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Lembo V, Bottegoni G. Systematic Investigation of Dual-Target-Directed Ligands. J Med Chem 2024; 67:10374-10385. [PMID: 38843874 PMCID: PMC11215722 DOI: 10.1021/acs.jmedchem.4c00838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 05/17/2024] [Accepted: 05/20/2024] [Indexed: 06/28/2024]
Abstract
Multitarget-directed ligands (MTDLs) are compounds rationally designed to affect multiple targets, aiming for a better therapeutic profile. For over 20 years, MTDLs have garnered increasing attention, the idea being that their full potential would have been achieved, thanks to unprecedented target combinations and advanced medicinal chemistry strategies. This study presents a literature mining effort resulting in a data set of dual-target-directed ligands (DTDLs), the fundamental example of MTDLs. We used this data set to evaluate the rationale behind target selection and the chemical novelty of DTDLs targeting specific protein combinations. Our analysis focused on DTDL targets in terms of biological function, disease association, structure, and chemogenomic traits. We also compared DTDLs with single-target compounds. We found that well-known target pathology associations often guide DTDL design, leveraging existing chemical scaffolds and binding pocket similarities. These findings highlight the current state of the field and suggest substantial untapped potential for rational polypharmacology.
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Affiliation(s)
- Vittorio Lembo
- Department
of Biomolecular Sciences, Università
degli Studi di Urbino Carlo Bo, Piazza Rinascimento 6, 61029 Urbino, Italy
- Computational
and Chemical Biology, Istituto Italiano
di Tecnologia, Via Morego
30, 16163 Genova, Italy
| | - Giovanni Bottegoni
- Department
of Biomolecular Sciences, Università
degli Studi di Urbino Carlo Bo, Piazza Rinascimento 6, 61029 Urbino, Italy
- Institute
of Clinical Sciences, University of Birmingham, Edgbaston, B15 2TT Birmingham, U.K.
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2
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Przedborski M, Smalley M, Thiyagarajan S, Goldman A, Kohandel M. Systems biology informed neural networks (SBINN) predict response and novel combinations for PD-1 checkpoint blockade. Commun Biol 2021; 4:877. [PMID: 34267327 PMCID: PMC8282606 DOI: 10.1038/s42003-021-02393-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 06/25/2021] [Indexed: 02/06/2023] Open
Abstract
Anti-PD-1 immunotherapy has recently shown tremendous success for the treatment of several aggressive cancers. However, variability and unpredictability in treatment outcome have been observed, and are thought to be driven by patient-specific biology and interactions of the patient's immune system with the tumor. Here we develop an integrative systems biology and machine learning approach, built around clinical data, to predict patient response to anti-PD-1 immunotherapy and to improve the response rate. Using this approach, we determine biomarkers of patient response and identify potential mechanisms of drug resistance. We develop systems biology informed neural networks (SBINN) to calculate patient-specific kinetic parameter values and to predict clinical outcome. We show how transfer learning can be leveraged with simulated clinical data to significantly improve the response prediction accuracy of the SBINN. Further, we identify novel drug combinations and optimize the treatment protocol for triple combination therapy consisting of IL-6 inhibition, recombinant IL-12, and anti-PD-1 immunotherapy in order to maximize patient response. We also find unexpected differences in protein expression levels between response phenotypes which complement recent clinical findings. Our approach has the potential to aid in the development of targeted experiments for patient drug screening as well as identify novel therapeutic targets.
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Affiliation(s)
- Michelle Przedborski
- Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada.
| | - Munisha Smalley
- Integrative Immuno Oncology Center, Mitra Biotech, Woburn, MA, USA
| | | | - Aaron Goldman
- Division of Engineering in Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Mohammad Kohandel
- Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada
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3
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Przedborski M, Sharon D, Chan S, Kohandel M. A mean-field approach for modeling the propagation of perturbations in biochemical reaction networks. Eur J Pharm Sci 2021; 165:105919. [PMID: 34175448 DOI: 10.1016/j.ejps.2021.105919] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 05/17/2021] [Accepted: 06/20/2021] [Indexed: 12/12/2022]
Abstract
Often, the time evolution of a biochemical reaction network is crucial for determining the effects of combining multiple pharmaceuticals. Here we illustrate a mathematical framework for modeling the dominant temporal behaviour of a complicated molecular pathway or biochemical reaction network in response to an arbitrary perturbation, such as resulting from the administration of a therapeutic agent. The method enables the determination of the temporal evolution of a target protein as the perturbation propagates through its regulatory network. The mathematical approach is particularly useful when the experimental data that is available for characterizing or parameterizing the regulatory network is limited or incomplete. To illustrate the method, we consider the examples of the regulatory networks for the target proteins c-Myc and Chop, which play an important role in venetoclax resistance in acute myeloid leukemia. First we show how the networks that regulate each target protein can be reduced to a mean-field model by identifying the distinct effects that groups of proteins in the regulatory network have on the target protein. Then we show how limited protein-level data can be used to further simplify the mean-field model to pinpoint the dominant effects of the network perturbation on the target protein. This enables a further reduction in the number of parameters in the model. The result is an ordinary differential equation model that captures the temporal evolution of the expression of a target protein when one or more proteins in its regulatory network have been perturbed. Finally, we show how the dominant effects predicted by the mathematical model agree with RNA sequencing data for the regulatory proteins comprising the molecular network, despite the model not having a priori knowledge of this data. Thus, while the approach gives a simplified model for the expression of the target protein, it allows for the interpretation of the effects of the perturbation on the regulatory network itself. This method can be easily extended to sets of target proteins to model components of a larger systems biology model, and provides an approach for partially integrating RNA sequencing data and protein expression data. Moreover, it is a general approach that can be used to study drug effects on specific protein(s) in any disease or condition.
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Affiliation(s)
- Michelle Przedborski
- Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada.
| | - David Sharon
- Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Steven Chan
- Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Mohammad Kohandel
- Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada
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4
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Tolani P, Gupta S, Yadav K, Aggarwal S, Yadav AK. Big data, integrative omics and network biology. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2021; 127:127-160. [PMID: 34340766 DOI: 10.1016/bs.apcsb.2021.03.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
A cell integrates various signals through a network of biomolecules that crosstalk to synergistically regulate the replication, transcription, translation and other metabolic activities of a cell. These networks regulate signal perception and processing that drives biological functions. The biological complexity cannot be fully captured by a single -omics discipline. The holistic study of an organism-in health, perturbation, exposure to environment and disease, is studied under systems biology. The bottom-up molecular approaches (genes, mRNA, protein, metabolite, etc.) have laid the foundation of current biological knowledge covering the horizon from viruses, bacteria, fungi, plants and animals. Yet, these techniques provide a rather myopic view of biology at the molecular level. To understand how the interconnected molecular components are formed and rewired in disease or exposure to environmental stimuli is the holy grail of modern biology. The omics era was heralded by the genomics revolution but advanced sequencing techniques are now also ubiquitous in transcriptomics, proteomics, metabolomics and lipidomics. Multi-omics data analysis and integration techniques are driving the quest for deeper insights into how the different layers of biomolecules talk to each other in diverse contexts.
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Affiliation(s)
- Priya Tolani
- Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, Haryana, India
| | - Srishti Gupta
- Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, Haryana, India; School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India
| | - Kirti Yadav
- Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, Haryana, India; Department of Pharmaceutical Biotechnology, Delhi Pharmaceutical Sciences and Research University, New Delhi, India
| | - Suruchi Aggarwal
- Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, Haryana, India; Department of Molecular Biology and Biotechnology, Cotton University, Guwahati, Assam, India
| | - Amit Kumar Yadav
- Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, Haryana, India.
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5
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Biophysical Insight into the Interaction of Human Lysozyme with Anticancer Drug Anastrozole: A Multitechnique Approach. ScientificWorldJournal 2020; 2020:8363685. [PMID: 32908463 PMCID: PMC7468670 DOI: 10.1155/2020/8363685] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 06/19/2020] [Indexed: 12/14/2022] Open
Abstract
In the present study, we employ fluorescence spectroscopy, dynamic light scattering, and molecular docking methods. Binding of anticancer drug anastrozole with human lysozyme (HL) is studied. Binding of anastrozole to HL is moderate but spontaneous. There is anastrozole persuaded hydrodynamic change in HL, leading to molecular compaction. Binding of anastrozole to HL also decreased in vitro lytic activity of HL. Molecular docking results suggest the electrostatic interactions and van der Waals forces played key role in binding interaction of anastrozole near the catalytic site. Binding interaction of anastrozole to proteins other than major transport proteins in blood can significantly affect pharmacokinetics of this molecule. Hence, rationalizing drug dosage is important. This study also points to unrelated effects that small molecules bring in the body that are considerable and need thorough investigation.
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de Ruyck J, Brysbaert G, Blossey R, Lensink MF. Molecular docking as a popular tool in drug design, an in silico travel. Adv Appl Bioinform Chem 2016; 9:1-11. [PMID: 27390530 PMCID: PMC4930227 DOI: 10.2147/aabc.s105289] [Citation(s) in RCA: 97] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
New molecular modeling approaches, driven by rapidly improving computational platforms, have allowed many success stories for the use of computer-assisted drug design in the discovery of new mechanism-or structure-based drugs. In this overview, we highlight three aspects of the use of molecular docking. First, we discuss the combination of molecular and quantum mechanics to investigate an unusual enzymatic mechanism of a flavoprotein. Second, we present recent advances in anti-infectious agents' synthesis driven by structural insights. At the end, we focus on larger biological complexes made by protein-protein interactions and discuss their relevance in drug design. This review provides information on how these large systems, even in the presence of the solvent, can be investigated with the outlook of drug discovery.
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Affiliation(s)
| | | | - Ralf Blossey
- University Lille, CNRS UMR8576 UGSF, Lille, France
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7
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Yan SK, Liu RH, Jin HZ, Liu XR, Ye J, Shan L, Zhang WD. "Omics" in pharmaceutical research: overview, applications, challenges, and future perspectives. Chin J Nat Med 2015; 13:3-21. [PMID: 25660284 DOI: 10.1016/s1875-5364(15)60002-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2014] [Indexed: 12/18/2022]
Abstract
In the post-genomic era, biological studies are characterized by the rapid development and wide application of a series of "omics" technologies, including genomics, proteomics, metabolomics, transcriptomics, lipidomics, cytomics, metallomics, ionomics, interactomics, and phenomics. These "omics" are often based on global analyses of biological samples using high through-put analytical approaches and bioinformatics and may provide new insights into biological phenomena. In this paper, the development and advances in these omics made in the past decades are reviewed, especially genomics, transcriptomics, proteomics and metabolomics; the applications of omics technologies in pharmaceutical research are then summarized in the fields of drug target discovery, toxicity evaluation, personalized medicine, and traditional Chinese medicine; and finally, the limitations of omics are discussed, along with the future challenges associated with the multi-omics data processing, dynamics omics analysis, and analytical approaches, as well as amenable solutions and future prospects.
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Affiliation(s)
- Shi-Kai Yan
- School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Run-Hui Liu
- School of Pharmacy, Second Military Medical University, Shanghai 200433, China
| | - Hui-Zi Jin
- School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xin-Ru Liu
- School of Pharmacy, Second Military Medical University, Shanghai 200433, China
| | - Ji Ye
- School of Pharmacy, Second Military Medical University, Shanghai 200433, China
| | - Lei Shan
- School of Pharmacy, Second Military Medical University, Shanghai 200433, China
| | - Wei-Dong Zhang
- School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China; School of Pharmacy, Second Military Medical University, Shanghai 200433, China; Shanghai Institute of Pharmaceutical Industry, Shanghai 200040, China.
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8
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Cobanoglu MC, Oltvai ZN, Taylor DL, Bahar I. BalestraWeb: efficient online evaluation of drug-target interactions. Bioinformatics 2015; 31:131-3. [PMID: 25192741 PMCID: PMC4271144 DOI: 10.1093/bioinformatics/btu599] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2014] [Revised: 07/03/2014] [Accepted: 08/31/2014] [Indexed: 12/28/2022] Open
Abstract
SUMMARY BalestraWeb is an online server that allows users to instantly make predictions about the potential occurrence of interactions between any given drug-target pair, or predict the most likely interaction partners of any drug or target listed in the DrugBank. It also permits users to identify most similar drugs or most similar targets based on their interaction patterns. Outputs help to develop hypotheses about drug repurposing as well as potential side effects. AVAILABILITY AND IMPLEMENTATION BalestraWeb is accessible at http://balestra.csb.pitt.edu/. The tool is built using a probabilistic matrix factorization method and DrugBank v3, and the latent variable models are trained using the GraphLab collaborative filtering toolkit. The server is implemented using Python, Flask, NumPy and SciPy.
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Affiliation(s)
- Murat Can Cobanoglu
- Department of Computational and Systems Biology, University of Pittsburgh, School of Medicine, Pittsburgh, PA, The University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA 15260 and Department of Pathology, University of Pittsburgh, School of Medicine, Pittsburgh, PA 15213, USA Department of Computational and Systems Biology, University of Pittsburgh, School of Medicine, Pittsburgh, PA, The University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA 15260 and Department of Pathology, University of Pittsburgh, School of Medicine, Pittsburgh, PA 15213, USA
| | - Zoltán N Oltvai
- Department of Computational and Systems Biology, University of Pittsburgh, School of Medicine, Pittsburgh, PA, The University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA 15260 and Department of Pathology, University of Pittsburgh, School of Medicine, Pittsburgh, PA 15213, USA Department of Computational and Systems Biology, University of Pittsburgh, School of Medicine, Pittsburgh, PA, The University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA 15260 and Department of Pathology, University of Pittsburgh, School of Medicine, Pittsburgh, PA 15213, USA
| | - D Lansing Taylor
- Department of Computational and Systems Biology, University of Pittsburgh, School of Medicine, Pittsburgh, PA, The University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA 15260 and Department of Pathology, University of Pittsburgh, School of Medicine, Pittsburgh, PA 15213, USA Department of Computational and Systems Biology, University of Pittsburgh, School of Medicine, Pittsburgh, PA, The University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA 15260 and Department of Pathology, University of Pittsburgh, School of Medicine, Pittsburgh, PA 15213, USA
| | - Ivet Bahar
- Department of Computational and Systems Biology, University of Pittsburgh, School of Medicine, Pittsburgh, PA, The University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA 15260 and Department of Pathology, University of Pittsburgh, School of Medicine, Pittsburgh, PA 15213, USA
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9
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Chiu YY, Tseng JH, Liu KH, Lin CT, Hsu KC, Yang JM. Homopharma: a new concept for exploring the molecular binding mechanisms and drug repurposing. BMC Genomics 2014; 15 Suppl 9:S8. [PMID: 25521038 PMCID: PMC4290623 DOI: 10.1186/1471-2164-15-s9-s8] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Background Drugs that simultaneously target multiple proteins often improve efficacy, particularly in the treatment of complex diseases such as cancers and central nervous system disorders. Many approaches have been proposed to identify the potential targets of a drug. Recently, we have introduced Space-Related Pharmamotif (SRPmotif) method to recognize the proteins that share similar binding environments. In addition, compounds with similar topology may bind to similar proteins and have similar protein-compound interactions. However, few studies have focused on exploring the relationships between binding environments and protein-compound interactions, which is important for understanding molecular binding mechanisms and helpful to be used in discovering drug repurposing. Results In this study, we propose a new concept of "Homopharma", combining similar binding environments and protein-compound interaction profiles, to explore the molecular binding mechanisms and drug repurposing. A Homopharma consists of a set of proteins which have the conserved binding environment and a set of compounds that share similar structures and functional groups. These proteins and compounds present conserved interactions and similar physicochemical properties. Therefore, these compounds are often able to inhibit the proteins in a Homopharma. Our experimental results show that the proteins and compounds in a Homopharma often have similar protein-compound interactions, comprising conserved specific residues and functional sites. Based on the Homopharma concept, we selected four flavonoid derivatives and 32 human protein kinases for enzymatic profiling. Among these 128 bioassays, the IC50 of 56 and 25 flavonoid-kinase inhibitions are less than 10 μM and 1 μM, respectively. Furthermore, these experimental results suggest that these flavonoids can be used as anticancer compounds, such as oral and colorectal cancer drugs. Conclusions The experimental results show that the Homopharma is useful for identifying key binding environments of proteins and compounds and discovering new inhibitory effects. We believe that the Homopharma concept can have the potential for understanding molecular binding mechanisms and providing new clues for drug development.
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Martell RE, Brooks DG, Wang Y, Wilcoxen K. Discovery of novel drugs for promising targets. Clin Ther 2014; 35:1271-81. [PMID: 24054704 DOI: 10.1016/j.clinthera.2013.08.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2013] [Revised: 06/27/2013] [Accepted: 08/13/2013] [Indexed: 11/18/2022]
Abstract
BACKGROUND Once a promising drug target is identified, the steps to actually discover and optimize a drug are diverse and challenging. OBJECTIVE The goal of this study was to provide a road map to navigate drug discovery. METHODS Review general steps for drug discovery and provide illustrating references. RESULTS A number of approaches are available to enhance and accelerate target identification and validation. Consideration of a variety of potential mechanisms of action of potential drugs can guide discovery efforts. The hit to lead stage may involve techniques such as high-throughput screening, fragment-based screening, and structure-based design, with informatics playing an ever-increasing role. Biologically relevant screening models are discussed, including cell lines, 3-dimensional culture, and in vivo screening. The process of enabling human studies for an investigational drug is also discussed. CONCLUSIONS Drug discovery is a complex process that has significantly evolved in recent years.
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Affiliation(s)
- Robert E Martell
- TESARO Inc, Waltham, Massachusetts; Tufts Medical Center, Boston, Massachusetts.
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11
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Csermely P, Korcsmáros T, Kiss HJM, London G, Nussinov R. Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review. Pharmacol Ther 2013; 138:333-408. [PMID: 23384594 PMCID: PMC3647006 DOI: 10.1016/j.pharmthera.2013.01.016] [Citation(s) in RCA: 512] [Impact Index Per Article: 46.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2013] [Accepted: 01/22/2013] [Indexed: 02/02/2023]
Abstract
Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.
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Affiliation(s)
- Peter Csermely
- Department of Medical Chemistry, Semmelweis University, P.O. Box 260, H-1444 Budapest 8, Hungary.
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12
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Bertolazzi P, Bock ME, Guerra C. On the functional and structural characterization of hubs in protein–protein interaction networks. Biotechnol Adv 2013; 31:274-86. [DOI: 10.1016/j.biotechadv.2012.12.002] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2012] [Revised: 11/13/2012] [Accepted: 12/01/2012] [Indexed: 01/07/2023]
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13
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Morrow JK, Zhang S. Computational prediction of protein hot spot residues. Curr Pharm Des 2012; 18:1255-65. [PMID: 22316154 DOI: 10.2174/138161212799436412] [Citation(s) in RCA: 67] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2011] [Accepted: 12/06/2011] [Indexed: 11/22/2022]
Abstract
Most biological processes involve multiple proteins interacting with each other. It has been recently discovered that certain residues in these protein-protein interactions, which are called hot spots, contribute more significantly to binding affinity than others. Hot spot residues have unique and diverse energetic properties that make them challenging yet important targets in the modulation of protein-protein complexes. Design of therapeutic agents that interact with hot spot residues has proven to be a valid methodology in disrupting unwanted protein-protein interactions. Using biological methods to determine which residues are hot spots can be costly and time consuming. Recent advances in computational approaches to predict hot spots have incorporated a myriad of features, and have shown increasing predictive successes. Here we review the state of knowledge around protein-protein interactions, hot spots, and give an overview of multiple in silico prediction techniques of hot spot residues.
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Affiliation(s)
- John Kenneth Morrow
- Department of Experimental Therapeutics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas 77054, USA
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14
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Srinivasan M, Dunker AK. Proline rich motifs as drug targets in immune mediated disorders. INTERNATIONAL JOURNAL OF PEPTIDES 2012; 2012:634769. [PMID: 22666276 PMCID: PMC3362030 DOI: 10.1155/2012/634769] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2011] [Accepted: 02/15/2012] [Indexed: 12/26/2022]
Abstract
The current version of the human immunome network consists of nearly 1400 interactions involving approximately 600 proteins. Intermolecular interactions mediated by proline-rich motifs (PRMs) are observed in many facets of the immune response. The proline-rich regions are known to preferentially adopt a polyproline type II helical conformation, an extended structure that facilitates transient intermolecular interactions such as signal transduction, antigen recognition, cell-cell communication and cytoskeletal organization. The propensity of both the side chain and the backbone carbonyls of the polyproline type II helix to participate in the interface interaction makes it an excellent recognition motif. An advantage of such distinct chemical features is that the interactions can be discriminatory even in the absence of high affinities. Indeed, the immune response is mediated by well-orchestrated low-affinity short-duration intermolecular interactions. The proline-rich regions are predominantly localized in the solvent-exposed regions such as the loops, intrinsically disordered regions, or between domains that constitute the intermolecular interface. Peptide mimics of the PRM have been suggested as potential antagonists of intermolecular interactions. In this paper, we discuss novel PRM-mediated interactions in the human immunome that potentially serve as attractive targets for immunomodulation and drug development for inflammatory and autoimmune pathologies.
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Affiliation(s)
- Mythily Srinivasan
- Department of Oral Pathology, Medicine and Radiology, Indiana University School of Dentistry, Indiana University Purdue University at Indianapolis 1121 West Michigan Street, DS290, Indianapolis, IN 46268, USA
| | - A. Keith Dunker
- Department of Biochemistry and Molecular Biology and School of Informatics, Indiana University School of Medicine, Indiana University Purdue University at Indianapolis, Indianapolis, IN, USA
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15
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Kuzu G, Keskin O, Gursoy A, Nussinov R. Expanding the conformational selection paradigm in protein-ligand docking. Methods Mol Biol 2012; 819:59-74. [PMID: 22183530 PMCID: PMC7455014 DOI: 10.1007/978-1-61779-465-0_5] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Conformational selection emerges as a theme in macromolecular interactions. Data validate it as a prevailing mechanism in protein-protein, protein-DNA, protein-RNA, and protein-small molecule drug recognition. This raises the question of whether this fundamental biomolecular binding mechanism can be used to improve drug docking and discovery. Actually, in practice this has already been taking place for some years in increasing numbers. Essentially, it argues for using not a single conformer, but an ensemble. The paradigm of conformational selection holds that because the ensemble is heterogeneous, within it there will be states whose conformation matches that of the ligand. Even if the population of this state is low, since it is favorable for binding the ligand, it will bind to it with a subsequent population shift toward this conformer. Here we suggest expanding it by first modeling all protein interactions in the cell by using Prism, an efficient motif-based protein-protein interaction modeling strategy, followed by ensemble generation. Such a strategy could be particularly useful for signaling proteins, which are major targets in drug discovery and bind multiple partners through a shared binding site, each with some-minor or major-conformational change.
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Affiliation(s)
- Guray Kuzu
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University Rumelifeneri Yolu, Istanbul, Turkey
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16
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Katsori AM, Chatzopoulou M, Dimas K, Kontogiorgis C, Patsilinakos A, Trangas T, Hadjipavlou-Litina D. Curcumin analogues as possible anti-proliferative & anti-inflammatory agents. Eur J Med Chem 2011; 46:2722-35. [PMID: 21514701 DOI: 10.1016/j.ejmech.2011.03.060] [Citation(s) in RCA: 70] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2011] [Revised: 03/21/2011] [Accepted: 03/29/2011] [Indexed: 01/25/2023]
Abstract
A series of novel curcumin analogues has been designed, synthesized and tested in vitro/in vivo as potential multi-target agents. Their anti-proliferative and anti-inflammatory activities were studied. Compounds 1b and 2b were stronger inhibitors of soybean lipoxygenase (LOX) than curcumin. Analogue 1b was also the most potent aldose reductase (ALR2) inhibitor. Two compounds, (1a and 1f) exhibited in vivo anti-inflammatory activity comparable to that of indomethacin, whereas derivative 1i exhibited even higher activity. The derivatives were also tested for their anti-proliferative activity using three different human cancer cell lines. Compounds 1a, 1b, 1d and 2b exhibited significant growth inhibitory activity as compared to curcumin, against all three cancer cell lines. Lipophilicity was determined as R(M) values using RPTLC and theoretically. The results are discussed in terms of the structural characteristics of the compounds. Docking simulations were performed on LOX and ALR2 inhibitor 1b and curcumin. Compound 1b is well fitted in the active site of ALR2, binding to the ALR2 enzyme in a similar way to curcumin. Allosteric interactions may govern the LOX-inhibitor binding.
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Affiliation(s)
- A-M Katsori
- Department of Pharmaceutical Chemistry, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
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The potassium channel KcsA: a model protein in studying membrane protein oligomerization and stability of oligomeric assembly? Arch Biochem Biophys 2011; 510:1-10. [PMID: 21458409 DOI: 10.1016/j.abb.2011.03.010] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2010] [Revised: 03/25/2011] [Accepted: 03/25/2011] [Indexed: 01/01/2023]
Abstract
Many membrane proteins are functional as stable oligomers. An understanding of the conditions that elicit and enhance oligomerization is important in many therapeutics. In this regard, protein-protein and protein-lipid interactions play crucial roles in the assembly and stability of oligomeric complexes. Recent years have seen a rapid increase in the mechanistic information on the importance of cytoplasmic termini in determining subunit assembly and stability of oligomeric complexes. In addition, the role of specific protein-lipid interaction between anionic phospholipids and "hot spots" on the protein surface has also become evident in stabilizing oligomeric assemblies. This review focuses on several contemporary developments of membrane proteins that stabilize oligomers by taking the potassium channel KcsA as an exemplary ion channel.
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Baths V, Roy U, Singh T. Disruption of cell wall fatty acid biosynthesis in Mycobacterium tuberculosis using a graph theoretic approach. Theor Biol Med Model 2011; 8:5. [PMID: 21453530 PMCID: PMC3087688 DOI: 10.1186/1742-4682-8-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2011] [Accepted: 03/31/2011] [Indexed: 12/01/2022] Open
Abstract
Fatty acid biosynthesis of Mycobacterium tuberculosis was analyzed using graph theory and influential (impacting) proteins were identified. The graphs (digraphs) representing this biological network provide information concerning the connectivity of each protein or metabolite in a given pathway, providing an insight into the importance of various components in the pathway, and this can be quantitatively analyzed. Using a graph theoretic algorithm, the most influential set of proteins (sets of {1, 2, 3}, etc.), which when eliminated could cause a significant impact on the biosynthetic pathway, were identified. This set of proteins could serve as drug targets. In the present study, the metabolic network of Mycobacterium tuberculosis was constructed and the fatty acid biosynthesis pathway was analyzed for potential drug targeting. The metabolic network was constructed using the KEGG LIGAND database and subjected to graph theoretical analysis. The nearness index of a protein was used to determine the influence of the said protein on other components in the network, allowing the proteins in a pathway to be ordered according to their nearness indices. A method for identifying the most strategic nodes to target for disrupting the metabolic networks is proposed, aiding the development of new drugs to combat this deadly disease.
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Affiliation(s)
- Veeky Baths
- Department of Biological Sciences, Birla Institute of Technology & Science (BITS) Pilani K K BIRLA Goa Campus, Goa 403 726, India.
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Geppert T, Hoy B, Wessler S, Schneider G. Context-Based Identification of Protein-Protein Interfaces and “Hot-Spot” Residues. ACTA ACUST UNITED AC 2011; 18:344-53. [DOI: 10.1016/j.chembiol.2011.01.005] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2010] [Revised: 12/03/2010] [Accepted: 01/05/2011] [Indexed: 02/07/2023]
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Schrattenholz A, Groebe K, Soskic V. Systems biology approaches and tools for analysis of interactomes and multi-target drugs. Methods Mol Biol 2010; 662:29-58. [PMID: 20824465 DOI: 10.1007/978-1-60761-800-3_2] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Systems biology is essentially a proteomic and epigenetic exercise because the relatively condensed information of genomes unfolds on the level of proteins. The flexibility of cellular architectures is not only mediated by a dazzling number of proteinaceous species but moreover by the kinetics of their molecular changes: The time scales of posttranslational modifications range from milliseconds to years. The genetic framework of an organism only provides the blue print of protein embodiments which are constantly shaped by external input. Indeed, posttranslational modifications of proteins represent the scope and velocity of these inputs and fulfil the requirements of integration of external spatiotemporal signal transduction inside an organism. The optimization of biochemical networks for this type of information processing and storage results in chemically extremely fine tuned molecular entities. The huge dynamic range of concentrations, the chemical diversity and the necessity of synchronisation of complex protein expression patterns pose the major challenge of systemic analysis of biological models. One further message is that many of the key reactions in living systems are essentially based on interactions of moderate affinities and moderate selectivities. This principle is responsible for the enormous flexibility and redundancy of cellular circuitries. In complex disorders such as cancer or neurodegenerative diseases, which initially appear to be rooted in relatively subtle dysfunctions of multimodal physiologic pathways, drug discovery programs based on the concept of high affinity/high specificity compounds ("one-target, one-disease"), which has been dominating the pharmaceutical industry for a long time, increasingly turn out to be unsuccessful. Despite improvements in rational drug design and high throughput screening methods, the number of novel, single-target drugs fell much behind expectations during the past decade, and the treatment of "complex diseases" remains a most pressing medical need. Currently, a change of paradigm can be observed with regard to a new interest in agents that modulate multiple targets simultaneously, essentially "dirty drugs." Targeting cellular function as a system rather than on the level of the single target, significantly increases the size of the drugable proteome and is expected to introduce novel classes of multi-target drugs with fewer adverse effects and toxicity. Multiple target approaches have recently been used to design medications against atherosclerosis, cancer, depression, psychosis and neurodegenerative diseases. A focussed approach towards "systemic" drugs will certainly require the development of novel computational and mathematical concepts for appropriate modelling of complex data. But the key is the extraction of relevant molecular information from biological systems by implementing rigid statistical procedures to differential proteomic analytics.
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Click TH, Ganguly D, Chen J. Intrinsically disordered proteins in a physics-based world. Int J Mol Sci 2010; 11:5292-309. [PMID: 21614208 PMCID: PMC3100817 DOI: 10.3390/ijms11125292] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2010] [Revised: 12/17/2010] [Accepted: 12/17/2010] [Indexed: 11/23/2022] Open
Abstract
Intrinsically disordered proteins (IDPs) are a newly recognized class of functional proteins that rely on a lack of stable structure for function. They are highly prevalent in biology, play fundamental roles, and are extensively involved in human diseases. For signaling and regulation, IDPs often fold into stable structures upon binding to specific targets. The mechanisms of these coupled binding and folding processes are of significant importance because they underlie the organization of regulatory networks that dictate various aspects of cellular decision-making. This review first discusses the challenge in detailed experimental characterization of these heterogeneous and dynamics proteins and the unique and exciting opportunity for physics-based modeling to make crucial contributions, and then summarizes key lessons from recent de novo simulations of the structure and interactions of several regulatory IDPs.
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Affiliation(s)
| | | | - Jianhan Chen
- Department of Biochemistry, Kansas State University, Manhattan, KS 66506, USA
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22
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Biochemical network-based drug-target prediction. Curr Opin Biotechnol 2010; 21:511-6. [DOI: 10.1016/j.copbio.2010.05.004] [Citation(s) in RCA: 55] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2010] [Revised: 05/18/2010] [Accepted: 05/21/2010] [Indexed: 01/09/2023]
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Logan JA, Kelly ME, Ayers D, Shipillis N, Baier G, Day PJR. Systems biology and modeling in neuroblastoma: practicalities and perspectives. Expert Rev Mol Diagn 2010; 10:131-45. [PMID: 20214533 DOI: 10.1586/erm.10.4] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Neuroblastoma (NB) is a common pediatric malignancy characterized by clinical and biological heterogeneity. A host of prognostic markers are available, contributing to accurate risk stratification and appropriate treatment allocation. Unfortunately, outcome is still poor for many patients, indicating the need for a new approach with enhanced utilization of the available biological data. Systems biology is a holistic approach in which all components of a biological system carry equal importance. Systems biology uses mathematical modeling and simulation to investigate dynamic interactions between system components, as a means of explaining overall system behavior. Systems biology can benefit the biomedical sciences by providing a more complete understanding of human disease, enhancing the development of targeted therapeutics. Systems biology is largely contiguous with current approaches in NB, which already employ an integrative and pseudo-holistic approach to disease management. Systems modeling of NB offers an optimal method for continuing progression in this field, and conferring additional benefit to current risk stratification and management. Likewise, NB provides an opportunity for systems biology to prove its utility in the context of human disease, since the biology of NB is comprehensively characterized and, therefore, suited to modeling. The purpose of this review is to outline the benefits, challenges and fundamental workings of systems modeling in human disease, using a specific example of bottom-up modeling in NB. The intention is to demonstrate practical requirements to begin bridging the gap between biological research and applied mathematical approaches for the mutual gain of both fields, and with additional benefits for clinical management.
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Affiliation(s)
- Jennifer A Logan
- Quantitative Molecular Medicine, Faculty of Medicine and Health Sciences, The Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
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Tuncbag N, Kar G, Gursoy A, Keskin O, Nussinov R. Towards inferring time dimensionality in protein-protein interaction networks by integrating structures: the p53 example. MOLECULAR BIOSYSTEMS 2010; 5:1770-8. [PMID: 19585003 PMCID: PMC2898629 DOI: 10.1039/b905661k] [Citation(s) in RCA: 61] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Structural data, efficient structural comparison algorithms and appropriate datasets and filters can assist in getting an insight into time dimensionality in interaction networks; in predicting which interactions can and cannot co-exist; and in obtaining concrete predictions consistent with experiment.
Inspection of protein–protein interaction maps illustrates that a hub protein can interact with a very large number of proteins, reaching tens and even hundreds. Since a single protein cannot interact with such a large number of partners at the same time, this presents a challenge: can we figure out which interactions can occur simultaneously and which are mutually excluded? Addressing this question adds a fourth dimension into interaction maps: that of time. Including the time dimension in structural networks is an immense asset; time dimensionality transforms network node-and-edge maps into cellular processes, assisting in the comprehension of cellular pathways and their regulation. While the time dimensionality can be further enhanced by linking protein complexes to time series of mRNA expression data, current robust, network experimental data are lacking. Here we outline how, using structural data, efficient structural comparison algorithms and appropriate datasets and filters can assist in getting an insight into time dimensionality in interaction networks; in predicting which interactions can and cannot co-exist; and in obtaining concrete predictions consistent with experiment. As an example, we present p53-linked processes.
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Affiliation(s)
- Nurcan Tuncbag
- Koc University, Center for Computational Biology and Bioinformatics, College of Engineering, Rumelifeneri Yolu, 34450 Sariyer Istanbul, Turkey
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25
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Abstract
The Protein Data Bank contains the description of approximately 27 000 protein-ligand binding sites. Most of the ligands at these sites are biologically active small molecules, affecting the biological function of the protein. The classification of their binding sites may lead to relevant results in drug discovery and design. Clusters of similar binding sites were created here by a hybrid, sequence and spatial structure-based approach, using the OPTICS clustering algorithm. A dissimilarity measure was defined: a distance function on the amino acid sequences of the binding sites. All the binding sites were clustered in the Protein Data Bank according to this distance function, and it was found that the clusters characterized well the Enzyme Commission numbers of the entries. The results, carefully color coded by the Enzyme Commission numbers of the proteins, containing the 20 967 binding sites clustered, are available as html files in three parts at http://pitgroup.org/seqclust/.
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Affiliation(s)
- Gábor Iván
- Protein Information Technology Group, Department of Computer Science, Eötvös University, Budapest, Hungary
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26
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Tsai CJ, Ma B, Nussinov R. Protein-protein interaction networks: how can a hub protein bind so many different partners? Trends Biochem Sci 2009; 34:594-600. [PMID: 19837592 PMCID: PMC7292551 DOI: 10.1016/j.tibs.2009.07.007] [Citation(s) in RCA: 104] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2009] [Revised: 07/08/2009] [Accepted: 07/28/2009] [Indexed: 01/30/2023]
Abstract
How can a single hub protein bind so many different partners? Numerous studies have sought differences between hubs and non-hubs to explain what makes a protein a hub and how a shared hub-binding site can be promiscuous, yet at the same time be specific. Here, we suggest that the problem is largely non-existent and resides in the popular representation of protein interaction networks: protein products derived from a single gene, even if different, are clustered in maps into a single node. This leads to the impression that a single protein binds to a very large number of partners. In reality, it does not; rather, protein networks reflect the combination of multiple proteins, each with a distinct conformation.
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Affiliation(s)
- Chung-Jung Tsai
- Center for Cancer Research Nanobiology Program, SAIC-Frederick, Inc., NCI-Frederick, Frederick, MD 21702, USA
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Targeting historically refractory interfaces: a partnership model that accelerates drug discovery within an expanded haystack. Future Med Chem 2009; 1:577-81. [PMID: 21426026 DOI: 10.4155/fmc.09.49] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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28
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Park MS, Dessal AL, Smrcka AV, Stern HA. Evaluating docking methods for prediction of binding affinities of small molecules to the G protein betagamma subunits. J Chem Inf Model 2009; 49:437-43. [PMID: 19434844 DOI: 10.1021/ci800384q] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Several studies have suggested that disrupting interactions of the G protein betagamma subunits with downstream binding partners might be a valuable study for pharmaceutical development. Recently, small molecules have been found which bind to Gbetagamma with high apparent affinity in an enzyme-linked immunosorbent assay (ELISA), have demonstrated selective inhibition of interactions of Gbetagamma with downstream signaling partners, and have been shown to increase antinociceptive effects of morphine and inhibit inflammation in vivo. In this paper we examine several docking and scoring protocols for estimating binding affinities for a set of 830 ligands from the NCI diversity set to the Gbeta1gamma2 subunit and compared these with IC50s measured in a competition ELISA with a high-affinity peptidic ligand. The best-performing docking protocol used a consensus score and ensemble docking and resulted in a 6-fold enrichment of high-affinity compounds in the top-ranked 5% of the ligand data set.
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Affiliation(s)
- Min-Sun Park
- Departments of Biochemistry and Biophysics, University of Rochester, Rochester, New York 14627, USA
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29
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Hwang T, Park T. Identification of differentially expressed subnetworks based on multivariate ANOVA. BMC Bioinformatics 2009; 10:128. [PMID: 19405941 PMCID: PMC2696448 DOI: 10.1186/1471-2105-10-128] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2008] [Accepted: 04/30/2009] [Indexed: 11/17/2022] Open
Abstract
Background Since high-throughput protein-protein interaction (PPI) data has recently become available for humans, there has been a growing interest in combining PPI data with other genome-wide data. In particular, the identification of phenotype-related PPI subnetworks using gene expression data has been of great concern. Successful integration for the identification of significant subnetworks requires the use of a search algorithm with a proper scoring method. Here we propose a multivariate analysis of variance (MANOVA)-based scoring method with a greedy search for identifying differentially expressed PPI subnetworks. Results Given the MANOVA-based scoring method, we performed a greedy search to identify the subnetworks with the maximum scores in the PPI network. Our approach was successfully applied to human microarray datasets. Each identified subnetwork was annotated with the Gene Ontology (GO) term, resulting in the phenotype-related functional pathway or complex. We also compared these results with those of other scoring methods such as t statistic- and mutual information-based scoring methods. The MANOVA-based method produced subnetworks with a larger number of proteins than the other methods. Furthermore, the subnetworks identified by the MANOVA-based method tended to consist of highly correlated proteins. Conclusion This article proposes a MANOVA-based scoring method to combine PPI data with expression data using a greedy search. This method is recommended for the highly sensitive detection of large subnetworks.
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Affiliation(s)
- Taeyoung Hwang
- Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Republic of Korea.
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30
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Abstract
The dominant paradigm in drug discovery is the concept of designing maximally selective ligands to act on individual drug targets. However, many effective drugs act via modulation of multiple proteins rather than single targets. Advances in systems biology are revealing a phenotypic robustness and a network structure that strongly suggests that exquisitely selective compounds, compared with multitarget drugs, may exhibit lower than desired clinical efficacy. This new appreciation of the role of polypharmacology has significant implications for tackling the two major sources of attrition in drug development--efficacy and toxicity. Integrating network biology and polypharmacology holds the promise of expanding the current opportunity space for druggable targets. However, the rational design of polypharmacology faces considerable challenges in the need for new methods to validate target combinations and optimize multiple structure-activity relationships while maintaining drug-like properties. Advances in these areas are creating the foundation of the next paradigm in drug discovery: network pharmacology.
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Affiliation(s)
- Andrew L Hopkins
- Division of Biological Chemistry and Drug Discovery, College of Life Science, University of Dundee, Dundee, UK.
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Espinoza-Fonseca LM. Knowledgebase for addiction-related genes: is it possible an extrapolation to rational multi-target drug design? Bioorg Med Chem 2008; 16:9346-8. [PMID: 18815048 DOI: 10.1016/j.bmc.2008.08.080] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2008] [Revised: 08/19/2008] [Accepted: 08/26/2008] [Indexed: 10/21/2022]
Abstract
In recent years the single-probe-single-target approach in drug design has started to be smoothly replaced by the single-probe-multiple-target (or multi-target) one, where a single drug is able to tackle different, but disease-related targets in a selective manner. However, the design of multi-target drugs has been hindered by a lack of a systematic network of disease-related common pathways. The recent development of the knowledgebase of addiction-related genes (KARG) has provided important hints on how to rationally design multi-target probes by connecting experimental techniques with available network models. In this perspective, the use of KARG as a template to build knowledgebases of disease-related genes for the rational multi-target drug design is suggested. Moreover, it is proposed that building knowledgebases of disease-related genes will become a necessary and ubiquitous tool in the rational design of multi-target drugs.
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Affiliation(s)
- L Michel Espinoza-Fonseca
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA.
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Abstract
Interactive proteomics addresses the physical associations among proteins and establishes global, disease-, and pathway-specific protein interaction networks. The inherent chemical and structural diversity of proteins, their different expression levels, and their distinct subcellular localizations pose unique challenges for the exploration of these networks, necessitating the use of a variety of innovative and ingenious approaches. Consequently, recent years have seen exciting developments in protein interaction mapping and the establishment of very large interaction networks, especially in model organisms. In the near future, attention will shift to the establishment of interaction networks in humans and their application in drug discovery and understanding of diseases. In this review, we present an impressive toolbox of different technologies that we expect to be crucial for interactive proteomics in the coming years.
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Xu H, Xu H, Lin M, Wang W, Li Z, Huang J, Chen Y, Chen X. Learning the drug target-likeness of a protein. Proteomics 2007; 7:4255-63. [DOI: 10.1002/pmic.200700062] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Computer-aided drug design: the next 20 years. J Comput Aided Mol Des 2007; 21:591-601. [PMID: 17989929 DOI: 10.1007/s10822-007-9142-y] [Citation(s) in RCA: 90] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2007] [Accepted: 10/18/2007] [Indexed: 10/22/2022]
Abstract
This perspectives article has been taken from a talk the author gave at the symposium in honor of Yvonne C. Martin's retirement, held at the American Chemical Society spring meeting in Chicago on March 25, 2007. The talk was intended as a somewhat lighthearted attempt to gaze into the future; inevitably, in print, things will come across more seriously than was intended. As we all know-the past is rarely predictive of the future.
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