Molecular dynamics simulations and in silico peptide ligand screening of the Elk-1 ETS domain

Background: The Elk-1 transcription factor is a member of a group of proteins called ternary complex factors, which serve as a paradigm for gene regulation in response to extracellular signals. Its deregulation has been linked to multiple human diseases including the development of tumours. The work herein aims to inform the design of potential peptidomimetic compounds that can inhibit the formation of the Elk-1 dimer, which is key to Elk-1 stability. We have conducted molecular dynamics simulations of the Elk-1 ETS domain followed by virtual screening. Results: We show the ETS dimerisation site undergoes conformational reorganisation at the a1b1 loop. Through exhaustive screening of di- and tri-peptide libraries against a collection of ETS domain conformations representing the dynamics of the loop, we identified a series of potential binders for the Elk-1 dimer interface. The di-peptides showed no particular preference toward the binding site; however, the tri-peptides made specific interactions with residues: Glu17, Gln18 and Arg49 that are pivotal to the dimer interface. Conclusions: We have shown molecular dynamics simulations can be combined with virtual peptide screening to obtain an exhaustive docking protocol that incorporates dynamic fluctuations in a receptor. Based on our findings, we suggest experimental binding studies to be performed on the 12 SILE ranked tri-peptides as possible compounds for the design of inhibitors of Elk-1 dimerisation. It would also be reasonable to consider the score ranked tri-peptides as a comparative test to establish whether peptide size is a determinant factor of binding to the ETS domain

Computational methodologies applied to Protein-Protein Interactions for molecular insights in Medicinal Chemistry

In living systems, proteins usually team up into \u201cmolecular machinery\u201d implementing several protein-to-protein physical contacts \u2013 or protein-protein interactions (PPIs) \u2013 to exert biological effects at both cellular and systems levels. Deregulations of protein-protein contacts have been associated with a huge number of diseases in a wide range of medical areas, such as oncology, cancer immunotherapy, infectious diseases, neurological disorders, heart failure, inflammation and oxidative stress. PPIs are very complex and usually characterised by specific shape, size and complementarity. The protein interfaces are generally large, broad and shallow, and frequently protein-protein contacts are established between non-continuous epitopes, that conversely are dislocated across the protein interfaces. For this reason, in the past two decades, PPIs were thought to be \u201cundruggable\u201d targets by the scientific research community with scarce or no chance of success. However, in recent years the Medicinal Chemistry frontiers have been changing and PPIs have gained popularity amongst the research groups due to their key roles in such a huge number of diseases. Until recently, PPIs were determined by experimental evidence through techniques specifically developed to target a small group of interactions. However, these methods present several limitations in terms of high costs and labour- and time-wasting. Nowadays, a large number of computational methods have been successfully applied to evaluate, validate, and deeply analyse the experimentally determined protein interactomes. In this context, a high number of computational tools and techniques have been developed, such as methods designed to construct interaction databases, quantum mechanics and molecular mechanics (QM/MM) to study the electronic properties, simulate chemical reactions, and calculate spectra, and all-atom molecular dynamics simulations to simulate temporal and spatial scales of inter- and intramolecular interactions. These techniques have allowed to explore PPI networks and predict the related functional features. In this PhD work, an extensive use of computational techniques has been reported as valuable tool to explore protein-protein interfaces, identify their hot spot residues, select small molecules and design peptides with the aim of inhibiting six different studied PPIs. Indeed, in this thesis, a success story of in silico approaches to PPI study has been described, where MD simulations, docking and pharmacophore screenings led to the identification of a set of PPI modulators. Among these, two molecules, RIM430 and RIM442, registered good inhibitory activity with IC50 values even within the nanomolar range against the interaction between MUC1 and CIN85 proteins in cancer disease. Furthermore, computational alanine scanning, all-atom molecular dynamics simulations, docking and pharmacophore screening were exploited to (1) rationally predict three potential interaction models of NLRP3PYD-ASCPYD complex involved in inflammatory and autoimmune diseases; (2) identify a potentially druggable region on the surface of SARS-CoV-2 Spike protein interface and select putative inhibitors of the interaction between Spike protein and the host ACE2 receptor against COVID-19 (CoronaVIrus Disease 2019); (3) investigate intramolecular modifications as a consequence of a point mutation on C3b protein (R102G) associated with the age-related macular degeneration (AMD) disease; (4) design non-standard peptides to inhibit transcriptional events associated with HOX-PBX complex involved in cancer diseases; and (5) to optimise a patented peptide sequence by designing helix-shaped peptides embedded with the hydrogen bond surrogate approach to tackle cocaine abuse relapses associated with Ras-RasGRF1 interaction. Although all the herein exploited techniques are based on predictive calculations and need experimental evidence to confirm the findings, the results and molecular insights retrieved and collected show the potential of the computer-aided drug design applied to the Medicinal Chemistry, guaranteeing labour- and time-saving to the research groups. On the other hand, computing ability, improved algorithms and fast-growing data sets are rapidly fostering advances in multiscale molecular modelling, providing a powerful emerging paradigm for drug discovery. It means that more and more research efforts will be done to invest in novel and more precise computational techniques and fine-tune the currently employed methodologies

High-Throughput Screening for Drug Discovery

The book focuses on various aspects and properties of high-throughput screening (HTS), which is of great importance in the development of novel drugs to treat communicable and non-communicable diseases. Chapters in this volume discuss HTS methodologies, resources, and technologies and highlight the significance of HTS in personalized and precision medicine

Understanding the functionality of the transcription factor ERG at a molecular level

The ETS transcription factor ERG is a master regulator of multiple vascular processes. Pathological dysregulation of endothelial ERG has been linked to inflammatory disorders. In non-endothelial cells, aberrant ectopic overexpression of ERG in cancer is oncogenic. Whilst many aspects of ERG’s biological and pathological role have been uncovered, the underlying molecular mechanisms are often not fully understood; likely impeded by the lack of a full-length crystal structure. I combined in silico Molecular Dynamics (MD) and in vitro assays to gain further insight into the functionality of ERG at the molecular level. A new ERG full-length structure was developed using MD. This model was used to study the structural basis for the binding specificity of ERG at DNA sequence variants of the ERG binding site. This identified key changes in ERG-DNA contacts depending on the binding motif. ERG activity can be modulated through posttranslational modifications including phosphorylation. Using the new ERG structural model, I gained a better understanding of the molecular consequences of ERG phosphorylation by ERK2. I found that upon Ser215 and Ser96 phosphorylation ERG undergoes conformational changes in the C- and N- termini, respectively. Using an electrophoresis mobility shift assay I showed that Ser215 phosphorylation increased the DNA binding of ERG. This goes in line with the in silico observed structural change in the C-terminus of ERG, where the DNA binding domain is located. Finally, I identified structural differences in the DNA binding ETS domain in an ERG variant found in a patient with primary lymphodoema. I showed changes in DNA binding, which was confirmed independently using in vitr oassays. Overall, the new structural modelwas successfully used to gain insight into the molecular mechanism underlying ERGactivity. Other aspects of ERG activity could be studied with the new model, potentially aiding the development of new therapeutic strategies.Open Acces

Recent Developments in Cancer Systems Biology

This ebook includes original research articles and reviews to update readers on the state of the art systems approach to not only discover novel diagnostic and prognostic biomarkers for several cancer types, but also evaluate methodologies to map out important genomic signatures. In addition, therapeutic targets and drug repurposing have been emphasized for a variety of cancer types. In particular, new and established researchers who desire to learn about cancer systems biology and why it is possibly the leading front to a personalized medicine approach will enjoy reading this book

What Controls the Controller: Structure and Function Characterizations of Transcription Factor PU.1 Uncover Its Regulatory Mechanism

The ETS family transcription factor PU.1/Spi-1 is a master regulator of the self-renewal of hematopoietic stem cells and their differentiation along both major lymphoid and myeloid branches. PU.1 activity is determined in a dosage-dependent manner as a function of both its expression and real-time regulation at the DNA level. While control of PU.1 expression is well established, the molecular mechanisms of its real-time regulation remain elusive. Our work is focused on discovering a complete regulatory mechanism that governs the molecular interactions of PU.1. Structurally, PU.1 exhibits a classic transcription factor architecture in which intrinsically disordered regions (IDR), consisting of 66% of its primary structure, are tethered to a well-structured DNA binding domain. The transcriptionally active form of PU.1 is a monomer that binds target DNA sites as a 1:1 complex. Our investigations show that IDRs of PU.1 reciprocally control two separate inactive dimeric forms, with and without DNA. At high concentrations, PU.1 forms a non-canonical 2:1 complex at a single DNA specific site. In the absence of DNA, PU.1 also forms a dimer, but it is incompatible with DNA binding. The DNA-free PU.1 dimer is further promoted by phosphomimetic mutants of IDR residues that are phosphorylated in B-lymphocytic activation. These results lead us to postulate a model of real-time PU.1 regulation, unknown in the ETS family, where independent dimeric forms antagonize each other to control the dosage of active PU.1 monomer at its target DNA sites. To demonstrate the biological relevance of our model, cellular assays probing PU.1-specific reporters and native target genes show that PU.1 transactivation exhibits a distinct dose response consistent with negative feedback. In summary, we have established the first model for the general real-time regulation of PU.1 at the DNA/protein level, without the need for recruiting specific binding partners. These novel interactions present potential therapeutic targets for correcting de-regulated PU.1 dosage in hematologic disorders, including leukemia, lymphoma, and myeloma

Analysis of the protein-Ligand and protein-peptide interactions using a combined sequence- and structure-based approach

Proteins participate in most of the important processes in cells, and their ability to perform their function ultimately depends on their three-dimensional structure. They usually act in these processes through interactions with other molecules. Because of the importance of their role, proteins are also the common target for small molecule drugs that inhibit their activity, which may include targeting protein interactions. Understanding protein interactions and how they are affected by mutations is thus crucial for combating drug resistance and aiding drug design. This dissertation combines bioinformatics studies of protein interactions at both primary sequence and structural level. We analyse protein-protein interactions through linear motifs, as well as protein-small molecule interactions, and study how mutations affect them. This is done in the context of two systems. In the first study of drug resistance mutations in the protease of the human immunodeficiency virus type 1, we successfully apply molecular dynamics simulations to estimate the effects of known resistance-associated mutations on the free binding energy, also revealing molecular mechanisms of resistance. In the second study, we analyse consensus profiles of linear motifs that mediate the recognition by the mitogen-activated protein kinases of their target proteins. We thus gain insights into the cellular processes these proteins are involved in.Proteine sind an den meisten wichtigen Prozessen in Zellen beteiligt, und ihre Fähigkeit, ihre Funktion zu erfüllen, hängt letztlich von ihrer dreidimensionalen Struktur ab. In diesen Prozessen wirken sie normalerweise durch Wechselwirkungen mit anderen Molekülen. Aufgrund der Bedeutung ihrer Rolle sind Proteine auch die häufigsten Angriffspunkte für niedermolekulare Wirkstoffe, die ihre Aktivität hemmen. Dies kann das Targeting von Proteinwechselwirkungen umfassen. Um Wechselwirkungen mit Medikamenten zu bekämpfen und das Wirkstoffdesign zu unterstützen, ist es wichtig, die Wechselwirkungen zwischen Proteinen und deren Einfluss auf Mutationen zu verstehen. Diese Dissertation kombiniert bioinformatische Studien zu Proteinwechselwirkungen sowohl auf primärer als auch auf struktureller Ebene. Wir analysieren Protein-Protein-Wechselwirkungen anhand linearer Motive sowie Protein-Kleinmolekül-Wechselwirkungen und untersuchen, wie sich Mutationen auf sie auswirken. Dies wird untersucht im Kontext von zwei Systemen. In der ersten Studie zu Resistenzmutationen in der Protease des humanen Immundefizienzvirus Typ 1 haben wir molekulardynamische Simulationen erfolgreich eingesetzt, um die Auswirkungen bekannter Resistenz-assoziierter Mutationen auf die freie Bindungsenergie abzuschätzen und molekulare Resistenzmechanismen aufzuzeigen. In der zweiten Studie analysieren wir Konsensusprofile von linearen Motiven, die die Erkennung der Zielproteine durch die Mitogen-aktivierten Proteinkinasen vermitteln. So gewinnen wir Einblick in die zellulären Prozesse, an denen diese Proteine beteiligt sind

Phosphoproteomics Analyses to Identify the Candidate Substrates and Signaling Intermediates of the Non-Receptor Tyrosine Kinase, SRMS

SRMS (Src-related kinase lacking C-terminal regulatory tyrosine and N-terminal myristoylaton sites) is a non-receptor tyrosine kinase that belongs to the BRK family kinases (BFKs) and is evolutionarily related to the Src family kinases (SFKs). Like SFKs and BFKs, the SRMS protein comprises of two domains involved in protein-protein interactions, namely, the Src-homology 3 domain (SH3) and Src-homology 2 domain (SH2) and one catalytic kinase domain. Unlike members of the BFKs and SFKs, the biochemical and cellular role of SRMS is poorly understood primarily due to the lack of information on the substrates and signaling intermediates regulated by the kinase. Previous biochemical studies have shown that wild type SRMS is enzymatically active and leads to the tyrosine-phosphorylation of several proteins, when expressed exogenously in mammalian cells. These tyrosine-phosphorylated proteins represent the candidate cellular substrates of SRMS which are largely unknown. Further, previous studies have determined that the SRMS protein displays a characteristic punctate cytoplasmic localization pattern in mammalian cells. These SRMS cytoplasmic puncta are uncharacterized and may provide insights into the biochemical and cellular role of the kinase. Here, we utilized mass spectrometry-based quantitative label-free phosphoproteomics to (a) identify the candidate SRMS cellular substrates and (b) candidate signaling intermediates regulated by SRMS, in HEK293 cells expressing ectopic SRMS. Specifically, using a phosphotyrosine enrichment strategy we identified 663 candidate SRMS substrates and consensus substrate-motifs of SRMS. We used customized peptide arrays and performed the high-throughput validation of a subset of the identified candidate SRMS substrates. Further, we independently validated Vimentin and Sam68 as bonafide SRMS substrates. Next, using Titanium dioxide (TiO2)-based phosphopeptide enrichment columns, we identified multiple signaling intermediates of SRMS. Functional gene enrichment analyses revealed several common and unique cellular processes regulated by the candidate SRMS substrates and signaling intermediates. Overall, these studies led to the identification of a significant number of novel and biologically relevant SRMS candidate substrates and signaling intermediates, which mapped to a number of cellular and biological processes primarily involved in cell cycle regulation, apoptosis, RNA processing, DNA repair and protein synthesis. These findings provide an important resource for future mechanistic studies to investigate the cellular and physiological functions of the SRMS. Studies towards characterizing the SRMS cytoplasmic puncta showed that the SRMS punctate structures do not colocalize with some of the major cellular organelles investigated, such as the mitochondria, endoplasmic reticulum, golgi bodies and lysosomes. However, studies investigating the involvement of the SRMS domains in puncta-localization revealed that the SRMS SH2 domain partly regulates this localization pattern. These results highlight the potential role of the SRMS SH2 domain in the localization of SRMS to these cytoplasmic sites and lay important groundwork for future characterization studies

Modeling Virus-Host Networks

Virus-host interactions are being cataloged at an increasing rate using protein interaction assays and small interfering RNA screens for host factors necessary for infection. These interactions can be viewed as a network, where genes or proteins are nodes, and edges correspond to associations between them. Virus-host interac- tion networks will eventually support the study and treatment of infection, but first require more data and better analysis techniques. This dissertation targets these goals with three aims. The first aim tackles the lack of data by providing a method for the computational prediction of virus-host protein interactions. We show that HIV-human protein interactions can be predicted using documented human peptide motifs found to be conserved on HIV proteins from different subtypes. We find that human proteins predicted to bind to HIV proteins are enriched in both documented HIV targeted proteins and pathways known to be utilized by HIV. The second aim seeks to improve peptide motif annotation on virus proteins, starting with the dock- ing site for protein kinases ERK1 and ERK2, which phosphorylate HIV proteins during infection. We find that the docking site motif, in spite of being suggestive of phosphorylation, is not present on all HIV subtypes for some HIV proteins, and we provide evidence that two variations of the docking site motif could explain phos- phorylation. In the third aim, we analyze virus-host networks and build on the observation that viruses target host hub proteins. We show that of the two hub types, date and party, HIV and influenza virus proteins prefer to interact with the latter. The methods presented here for prediction and motif refinement, as well as the analysis of virus targeted hubs, provide a useful set of tools and hypotheses for the study of virus-host interactions

Characterization of acetylcholinesterase & its promoter region in Tetraodon nigroviridis

Lau Suk Kwan.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references (leaves 128-150).Abstracts in English and Chinese.Acknowledgment --- p.iTable of content --- p.iiList of Figures --- p.xList of Tables --- p.xivAbbreviation --- p.xvAbstract --- p.xviii論文摘要 --- p.xxChapter 1 --- Chapter 1 Introduction --- p.1Chapter 1.1 --- Tetraodon nigroviridis --- p.1Chapter 1.1.1 --- Background --- p.1Chapter 1.1.2 --- Genomic Sequencing Project --- p.3Chapter 1.1.3 --- Tetraodon nigroviridis as Study Model --- p.4Chapter 1.1.3.1 --- Genomic Comparison --- p.4Chapter 1.1.3.2 --- Gene Order and Structural Studies --- p.5Chapter 1.1.3.3 --- Genomic Evolution --- p.6Chapter 1.2 --- Transcriptional Regulation and Transcription Factors Binding Sites Prediction --- p.7Chapter 1.2.1 --- Transcriptional Regulation --- p.7Chapter 1.2.1.1 --- Chromatin Remodeling --- p.7Chapter 1.2.1.2 --- Locus Control Regions (LCR) and Boundary Elements --- p.8Chapter 1.2.1.3 --- Promoter Structure --- p.9Chapter 1.2.1.4 --- Transcriptional Machinery Assembly --- p.10Chapter 1.2.2 --- Transcription Factors and Their Binding Sites --- p.11Chapter 1.2.3 --- Transcription Factor Binding Site Prediction --- p.12Chapter 1.3 --- Acetylcholinesterase --- p.15Chapter 1.3.1 --- Background --- p.15Chapter 1.3.2 --- Regulation ofAChE --- p.17Chapter 1.3.2.1 --- Transcriptional Level --- p.17Chapter 1.3.2.2 --- Post-transcriptional Level --- p.19Chapter 1.3.2.3 --- Post-translational Level --- p.20Chapter 1.3.2.3.1 --- Oligomerization --- p.20Chapter 1.3.2.3.2 --- Glycosylation --- p.21Chapter 1.3.2.3.3 --- Phosphroylation --- p.22Chapter 1.3.3 --- Functions of AChE --- p.23Chapter 1.3.3.1 --- Hydrolysis Acetylcholine --- p.23Chapter 1.3.3.2 --- Embryonic Development --- p.23Chapter 1.3.3.3 --- Haemotopotesis and Thrombopsiesis --- p.24Chapter 1.3.3.4 --- Neuritogensis --- p.24Chapter 1.3.3.5 --- Amyloid Fibre Assembly --- p.24Chapter 1.3.3.6 --- Apoptosis --- p.25Chapter 1.3.4 --- AChE and Alzheimer's disease --- p.25Chapter 1.3.4.1 --- Treatment for AD Patients --- p.27Chapter 1.4 --- Inducible Cell Expression Systems --- p.28Chapter 1.5 --- Objectives --- p.32Chapter 2 --- Chapter 2 Materials and Methods --- p.33Chapter 2.1 --- Materials --- p.33Chapter 2.2 --- Methods --- p.34Chapter 2.2.1 --- Primer Design --- p.34Chapter 2.2.2 --- Cell Culture --- p.34Chapter 2.2.3 --- Transformation --- p.35Chapter 2.2.4 --- Plasmids Preparation --- p.35Chapter 2.2.5 --- Plasmids Screening --- p.36Chapter 2.2.6 --- RNA Extraction --- p.36Chapter 2.2.7 --- Reverse Transcriptase Polymerase Chain Reaction and Construction tnAChE/pCR4 vector --- p.37Chapter 2.2.8 --- Genomic Analysis --- p.37Chapter 2.2.9 --- Protein Sequence Analysis --- p.38Chapter 2.2.10 --- Genomic DNA Extraction --- p.39Chapter 2.2.11 --- Construction of Reporter Vectors ptnAChE_565/pGL3 and ptnAChK1143/pGL3 --- p.39Chapter 2.2.12 --- Luciferase Assay --- p.40Chapter 2.2.13 --- Transcription Factors and Promoter Prediction --- p.40Chapter 2.2.14 --- Protein Assay --- p.41Chapter 2.2.15 --- AChE Activity Determined by Ellman's Method --- p.41Chapter 2.2.16 --- Histochemistry --- p.42Chapter 2.2.17 --- Protein Extraction from Tissues --- p.42Chapter 2.2.18 --- Construction of Bacterial Expression Vector His-MBP-tnAChEAC/pHISMAL --- p.43Chapter 2.2.19 --- Protein Expression in Bacterial Expression System --- p.43Chapter 2.2.20 --- Purification and Thrombin Cleavage of His-MBP- tnAChEAC --- p.44Chapter 2.2.21 --- SDS Electrophoresis --- p.44Chapter 2.2.22 --- Western Blotting --- p.45Chapter 2.2.23 --- Construction of Tet-Off Expression Vector --- p.45Chapter 2.2.24 --- Transient Expression of tnAChEAC --- p.46Chapter 2.2.25 --- Establishment of Stable Tet-Off CHO Cell Lines Overexpressing tnAChEAC --- p.47Chapter 2.2.26 --- MTT Assay --- p.47Chapter 2.2.27 --- Partial Purification of tnAChEΔC --- p.48Chapter 3 --- Chapter 3 Sequence Analysis of AChE Gene of Tetraodon nigroviridis --- p.49Chapter 3.1 --- Results --- p.49Chapter 3.1.1 --- Cloning of tnAChE from Tetraodon nigroviridis Brain --- p.49Chapter 3.1.2 --- "Comparative genomic analysis of tnAChE with Human, Rat, Mouse, Takifugu rubripes, ZebrafishAChE" --- p.49Chapter 3.1.3 --- Primary Sequence Analysis --- p.52Chapter 3.1.4 --- Promoter and Transcriptional Factors Predictedin tnAChE Promoter Region --- p.60Chapter 3.1.4.1 --- Promoter Region Analysis In Silico --- p.60Chapter 3.1.4.2 --- Promoter Activity Analysis --- p.76Chapter 3.2 --- Discussion --- p.78Chapter 4 --- Characterization of tnAChE in Prokaryotic and Eukaryotic Tet-Off Inducible Expression System --- p.91Chapter 4.1 --- Results --- p.91Chapter 4.1.1 --- AChE Expresses in Tetraodon nigroviridis --- p.91Chapter 4.1.2 --- Expression of recombinant tnAChE in Bacterial Expression System --- p.94Chapter 4.1.2.1 --- Construction of His-MBP-tnAChEΔC/pHISMAL Construct --- p.94Chapter 4.1.2.2 --- His-MBP-tnAChEAC Expression in E. coli Strains BL21 (DE) and C41 --- p.94Chapter 4.1.3 --- Expression of tnAChEAC in Mammalian Expression System --- p.99Chapter 4.1.3.1 --- Construction of tnAChEAC/pTRE2hgyo Mammalian Expression Vector --- p.99Chapter 4.1.3.2 --- Transient Expression of tnAChEAC --- p.99Chapter 4.1.3.3 --- Establishment of Tet-Off CHO Cells Stably Expressing the Inducible tnAChEAC --- p.101Chapter 4.1.3.4 --- Characterization of Tet-Off tnAChEAC Stably Transfected Cell Clones --- p.103Chapter 4.1.3.5 --- Effect of Over Expressed tnAChEAC on cell viability --- p.103Chapter 4.1.3.6 --- Partial Purification of tnAChEAC from Stably Transfected Cells --- p.107Chapter 4.1.3.7 --- tnAChE and tnAChEAC in Different pH Values --- p.112Chapter 4.1.3.8 --- Kinetic Study of tnAChEAC --- p.112Chapter 4.1.3.9 --- Inhibition of AChE Activity of Partial Purified tnAChEAC by Huperzine --- p.112Chapter 4.2 --- Discussion --- p.116Chapter 4.2.1 --- Bacterial Expression System --- p.116Chapter 4.2.2 --- Expression of tnAChEΔC in Mammalian System --- p.119Chapter 5 --- General Discussion --- p.124Chapter 5.1 --- Summaries --- p.124Chapter 5.2 --- Further works --- p.126Chapter 6 --- References --- p.128Appendix 1 internet software and database used in this project --- p.151Appendix 2 tnAChE mRNA sequence --- p.152Appendix 3 ptnAChE-1143 sequence --- p.154Appendix 4 Six open reading frame translation of ptnAChE-1143 --- p.15