DOCSLIB.ORG

Simulation of Protein Dynamics for Mechanistic Insight and Drug Design

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Simulation of Protein Dynamics for Mechanistic Insight and Drug Design Simulation of protein dynamics for mechanistic insight and drug design A thesis submitted to The University of Manchester for the degree of Master of Philosophy in the Faculty of Biology, Medicine and Health 2019 YANG, Zhang School of Health Sciences/Division of Pharmacy List of contents List of figures ............................................................................................................................ 5 List of tables ........................................................................................................................... 14 List of abbreviations ............................................................................................................... 16 Abstract .................................................................................................................................. 18 Declaration ............................................................................................................................. 20 Copyright Statement .............................................................................................................. 21 Acknowledgements ................................................................................................................ 22 Introduction to protein dynamics in drug design .................................................................. 23 In silico design of anti-cancer compounds targeting R-spondin using molecular dynamics and free energy calculations .................................................................................................. 24 1. Background ........................................................................................................................ 24 1.1 Wnt signalling pathway ............................................................................................ 24 1.2 R-spondin signalling and disease ............................................................................. 26 1.3 Structure basis of R-spondin signalling .................................................................... 29 1.4 Protein dynamics...................................................................................................... 31 1.5 Aim of this work ....................................................................................................... 33 2. Computational Theory ...................................................................................................... 35 2.1 Protein-ligand binding site prediction ..................................................................... 35 2.1.1 Shape-based methodology ............................................................................... 36 2.1.2 Energy-based methodology .............................................................................. 37 2.2 Molecular docking .................................................................................................... 38 2.2.1 Molecular docking algorithms .......................................................................... 39 2.2.2 Scoring functions ............................................................................................... 41 2.3 Molecular dynamics ................................................................................................. 43 2.3.1 Molecular mechanics ........................................................................................ 43 2.3.2 Water solvent models ....................................................................................... 45 2.3.3 Periodical boundary conditions ........................................................................ 46 2.3.4 Temperature and pressure regulation .............................................................. 47 2.4 Free energy calculation ............................................................................................ 48 2.4.1 Poisson Boltzmann potential ............................................................................ 48 2.4.2 Generalized Born model ................................................................................... 49 2.4.3 MM-GBSA method ............................................................................................ 50 3. High throughput virtual screening .................................................................................... 51 2 3.1 Compound database refinement ............................................................................. 53 3.2 Binding pocket identification ................................................................................... 55 3.2.1 Closed and Open2 R-spondin binding pockets ................................................. 55 3.2.2 RNF43 binding pockets ..................................................................................... 61 3.2.3 LGR5 binding pockets ........................................................................................ 63 3.3 Molecular docking and protein-ligand interactions ................................................. 66 3.3.1 Methods ............................................................................................................ 66 3.3.2 Closed R-spondin interactions .......................................................................... 67 3.3.3 Open2 R-spondin interactions .......................................................................... 70 3.3.4 RNF43 interactions ............................................................................................ 72 3.3.5 LGR5 interactions .............................................................................................. 76 4 Molecular dynamics simulation ......................................................................................... 79 4.1 Methods ................................................................................................................... 80 4.1.1 Model construction ........................................................................................... 80 4.1.2 Molecular dynamics protocol ........................................................................... 81 4.1.3 Compounds selection and optimization ........................................................... 81 4.1.4 Analysis tools..................................................................................................... 82 4.2 Results: Closed R-spondin ligands ............................................................................ 83 4.2.1 Compound selection ......................................................................................... 83 4.2.2 Ligand Binding stabilities................................................................................... 90 4.2.3 Ligand structure refinement ............................................................................. 94 4.2.4 Special cases .................................................................................................... 100 4.3 Results: Open2 R-spondin ligands .......................................................................... 105 4.3.1 Compound selection ....................................................................................... 105 4.3.2 Ligand Binding stabilities................................................................................. 110 4.3.3 Ligand structure refinement ........................................................................... 113 4.3.4 Special cases .................................................................................................... 118 4.4 Results: RNF43 ligands ........................................................................................... 123 4.4.1 Compound selection ....................................................................................... 123 4.4.2 Ligand Binding stabilities................................................................................. 128 4.4.3 Ligand structure refinement ........................................................................... 137 4.5 Results: LGR5 ligands ............................................................................................. 141 4.5.1 Compound selection ....................................................................................... 141 4.5.2 Ligand Binding stabilities................................................................................. 143 4.5.3 Ligand structure refinement ........................................................................... 149 4.5.4 Special cases .................................................................................................... 155 3 5. Free energy calculation ................................................................................................... 157 5.1 Methods ................................................................................................................. 157 5.2 Binding free energy and energy decomposition .................................................... 159 5.2.1 Closed R-spondin ............................................................................................ 159 5.2.2 Open2 R-spondin ............................................................................................ 162 5.2.3 RNF43 .............................................................................................................. 169 5.2.4 LGR5 ................................................................................................................ 173 6. Discussion .......................................................................................................................
Load more

Recommended publications
  • Mutational Landscape Differences Between Young-Onset and Older-Onset Breast Cancer Patients Nicole E
    Mealey et al. BMC Cancer (2020) 20:212 https://doi.org/10.1186/s12885-020-6684-z RESEARCH ARTICLE Open Access Mutational landscape differences between young-onset and older-onset breast cancer patients Nicole E. Mealey1 , Dylan E. O’Sullivan2 , Joy Pader3 , Yibing Ruan3 , Edwin Wang4 , May Lynn Quan1,5,6 and Darren R. Brenner1,3,5* Abstract Background: The incidence of breast cancer among young women (aged ≤40 years) has increased in North America and Europe. Fewer than 10% of cases among young women are attributable to inherited BRCA1 or BRCA2 mutations, suggesting an important role for somatic mutations. This study investigated genomic differences between young- and older-onset breast tumours. Methods: In this study we characterized the mutational landscape of 89 young-onset breast tumours (≤40 years) and examined differences with 949 older-onset tumours (> 40 years) using data from The Cancer Genome Atlas. We examined mutated genes, mutational load, and types of mutations. We used complementary R packages “deconstructSigs” and “SomaticSignatures” to extract mutational signatures. A recursively partitioned mixture model was used to identify whether combinations of mutational signatures were related to age of onset. Results: Older patients had a higher proportion of mutations in PIK3CA, CDH1, and MAP3K1 genes, while young- onset patients had a higher proportion of mutations in GATA3 and CTNNB1. Mutational load was lower for young- onset tumours, and a higher proportion of these mutations were C > A mutations, but a lower proportion were C > T mutations compared to older-onset tumours. The most common mutational signatures identified in both age groups were signatures 1 and 3 from the COSMIC database.
    [Show full text]
  • Ncomms3787.Pdf
    ARTICLE Received 6 Aug 2013 | Accepted 17 Oct 2013 | Published 14 Nov 2013 DOI: 10.1038/ncomms3787 OPEN Structural and molecular basis of ZNRF3/RNF43 transmembrane ubiquitin ligase inhibition by the Wnt agonist R-spondin Matthias Zebisch1, Yang Xu2,3, Christos Krastev1, Bryan T. MacDonald2, Maorong Chen2, Robert J.C. Gilbert1,XiHe2 & E. Yvonne Jones1 The four R-spondin (Rspo) proteins are secreted agonists of Wnt signalling in vertebrates, functioning in embryogenesis and adult stem cell biology. Through ubiquitination and degradation of Wnt receptors, the transmembrane E3 ubiquitin ligase ZNRF3 and related RNF43 antagonize Wnt signalling. Rspo ligands have been reported to inhibit the ligase activity through direct interaction with ZNRF3 and RNF43. Here we report multiple crystal structures of the ZNRF3 ectodomain (ZNRF3ecto), a signalling-competent Furin1–Furin2 (Fu1–Fu2) fragment of Rspo2 (Rspo2Fu1–Fu2), and Rspo2Fu1–Fu2 in complex with ZNRF3ecto,or RNF43ecto. A prominent loop in Fu1 clamps into equivalent grooves in the ZNRF3ecto and RNF43ecto surface. Rspo binding enhances dimerization of ZNRF3ecto but not of RNF43ecto. Comparison of the four Rspo proteins, mutants and chimeras in biophysical and cellular assays shows that their signalling potency depends on their ability to recruit ZNRF3 or RNF43 via Fu1 into a complex with LGR receptors, which interact with Rspo via Fu2. 1 Division of Structural Biology, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK. 2 F.M. Kirby Neurobiology Center, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA. 3 Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, College of Life Science, Jilin University, Changchun 130012, China.
    [Show full text]
  • Modeling the Genetic Basis of Individual Differences In
    brain sciences Article Modeling the Genetic Basis of Individual Differences in Susceptibility to Gulf War Illness Byron C. Jones 1,*, Diane B. Miller 2, Lu Lu 1, Wenyuan Zhao 1, David G. Ashbrook 1 , Fuyi Xu 1 , Megan K. Mulligan 1, Robert W. Williams 1, Daming Zhuang 1, Carolina Torres-Rojas 1 and James P. O’Callaghan 2,* 1 Department of Genetics, Genomics and Informatics, Department of Pharmacology, University of Tennessee Health Science Center, 71 South Manassas Street, Memphis, TN 38163, USA; [email protected] (L.L.); [email protected] (W.Z.); [email protected] (D.G.A.); [email protected] (F.X.); [email protected] (M.K.M.); [email protected] (R.W.W.); [email protected] (D.Z.); [email protected] (C.T.-R.) 2 Molecular Neurotoxicology Laboratory, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, Morgantown, WV 26505, USA; [email protected] * Correspondence: [email protected] (B.C.J.); [email protected] (J.P.O.); Tel.: +901-448-2814 (B.C.J.); +304-285-6079 (J.P.O.) Received: 6 January 2020; Accepted: 20 February 2020; Published: 2 March 2020 Abstract: Between 25% and 30% of the nearly one million military personnel who participated in the 1991 Persian Gulf War became ill with chronic symptoms ranging from gastrointestinal to nervous system dysfunction. This disorder is now referred to as Gulf War Illness (GWI) and the underlying pathophysiology has been linked to exposure-based neuroinflammation caused by organophosphorous (OP) compounds coupled with high circulating glucocorticoids. In a mouse model of GWI we developed, corticosterone was shown to act synergistically with an OP (diisopropylflurophosphate) to dramatically increase proinflammatory cytokine gene expression in the brain.
    [Show full text]
  • Transdifferentiation of Human Mesenchymal Stem Cells
    Transdifferentiation of Human Mesenchymal Stem Cells Dissertation zur Erlangung des naturwissenschaftlichen Doktorgrades der Julius-Maximilians-Universität Würzburg vorgelegt von Tatjana Schilling aus San Miguel de Tucuman, Argentinien Würzburg, 2007 Eingereicht am: Mitglieder der Promotionskommission: Vorsitzender: Prof. Dr. Martin J. Müller Gutachter: PD Dr. Norbert Schütze Gutachter: Prof. Dr. Georg Krohne Tag des Promotionskolloquiums: Doktorurkunde ausgehändigt am: Hiermit erkläre ich ehrenwörtlich, dass ich die vorliegende Dissertation selbstständig angefertigt und keine anderen als die von mir angegebenen Hilfsmittel und Quellen verwendet habe. Des Weiteren erkläre ich, dass diese Arbeit weder in gleicher noch in ähnlicher Form in einem Prüfungsverfahren vorgelegen hat und ich noch keinen Promotionsversuch unternommen habe. Gerbrunn, 4. Mai 2007 Tatjana Schilling Table of contents i Table of contents 1 Summary ........................................................................................................................ 1 1.1 Summary.................................................................................................................... 1 1.2 Zusammenfassung..................................................................................................... 2 2 Introduction.................................................................................................................... 4 2.1 Osteoporosis and the fatty degeneration of the bone marrow..................................... 4 2.2 Adipose and bone
    [Show full text]
  • Downloaded Per Proteome Cohort Via the Web- Site Links of Table 1, Also Providing Information on the Deposited Spectral Datasets
    www.nature.com/scientificreports OPEN Assessment of a complete and classifed platelet proteome from genome‑wide transcripts of human platelets and megakaryocytes covering platelet functions Jingnan Huang1,2*, Frauke Swieringa1,2,9, Fiorella A. Solari2,9, Isabella Provenzale1, Luigi Grassi3, Ilaria De Simone1, Constance C. F. M. J. Baaten1,4, Rachel Cavill5, Albert Sickmann2,6,7,9, Mattia Frontini3,8,9 & Johan W. M. Heemskerk1,9* Novel platelet and megakaryocyte transcriptome analysis allows prediction of the full or theoretical proteome of a representative human platelet. Here, we integrated the established platelet proteomes from six cohorts of healthy subjects, encompassing 5.2 k proteins, with two novel genome‑wide transcriptomes (57.8 k mRNAs). For 14.8 k protein‑coding transcripts, we assigned the proteins to 21 UniProt‑based classes, based on their preferential intracellular localization and presumed function. This classifed transcriptome‑proteome profle of platelets revealed: (i) Absence of 37.2 k genome‑ wide transcripts. (ii) High quantitative similarity of platelet and megakaryocyte transcriptomes (R = 0.75) for 14.8 k protein‑coding genes, but not for 3.8 k RNA genes or 1.9 k pseudogenes (R = 0.43–0.54), suggesting redistribution of mRNAs upon platelet shedding from megakaryocytes. (iii) Copy numbers of 3.5 k proteins that were restricted in size by the corresponding transcript levels (iv) Near complete coverage of identifed proteins in the relevant transcriptome (log2fpkm > 0.20) except for plasma‑derived secretory proteins, pointing to adhesion and uptake of such proteins. (v) Underrepresentation in the identifed proteome of nuclear‑related, membrane and signaling proteins, as well proteins with low‑level transcripts.
    [Show full text]
  • Multi Gene Mutation Signatures in Colorectal Cancer
    Zhuang et al. BMC Cancer (2021) 21:380 https://doi.org/10.1186/s12885-021-08108-9 RESEARCH ARTICLE Open Access Multi gene mutation signatures in colorectal cancer patients: predict for the diagnosis, pathological classification, staging and prognosis Yan Zhuang1†, Hailong Wang2†, Da Jiang3, Ying Li3, Lixia Feng4, Caijuan Tian5, Mingyu Pu5, Xiaowei Wang6, Jiangyan Zhang6, Yuanjing Hu7* and Pengfei Liu2* Abstract Background: Identifying gene mutation signatures will enable a better understanding for the occurrence and development of colorectal cancer (CRC), and provide some potential biomarkers for clinical practice. Currently, however, there is still few effective biomarkers for early diagnosis and prognostic judgment in CRC patients. The purpose was to identify novel mutation signatures for the diagnosis and prognosis of CRC. Methods: Clinical information of 531 CRC patients and their sequencing data were downloaded from TCGA database (training group), and 53 clinical patients were collected and sequenced with targeted next generation sequencing (NGS) technology (validation group). The relationship between the mutation genes and the diagnosis, pathological type, stage and prognosis of CRC were compared to construct signatures for CRC, and then analyzed their relationship with RNA expression, immunocyte infiltration and tumor microenvironment (TME). (Continued on next page) * Correspondence: [email protected]; [email protected] †Yan Zhuang and Hailong Wang contributed equally to this work. 7Department of Gynecological Oncology, Tianjin Central Hospital of Obstetrics & Gynecology, No. 156 Nankai Third Road, Nankai District, Tianjin 300100, China 2Department of Oncology, Tianjin Academy of Traditional Chinese Medicine Affiliated Hospital, No.354 Beima Road, Hongqiao District, Tianjin 300120, China Full list of author information is available at the end of the article © The Author(s).
    [Show full text]
  • Inactivating Mutations of RNF43 Confer Wnt Dependency in Pancreatic Ductal Adenocarcinoma
    Inactivating mutations of RNF43 confer Wnt dependency in pancreatic ductal adenocarcinoma Xiaomo Jianga,1, Huai-Xiang Haoa,1, Joseph D. Growneyb, Steve Woolfendenb, Cindy Bottigliob, Nicholas Ngc,BoLua, Mindy H. Hsiehc, Linda Bagdasarianb, Ronald Meyerb, Timothy R. Smithc, Monika Avelloa, Olga Charlata, Yang Xiea, Jeffery A. Portera, Shifeng Panc, Jun Liuc, Margaret E. McLaughlinb, and Feng Conga,2 aDevelopmental and Molecular Pathways, Novartis Institutes for Biomedical Research, Cambridge, MA 02139; bOncology Translational Medicine, Novartis Pharma, Cambridge, MA 02139; and cGenomics Institute of Novartis Research Foundation, San Diego, CA 92121 Edited by Jeremy Nathans, Johns Hopkins University, Baltimore, MD, and approved May 31, 2013 (received for review April 16, 2013) A growing number of agents targeting ligand-induced Wnt/β-cat- including LRP6 antibody (9, 10), Frizzled antibody (11), and Por- enin signaling are being developed for cancer therapy. However, cupine inhibitor (12), are being developed. However, it is chal- clinical development of these molecules is challenging because of lenging to develop therapeutic agents without a defined patient the lack of a genetic strategy to identify human tumors dependent population, and we do not have enough knowledge about human on ligand-induced Wnt/β-catenin signaling. Ubiquitin E3 ligase tumors dependent on ligand-induced Wnt/β-catenin signaling. ring finger 43 (RNF43) has been suggested as a negative regulator We have shown that transmembrane E3 ubiqutin ligase ZNRF3 of Wnt signaling, and mutations of RNF43 have been identified in negatively regulates Wnt/β-catenin signaling through promoting various tumors, including cystic pancreatic tumors. However, loss the degradation of Frizzled, and the activity of ZNRF3 is inhibited of function study of RNF43 in cell culture has not been conducted, by R-spondin proteins (13).
    [Show full text]
  • Molecular Role of RNF43 in Canonical and Noncanonical Wnt Signaling
    Title Molecular Role of RNF43 in Canonical and Noncanonical Wnt Signaling Tsukiyama, Tadasuke; Fukui, Akimasa; Terai, Sayuri; Fujioka, Yoichiro; Shinada, Keisuke; Takahashi, Hidehisa; Author(s) Yamaguchi, Terry P.; Ohba, Yusuke; Hatakeyama, Shigetsugu Molecular and cellular biology, 35(11), 2007-2023 Citation https://doi.org/10.1128/MCB.00159-15 Issue Date 2015-06 Doc URL http://hdl.handle.net/2115/60266 Type article (author version) File Information R43_MCB_Hatakeyama.pdf Instructions for use Hokkaido University Collection of Scholarly and Academic Papers : HUSCAP Molecular role of RNF43 in canonical and noncanonical Wnt signaling Tadasuke Tsukiyamaa, Akimasa Fukuib, Sayuri Teraia, Yoichiro Fujiokac, Keisuke Shinadaa, Hidehisa Takahashia, Terry P. Yamaguchid, Yusuke Ohbac, and Shigetsugu Hatakeyamaa Department of Biochemistry, Hokkaido University Graduate School of Medicine, Sapporo, Hokkaido, Japana; Laboratory of Tissue and Polymer Sciences, Division of Advanced Interdisciplinary Science, Faculty of Advanced Life Science, Hokkaido University, Sapporo, Hokkaido, Japanb; Department of Cell Physiology, Hokkaido University Graduate School of Medicine, Sapporo, Hokkaido, Japanc; Cancer and Developmental Biology Laboratory, Center for Cancer Research, National Cancer Institute-Frederick, NIH, 1050 Boyles St., Bldg. 539, Rm. 218, Frederick, Maryland, USAd Running title: Molecular role of RNF43 in Wnt signaling #Address correspondence to Shigetsugu Hatakeyama, [email protected] Word count for the Materials and Methods section: 1,934 Combined word count for the Introduction, Results and Discussion sections: 4,680 1 ABSTRACT Wnt signaling pathways are tightly regulated by ubiquitination, and dysregulation of these pathways promotes tumorigenesis. It has been reported that the ubiquitin ligase RNF43 plays an important role in frizzled-dependent regulation of the Wnt/βcatenin pathway.
    [Show full text]
  • Reelin/Disabled-1-Signaling in Glioma Cell Migration and Invasion
    AUS DER ABTEILUNG FÜR NEUROPATHOLOGIE Prof. Dr. med. Markus J. Riemenschneider DER FAKULTÄT FÜR MEDIZIN DER UNIVERSITÄT REGENSBURG REELIN/DISABLED-1-SIGNALING IN GLIOMA CELL MIGRATION AND INVASION Inaugural Dissertation zur Erlangung des Doktorgrades der Medizin der Fakultät für Medizin der Universität Regensburg vorgelegt von Stefan Bernd Swoboda 2018 AUS DER ABTEILUNG FÜR NEUROPATHOLOGIE Prof. Dr. med. Markus J. Riemenschneider DER FAKULTÄT FÜR MEDIZIN DER UNIVERSITÄT REGENSBURG REELIN/DISABLED-1-SIGNALING IN GLIOMA CELL MIGRATION AND INVASION Inaugural Dissertation zur Erlangung des Doktorgrades der Medizin der Fakultät für Medizin der Universität Regensburg vorgelegt von Stefan Bernd Swoboda 2018 Dekan: Prof. Dr. Dr. Torsten E. Reichert 1. Berichterstatter: Prof. Dr. med. Markus J. Riemenschneider 2. Berichterstatter: Prof. Dr. rer. nat. Eugen Kerkhoff Table of Contents Table of Contents Abbreviations .................................................................... - 7 - 1 Introduction ............................................................... - 9 - 1.1 Reelin/Disabled-1 signaling pathway ........................................................................ - 9 - 1.1.1 Reelin ................................................................................................................ - 9 - 1.1.2 Disabled-1 ....................................................................................................... - 15 - 1.1.3 Overview of known downstream targets ......................................................... -
    [Show full text]
  • RNF43 Is Frequently Mutated in Colorectal and Endometrial Cancers
    RNF43 Is Frequently Mutated in Colorectal and Endometrial Cancers The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Hodis, Eran. 2020. RNF43 Is Frequently Mutated in Colorectal and Endometrial Cancers. Doctoral dissertation, Harvard Medical School. Citable link https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37365198 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA RNF43 is frequently mutated in colorectal and endometrial cancers a thesis presented by Eran Hodis, Ph.D. Submitted in Partial Fulfillment of the Requirements for the M.D. Degree with Honors in a Special Field at Harvard Medical School May 2020 Advisor: Levi A. Garraway, M.D., Ph.D. Eran Hodis, Ph.D. RNF43 is frequently mutated in colorectal and endometrial cancers Abstract We report somatic mutations of RNF43 in over 18% of colorectal adenocarci- nomas and endometrial carcinomas. RNF43 encodes an E3 ubiquitin ligase that negatively regulates Wnt signaling. Truncating mutations of RNF43 are more prevalent in microsatellite-unstable tumors and show mutual exclusivity with in- activating APC mutations in colorectal adenocarcinomas. These results indicate that RNF43 is one of the most commonly mutated genes in colorectal and en- dometrial cancers.∗ ∗The material in this thesis is reproduced, in accordance with Springer Nature’s author reuse guidelines, from Giannakis, M.#, Hodis E.# et al. Nature Genetics 2014.
    [Show full text]
  • RSPO2 Inhibition of RNF43 and ZNRF3 Governs Limb Development
    LETTER https://doi.org/10.1038/s41586-018-0118-y RSPO2 inhibition of RNF43 and ZNRF3 governs limb development independently of LGR4/5/6 Emmanuelle Szenker-Ravi1,17, Umut Altunoglu2,17, Marc Leushacke1,17, Célia Bosso-Lefèvre1,3, Muznah Khatoo1, Hong Thi Tran4, Thomas Naert4, Rivka Noelanders4, Amin Hajamohideen1, Claire Beneteau5, Sergio B. de Sousa6,7, Birsen Karaman2, Xenia Latypova5, Seher Başaran2, Esra Börklü Yücel8, Thong Teck Tan1, Lena Vlaeminck4,16, Shalini S. Nayak9, Anju Shukla9, Katta Mohan Girisha9, Cédric Le Caignec5,10, Natalia Soshnikova11, Zehra Oya Uyguner2, Kris Vleminckx4*, Nick Barker1,12,13*, Hülya Kayserili2,8* & Bruno Reversade1,3,8,14,15* The four R-spondin secreted ligands (RSPO1–RSPO4) act via RSPO2 (Extended Data Fig. 1b) that affects a residue conserved in all their cognate LGR4, LGR5 and LGR6 receptors to amplify WNT R-spondin paralogues and homologues (Fig. 1c and Extended Data signalling1–3. Here we report an allelic series of recessive RSPO2 Fig. 2a). The analogous mutation p.Arg64Cys in RSPO4 was shown to mutations in humans that cause tetra-amelia syndrome, which is cause congenital anonchia15. The seven affected fetuses from families 2 characterized by lung aplasia and a total absence of the four limbs. to 5 presented with complete absence of four limbs, lung hypo/aplasia, Functional studies revealed impaired binding to the LGR4/5/6 cleft lip-palate, and labioscrotal fold aplasia, all characteristic of receptors and the RNF43 and ZNRF3 transmembrane ligases, and TETAMS (Fig. 1a, b, Extended Data Fig. 1a and Extended Data Table 1). reduced WNT potentiation, which correlated with allele severity.
    [Show full text]
  • Dynamics, Nanomechanics and Signal Transduction in Reelin Repeats Karolina Mikulska-Ruminska *, Janusz Strzelecki & Wieslaw Nowak *
    www.nature.com/scientificreports OPEN Dynamics, nanomechanics and signal transduction in reelin repeats Karolina Mikulska-Ruminska *, Janusz Strzelecki & Wieslaw Nowak * Reelin is a large glycoprotein controlling brain development and cell adhesion. It regulates the positioning of neurons, as well as neurotransmission and memory formation. Perturbations in reelin signaling are linked to psychiatric disorders. Reelin participates in signal transduction by binding to the lipoprotein receptors VLDLR and ApoER2 through its central region. This part is rich in repeating BNR-EGF-BNR modules. We used standard molecular dynamics, steered molecular dynamics, and perturbation response scanning computational methods to characterize unique dynamical properties of reelin modules involved in signaling. Each module has specifc sensors and efectors arranged in a similar topology. In the modules studied, disulfde bridges play a protective role, probably making both selective binding and protease activity of reelin possible. Results of single reelin molecule stretching by atomic force microscopy provide the frst data on the mechanical stability of individual reelin domains. The forces required for partial unfolding of the modules studied are below 60 pN. Proteins control signal transduction between cells1. Defects in their sequence, structure, folding, dynamics or interactions may result in neurological diseases such as autism spectrum disorders (ASD), schizophrenia, bipolar disorder, depression or Alzheimer’s disease2–4. A murine mutant line called reeler was reported several decades ago5 and had a profound impact on the understanding of mammalian brain development mechanisms. In the reeler phenotype mice, the Reln gene is missing6 and therefore the process of neuron migration and brain archi- tecture are compromised. Tis knockout results in an inverted cerebral cortex7.
    [Show full text]