Modeling and simulations of functionalized magnetic nanoparticles as drug delivery systems

Τα νανοσωματίδια (NPs) ως συστήματα μεταφοράς φαρμάκων αφορούν τεχνολογίες μηχανικής για τη στοχευμένη μεταφορά και την ελεγχόμενη απελευθέρωση θεραπευτικών παραγόντων και παρουσιάζουν σημαντική επιστημονική υπόσχεση στη θεραπεία του καρκίνου, όπου χρησιμοποιούνται στη βελτίωση της βιοκατανομής των αντικαρκινικών φαρμάκων. Τα μαγνητικά νανοσωματίδια (MNPs) αποτελούν μια κατηγορία νανοσωματιδίων, τα οποία μπορούν να χρησιμοποιηθούν κάτω από την επίδραση έντασης μαγνητικού πεδίου με σκοπό να φτάσουν στη στοχευμένη περιοχή και να απελευθερώσουν το φάρμακο πιο γρήγορα και πιο αποτελεσματικά. Τέτοια σωματίδια συνήθως αποτελούνται από βιοσυμβατά MNPs οξειδίου του σιδήρου όπως είναι ο μαγνητίτης (Fe3O4) και η οξειδωμένη μορφή του, ο μαγεμίτης (γ-Fe2O3), με κατάλληλη αρχιτεκτονική επιφάνειας και συζευγμένα μόρια/πρωτείνες. Η πρώτη εκτίμηση σχετικά με την τοξικότητα των MNPs καθώς και της αποτελεσματικής μεταφοράς τους στο κύτταρο, είναι η αλληλεπίδραση του MNP με την κυτταρική μεμβράνη. Στην παρούσα διπλωματική εργασία, χρησιμοποιήθηκαν υπολογιστικές προσεγγίσεις για την κατασκευή νανοσωματιδίων μαγνητίτη διαφορετικού σχήματος, μεγέθους και χημείας της επιφάνειάς τους. Ακολούθως, προσομοιώσεις μοριακής δυναμικής εφαρμόστηκαν για τη διερεύνηση της επαφής του MNP με την κυτταρική μεμβράνη, προκειμένου να αποκτηθεί γνώση των φυσικοχημικών ιδιοτήτων που διέπουν τις αλληλεπιδράσεις μεταξύ διαφορετικών κατηγοριών MNPs και της μεμβράνης. Αρχικά, ένας γενικός κώδικας αναπτύχθηκε για την κατασκευή του μοντέλου πυρήνα νανοσωματιδίων δεδομένου μεγέθους και αρχιτεκτονικής επιφάνειας. Tα επίπεδα ανάπτυξης του Fe3O4 κρυστάλλου, τα οποία σχετίζονται με την ελάχιστη ενέργεια επιφάνειας, χρησιμοποιήθηκαν για την επέκταση του μεγέθους και σχήματος του νανοσωματιδίου. Η προσέγγιση αυτή γενικεύτηκε με την ανάπτυξη ενός αλγορίθμου κατασκευής διαφορετικών μορφολογιών κρυστάλλου με βάση τα επίπεδα ανάπτυξης του κρυστάλλου, τους δείκτες Miller και του κρυσταλλικού μεγέθους καθορισμένο από τον χρήστη. Ακολούθως, η υλοποίηση ενός άλλου προγράμματος επιτρέπει την πρόσδεση των αλυσίδων πολυβινυλικής αλκοόλης (PVA) και πολυακριλικού οξέως (ARA) στον Fe3O4 MNP πυρήνα. Η λιπιδική διπλοστοιβάδα (dipalmitoylphosphatidylcholine - DPPC) κατασκευάστηκε στη συνέχεια ως μοντέλο της κυτταρικής μεμβράνης. Τέλος, τα δύο μοντέλα MNPs τοποθετήθηκαν στην υδάτινη περιοχή της λιπιδικής διπλοστοιβάδας και ατομικιστικές προσομοιώσεις μοριακής δυναμικής εφαρμόστηκαν με σκοπό την περιγραφή των αλληλεπιδράσεων νανοσωματιδίου-μεμβράνης σε ατομικό επίπεδο. Τα αποτελέσματα της προσομοίωσης συγκρίνονται περαιτέρω με τα διαθέσιμα πειραματικά δεδομένα από τους συνεργάτες μας και τα συμπεράσματα συντάσσονται ως πρός τις διαφορετικές αλληλεπιδράσεις μεταξύ διαφορετικών προσδετών του NP και του μοντέλου κυτταρικής μεμβράνης.Nanoparticles (NPs) as drug delivery systems are engineered technologies for the delivery of therapeutic agents to their targets in a controlled manner and have shown significant potential to be employed in cancer treatment, with the aim to improve the biodistribution of cancer drugs. Magnetic nanoparticles (MNPs) are a class of nanoparticles, which can be manipulated using magnetic field gradients in order to reach the target site of interest and deliver the drug faster and more efficiently. Common MNPs consist of biocompatible iron oxide MNPs such as magnetite (Fe3O4) and its oxidized form maghemite (γ-Fe2O3) with proper surface architecture and conjugated targeting ligands/proteins. A first consideration in assessing MNP toxicity as well as efficiency of translocation in a cell is the interaction of the MNP with the cell membrane. In the present thesis, computational approaches were used for the construction of functionalized magnetite MNPs of different shape, size, and surface chemistry. Subsequently, Molecular Dynamics (MD) simulations were performed to investigate the MNP in contact with a model cell membrane in order to gain insights into the physicochemical properties that govern the interactions between different classes of MNPs and the membrane. Initially, a generic code that builds the model of the NP core of a given size and surface architecture was developed. The growing planes of the Fe3O4 crystal, which are analogous to the minimum surface energies, were used to extend the size and shape of the NP. This approach was generalized by developing an algorithm that constructs different crystal morphologies for a given crystal based on its preferred growing planes, the Miller indices and a user-defined size of the crystal. Subsequently, another algorithm was developed to attach polyvinyl alcohol (PVA) and polyarabic acid (ARA) ligands to the Fe3O4 MNP core. A dipalmitoylphosphatidylcholine (DPPC) lipid bilayer was then built as a model cell membrane. Finally, the two model MNPs were placed in the water phase of the lipid bilayer and atomistic MD simulations were performed in order to describe the nanoparticle-membrane interactions in atomic-level detail. The results from our simulations were further compared to available experimental data from our collaborators and conclusions were drawn for the distinct interactions between the different ligand coating of the NP and the model cell membrane

Graph-Based Analyses of Dynamic Water-Mediated Hydrogen-Bond Networks in Phosphatidylserine: Cholesterol Membranes

Phosphatidylserine lipids are anionic molecules present in eukaryotic plasma membranes, where they have essential physiological roles. The altered distribution of phosphatidylserine in cells such as apoptotic cancer cells, which, unlike healthy cells, expose phosphatidylserine, is of direct interest for the development of biomarkers. We present here applications of a recently implemented Depth-First-Search graph algorithm to dissect the dynamics of transient water-mediated lipid clusters at the interface of a model bilayer composed of 1-palmytoyl-2-oleoyl-sn-glycero-2-phosphatidylserine (POPS) and cholesterol. Relative to a reference POPS bilayer without cholesterol, in the POPS:cholesterol bilayer there is a somewhat less frequent sampling of relatively complex and extended water-mediated hydrogen-bond networks of POPS headgroups. The analysis protocol used here is more generally applicable to other lipid:cholesterol bilayers

A nexus of intrinsic dynamics underlies translocase priming

The cytoplasmic ATPase SecA and the membrane-embedded SecYEG channel assemble to form the Sec translocase. How this interaction primes and catalytically activates the translocase remains unclear. We show that priming exploits a nexus of intrinsic dynamics in SecA. Using atomistic simulations, smFRET, and HDX-MS, we reveal multiple dynamic islands that cross-talk with domain and quaternary motions. These dynamic elements are functionally important and conserved. Central to the nexus is a slender stem through which rotation of the preprotein clamp of SecA is biased by ATPase domain motions between open and closed clamping states. An H-bonded framework covering most of SecA enables multi-tier dynamics and conformational alterations with minimal energy input. As a result, cognate ligands select preexisting conformations and alter local dynamics to regulate catalytic activity and clamp motions. These events prime the translocase for high-affinity reception of non-folded preprotein clients. Dynamics nexuses are likely universal and essential in multi-liganded proteins.</p

Preproteins couple the intrinsic dynamics of SecA to its ATPase cycle to translocate via a catch and release mechanism

Protein machines undergo conformational motions to interact with and manipulate polymeric substrates. The Sec translocase promiscuously recognizes, becomes activated, and secretes >500 non-folded preprotein clients across bacterial cytoplasmic membranes. Here, we reveal that the intrinsic dynamics of the translocase ATPase, SecA, and of preproteins combine to achieve translocation. SecA possesses an intrinsically dynamic preprotein clamp attached to an equally dynamic ATPase motor. Alternating motor conformations are finely controlled by the γ-phosphate of ATP, while ADP causes motor stalling, independently of clamp motions. Functional preproteins physically bridge these independent dynamics. Their signal peptides promote clamp closing; their mature domain overcomes the rate-limiting ADP release. While repeated ATP cycles shift the motor between unique states, multiple conformationally frustrated prongs in the clamp repeatedly “catch and release” trapped preprotein segments until translocation completion. This universal mechanism allows any preprotein to promiscuously recognize the translocase, usurp its intrinsic dynamics, and become secreted

A graph-based approach identifies dynamic H-bond communication networks in spike protein S of SARS-CoV-2

We apply graph-based approaches to identify H-bond clusters in protein complexes. Three conformations of spike protein S have distinct H-bond clusters at key sites. Hydrogen-bond clusters could govern structural plasticity of spike protein S. Protein S binds to ACE2 receptor via H-bond clusters extending deep across interface.Corona virus spike protein S is a large homo-trimeric protein anchored in the membrane of the virion particle. Protein S binds to angiotensin-converting-enzyme 2, ACE2, of the host cell, followed by proteolysis of the spike protein, drastic protein conformational change with exposure of the fusion peptide of the virus, and entry of the virion into the host cell. The structural elements that govern conformational plasticity of the spike protein are largely unknown. Here, we present a methodology that relies upon graph and centrality analyses, augmented by bioinformatics, to identify and characterize large H-bond clusters in protein structures. We apply this methodology to protein S ectodomain and find that, in the closed conformation, the three protomers of protein S bring the same contribution to an extensive central network of H-bonds, and contribute symmetrically to a relatively large H-bond cluster at the receptor binding domain, and to a cluster near a protease cleavage site. Markedly different H-bonding at these three clusters in open and pre-fusion conformations suggest dynamic H-bond clusters could facilitate structural plasticity and selection of a protein S protomer for binding to the host receptor, and proteolytic cleavage. From analyses of spike protein sequences we identify patches of histidine and carboxylate groups that could be involved in transient proton binding.PSI COVID19 Emergency Science FundSpanish Ministry of Science, Innovation and Universities RTI2018-098983-B-I00Excellence Initiative of the German Federal and State Governments via the Freie Universitat BerlinGerman Research Foundation (DFG) SFB 107

Understanding Conformational Dynamics of Complex Lipid Mixtures Relevant to Biology

This is a perspective article entitled “Frontiers in computational biophysics: understanding conformational dynamics of complex lipid mixtures relevant to biology” which is following a CECAM meeting with the same name.Fil: Friedman, Ran. Linnæus University; ArgentinaFil: Khalid, Syma. University of Southampton; Reino UnidoFil: Aponte Santamaría, Camilo. Ruprecht-Karls-Universität Heidelberg; Alemania. Universidad de los Andes; ColombiaFil: Arutyunova, Elena. University of Alberta; CanadáFil: Becker, Marlon. Westfälische Wilhelms Universität; AlemaniaFil: Boyd, Kevin J.. University of Connecticut; Estados UnidosFil: Christensen, Mikkel. University Aarhus; DinamarcaFil: Coimbra, João T. S.. Universidad de Porto; PortugalFil: Concilio, Simona. Universita di Salerno; ItaliaFil: Daday, Csaba. Heidelberg Institute for Theoretical Studies; AlemaniaFil: Eerden, Floris J. van. University of Groningen; Países BajosFil: Fernandes, Pedro A.. Universidad de Porto; PortugalFil: Gräter, Frauke. Heidelberg University; Alemania. Heidelberg Institute for Theoretical Studies; AlemaniaFil: Hakobyan, Davit. Westfälische Wilhelms Universität; AlemaniaFil: Heuer, Andreas. Westfälische Wilhelms Universität; AlemaniaFil: Karathanou, Konstantina. Freie Universität Berlin; AlemaniaFil: Keller, Fabian. Westfälische Wilhelms Universität; AlemaniaFil: Lemieux, M. Joanne. University of Alberta; CanadáFil: Marrink, Siewert J.. University of Groningen; Países BajosFil: May, Eric R.. University of Connecticut; Estados UnidosFil: Mazumdar, Antara. University of Groningen; Países BajosFil: Naftalin, Richard. Colegio Universitario de Londres; Reino UnidoFil: Pickholz, Mónica Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Piotto, Stefano. Universita di Salerno; ItaliaFil: Pohl, Peter. Johannes Kepler University; AustriaFil: Quinn, Peter. Colegio Universitario de Londres; Reino UnidoFil: Ramos, Maria J.. Universidad de Porto; PortugalFil: Schiøtt, Birgit. University Aarhus; DinamarcaFil: Sengupta, Durba. National Chemical Laboratory India; IndiaFil: Sessa, Lucia. Universita di Salerno; ItaliaFil: Vanni, Stefano. University Of Fribourg;Fil: Zeppelin, Talia. University Aarhus; DinamarcaFil: Zoni, Valeria. University of Fribourg; SuizaFil: Bondar, Ana-Nicoleta. Freie Universität Berlin; AlemaniaFil: Domene, Carmen. University of Oxford; Reino Unido. University of Bath; Reino Unid

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Correction to: Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

The original version of this article unfortunately contained a mistake

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Graph-Theory Algorithms for Dynamic Hydrogen-Bonded Networks in Proteins and Lipid Membranes

Computer simulations can give essential insights into the dynamics of biomolecular systems but raise significant big data challenges still to be sorted out. To overcome the challenge of large data sets combined with the complexity of biomolecular interactions, I implemented a set of robust algorithms, as part of this doctoral thesis, inspired by graph theory that allows us to use large data sets from atomistic molecular dynamics (MD) simulations and derive simple graphical representations of the hydrogen bond (H-bond) networks of lipid membrane models, proteins in different intermediate states, and of the response of the proteins to mutations. These representations are valuable for the interpretation of data from experiments and computations. Our algorithms facilitate highly efficient analyses of dynamic H-bond networks at the lipid membrane interface. We introduce the implementation of a Connected Components algorithm to cluster lipid molecules and a Depth First Search (DFS) algorithm that allows us to characterize the topology of dynamic H-bond clusters sampled by lipid headgroups in MD simulations. With the algorithm we developed, we identify the transient sampling of four main types of lipid H-bond clusters: linear, star, circular and extensive networks combining these topologies. Water bridges between lipid headgroups are dynamic with lifetimes lasting for a few picoseconds. Our algorithms are further extended to study conformational dynamics in proteins. An example is SecA, a protein motor that couples Adenosine triphosphate (ATP) binding and hydrolysis with the pre-protein substrate's translocation through the membrane embedded SecYEG protein translocon. However, the exact mechanism of SecA’s conformational coupling remains unclear. We present a methodology of applying graph-based approaches to characterize the dynamics of the SecA protein motor by computing long-distance H-bond pathways that inter-connect the nucleotide-binding pocket and the pre-protein binding site, shortest-distance routes and centrality measures that reveal amino acids with a central role in the total connectivity of the protein graph. A key finding enabled by the graph-based approach developed as part of this doctoral thesis is that mutations near the nucleotide-binding site associate with modified dynamics at the pre-protein binding domain. Water molecules participate in extended H-bonded water chains contributing to long-distance conformational coupling. Our methodologies are also applied to protein VASA, a DEAD-box enzyme involved in the cell cycle with ATP and Ribonucleic Acid (RNA) binding sites and explore the conformational coupling between the two binding sites and Channelrhodopsin’s C1C2 lipid-protein H-bond molecular dynamics. Lastly, our algorithms are applied to the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) protein S crystal structures. Protein S undergoes conformational changes and symmetry loss of core H-bonded clusters as it transitions from the closed to the pre-fusion conformation. Our study has identified N501 as a central residue of the H-bond network that interconnects the spike protein S to Angiotensin-Converting Enzyme 2 (ACE2), and that subsequently became mutated into TYR in a new COVID-19 variant.Computersimulationen können wesentliche Einblicke in die Dynamik biomolekularer Systeme geben, werfen aber auch erhebliche Herausforderungen in Bezug auf große Datenmengen auf, die noch zu bewältigen sind. Um die Herausforderung großer Datenmengen in Verbindung mit der Komplexität biomolekularer Wechselwirkungen zu bewältigen, habe ich im Rahmen dieser Doktorarbeit eine Reihe robuster Algorithmen implementiert, die von der Graphentheorie inspiriert sind und es uns ermöglichen, große Datenmengen aus atomistischen Molekulardynamiksimulationen (MD-Simulationen) zu verwenden und einfache grafische Darstellungen der Wasserstoffbrückenbindungen (H-Bindungen) von Lipidmembranmodellen, Proteinen in verschiedenen Zwischenzuständen und der Reaktion der Proteine auf Mutationen abzuleiten. Diese Darstellungen sind wertvoll für die Interpretation von Daten aus Experimenten und Berechnungen. Unsere Algorithmen ermöglichen hocheffiziente Analysen von dynamischen H-Bindungsnetzwerken an der Grenzfläche von Lipidmembranen. Wir stellen die Implementierung eines Algorithmus für verbundene Komponenten zum Clustern von H-gebundenen Lipidmolekülen und einen DFS-Algorithmus (Depth First Search) vor, der es uns ermöglicht, die Topologie von dynamischen H-Bindungsclustern zu charakterisieren, die von Lipidkopfgruppen in MD-Simulationen gesampelt werden. Mit dem von uns entwickelten Algorithmus identifizieren wir die vorübergehenden Probenahmen von vier Haupttypen von Lipid-H-Bindungsclustern: lineare, sternförmige, zirkuläre und umfangreiche Netzwerke, die diese Topologien kombinieren. Wasserbrücken zwischen Lipid-Kopfgruppen sind dynamisch und haben eine Lebensdauer in einer Größenordnung von Pikosekunden. Unsere Algorithmen werden weiter ausgebaut, um die Konformationsdynamik von Proteinen zu untersuchen. Ein Beispiel ist SecA, ein Proteinmotor, der die Bindung und Hydrolyse von Adenosintriphosphat (ATP) mit der Translokation des Präproteinsubstrats durch das in die Membran eingebettete SecYEG-Protein-Translokon verbindet. Der genaue Mechanismus der SecA-Konformationskopplung bleibt jedoch unklar. Wir stellen eine Methode zur Anwendung graphbasierter Ansätze vor, um die Dynamik des SecA-Proteinmotors zu charakterisieren, indem wir die langen H-Bindungen, die die Nukleotid-Bindungstasche und die Prä-Protein-Bindungsstelle miteinander verbinden, sowie die kürzesten Entfernungen und Zentralitätsmaße berechnen, die die Aminosäuren mit einer zentralen Rolle in der Gesamtkonnektivität des Proteingraphen aufzeigen. Eine wichtige Erkenntnis, die durch den im Rahmen dieser Doktorarbeit entwickelten graphbasierten Ansatz ermöglicht wurde, ist, dass Mutationen in der Nähe der Nukleotid-Bindungsstelle mit einer veränderten Dynamik im Bereich der Prä-Proteinbindung einhergehen. Wassermoleküle sind an langen H-gebundenen Wasserketten beteiligt und tragen zur Konformationskopplung über längere Distanzen bei. Unsere Methoden werden auch auf das Protein VASA angewandt, ein am Zellzyklus beteiligtes DEAD-Box-Enzym mit ATP- und RNA-Bindungsstellen, und untersuchen die Konformationskopplung zwischen den beiden Bindungsstellen und die molekulare Dynamik der C1C2-Lipid-Protein-H-Bindung von Kanalrhodopsin. Zudem werden unsere Algorithmen auf die SARS-COV-2-Protein-S-Kristallstrukturen angewendet. Protein S unterliegt Konformationsänderungen und dem Verlust der Symmetrie der H-gebundenen Kerncluster, wenn es von der geschlossenen in die Präfusionskonformation übergeht. In unserer Studie wurde N501 als zentraler Rest des H-Bindungsnetzwerks identifiziert, das das Spike-Protein S mit dem Angiotensin Converting Enzym 2 (ACE2) verbindet, und das anschließend in einer neuen COVID-19-Variante zu TYR mutiert wurde