1
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Cankara F, Senyuz S, Sayin AZ, Gursoy A, Keskin O. DiPPI: A Curated Data Set for Drug-like Molecules in Protein-Protein Interfaces. J Chem Inf Model 2024; 64:5041-5051. [PMID: 38907989 DOI: 10.1021/acs.jcim.3c01905] [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: 06/24/2024]
Abstract
Proteins interact through their interfaces, and dysfunction of protein-protein interactions (PPIs) has been associated with various diseases. Therefore, investigating the properties of the drug-modulated PPIs and interface-targeting drugs is critical. Here, we present a curated large data set for drug-like molecules in protein interfaces. We further introduce DiPPI (Drugs in Protein-Protein Interfaces), a two-module web site to facilitate the search for such molecules and their properties by exploiting our data set in drug repurposing studies. In the interface module of the web site, we present several properties, of interfaces, such as amino acid properties, hotspots, evolutionary conservation of drug-binding amino acids, and post-translational modifications of these residues. On the drug-like molecule side, we list drug-like small molecules and FDA-approved drugs from various databases and highlight those that bind to the interfaces. We further clustered the drugs based on their molecular fingerprints to confine the search for an alternative drug to a smaller space. Drug properties, including Lipinski's rules and various molecular descriptors, are also calculated and made available on the web site to guide the selection of drug molecules. Our data set contains 534,203 interfaces for 98,632 protein structures, of which 55,135 are detected to bind to a drug-like molecule. 2214 drug-like molecules are deposited on our web site, among which 335 are FDA-approved. DiPPI provides users with an easy-to-follow scheme for drug repurposing studies through its well-curated and clustered interface and drug data and is freely available at http://interactome.ku.edu.tr:8501.
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Affiliation(s)
- Fatma Cankara
- Graduate School of Sciences and Engineering, Koç University, İstanbul 34450, Turkey
| | - Simge Senyuz
- Graduate School of Sciences and Engineering, Koç University, İstanbul 34450, Turkey
| | - Ahenk Zeynep Sayin
- Department of Chemical and Biological Engineering, Koç University, İstanbul 34450, Turkey
| | - Attila Gursoy
- Department of Computer Engineering, Koç University, İstanbul 34450, Turkey
| | - Ozlem Keskin
- Department of Chemical and Biological Engineering, Koç University, İstanbul 34450, Turkey
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2
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Joseph BP, Weber V, Knüpfer L, Giorgetti A, Alfonso-Prieto M, Krauß S, Carloni P, Rossetti G. Low Molecular Weight Inhibitors Targeting the RNA-Binding Protein HuR. Int J Mol Sci 2023; 24:13127. [PMID: 37685931 PMCID: PMC10488267 DOI: 10.3390/ijms241713127] [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: 07/31/2023] [Revised: 08/18/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
The RNA-binding protein human antigen R (HuR) regulates stability, translation, and nucleus-to-cytoplasm shuttling of its target mRNAs. This protein has been progressively recognized as a relevant therapeutic target for several pathologies, like cancer, neurodegeneration, as well as inflammation. Inhibitors of mRNA binding to HuR might thus be beneficial against a variety of diseases. Here, we present the rational identification of structurally novel HuR inhibitors. In particular, by combining chemoinformatic approaches, high-throughput virtual screening, and RNA-protein pulldown assays, we demonstrate that the 4-(2-(2,4,6-trioxotetrahydropyrimidin-5(2H)-ylidene)hydrazineyl)benzoate ligand exhibits a dose-dependent HuR inhibition effect in binding experiments. Importantly, the chemical scaffold is new with respect to the currently known HuR inhibitors, opening up a new avenue for the design of pharmaceutical agents targeting this important protein.
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Affiliation(s)
- Benjamin Philipp Joseph
- Institute for Neuroscience and Medicine and Institute for Advanced Simulations (INM-9/IAS-5), Computational Biomedicine, Forschungszentrum Jülich, 52425 Jülich, Germany; (B.P.J.); (V.W.); (A.G.); (M.A.-P.); (G.R.)
- Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University, 52062 Aachen, Germany
| | - Verena Weber
- Institute for Neuroscience and Medicine and Institute for Advanced Simulations (INM-9/IAS-5), Computational Biomedicine, Forschungszentrum Jülich, 52425 Jülich, Germany; (B.P.J.); (V.W.); (A.G.); (M.A.-P.); (G.R.)
- Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University, 52062 Aachen, Germany
| | - Lisa Knüpfer
- Institute of Biology, University of Siegen, 57076 Siegen, Germany;
| | - Alejandro Giorgetti
- Institute for Neuroscience and Medicine and Institute for Advanced Simulations (INM-9/IAS-5), Computational Biomedicine, Forschungszentrum Jülich, 52425 Jülich, Germany; (B.P.J.); (V.W.); (A.G.); (M.A.-P.); (G.R.)
- Department of Biotechnology, University of Verona, 37134 Verona, Italy
| | - Mercedes Alfonso-Prieto
- Institute for Neuroscience and Medicine and Institute for Advanced Simulations (INM-9/IAS-5), Computational Biomedicine, Forschungszentrum Jülich, 52425 Jülich, Germany; (B.P.J.); (V.W.); (A.G.); (M.A.-P.); (G.R.)
| | - Sybille Krauß
- Institute of Biology, University of Siegen, 57076 Siegen, Germany;
| | - Paolo Carloni
- Institute for Neuroscience and Medicine and Institute for Advanced Simulations (INM-9/IAS-5), Computational Biomedicine, Forschungszentrum Jülich, 52425 Jülich, Germany; (B.P.J.); (V.W.); (A.G.); (M.A.-P.); (G.R.)
- Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University, 52062 Aachen, Germany
| | - Giulia Rossetti
- Institute for Neuroscience and Medicine and Institute for Advanced Simulations (INM-9/IAS-5), Computational Biomedicine, Forschungszentrum Jülich, 52425 Jülich, Germany; (B.P.J.); (V.W.); (A.G.); (M.A.-P.); (G.R.)
- Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich, 52425 Jülich, Germany
- Department of Neurology, RWTH Aachen University, 44517 Aachen, Germany
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3
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Bolz SN, Schroeder M. Promiscuity in drug discovery on the verge of the structural revolution: recent advances and future chances. Expert Opin Drug Discov 2023; 18:973-985. [PMID: 37489516 DOI: 10.1080/17460441.2023.2239700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 07/19/2023] [Indexed: 07/26/2023]
Abstract
INTRODUCTION Promiscuity denotes the ability of ligands and targets to specifically interact with multiple binding partners. Despite negative aspects like side effects, promiscuity is receiving increasing attention in drug discovery as it can enhance drug efficacy and provides a molecular basis for drug repositioning. The three-dimensional structure of ligand-target complexes delivers exclusive insights into the molecular mechanisms of promiscuity and structure-based methods enable the identification of promiscuous interactions. With the recent breakthrough in protein structure prediction, novel possibilities open up to reveal unknown connections in ligand-target interaction networks. AREAS COVERED This review highlights the significance of structure in the identification and characterization of promiscuity and evaluates the potential of protein structure prediction to advance our knowledge of drug-target interaction networks. It discusses the definition and relevance of promiscuity in drug discovery and explores different approaches to detecting promiscuous ligands and targets. EXPERT OPINION Examination of structural data is essential for understanding and quantifying promiscuity. The recent advancements in structure prediction have resulted in an abundance of targets that are well-suited for structure-based methods like docking. In silico approaches may eventually completely transform our understanding of drug-target networks by complementing the millions of predicted protein structures with billions of predicted drug-target interactions.
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Affiliation(s)
- Sarah Naomi Bolz
- Biotechnology Center (BIOTEC), CMCB, Technische Universität Dresden, Dresden, Germany
| | - Michael Schroeder
- Biotechnology Center (BIOTEC), CMCB, Technische Universität Dresden, Dresden, Germany
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4
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Trisciuzzi D, Siragusa L, Baroni M, Cruciani G, Nicolotti O. An Integrated Machine Learning Model To Spot Peptide Binding Pockets in 3D Protein Screening. J Chem Inf Model 2022; 62:6812-6824. [PMID: 36320100 DOI: 10.1021/acs.jcim.2c00583] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The prediction of peptide-protein binding sites is of utmost importance to tackle the onset of severe neurodegenerative diseases and cancer. In this work, we detail a novel machine learning model based on Linear Discriminant Analysis (LDA) demonstrating to be highly predictive in detecting the putative protein binding regions of small peptides. Starting from 439 high-quality pockets derived from peptide-protein crystallographic complexes, three sets of well-established peptide-binding regions were first selected through a Partitioning Around Medoids (PAM) clustering algorithm based on morphological and energetic 3D GRID-MIF molecular descriptors. Next, the best combination between all the putative interacting peptide pockets and related GRID-MIF scores was automatically explored by using the LDA-based protocol implemented in BioGPS. This approach proved successful to recognize the actual interacting peptide regions (that is, AUC = 0.86 and partial ROC enrichment at 5% of 0.48) from all the other pockets of the protein. Validated on two external collections sets, including 445 and 347 crystallographic peptide-protein complexes, our LDA-based model could be effective to further run peptide-protein virtual screening campaigns.
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Affiliation(s)
- Daniela Trisciuzzi
- Department of Pharmacy-Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", 70125Bari, Italy.,Molecular Discovery Ltd., Kinetic Business Centre, Theobald Street, Elstree, Borehamwood, HertfordshireWD6 4PJ, United Kingdom
| | - Lydia Siragusa
- Molecular Horizon s.r.l., Via Montelino, 30, 06084Bettona (PG), Italy.,Molecular Discovery Ltd., Kinetic Business Centre, Theobald Street, Elstree, Borehamwood, HertfordshireWD6 4PJ, United Kingdom
| | - Massimo Baroni
- Molecular Discovery Ltd., Kinetic Business Centre, Theobald Street, Elstree, Borehamwood, HertfordshireWD6 4PJ, United Kingdom
| | - Gabriele Cruciani
- Department of Chemistry, Biology and Biotechnology, Università degli Studi di Perugia, via Elce di Sotto, 8, 06123Perugia (PG), Italy
| | - Orazio Nicolotti
- Department of Pharmacy-Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", 70125Bari, Italy
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5
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Spennato M, Roggero OM, Varriale S, Asaro F, Cortesi A, Kašpar J, Tongiorgi E, Pezzella C, Gardossi L. Neuroprotective Properties of Cardoon Leaves Extracts against Neurodevelopmental Deficits in an In Vitro Model of Rett Syndrome Depend on the Extraction Method and Harvest Time. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27248772. [PMID: 36557905 PMCID: PMC9783035 DOI: 10.3390/molecules27248772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 12/06/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022]
Abstract
This study investigates the bioactive properties of different extracts of cardoon leaves in rescuing neuronal development arrest in an in vitro model of Rett syndrome (RTT). Samples were obtained from plants harvested at different maturity stages and extracted with two different methodologies, namely Naviglio® and supercritical carbon dioxide (scCO2). While scCO2 extracts more hydrophobic fractions, the Naviglio® method extracts phenolic compounds and less hydrophobic components. Only the scCO2 cardoon leaves extract obtained from plants harvested in spring induced a significant rescue of neuronal atrophy in RTT neurons, while the scCO2 extract from the autumn harvest stimulated dendrite outgrowth in Wild-Type (WT) neurons. The scCO2 extracts were the richest in squalene, 3ß-taraxerol and lupeol, with concentrations in autumn harvest doubling those in spring harvest. The Naviglio® extract was rich in cynaropicrin and exerted a toxic effect at 20 µM on both WT and RTT neurons. When cynaropicrin, squalene, lupeol and 3ß-taraxerol were tested individually, no positive effect was observed, whereas a significant neurotoxicity of cynaropicrin and lupeol was evident. In conclusion, cardoon leaves extracts with high content of hydrophobic bioactive molecules and low cynaropicrin and lupeol concentrations have pharmacological potential to stimulate neuronal development in RTT and WT neurons in vitro.
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Affiliation(s)
- Mariachiara Spennato
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, Via L. Giorgieri 1, 34127 Trieste, Italy
| | - Ottavia Maria Roggero
- Department of Life Sciences, University of Trieste, Via L. Giorgieri 5, 34127 Trieste, Italy
| | - Simona Varriale
- Department of Chemical Sciences, University Federico II of Naples, Via Cinthia, 4, 80126 Napoli, Italy
| | - Fioretta Asaro
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, Via L. Giorgieri 1, 34127 Trieste, Italy
| | - Angelo Cortesi
- Department of Engineering and Architecture, University of Trieste, Via Alfonso Valerio 6/A, 34127 Trieste, Italy
| | - Jan Kašpar
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, Via L. Giorgieri 1, 34127 Trieste, Italy
| | - Enrico Tongiorgi
- Department of Life Sciences, University of Trieste, Via L. Giorgieri 5, 34127 Trieste, Italy
| | - Cinzia Pezzella
- Department of Chemical Sciences, University Federico II of Naples, Via Cinthia, 4, 80126 Napoli, Italy
| | - Lucia Gardossi
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, Via L. Giorgieri 1, 34127 Trieste, Italy
- Correspondence:
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6
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New Drug Design Avenues Targeting Alzheimer's Disease by Pharmacoinformatics-Aided Tools. Pharmaceutics 2022; 14:pharmaceutics14091914. [PMID: 36145662 PMCID: PMC9503559 DOI: 10.3390/pharmaceutics14091914] [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: 08/05/2022] [Revised: 09/03/2022] [Accepted: 09/06/2022] [Indexed: 11/30/2022] Open
Abstract
Neurodegenerative diseases (NDD) have been of great interest to scientists for a long time due to their multifactorial character. Among these pathologies, Alzheimer’s disease (AD) is of special relevance, and despite the existence of approved drugs for its treatment, there is still no efficient pharmacological therapy to stop, slow, or repair neurodegeneration. Existing drugs have certain disadvantages, such as lack of efficacy and side effects. Therefore, there is a real need to discover new drugs that can deal with this problem. However, as AD is multifactorial in nature with so many physiological pathways involved, the most effective approach to modulate more than one of them in a relevant manner and without undesirable consequences is through polypharmacology. In this field, there has been significant progress in recent years in terms of pharmacoinformatics tools that allow the discovery of bioactive molecules with polypharmacological profiles without the need to spend a long time and excessive resources on complex experimental designs, making the drug design and development pipeline more efficient. In this review, we present from different perspectives how pharmacoinformatics tools can be useful when drug design programs are designed to tackle complex diseases such as AD, highlighting essential concepts, showing the relevance of artificial intelligence and new trends, as well as different databases and software with their main results, emphasizing the importance of coupling wet and dry approaches in drug design and development processes.
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7
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Salerno A, Seghetti F, Caciolla J, Uliassi E, Testi E, Guardigni M, Roberti M, Milelli A, Bolognesi ML. Enriching Proteolysis Targeting Chimeras with a Second Modality: When Two Are Better Than One. J Med Chem 2022; 65:9507-9530. [PMID: 35816671 PMCID: PMC9340767 DOI: 10.1021/acs.jmedchem.2c00302] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
![]()
Proteolysis targeting chimera (PROTAC)-mediated protein
degradation
has prompted a radical rethink and is at a crucial stage in driving
a drug discovery transition. To fully harness the potential of this
technology, a growing paradigm toward enriching PROTACs with other
therapeutic modalities has been proposed. Could researchers successfully
combine two modalities to yield multifunctional PROTACs
with an expanded profile? In this Perspective, we try to answer this
question. We discuss how this possibility encompasses different approaches,
leading to multitarget PROTACs, light-controllable PROTACs, PROTAC conjugates, and macrocycle- and oligonucleotide-based PROTACs. This possibility
promises to further enhance PROTAC efficacy and selectivity, minimize
side effects, and hit undruggable targets. While PROTACs have reached
the clinical investigation stage, additional steps must be taken toward
the translational development of multifunctional PROTACs.
A deeper and detailed understanding of the most critical challenges
is required to fully exploit these opportunities and decisively enrich
the PROTAC toolbox.
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Affiliation(s)
- Alessandra Salerno
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum - University of Bologna, Via Belmeloro 6, 40126 Bologna, Italy
| | - Francesca Seghetti
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum - University of Bologna, Via Belmeloro 6, 40126 Bologna, Italy
| | - Jessica Caciolla
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum - University of Bologna, Via Belmeloro 6, 40126 Bologna, Italy
| | - Elisa Uliassi
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum - University of Bologna, Via Belmeloro 6, 40126 Bologna, Italy
| | - Eleonora Testi
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum - University of Bologna, Via Belmeloro 6, 40126 Bologna, Italy
| | - Melissa Guardigni
- Department for Life Quality Studies, Alma Mater Studiorum - University of Bologna, Corso d'Augusto 237, 47921 Rimini, Italy
| | - Marinella Roberti
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum - University of Bologna, Via Belmeloro 6, 40126 Bologna, Italy
| | - Andrea Milelli
- Department for Life Quality Studies, Alma Mater Studiorum - University of Bologna, Corso d'Augusto 237, 47921 Rimini, Italy
| | - Maria Laura Bolognesi
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum - University of Bologna, Via Belmeloro 6, 40126 Bologna, Italy
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8
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Discovery of novel SARS-CoV-2 inhibitors targeting the main protease M pro by virtual screenings and hit optimization. Antiviral Res 2022; 204:105350. [PMID: 35688349 PMCID: PMC9172283 DOI: 10.1016/j.antiviral.2022.105350] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 05/25/2022] [Accepted: 05/30/2022] [Indexed: 11/21/2022]
Abstract
Two years after its emergence, SARS-CoV-2 still represents a serious and global threat to human health. Antiviral drug development usually takes a long time and, to increase the chances of success, chemical variability of hit compounds represents a valuable source for the discovery of new antivirals. In this work, we applied a platform of variably oriented virtual screening campaigns to seek for novel chemical scaffolds for SARS-CoV-2 main protease (Mpro) inhibitors. The study on the resulting 30 best hits led to the identification of a series of structurally unrelated Mpro inhibitors. Some of them exhibited antiviral activity in the low micromolar range against SARS-CoV-2 and other human coronaviruses (HCoVs) in different cell lines. Time-of-addition experiments demonstrated an antiviral effect during the viral replication cycle at a time frame consistent with the inhibition of SARS-CoV-2 Mpro activity. As a proof-of-concept, to validate the pharmaceutical potential of the selected hits against SARS-CoV-2, we rationally optimized one of the hit compounds and obtained two potent SARS-CoV-2 inhibitors with increased activity against Mpro both in vitro and in a cellular context, as well as against SARS-CoV-2 replication in infected cells. This study significantly contributes to the expansion of the chemical variability of SARS-CoV-2 Mpro inhibitors and provides new scaffolds to be exploited for pan-coronavirus antiviral drug development.
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9
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Ali TFS, Ciftci HI, Radwan MO, Roshdy E, Shawky AM, Abourehab MAS, Tateishi H, Otsuka M, Fujita M. Discovery of Azaindolin-2-One as a Dual Inhibitor of GSK3β and Tau Aggregation with Potential Neuroprotective Activity. Pharmaceuticals (Basel) 2022; 15:ph15040426. [PMID: 35455423 PMCID: PMC9029746 DOI: 10.3390/ph15040426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 03/22/2022] [Accepted: 03/29/2022] [Indexed: 11/29/2022] Open
Abstract
The inhibition of glycogen synthase kinase 3β (GSK3β) activity through pharmacological intervention represents a promising approach for treating challenging neurodegenerative disorders like Alzheimer’s disease. Similarly, abnormal tau aggregate accumulation in neurons is a hallmark of various neurodegenerative diseases. We introduced new dual GSK3β/tau aggregation inhibitors due to the excellent clinical outcome of multitarget drugs. Compound (E)-2f stands out among the synthesized inhibitors as a promising GSK3β inhibitor (IC50 1.7 µM) with a pronounced tau anti-aggregation effect in a cell-based model of tauopathy. Concurrently, (E)-2f was demonstrated to be non-toxic to normal cells, making it a promising neuroprotective lead compound that needs further investigation.
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Affiliation(s)
- Taha F. S. Ali
- Medicinal Chemistry Department, Faculty of Pharmacy, Minia University, Minia 61519, Egypt;
- Medicinal and Biological Chemistry Science Farm Joint Research Laboratory, Faculty of Life Sciences, Kumamoto University, Kumamoto 862-0973, Japan; (H.I.C.); (M.O.R.); (H.T.); (M.O.)
- Correspondence: (T.F.S.A.); (M.F.); Tel.: +20-10-6983-5295 (T.F.S.A.); +81-96-371-4622 (M.F.)
| | - Halil I. Ciftci
- Medicinal and Biological Chemistry Science Farm Joint Research Laboratory, Faculty of Life Sciences, Kumamoto University, Kumamoto 862-0973, Japan; (H.I.C.); (M.O.R.); (H.T.); (M.O.)
- Department of Drug Discovery, Science Farm, Ltd., Kumamoto 862-0976, Japan
| | - Mohamed O. Radwan
- Medicinal and Biological Chemistry Science Farm Joint Research Laboratory, Faculty of Life Sciences, Kumamoto University, Kumamoto 862-0973, Japan; (H.I.C.); (M.O.R.); (H.T.); (M.O.)
- National Research Centre, Chemistry of Natural Compounds Department, Pharmaceutical and Drug Industries Research Division, Dokki, Cairo 12622, Egypt
| | - Eslam Roshdy
- Medicinal Chemistry Department, Faculty of Pharmacy, Minia University, Minia 61519, Egypt;
- Department of Chemistry, Graduate School of Science, Hiroshima University, Higashi-Hiroshima, Hiroshima 739-8526, Japan
| | - Ahmed M. Shawky
- Science and Technology Unit (STU), Umm Al-Qura University, Makkah 21955, Saudi Arabia;
- Central Laboratory for Micro-Analysis, Minia University, Minia 61519, Egypt
| | - Mohammed A. S. Abourehab
- Department of Pharmaceutics, Faculty of Pharmacy, Umm Al-Qura University, Makkah 21955, Saudi Arabia;
| | - Hiroshi Tateishi
- Medicinal and Biological Chemistry Science Farm Joint Research Laboratory, Faculty of Life Sciences, Kumamoto University, Kumamoto 862-0973, Japan; (H.I.C.); (M.O.R.); (H.T.); (M.O.)
| | - Masami Otsuka
- Medicinal and Biological Chemistry Science Farm Joint Research Laboratory, Faculty of Life Sciences, Kumamoto University, Kumamoto 862-0973, Japan; (H.I.C.); (M.O.R.); (H.T.); (M.O.)
- Department of Drug Discovery, Science Farm, Ltd., Kumamoto 862-0976, Japan
| | - Mikako Fujita
- Medicinal and Biological Chemistry Science Farm Joint Research Laboratory, Faculty of Life Sciences, Kumamoto University, Kumamoto 862-0973, Japan; (H.I.C.); (M.O.R.); (H.T.); (M.O.)
- Correspondence: (T.F.S.A.); (M.F.); Tel.: +20-10-6983-5295 (T.F.S.A.); +81-96-371-4622 (M.F.)
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10
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Trisciuzzi D, Siragusa L, Baroni M, Autiero I, Nicolotti O, Cruciani G. Getting Insights into Structural and Energetic Properties of Reciprocal Peptide-Protein Interactions. J Chem Inf Model 2022; 62:1113-1125. [PMID: 35148095 DOI: 10.1021/acs.jcim.1c01343] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Peptide-protein interactions play a key role for many cellular and metabolic processes involved in the onset of largely spread diseases such as cancer and neurodegenerative pathologies. Despite the progress in the structural characterization of peptide-protein interfaces, the in-depth knowledge of the molecular details behind their interactions is still a daunting task. Here, we present the first comprehensive in silico morphological and energetic study of peptide binding sites by focusing on both peptide and protein standpoints. Starting from the PixelDB database, a nonredundant benchmark collection of high-quality 3D crystallographic structures of peptide-protein complexes, a classification analysis of the most representative categories based on the nature of each cocrystallized peptide has been carried out. Several interpretable geometrical and energetic descriptors have been computed both from peptide and target protein sides in the attempt to unveil physicochemical and structural causative correlations. Finally, we investigated the most frequent peptide-protein residue pairs at the binding interface and made extensive energetic analyses, based on GRID MIFs, with the aim to study the peptide affinity-enhancing interactions to be further exploited in rational drug design strategies.
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Affiliation(s)
- Daniela Trisciuzzi
- Department of Pharmacy, Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", 70125 Bari, Italy.,Molecular Horizon s.r.l., Via Montelino, 30, 06084 Bettona (PG), Italy
| | - Lydia Siragusa
- Molecular Horizon s.r.l., Via Montelino, 30, 06084 Bettona (PG), Italy.,Molecular Discovery Ltd., Kinetic Business Centre, Theobald Street, Elstree, Borehamwood, Hertfordshire WD6 4PJ, United Kingdom
| | - Massimo Baroni
- Molecular Discovery Ltd., Kinetic Business Centre, Theobald Street, Elstree, Borehamwood, Hertfordshire WD6 4PJ, United Kingdom
| | - Ida Autiero
- Molecular Horizon s.r.l., Via Montelino, 30, 06084 Bettona (PG), Italy.,National Research Council, Institute of Biostructures and Bioimaging, 80138 Naples, Italy
| | - Orazio Nicolotti
- Department of Pharmacy, Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", 70125 Bari, Italy
| | - Gabriele Cruciani
- Department of Chemistry, Biology and Biotechnology, Università degli Studi di Perugia, via Elce di Sotto, 8, 06123 Perugia (PG), Italy
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11
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Polypharmacology: The science of multi-targeting molecules. Pharmacol Res 2022; 176:106055. [PMID: 34990865 DOI: 10.1016/j.phrs.2021.106055] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 12/23/2021] [Accepted: 12/31/2021] [Indexed: 12/28/2022]
Abstract
Polypharmacology is a concept where a molecule can interact with two or more targets simultaneously. It offers many advantages as compared to the conventional single-targeting molecules. A multi-targeting drug is much more efficacious due to its cumulative efficacy at all of its individual targets making it much more effective in complex and multifactorial diseases like cancer, where multiple proteins and pathways are involved in the onset and development of the disease. For a molecule to be polypharmacologic in nature, it needs to possess promiscuity which is the ability to interact with multiple targets; and at the same time avoid binding to antitargets which would otherwise result in off-target adverse effects. There are certain structural features and physicochemical properties which when present would help researchers to predict if the designed molecule would possess promiscuity or not. Promiscuity can also be identified via advanced state-of-the-art computational methods. In this review, we also elaborate on the methods by which one can intentionally incorporate promiscuity in their molecules and make them polypharmacologic. The polypharmacology paradigm of "one drug-multiple targets" has numerous applications especially in drug repurposing where an already established drug is redeveloped for a new indication. Though designing a polypharmacological drug is much more difficult than designing a single-targeting drug, with the current technologies and information regarding different diseases and chemical functional groups, it is plausible for researchers to intentionally design a polypharmacological drug and unlock its advantages.
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12
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Fernández-Torras A, Comajuncosa-Creus A, Duran-Frigola M, Aloy P. Connecting chemistry and biology through molecular descriptors. Curr Opin Chem Biol 2021; 66:102090. [PMID: 34626922 DOI: 10.1016/j.cbpa.2021.09.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 08/23/2021] [Accepted: 09/03/2021] [Indexed: 01/14/2023]
Abstract
Through the representation of small molecule structures as numerical descriptors and the exploitation of the similarity principle, chemoinformatics has made paramount contributions to drug discovery, from unveiling mechanisms of action and repurposing approved drugs to de novo crafting of molecules with desired properties and tailored targets. Yet, the inherent complexity of biological systems has fostered the implementation of large-scale experimental screenings seeking a deeper understanding of the targeted proteins, the disrupted biological processes and the systemic responses of cells to chemical perturbations. After this wealth of data, a new generation of data-driven descriptors has arisen providing a rich portrait of small molecule characteristics that goes beyond chemical properties. Here, we give an overview of biologically relevant descriptors, covering chemical compounds, proteins and other biological entities, such as diseases and cell lines, while aligning them to the major contributions in the field from disciplines, such as natural language processing or computer vision. We now envision a new scenario for chemical and biological entities where they both are translated into a common numerical format. In this computational framework, complex connections between entities can be unveiled by means of simple arithmetic operations, such as distance measures, additions, and subtractions.
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Affiliation(s)
- Adrià Fernández-Torras
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Arnau Comajuncosa-Creus
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Miquel Duran-Frigola
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain; Ersilia Open Source Initiative, Cambridge, United Kingdom
| | - Patrick Aloy
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain; Institució Catalana de Recerca I Estudis Avançats (ICREA), Barcelona, Catalonia, Spain.
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13
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Mangiatordi GF, Intranuovo F, Delre P, Abatematteo FS, Abate C, Niso M, Creanza TM, Ancona N, Stefanachi A, Contino M. Cannabinoid Receptor Subtype 2 (CB2R) in a Multitarget Approach: Perspective of an Innovative Strategy in Cancer and Neurodegeneration. J Med Chem 2020; 63:14448-14469. [PMID: 33094613 DOI: 10.1021/acs.jmedchem.0c01357] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The cannabinoid receptor subtype 2 (CB2R) represents an interesting and new therapeutic target for its involvement in the first steps of neurodegeneration as well as in cancer onset and progression. Several studies, focused on different types of tumors, report a promising anticancer activity induced by CB2R agonists due to their ability to reduce inflammation and cell proliferation. Moreover, in neuroinflammation, the stimulation of CB2R, overexpressed in microglial cells, exerts beneficial effects in neurodegenerative disorders. With the aim to overcome current treatment limitations, new drugs can be developed by specifically modulating, together with CB2R, other targets involved in such multifactorial disorders. Building on successful case studies of already developed multitarget strategies involving CB2R, in this Perspective we aim at prompting the scientific community to consider new promising target associations involving HDACs (histone deacetylases) and σ receptors by employing modern approaches based on molecular hybridization, computational polypharmacology, and machine learning algorithms.
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Affiliation(s)
| | - Francesca Intranuovo
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Via Orabona 4, 70125 Bari, Italy
| | - Pietro Delre
- CNR-Institute of Crystallography, Via Amendola 122/o, 70126 Bari, Italy.,Dipartimento di Chimica, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Francesca Serena Abatematteo
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Via Orabona 4, 70125 Bari, Italy
| | - Carmen Abate
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Via Orabona 4, 70125 Bari, Italy
| | - Mauro Niso
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Via Orabona 4, 70125 Bari, Italy
| | - Teresa Maria Creanza
- CNR-Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, Via Amendola 122/o, 70126 Bari, Italy
| | - Nicola Ancona
- CNR-Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, Via Amendola 122/o, 70126 Bari, Italy
| | - Angela Stefanachi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Via Orabona 4, 70125 Bari, Italy
| | - Marialessandra Contino
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Via Orabona 4, 70125 Bari, Italy
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14
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Chaudhari R, Fong LW, Tan Z, Huang B, Zhang S. An up-to-date overview of computational polypharmacology in modern drug discovery. Expert Opin Drug Discov 2020; 15:1025-1044. [PMID: 32452701 PMCID: PMC7415563 DOI: 10.1080/17460441.2020.1767063] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 05/06/2020] [Indexed: 12/30/2022]
Abstract
INTRODUCTION In recent years, computational polypharmacology has gained significant attention to study the promiscuous nature of drugs. Despite tremendous challenges, community-wide efforts have led to a variety of novel approaches for predicting drug polypharmacology. In particular, some rapid advances using machine learning and artificial intelligence have been reported with great success. AREAS COVERED In this article, the authors provide a comprehensive update on the current state-of-the-art polypharmacology approaches and their applications, focusing on those reports published after our 2017 review article. The authors particularly discuss some novel, groundbreaking concepts, and methods that have been developed recently and applied to drug polypharmacology studies. EXPERT OPINION Polypharmacology is evolving and novel concepts are being introduced to counter the current challenges in the field. However, major hurdles remain including incompleteness of high-quality experimental data, lack of in vitro and in vivo assays to characterize multi-targeting agents, shortage of robust computational methods, and challenges to identify the best target combinations and design effective multi-targeting agents. Fortunately, numerous national/international efforts including multi-omics and artificial intelligence initiatives as well as most recent collaborations on addressing the COVID-19 pandemic have shown significant promise to propel the field of polypharmacology forward.
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Affiliation(s)
- Rajan Chaudhari
- Intelligent Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, United States
| | - Long Wolf Fong
- Intelligent Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, United States
- MD Anderson UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Avenue, Houston, Texas 77030, United States
| | - Zhi Tan
- Intelligent Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, United States
| | - Beibei Huang
- Intelligent Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, United States
| | - Shuxing Zhang
- Intelligent Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, United States
- MD Anderson UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Avenue, Houston, Texas 77030, United States
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15
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Naderi M, Lemoine JM, Govindaraj RG, Kana OZ, Feinstein WP, Brylinski M. Binding site matching in rational drug design: algorithms and applications. Brief Bioinform 2019; 20:2167-2184. [PMID: 30169563 PMCID: PMC6954434 DOI: 10.1093/bib/bby078] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 07/18/2018] [Accepted: 07/29/2018] [Indexed: 01/06/2023] Open
Abstract
Interactions between proteins and small molecules are critical for biological functions. These interactions often occur in small cavities within protein structures, known as ligand-binding pockets. Understanding the physicochemical qualities of binding pockets is essential to improve not only our basic knowledge of biological systems, but also drug development procedures. In order to quantify similarities among pockets in terms of their geometries and chemical properties, either bound ligands can be compared to one another or binding sites can be matched directly. Both perspectives routinely take advantage of computational methods including various techniques to represent and compare small molecules as well as local protein structures. In this review, we survey 12 tools widely used to match pockets. These methods are divided into five categories based on the algorithm implemented to construct binding-site alignments. In addition to the comprehensive analysis of their algorithms, test sets and the performance of each method are described. We also discuss general pharmacological applications of computational pocket matching in drug repurposing, polypharmacology and side effects. Reflecting on the importance of these techniques in drug discovery, in the end, we elaborate on the development of more accurate meta-predictors, the incorporation of protein flexibility and the integration of powerful artificial intelligence technologies such as deep learning.
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Affiliation(s)
- Misagh Naderi
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Jeffrey Mitchell Lemoine
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
- Division of Computer Science and Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
| | | | - Omar Zade Kana
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Wei Pan Feinstein
- High-Performance Computing, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
- Center for Computation & Technology, Louisiana State University, Baton Rouge, LA 70803, USA
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16
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Scheibenreif L, Littmann M, Orengo C, Rost B. FunFam protein families improve residue level molecular function prediction. BMC Bioinformatics 2019; 20:400. [PMID: 31319797 PMCID: PMC6639920 DOI: 10.1186/s12859-019-2988-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Accepted: 07/09/2019] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND The CATH database provides a hierarchical classification of protein domain structures including a sub-classification of superfamilies into functional families (FunFams). We analyzed the similarity of binding site annotations in these FunFams and incorporated FunFams into the prediction of protein binding residues. RESULTS FunFam members agreed, on average, in 36.9 ± 0.6% of their binding residue annotations. This constituted a 6.7-fold increase over randomly grouped proteins and a 1.2-fold increase (1.1-fold on the same dataset) over proteins with the same enzymatic function (identical Enzyme Commission, EC, number). Mapping de novo binding residue prediction methods (BindPredict-CCS, BindPredict-CC) onto FunFam resulted in consensus predictions for those residues that were aligned and predicted alike (binding/non-binding) within a FunFam. This simple consensus increased the F1-score (for binding) 1.5-fold over the original prediction method. Variation of the threshold for how many proteins in the consensus prediction had to agree provided a convenient control of accuracy/precision and coverage/recall, e.g. reaching a precision as high as 60.8 ± 0.4% for a stringent threshold. CONCLUSIONS The FunFams outperformed even the carefully curated EC numbers in terms of agreement of binding site residues. Additionally, we assume that our proof-of-principle through the prediction of protein binding residues will be relevant for many other solutions profiting from FunFams to infer functional information at the residue level.
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Affiliation(s)
- Linus Scheibenreif
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany.
| | - Maria Littmann
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany.
| | - Christine Orengo
- Department of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Burkhard Rost
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany
- Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, 85748, Garching/Munich, Germany
- TUM School of Life Sciences Weihenstephan (WZW), Alte Akademie 8, Freising, Germany
- Department of Biochemistry and Molecular Biophysics & New York Consortium on Membrane Protein Structure (NYCOMPS), Columbia University, 701 West, 168th Street, New York, NY 10032, USA
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17
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Cerisier N, Petitjean M, Regad L, Bayard Q, Réau M, Badel A, Camproux AC. High Impact: The Role of Promiscuous Binding Sites in Polypharmacology. Molecules 2019; 24:molecules24142529. [PMID: 31295958 PMCID: PMC6680532 DOI: 10.3390/molecules24142529] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 06/27/2019] [Accepted: 06/27/2019] [Indexed: 02/06/2023] Open
Abstract
The literature focuses on drug promiscuity, which is a drug’s ability to bind to several targets, because it plays an essential role in polypharmacology. However, little work has been completed regarding binding site promiscuity, even though its properties are now recognized among the key factors that impact drug promiscuity. Here, we quantified and characterized the promiscuity of druggable binding sites from protein-ligand complexes in the high quality Mother Of All Databases while using statistical methods. Most of the sites (80%) exhibited promiscuity, irrespective of the protein class. Nearly half were highly promiscuous and able to interact with various types of ligands. The corresponding pockets were rather large and hydrophobic, with high sulfur atom and aliphatic residue frequencies, but few side chain atoms. Consequently, their interacting ligands can be large, rigid, and weakly hydrophilic. The selective sites that interacted with one ligand type presented less favorable pocket properties for establishing ligand contacts. Thus, their ligands were highly adaptable, small, and hydrophilic. In the dataset, the promiscuity of the site rather than the drug mainly explains the multiple interactions between the drug and target, as most ligand types are dedicated to one site. This underlines the essential contribution of binding site promiscuity to drug promiscuity between different protein classes.
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Affiliation(s)
- Natacha Cerisier
- Université de Paris, Biologie Fonctionnelle et Adaptative, UMR 8251, CNRS, ERL U1133, INSERM, Computational Modeling of Protein Ligand Interactions, F-75013 Paris, France
| | - Michel Petitjean
- Université de Paris, Biologie Fonctionnelle et Adaptative, UMR 8251, CNRS, ERL U1133, INSERM, Computational Modeling of Protein Ligand Interactions, F-75013 Paris, France
| | - Leslie Regad
- Université de Paris, Biologie Fonctionnelle et Adaptative, UMR 8251, CNRS, ERL U1133, INSERM, Computational Modeling of Protein Ligand Interactions, F-75013 Paris, France
| | - Quentin Bayard
- Centre de Recherche des Cordeliers, Sorbonne Universités, INSERM, USPC, Université Paris Descartes, Université Paris Diderot, Université Paris 13, Functional Genomics of Solid Tumors Laboratory, F-75006 Paris, France
| | - Manon Réau
- Laboratoire Génomique Bioinformatique et Chimie Moléculaire, EA 7528, Conservatoire National des Arts et Métiers, F-75003 Paris, France
| | - Anne Badel
- Université de Paris, Biologie Fonctionnelle et Adaptative, UMR 8251, CNRS, ERL U1133, INSERM, Computational Modeling of Protein Ligand Interactions, F-75013 Paris, France
| | - Anne-Claude Camproux
- Université de Paris, Biologie Fonctionnelle et Adaptative, UMR 8251, CNRS, ERL U1133, INSERM, Computational Modeling of Protein Ligand Interactions, F-75013 Paris, France.
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18
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Zhou J, Jiang X, He S, Jiang H, Feng F, Liu W, Qu W, Sun H. Rational Design of Multitarget-Directed Ligands: Strategies and Emerging Paradigms. J Med Chem 2019; 62:8881-8914. [PMID: 31082225 DOI: 10.1021/acs.jmedchem.9b00017] [Citation(s) in RCA: 155] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Due to the complexity of multifactorial diseases, single-target drugs do not always exhibit satisfactory efficacy. Recently, increasing evidence indicates that simultaneous modulation of multiple targets may improve both therapeutic safety and efficacy, compared with single-target drugs. However, few multitarget drugs are on market or in clinical trials, despite the best efforts of medicinal chemists. This article discusses the systematic establishment of target combination, lead generation, and optimization of multitarget-directed ligands (MTDLs). Moreover, we analyze some MTDLs research cases for several complex diseases in recent years and the physicochemical properties of 117 clinical multitarget drugs, with the aim to reveal the trends and insights of the potential use of MTDLs.
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Affiliation(s)
- Junting Zhou
- Department of Medicinal Chemistry , China Pharmaceutical University , Nanjing 211198 , People's Republic of China.,Department of Natural Medicinal Chemistry , China Pharmaceutical University , Nanjing , 211198 , People's Republic of China
| | - Xueyang Jiang
- Department of Medicinal Chemistry , China Pharmaceutical University , Nanjing 211198 , People's Republic of China.,Department of Natural Medicinal Chemistry , China Pharmaceutical University , Nanjing , 211198 , People's Republic of China
| | - Siyu He
- Department of Medicinal Chemistry , China Pharmaceutical University , Nanjing 211198 , People's Republic of China
| | - Hongli Jiang
- Department of Medicinal Chemistry , China Pharmaceutical University , Nanjing 211198 , People's Republic of China.,Department of Natural Medicinal Chemistry , China Pharmaceutical University , Nanjing , 211198 , People's Republic of China
| | - Feng Feng
- Department of Natural Medicinal Chemistry , China Pharmaceutical University , Nanjing , 211198 , People's Republic of China.,Jiangsu Food and Pharmaceutical Science College , Huaian 223003 , People's Republic of China
| | - Wenyuan Liu
- Department of Analytical Chemistry , China Pharmaceutical University , Nanjing 210009 , People's Republic of China
| | - Wei Qu
- Department of Natural Medicinal Chemistry , China Pharmaceutical University , Nanjing , 211198 , People's Republic of China
| | - Haopeng Sun
- Department of Medicinal Chemistry , China Pharmaceutical University , Nanjing 211198 , People's Republic of China
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19
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Shi D, Khan F, Abagyan R. Extended Multitarget Pharmacology of Anticancer Drugs. J Chem Inf Model 2019; 59:3006-3017. [PMID: 31025863 DOI: 10.1021/acs.jcim.9b00031] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Multitarget pharmacology of small-molecule cancer drugs significantly contributes to their mechanism of action, side effects, and emergence of drug resistance and opens ways to repurpose, combine, or customize drug therapy. In most cases, the set of targets affected at therapeutic concentrations is not fully characterized and/or the interaction efficacy values are not accurately quantified. We collected information about multiple targets for each cancer drug along with their experimental effective concentrations or binding activities from multiple sources. All multitarget activity values for each drug then were used to build two proximity network pharmacology maps of anticancer drugs and targets of those drugs, respectively. Together with the network map, we showed that the majority of the cancer drugs had substantial multitarget pharmacology based on our current knowledge. In addition, most of the cancer drugs simultaneously affect macromolecular targets from different classes and types. The target subset can further be accentuated and personalized by patient sample-specific expression data. The network maps of cancer drugs and targets as well as all quantified activity data were integrated into a freely available database, CancerDrugMap (http://ruben.ucsd.edu/dnet/maps/drugnet.html). The identified multitarget pharmacology of cancer drugs is essential for improving the efficacy of individually prescribed drugs and drug combinations and minimization of adverse effects.
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Affiliation(s)
- Da Shi
- Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California, San Diego , La Jolla , California 92093-0747 , United States
| | - Feroz Khan
- Metabolic and Structural Biology Department , CSIR-Central Institute of Medicinal and Aromatic Plants (CIMAP) , Lucknow 226015 , Uttar Pradesh , India
| | - Ruben Abagyan
- Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California, San Diego , La Jolla , California 92093-0747 , United States
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20
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Abstract
INTRODUCTION The success of binding site comparisons in drug discovery is based on the recognized fact that many different proteins have similar binding sites. Indeed, binding site comparisons have found many uses in drug development and have the potential to dramatically cut the cost and shorten the time necessary for the development of new drugs. Areas covered: The authors review recent methods for comparing protein binding sites and their use in drug repurposing and polypharmacology. They examine emerging fields including the use of binding site comparisons in precision medicine, the prediction of structured water molecules, the search for targets of natural compounds, and their application in the development of protein-based drugs by loop modeling and for comparison of RNA binding sites. Expert opinion: Binding site comparisons have produced many interesting results in drug development, but relatively little work has been done on protein-protein interaction sites, which are particularly relevant in view of the success of biological drugs. Growth of protein loop modeling for modulating biological drugs is anticipated. The fusion of currently distinct methods for the comparison of RNA and protein binding sites into a single comprehensive approach could allow the search for new selective ribosomal antibiotics and initiate pharmaceutical research into other nucleoproteins.
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Affiliation(s)
- Janez Konc
- a Theory Department , National Institute of Chemistry , Ljubljana , Slovenia.,b Faculty of Pharmacy , University of Ljubljana , Ljubljana , Slovenia.,c Faculty of Mathematics , Natural Sciences and Information Technologies, University of Primorska , Koper , Slovenia.,d Faculty of Chemistry and Chemical Technology , University of Maribor , Maribor , Slovenia
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21
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Pu L, Govindaraj RG, Lemoine JM, Wu HC, Brylinski M. DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network. PLoS Comput Biol 2019; 15:e1006718. [PMID: 30716081 PMCID: PMC6375647 DOI: 10.1371/journal.pcbi.1006718] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 02/14/2019] [Accepted: 12/16/2018] [Indexed: 01/19/2023] Open
Abstract
Comprehensive characterization of ligand-binding sites is invaluable to infer molecular functions of hypothetical proteins, trace evolutionary relationships between proteins, engineer enzymes to achieve a desired substrate specificity, and develop drugs with improved selectivity profiles. These research efforts pose significant challenges owing to the fact that similar pockets are commonly observed across different folds, leading to the high degree of promiscuity of ligand-protein interactions at the system-level. On that account, novel algorithms to accurately classify binding sites are needed. Deep learning is attracting a significant attention due to its successful applications in a wide range of disciplines. In this communication, we present DeepDrug3D, a new approach to characterize and classify binding pockets in proteins with deep learning. It employs a state-of-the-art convolutional neural network in which biomolecular structures are represented as voxels assigned interaction energy-based attributes. The current implementation of DeepDrug3D, trained to detect and classify nucleotide- and heme-binding sites, not only achieves a high accuracy of 95%, but also has the ability to generalize to unseen data as demonstrated for steroid-binding proteins and peptidase enzymes. Interestingly, the analysis of strongly discriminative regions of binding pockets reveals that this high classification accuracy arises from learning the patterns of specific molecular interactions, such as hydrogen bonds, aromatic and hydrophobic contacts. DeepDrug3D is available as an open-source program at https://github.com/pulimeng/DeepDrug3D with the accompanying TOUGH-C1 benchmarking dataset accessible from https://osf.io/enz69/.
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Affiliation(s)
- Limeng Pu
- Division of Electrical & Computer Engineering, Louisiana State University, Baton Rouge, LA, United States of America
| | - Rajiv Gandhi Govindaraj
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, United States of America
| | - Jeffrey Mitchell Lemoine
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, United States of America
- Division of Computer Science and Engineering, Louisiana State University, Baton Rouge, LA, United States of America
| | - Hsiao-Chun Wu
- Division of Electrical & Computer Engineering, Louisiana State University, Baton Rouge, LA, United States of America
| | - Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, United States of America
- Center for Computation & Technology, Louisiana State University, Baton Rouge, LA, United States of America
- * E-mail:
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22
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Ozdemir ES, Halakou F, Nussinov R, Gursoy A, Keskin O. Methods for Discovering and Targeting Druggable Protein-Protein Interfaces and Their Application to Repurposing. Methods Mol Biol 2019; 1903:1-21. [PMID: 30547433 PMCID: PMC8185533 DOI: 10.1007/978-1-4939-8955-3_1] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Abstract
Drug repurposing is a creative and resourceful approach to increase the number of therapies by exploiting available and approved drugs. However, identifying new protein targets for previously approved drugs is challenging. Although new strategies have been developed for drug repurposing, there is broad agreement that there is room for further improvements. In this chapter, we review protein-protein interaction (PPI) interface-targeting strategies for drug repurposing applications. We discuss certain features, such as hot spot residue and hot region prediction and their importance in drug repurposing, and illustrate common methods used in PPI networks to identify drug off-targets. We also collect available online resources for hot spot prediction, binding pocket identification, and interface clustering which are effective resources in polypharmacology. Finally, we provide case studies showing the significance of protein interfaces and hot spots in drug repurposing.
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Affiliation(s)
- E Sila Ozdemir
- Department of Chemical and Biological Engineering, Koc University, Istanbul, Turkey
| | - Farideh Halakou
- Department of Computer Engineering, Koc University, Istanbul, Turkey
| | - Ruth Nussinov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD, USA
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Attila Gursoy
- Department of Computer Engineering, Koc University, Istanbul, Turkey.
| | - Ozlem Keskin
- Department of Chemical and Biological Engineering, Koc University, Istanbul, Turkey.
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23
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Sharma A, Manolakos ES. Multi-criteria protein structure comparison and structural similarities analysis using pyMCPSC. PLoS One 2018; 13:e0204587. [PMID: 30332415 PMCID: PMC6192565 DOI: 10.1371/journal.pone.0204587] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Accepted: 09/11/2018] [Indexed: 01/20/2023] Open
Abstract
Protein Structure Comparison (PSC) is a well developed field of computational proteomics with active interest from the research community, since it is widely used in structural biology and drug discovery. With new PSC methods continuously emerging and no clear method of choice, Multi-Criteria Protein Structure Comparison (MCPSC) is commonly employed to combine methods and generate consensus structural similarity scores. We present pyMCPSC, a Python based utility we developed to allow users to perform MCPSC efficiently, by exploiting the parallelism afforded by the multi-core CPUs of today’s desktop computers. We show how pyMCPSC facilitates the analysis of similarities in protein domain datasets and how it can be extended to incorporate new PSC methods as they are becoming available. We exemplify the power of pyMCPSC using a case study based on the Proteus_300 dataset. Results generated using pyMCPSC show that MCPSC scores form a reliable basis for identifying the true classification of a domain, as evidenced both by the ROC analysis as well as the Nearest-Neighbor analysis. Structure similarity based “Phylogenetic Trees” representation generated by pyMCPSC provide insight into functional grouping within the dataset of domains. Furthermore, scatter plots generated by pyMCPSC show the existence of strong correlation between protein domains belonging to SCOP Class C and loose correlation between those of SCOP Class D. Such analyses and corresponding visualizations help users quickly gain insights about their datasets. The source code of pyMCPSC is available under the GPLv3.0 license through a GitHub repository (https://github.com/xulesc/pymcpsc).
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Affiliation(s)
- Anuj Sharma
- Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece
| | - Elias S. Manolakos
- Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece
- Northeastern University, Boston, Massachusetts, United States of America
- * E-mail:
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24
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Pinzi L, Caporuscio F, Rastelli G. Selection of protein conformations for structure-based polypharmacology studies. Drug Discov Today 2018; 23:1889-1896. [PMID: 30099123 DOI: 10.1016/j.drudis.2018.08.007] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Revised: 08/03/2018] [Accepted: 08/06/2018] [Indexed: 11/29/2022]
Abstract
Several drugs exert their therapeutic effect through the modulation of multiple targets. Structure-based approaches hold great promise for identifying compounds with the desired polypharmacological profiles. These methods use knowledge of the protein binding sites to identify stereoelectronically complementary ligands. The selection of the most suitable protein conformations to be used in the design process is vital, especially for multitarget drug design in which the same ligand has to be accommodated in multiple binding pockets. Herein, we focus on currently available techniques for the selection of the most suitable protein conformations for multitarget drug design, compare the potential advantages and limitations of each method, and comment on how their combination could help in polypharmacology drug design.
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Affiliation(s)
- Luca Pinzi
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Giuseppe Campi 103, 41125, Modena, Italy
| | - Fabiana Caporuscio
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Giuseppe Campi 103, 41125, Modena, Italy
| | - Giulio Rastelli
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Giuseppe Campi 103, 41125, Modena, Italy.
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25
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Gandini A, Bartolini M, Tedesco D, Martinez-Gonzalez L, Roca C, Campillo NE, Zaldivar-Diez J, Perez C, Zuccheri G, Miti A, Feoli A, Castellano S, Petralla S, Monti B, Rossi M, Moda F, Legname G, Martinez A, Bolognesi ML. Tau-Centric Multitarget Approach for Alzheimer’s Disease: Development of First-in-Class Dual Glycogen Synthase Kinase 3β and Tau-Aggregation Inhibitors. J Med Chem 2018; 61:7640-7656. [DOI: 10.1021/acs.jmedchem.8b00610] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Affiliation(s)
- Annachiara Gandini
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum—University of Bologna, Via Belmeloro 6, I-40126 Bologna, Italy
- Laboratory of Prion Biology, Department of Neuroscience, Scuola Internazionale Superiore di Studi Avanzati (SISSA), Via Bonomea 265, I-34136 Trieste, Italy
| | - Manuela Bartolini
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum—University of Bologna, Via Belmeloro 6, I-40126 Bologna, Italy
| | - Daniele Tedesco
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum—University of Bologna, Via Belmeloro 6, I-40126 Bologna, Italy
| | | | - Carlos Roca
- Centro de Investigaciones Biologica, CSIC, Ramiro de Maeztu 9, 28040 Madrid, Spain
| | - Nuria E. Campillo
- Centro de Investigaciones Biologica, CSIC, Ramiro de Maeztu 9, 28040 Madrid, Spain
| | - Josefa Zaldivar-Diez
- Centro de Investigaciones Biologica, CSIC, Ramiro de Maeztu 9, 28040 Madrid, Spain
| | - Concepción Perez
- Instituto de Quimica Medica, CSIC, Calle Juan de la Cierva 3, 28006 Madrid, Spain
| | - Giampaolo Zuccheri
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum—University of Bologna, Via Belmeloro 6, I-40126 Bologna, Italy
- S3 Center of the Institute of Nanosciences, Italian National Research Council (CNR), I-41125 Modena, Italy
| | - Andrea Miti
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum—University of Bologna, Via Belmeloro 6, I-40126 Bologna, Italy
- S3 Center of the Institute of Nanosciences, Italian National Research Council (CNR), I-41125 Modena, Italy
| | - Alessandra Feoli
- EpigeneticMedChemLab, Department of Pharmacy, University of Salerno, Via Giovanni Paolo II 132, I-84084 Fisciano, Italy
| | - Sabrina Castellano
- EpigeneticMedChemLab, Department of Pharmacy, University of Salerno, Via Giovanni Paolo II 132, I-84084 Fisciano, Italy
| | - Sabrina Petralla
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum—University of Bologna, Via Belmeloro 6, I-40126 Bologna, Italy
| | - Barbara Monti
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum—University of Bologna, Via Belmeloro 6, I-40126 Bologna, Italy
| | - Martina Rossi
- Laboratory of Prion Biology, Department of Neuroscience, Scuola Internazionale Superiore di Studi Avanzati (SISSA), Via Bonomea 265, I-34136 Trieste, Italy
| | - Fabio Moda
- Fondazione IRCCS Istituto Neurologico Carlo Besta, via Celoria 11, I-20133 Milan, Italy
| | - Giuseppe Legname
- Laboratory of Prion Biology, Department of Neuroscience, Scuola Internazionale Superiore di Studi Avanzati (SISSA), Via Bonomea 265, I-34136 Trieste, Italy
| | - Ana Martinez
- Centro de Investigaciones Biologica, CSIC, Ramiro de Maeztu 9, 28040 Madrid, Spain
| | - Maria Laura Bolognesi
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum—University of Bologna, Via Belmeloro 6, I-40126 Bologna, Italy
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26
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Kakhlon O. Pharmacological approaches for treating glycogen storage disorders involving polyglucosan body accumulation. Expert Opin Orphan Drugs 2017. [DOI: 10.1080/21678707.2017.1405804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Or Kakhlon
- Department of Neurology, Hadassah Medical Association, Ein Kerem, Jerusalem, Israel
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27
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Lee J, Konc J, Janežič D, Brooks BR. Global organization of a binding site network gives insight into evolution and structure-function relationships of proteins. Sci Rep 2017; 7:11652. [PMID: 28912495 PMCID: PMC5599562 DOI: 10.1038/s41598-017-10412-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Accepted: 08/07/2017] [Indexed: 01/06/2023] Open
Abstract
The global organization of protein binding sites is analyzed by constructing a weighted network of binding sites based on their structural similarities and detecting communities of structurally similar binding sites based on the minimum description length principle. The analysis reveals that there are two central binding site communities that play the roles of the network hubs of smaller peripheral communities. The sizes of communities follow a power-law distribution, which indicates that the binding sites included in larger communities may be older and have been evolutionary structural scaffolds of more recent ones. Structurally similar binding sites in the same community bind to diverse ligands promiscuously and they are also embedded in diverse domain structures. Understanding the general principles of binding site interplay will pave the way for improved drug design and protein design.
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Affiliation(s)
- Juyong Lee
- Department of Chemistry, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon, 24341, Republic of Korea. .,Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, 20892, United States.
| | - Janez Konc
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, SI-6000, Koper, Slovenia.,National Institute of Chemistry, Hajdrihova 19, SI-1000, Ljubljana, Slovenia
| | - Dušanka Janežič
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, SI-6000, Koper, Slovenia
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, 20892, United States
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