha Y Niv
- Institute of Biochemistry, Food Science and Nutrition, Robert H Smith Faculty of Agriculture Food and Environment, The Hebrew University, Jerusalem, Israel
| | - Ruth Nussinov
- Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, USA; Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Chung-Jung Tsai
- Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, USA
| | - Hyunbum Jang
- Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, USA
| | - Dzmitry Padhorny
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA
| | - Tom McLeish
- Department of Physics, University of York, York, UK
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20
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Miller MS, Thompson PE, Gabelli SB. Structural Determinants of Isoform Selectivity in PI3K Inhibitors. Biomolecules 2019; 9:biom9030082. [PMID: 30813656 PMCID: PMC6468644 DOI: 10.3390/biom9030082] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Accepted: 02/21/2019] [Indexed: 01/17/2023] Open
Abstract
Phosphatidylinositol 3-kinases (PI3Ks) are important therapeutic targets for the treatment of cancer, thrombosis, and inflammatory and immune diseases. The four highly homologous Class I isoforms, PI3K, PI3K, PI3K and PI3K have unique, non-redundant physiological roles and as such, isoform selectivity has been a key consideration driving inhibitor design and development. In this review, we discuss the structural biology of PI3Ks and how our growing knowledge of structure has influenced the medicinal chemistry of PI3K inhibitors. We present an analysis of the available structure-selectivity-activity relationship data to highlight key insights into how the various regions of the PI3K binding site influence isoform selectivity. The picture that emerges is one that is far from simple and emphasizes the complex nature of protein-inhibitor binding, involving protein flexibility, energetics, water networks and interactions with non-conserved residues.
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Affiliation(s)
- Michelle S Miller
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
| | - Philip E Thompson
- Medicinal Chemistry, Monash Institute of Pharmaceutical Sciences, Parkville, Victoria 3052, Australia.
| | - Sandra B Gabelli
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
- Departments of Medicine, Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
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21
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Wang J, Gong GQ, Zhou Y, Lee WJ, Buchanan CM, Denny WA, Rewcastle GW, Kendall JD, Dickson JMJ, Flanagan JU, Shepherd PR, Yang DH, Wang MW. High-throughput screening campaigns against a PI3Kα isoform bearing the H1047R mutation identified potential inhibitors with novel scaffolds. Acta Pharmacol Sin 2018; 39:1816-1822. [PMID: 29991713 DOI: 10.1038/s41401-018-0057-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Accepted: 05/30/2018] [Indexed: 12/30/2022] Open
Abstract
The phosphatidylinositol 3-kinase (PI3K) pathway is involved in many cellular functions including cell growth, metabolism, and transformation. Hyperactivation of this pathway contributes to tumorigenesis, therefore, PI3K is a major target for anticancer drug discovery. Since the PI3Kα isoform is implicated mostly in cancer, we conducted a high-throughput screening (HTS) campaign using a 3-step PI3K homogenous time-resolved fluorescence assay against this isoform bearing the H1047R mutation. A total of 288,000 synthetic and natural product-derived compounds were screened and of which, we identified 124 initial hits that were further selected by considering the predicted binding mode, relationship to known pan-assay interference compounds and previous descriptions as a lipid kinase inhibitor. A total of 24 compounds were then tested for concentration-dependent responses. These hit compounds provide novel scaffolds that can potentially be optimized to create novel PI3K inhibitors.
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22
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Leontiadou H, Galdadas I, Athanasiou C, Cournia Z. Insights into the mechanism of the PIK3CA E545K activating mutation using MD simulations. Sci Rep 2018; 8:15544. [PMID: 30341384 PMCID: PMC6195558 DOI: 10.1038/s41598-018-27044-6] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Accepted: 05/04/2018] [Indexed: 12/19/2022] Open
Abstract
Phosphoinositide 3-kinase alpha (PI3Kα) is involved in fundamental cellular processes including cell proliferation and differentiation and is frequently mutated in human malignancies. One of the most common mutations is E545K, which results in an amino acid substitution of opposite charge. It has been recently proposed that in this oncogenic charge-reversal mutation, the interactions between the protein catalytic and regulatory subunits are abrogated, resulting in loss of regulation and constitutive PI3Kα activity, which can lead to oncogenesis. To assess the mechanism of the PI3Kα E545K activating mutation, extensive Molecular Dynamics simulations were performed to examine conformational changes differing between the wild type (WT) and mutant proteins as they occur in microsecond simulations. In the E545K mutant PI3Kα, we observe a spontaneous detachment of the nSH2 PI3Kα domain (regulatory subunit, p85α) from the helical domain (catalytic subunit, p110α) causing significant loss of communication between the regulatory and catalytic subunits. We examine the allosteric network of the two proteins and show that a cluster of residues around the mutation is important for delivering communication signals between the catalytic and regulatory subunits. Our results demonstrate the dynamical and structural effects induced by the p110α E545K mutation in atomic level detail and indicate a possible mechanism for the loss of regulation that E545K confers on PI3Kα.
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Affiliation(s)
- Hari Leontiadou
- Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 11527, Athens, Greece
| | - Ioannis Galdadas
- Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 11527, Athens, Greece
| | - Christina Athanasiou
- Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 11527, Athens, Greece
| | - Zoe Cournia
- Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 11527, Athens, Greece.
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23
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Molecular Dynamics Simulations to Investigate the Binding Mode of the Natural Product Liphagal with Phosphoinositide 3-Kinase α. Molecules 2016; 21:molecules21070857. [PMID: 27367663 PMCID: PMC6274547 DOI: 10.3390/molecules21070857] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2016] [Revised: 06/23/2016] [Accepted: 06/23/2016] [Indexed: 11/16/2022] Open
Abstract
Phosphatidylinositol 3-kinase α (PI3Kα) is an attractive target for anticancer drug design. Liphagal, isolated from the marine sponge Aka coralliphaga, possesses the special “liphagane” meroterpenoid carbon skeleton and has been demonstrated as a PI3Kα inhibitor. Molecular docking and molecular dynamics simulations were performed to explore the dynamic behaviors of PI3Kα binding with liphagal, and free energy calculations and energy decomposition analysis were carried out by use of molecular mechanics/Poisson-Boltzmann (generalized Born) surface area (MM/PB(GB)SA) methods. The results reveal that the heteroatom rich aromatic D-ring of liphagal extends towards the polar region of the binding site, and the D-ring 15-hydroxyl and 16-hydroxyl form three hydrogen bonds with Asp810 and Tyr836. The cyclohexyl A-ring projects up into the upper pocket of the lipophilic region, and the hydrophobic/van der Waals interactions with the residues Met772, Trp780, Ile800, Ile848, Val850, Met922, Phe930, Ile932 could be the key interactions for the affinity of liphagal to PI3Kα. Thus, a new strategy for the rational design of more potent analogs of liphagal against PI3Kα is provided. Our proposed PI3Kα/liphagal binding mode would be beneficial for the discovery of new active analogs of liphagal against PI3Kα.
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Immobilization of the N-terminal helix stabilizes prefusion paramyxovirus fusion proteins. Proc Natl Acad Sci U S A 2016; 113:E3844-51. [PMID: 27335462 DOI: 10.1073/pnas.1608349113] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Parainfluenza virus 5 (PIV5) is an enveloped, single-stranded, negative-sense RNA virus of the Paramyxoviridae family. PIV5 fusion and entry are mediated by the coordinated action of the receptor-binding protein, hemagglutinin-neuraminidase (HN), and the fusion protein (F). Upon triggering by HN, F undergoes an irreversible ATP- and pH-independent conformational change, going down an energy gradient from a metastable prefusion state to a highly stable postfusion state. Previous studies have highlighted key conformational changes in the F-protein refolding pathway, but a detailed understanding of prefusion F-protein metastability remains elusive. Here, using two previously described F-protein mutations (S443D or P22L), we examine the capacity to modulate PIV5 F stability and the mechanisms by which these point mutants act. The S443D mutation destabilizes prefusion F proteins by disrupting a hydrogen bond network at the base of the F-protein globular head. The introduction of a P22L mutation robustly rescues destabilized F proteins through a local hydrophobic interaction between the N-terminal helix and a hydrophobic pocket. Prefusion stabilization conferred by a P22L-homologous mutation is demonstrated in the F protein of Newcastle disease virus, a paramyxovirus of a different genus, suggesting a conserved stabilizing structural element within the paramyxovirus family. Taken together, the available data suggest that movement of the N-terminal helix is a necessary early step for paramyxovirus F-protein refolding and presents a novel target for structure-based drug design.
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