Using Machine Learning in the Fight against COVID-19

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Using Machine Learning in the Fight against COVID-19

Using Machine Learning in the Fight against COVID-19

BIFOLD Fellow Prof. Dr. Frank Noé, who leads the research group AI for the Sciences, together with an international team, identified a potential drug candidate for the therapy of COVID-19. Among other methods, they used deep learning models and molecular dynamics simulations in order to identify the drug Otamixaban as a potential inhibitor of the human target enzyme which is required by SARS-CoV-2 in order to enter into lung cells. According to their findings, Otamixaban works in synergy with other drugs such as Camostat and Nafamostat and may present an effective early treatment option for COVID-19. Their work was now published in Chemical Science.

Visualization of the SARS-CoV-2 virus.
(© Unsplash / Fusion Medical Information)

While the availability of COVID-19 vaccines created some relief during the ongoing pandemic, there is still no effective therapy against the virus. One therapeutic approach pursues the strategy to prevent the virus from entering human cells.

In their publication, Frank Noé, who heads an interdisciplinary research unit at Freie Universität Berlin, and his colleagues at FU Berlin, German Primate Center, National Center for Advancing Translational Sciences (MD, USA), Fraunhofer Institute for Toxicology and Experimental Medicine, and Universität Göttingen could show that the late-stage drug candidate, Otamixaban, works as an effective inhibitor of SARS-Cov-2 lung cell entry by suppressing the activity of an enzyme called “transmembrane serine protease 2” (TMPRSS2). The SARS-CoV-2 virus uses its so-called spike protein (S-protein) to connect to an enzyme (ACE2) on the surface of a human lung cell. Subsequently the S-protein is cleaved by the enzyme TMPRSS2 thereby enabling the virus to enter the cell. Inhibiting TMPRSS2 with Otamixaban prevents the cell entry weakly, but this inhibitory effect is found to be profoundly amplified when combining Otamixaban with other known TMPRSS2-inhibiting drugs such as Nafamostat and Camostat.

Otamixaban prevents TMPRSS2 from activating the Coronavirus’ spike protein, resulting in its inability to bind to the ACE2 receptor and enter the human cell.
(© Tim Hempel et al.)

Frank Noé and his team analyzed the inhibitory effects of Otamixaban in silico, i.e. by machine learning and computer simulation. They combined deep learning methods and molecular dynamics simulation in order to screen a database of druglike molecules for potential inhibitors of TMPRSS2. Otamixaban was one of the proposed candidates that was confirmed to be active in the experimental assay. Subsequently, the Noé group conducted extensive molecular dynamics simulations of the TMPRSS2-Otamixaban complex and applied big data analytics in order to understand the inhibition mechanism in detail, while in parallel the inhibitor effect of Otamixaban was confirmed in cells and lung tissue.

Prof. Dr. Frank Noé

“The new machine learning methods that we develop at BIFOLD do not only help to solve fundamental problems in molecular and quantum physics, they are also increasingly important in application-oriented biochemical research. I believe it is very likely that if we hopefully end up with effective therapy options against COVID-19, machine learning will have played a key role in identifying them.”

Otaxamiban, originally developed for other medical conditions, is particularly interesting as it had already entered the third phase of clinical trials for a different indication, potential alleviating the trajectory towards clinical trials of the new formulation presented here. The researchers filed an EU patent application for the active agent combination.

The publications in detail:


Synergistic inhibition of SARS-CoV-2 cell entry by otamixaban and covalent protease inhibitors: pre-clinical assessment of pharmacological and molecular properties

Authors:
Tim Hempel, Katarina Elez, Nadine Krüger, Lluís Raich, Jonathan H. Shrimp, Olga Danov, Danny Jonigk, Armin Braun, Min Shen, Matthew D. Hall, Stefan Pöhlmann, Markus Hoffmann, Frank Noé

Abstract:
SARS-CoV-2, the cause of the COVID-19 pandemic, exploits host cell proteins for viral entry into human lung cells. One of them, the protease TMPRSS2, is required to activate the viral spike protein (S). Even though two inhibitors, camostat and nafamostat, are known to inhibit TMPRSS2 and block cell entry of SARS-CoV-2, finding further potent therapeutic options is still an important task. In this study, we report that a late-stage drug candidate, otamixaban, inhibits SARS-CoV-2 cell entry. We show that otamixaban suppresses TMPRSS2 activity and SARS-CoV-2 infection of a human lung cell line, although with lower potency than camostat or nafamostat. In contrast, otamixaban inhibits SARS-CoV-2 infection of precision cut lung slices with the same potency as camostat. Furthermore, we report that otamixaban’s potency can be significantly enhanced by (sub-) nanomolar nafamostat or camostat supplementation. Dominant molecular TMPRSS2-otamixaban interactions are assessed by extensive 109 μs of atomistic molecular dynamics simulations. Our findings suggest that combinations of otamixaban with supplemental camostat or nafamostat are a promising option for the treatment of COVID-19.

Publications:
Chemical Science (2021)
https://doi.org/10.1039/D1SC01494C

Molecular mechanism of inhibiting the SARS-CoV-2 cell entry facilitator TMPRSS2 with camostat and nafamostat

Authors:
Tim Hempel, Lluís Raich, Simon Olsson, Nurit P. Azouz, Andrea M. Klingler, Markus Hoffmann, Stefan Pöhlmann, Marc E. Rothenberg, Frank Noé

Abstract:
The entry of the coronavirus SARS-CoV-2 into human lung cells can be inhibited by the approved drugs camostat and nafamostat. Here we elucidate the molecular mechanism of these drugs by combining experiments and simulations. In vitro assays confirm that both drugs inhibit the human protein TMPRSS2, a SARS-Cov-2 spike protein activator. As no experimental structure is available, we provide a model of the TMPRSS2 equilibrium structure and its fluctuations by relaxing an initial homology structure with extensive 330 microseconds of all-atom molecular dynamics (MD) and Markov modeling. Through Markov modeling, we describe the binding process of both drugs and a metabolic product of camostat (GBPA) to TMPRSS2, reaching a Michaelis complex (MC) state, which precedes the formation of a long-lived covalent inhibitory state. We find that nafamostat has a higher MC population than camostat and GBPA, suggesting that nafamostat is more readily available to form the stable covalent enzyme–substrate intermediate, effectively explaining its high potency. This model is backed by our in vitro experiments and consistent with previous virus cell entry assays. Our TMPRSS2–drug structures are made public to guide the design of more potent and specific inhibitors.

Publication:
Chemical Science 12(3): 983-992 (2020)
https://doi.org/10.1039/D0SC05064D

More information is available from:

Prof. Dr. Frank Noé

Freie Universität Berlin
Computational Molecular Biology
Arnimallee 6
D-4195 Berlin

Email: frank.noe@fu-berlin.de

COVID-19: A Stresstest for the Internet

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COVID-19: A Stresstest for the Internet

COVID-19: A Stress Test for the Internet

25 percent increase in Internet traffic within only a few days

On 11 March 2020, the day the WHO declared the coronavirus a global pandemic, the impact of SARS-CoV-2 also spread to the World Wide Web. Following this announcement, governments around the world began enacting stay-at-home orders and other regulations for working from home and homeschooling. Within a single week, Internet traffic volume increased by 25 percent – an increase which under normal circumstances is usually observed over the course of a year. Taking account of increased use during the second lockdown in fall 2020, the overall use of Internet services in 2020 increased between 35 and 50 percent, depending on the network. An international, interdisciplinary group of researchers led by Professor Dr. Georgios Smaragdakis, professor of Internet measurement and analysis at TU Berlin and Fellow of the Berlin Institute for the Foundations of Learning and Data (BIFOLD), has published these figures and other findings in a paper in Communications of the Association for Computing Machinery (ACM). The leading professional association recently named the paper a research highlight.

Despite the worldwide restrictions necessitated by COVID-19, life continued with the Internet playing an important role.

From essentially one day to the next, almost nothing was possible without a stable Internet connection. Since March of last year, team meetings, school lessons, and even private celebrations have primarily been held via digital screens. Those without a broadband connection or sufficient electronic devices have missed out. Despite the worldwide restrictions necessitated by COVID-19, life continued with the Internet playing an important role for companies, the education sector, entertainment, retail, and social interactions. “In the spring of 2020, no one could say with certainty whether the Internet would be able to withstand this rush demand,” explains Georgios Smaragdakis. “No one had previously expected a sudden surge in Internet traffic of such proportions.” In their project, financed in part by BIFOLD, the researchers investigated Internet data streams from different Internet providers across Europe. “Together they provide us with a good understanding of the impacts that the COVID-19 waves and lockdown measures had on Internet traffic,” continues Georgios Smaragdakis.

Within a year of the implementation of the first lockdown measures, the aggregate volume of Internet data traffic increased by approximately 40 percent, significantly more than the expected annual growth. At the same time, mobile data traffic first slightly decreased and then only grew moderately, as people were out and about less, thus using less mobile data. “Our calculations show that the use of services such as video conferencing and VPNs increased by up to 300 percent. Gaming applications also significantly increased. After moderate growth during the spring lockdown, use increased by about 300 percent during the fall lockdown. And while these applications were primarily used in the evening or on the weekend pre-pandemic, gaming usage increases were evenly distributed across each day of the week during the second lockdown, mainly in the mornings,” remarks Georgios Smaragdakis.

Overall traffic patterns in Internet usage have clearly changed: While peak times before the pandemic were on the weekend and in the evening, the sudden growth in Internet usage primarily occurred on weekdays during working hours. This asynchronous growth is precisely one reason why researchers believe the Internet was able to handle the increased traffic relatively well. Smaragdakis believes the good structure and overprovisioning of the network operators also helped.

“In terms of digitalization, the last months have been a tremendous success,” says Smaragdakis. “In just a matter of weeks, German universities and government authorities adopted developments that they had previously failed to implement in years. These days, a broadband connection is not just something that is nice to have, but rather an essential requirement to be able to work. This level of digitalization is the new normal. It will not be possible to return to previous practices.”

The researchers’ study also shows that overprovisioning, proactive network management, and automatization were key to providing resistant networks which could cope with the drastic and unexpected fluctuations in demand like those experienced during the COVID-19 pandemic. “Many, but not all, network providers succeeded in doing this. With the pandemic set to continue for some time, it is important that we continue to examine data traffic to understand how usage changes during these unprecedented times,” he concludes.

The Publication in Detail

Authors:
Anja Feldmann, Oliver Gasser, Franziska Lichtblau, Enric Pujol, Ingmar Poese, Christoph Dietzel, Daniel Wagner, Matthias Wichtlhuber, Juan Tapiador, Narseo Vallina-Rodriguez, Oliver Hohlfeld, Georgios Smaragdakis
Abstract:
In March 2020, the World Health Organization declared the Corona Virus 2019 (COVID-19) outbreak a global pandemic. As a result, billions of people were either encouraged or forced by their governments to stay home to reduce the spread of the virus. This caused many to turn to the Internet for work, education, social interaction, and entertainment. With the Internet demand rising at an unprecedented rate, the question of whether the Internet could sustain this additional load emerged. To answer this question, this paper will review the impact of the first year of the COVID-19 pandemic on Internet traffic in order to analyze its performance. In order to keep our study broad, we collect and analyze Internet traffic data from multiple locations at the core and edge of the Internet. From this, we characterize how traffic and application demands change, to describe the “new normal,” and explain how the Internet reacted during these unprecedented times.
Publication:
Communications of the ACM, July 2021, Vol. 64 No. 7, Pages 101-108
https://cacm.acm.org/magazines/2021/7/253468-a-year-in-lockdown/fulltext

For further information please contact:

Prof. Dr. Georgios Smaragdakis
TU Berlin
Tel.: 030 314-75169
Email: georgios@inet.tu-berlin.de