“Honey, I shrunk the cookbook” – New approach to vaccine development
Bioinformatics: Publication in Cell Systems
Peer-Reviewed PublicationIMAGE:
WITH THE HOGVAX TOOL, SO-CALLED EPITOPES – SHORT PROTEIN FRAGMENTS OF A PATHOGEN THAT TRIGGER AN IMMUNE RESPONSE – CAN BE COMBINED TO CREATE NOVEL VACCINES. THE AIM IS TO MAXIMISE POPULATION COVERAGE. (FIG.: HHU/SARA SCHULTE)
view moreCREDIT: HHU/SARA SCHULTE
Vaccine development aims at protecting as many people as possible from infections. Short protein fragments of pathogens, so-called epitopes, are seen as a promising new approach for vaccine development. In the scientific journal Cell Systems, bioinformaticians from Heinrich Heine University Düsseldorf (HHU) now present a method for identifying those epitopes that promise safe immunisation across the broadest possible population group. They have also computed vaccine candidates against the coronavirus SARS-CoV-2 using their HOGVAX tool.
During the coronavirus pandemic, so-called mRNA vaccines proved particularly successful and flexible. These vaccines target the so-called spike proteins – characteristic structures on the surface of the virus. The mRNA contains the sequence of the spike protein, which is produced in the body after vaccination and then trains the human immune system.
“Epitopes” – short fragments of pathogen proteins that are capable of triggering an immune response – are seen as an alternative method to mRNA and a promising approach for obtaining targeted immune responses quickly, cost-effectively and safely.
Everyone has a unique immune system: Depending on their infection history, the immune system is trained to handle and react to different proteins. “This is a fundamental problem of vaccines based on epitopes,” explains Professor Dr Gunnar Klau, holder of the Chair of Algorithmic Bioinformatics at HHU. Together with his PhD student Sara Schulte and Professor Dr Alexander Dilthey from the Institute of Medical Microbiology and Hospital Hygiene, he considered a new approach to developing such vaccines.
Professor Klau compares the problem with a chef who needs to create a new dish for a large event: “Some guests have allergies, while others do not like certain ingredients, so the chef needs to select ingredients that as many of the guests as possible can eat and will enjoy.”
Translated to vaccine development, this means that they are seeking epitopes that trigger a good immune response in as many people as possible. This is necessary because it is not possible to pack an unlimited number of protein fragments into a vaccine so that the various immune systems can seek out the sequences suitable for them – the carrier medium simply does not have sufficient capacity.
The team of three researchers took a special approach with their bioinformatic tool “HOGVAX”. Sara Schulte: “Instead of stringing the epitopes for the vaccine together end-to-end, we use identical sequences at the beginning and end of the epitopes so we can overlay them. The identical section, known as the ‘overlap’, is thus only represented once in the vaccine, which enables us to save a huge amount of space.” This in turn enables many more epitopes to be included in a vaccine.
In order to manage the epitopes and their longest overlaps efficiently, the researchers use a data structure known as a “hierarchical overlap graph” (for short: HOG). Klau: “To stay with the cooking analogy: HOG corresponds to a compressed or shrunk cookbook, from which the chef can now select the recipes that are suitable for all guests.”
Professor Dilthey: “As a test, we applied HOGVAX to data for the SARS-CoV-2 virus and we were able to integrate significantly more epitopes than other tools. According to our calculations, we would be able to reach – and immunise – more than 98% of the world population.”
Sara Schulte comments on the further perspectives for their results: “In the future, we will work on adapting HOGVAX for use in cancer therapy. The aim here is to develop agents specifically designed for individual patients that attack tumour cells in a targeted manner.”
Original publication:
Sara C. Schulte, Alexander T. Dilthey, Gunnar W. Klau; HOGVAX: Exploiting Epitope Overlaps to Maximize Population Coverage in Vaccine Design with Application to SARS-CoV-2; Cell Systems 14, 1-9 December 20, 2023.
DOI: 10.1016/j.cels.2023.11.001
Functional principle of the HOGVAX tool. (Fig.: HHU/Sara Schulte)
CREDIT
HHU/Sara Schulte
JOURNAL
Cell Systems
ARTICLE TITLE
HOGVAX: Exploiting Epitope Overlaps to Maximize Population Coverage in Vaccine Design with Application to SARS-CoV-2
ARTICLE PUBLICATION DATE
20-Dec-2023
Catalyzing drug discovery with explainable deep learning
Researchers at MIT, the Broad Institute of MIT and Harvard, Integrated Biosciences, the Wyss Institute for Biologically Inspired Engineering, and the Leibniz Institute of Polymer Research have identified a new structural class of antibiotics.
Peer-Reviewed PublicationScientists have discovered one of the first new classes of antibiotics identified in the past 60 years, and the first discovered leveraging an AI-powered platform built around explainable deep learning.
Published in Nature today, the peer-reviewed paper, entitled “Discovery of a structural class of antibiotics with explainable deep learning,” was co-authored by a team of 21 researchers, led by Felix Wong, Ph.D., co-founder of Integrated Biosciences, and James J. Collins, Ph.D., Termeer Professor of Medical Engineering and Science at MIT and founding chair of the Integrated Biosciences Scientific Advisory Board.
Additional collaborators included researchers at the Massachusetts Institute of Technology (MIT), the Broad Institute of MIT and Harvard, the Wyss Institute for Biologically Inspired Engineering, and the Leibniz Institute of Polymer Research in Dresden, Germany. In their study, the researchers virtually screened more than 12 million candidate compounds to identify this new class of antibiotics, which show potential to address antibiotic resistance.
In this groundbreaking approach, the team of researchers trained deep learning models on experimentally generated data to predict the antibiotic activity and toxicity of any compound. Drawing inspiration from AI used in other contexts, including DeepMind’s AlphaGo gaming technology, the authors designed new models to explain which parts of a molecule were important for antibiotic activity.
The result was the identification of a new class of antibiotics with potent activity against multidrug-resistant pathogens. In one series of experiments, the researchers tested a candidate antibiotic in mouse models of MRSA infection and found that it was efficacious both topically and systemically, indicating that the compound could be suitable for further development as a treatment for severe and sepsis-related bacterial infections.
“This discovery of a new class of antibiotics is a breakthrough result showing that artificial intelligence and explainable deep learning are uniquely capable of catalyzing drug discovery,” said Dr. Wong. “Our work makes publicly available several high-powered models to accurately predict both antibiotic activity and toxicity. Importantly, this is one of the first demonstrations that deep learning models can explain what they are predicting, with immediate and far-reaching implications to how drug discovery is done and how efficiently we can find new drugs using AI.”
Dr. Collins said, “This is an important validation of how important the integration of AI and explainable deep learning will be to overcoming some of the most vexing challenges in medicine, in this case antibiotic resistance. Building on these validating studies and similar approaches, the Integrated Biosciences team is poised to further accelerate their integration of synthetic biology and a deep understanding of cellular stress to address a significant unmet need for new treatments targeting age-related diseases.”
Satotaka Omori, Ph.D., founding member and Head of Aging Biology at Integrated Biosciences, and a contributing author on the publication, said, “An important implication of this study is that deep learning models in drug discovery can, and in many cases should, be made explainable. While AI continues to make an impact, it is also limited by the many black box models that are commonly used and obfuscate the underlying decision-making process. By opening up these black boxes, we aim to create more generalizable insights that may be more useful in accelerating the use and development of next-generation approaches to drug discovery.”
Alicia Li, a research associate at Integrated Biosciences and a contributing author on the publication, added, “It’s really exciting to see how we’ve been able to demonstrate a new way to predict how useful a compound will be as an antibiotic, the likelihood that the compound will progress in Phase I trials, and whether or not the compound is one of potentially many other members in a novel class of drugs.”
Integrated Biosciences has built a body of research that, in addition to this new Nature publication, includes a Nature Aging paper published in May demonstrating how AI can be used to discover novel senolytics, anti-aging compounds that selectively eliminate senescent “zombie” cells. These compounds have shown promise in their ability to treat age-related diseases, such as fibrosis, inflammation, and cancer.
A Cell Systems paper published in July demonstrated a synthetic biology-based platform allowing human control over aging-associated stress responses, enabling accelerated drug screens for targeting aging.
The publication, “Discovery of a structural class of antibiotics with explainable deep learning,” can be accessed on the Nature website at: https://www.nature.com/articles/s41586-023-06887-8.
JOURNAL
Nature
METHOD OF RESEARCH
Experimental study
SUBJECT OF RESEARCH
Animals
ARTICLE TITLE
Discovery of a structural class of antibiotics with explainable deep learning
ARTICLE PUBLICATION DATE
20-Dec-2023
Scientists in Germany develop a new analytical method, which enables improved insight into (mRNA) nanoparticles and similar pharmaceutical and non-pharmaceutical products
A new technology provides new insights into mRNA pharmaceuticals and other nanomedicines, which can be helpful for the development of new products
IMAGE:
THE NEW METHOD ALLOWS RESEARCHERS TO DIRECTLY SEPARATE THE NANOPARTICLES ACCORDING TO THEIR SIZE AND INVESTIGATE THEIR STRUCTURE BY X-RAY SCATTERING.
view moreCREDIT: ISABEL ROMERO CALVO/EMBL
Messenger RNA (mRNA) nanomedicines, a ground-breaking technology that has led to the development of the first approved COVID-19 vaccine, was recently recognised by the Nobel Prize in Medicine or Physiology. But mRNA’s potential for pharmaceutical application is expected to go much beyond this – it could open up new opportunities for the treatment and prevention of diseases, such as viral and bacterial infections, cancer, cardiovascular diseases, and inflammatory and auto-immune diseases. It could also transform the large field of interventions by therapeutic proteins.
Many novel mRNA nanomedicines, which are currently in different stages of development, may become available in the future. One requirement for all applications of mRNA in pharmaceutical products is that they need to be formulated in suitable delivery systems, each designed for different functions and optimised for therapeutic product needs based on the intended application and route of delivery.
Lipid-based nanoparticles are tiny droplets of fat-like molecules that serve as protective packaging for the mRNA. Their properties depend on composition, structure, manufacturing protocol, and other conditions. An important aspect of nanoparticles is their size. By their nature, nanoparticles can vary a little bit in size, some being a bit smaller, some a bit larger than the average value. The particle size can have an influence, for example, on the stability and the behaviour of the formulations after administration. It is therefore important to control the particle size inside a pharmaceutical product to evaluate and ensure its quality.
Scientists at EMBL Hamburg, Johannes Gutenberg University Mainz, Postnova Analytics GmbH, and BioNTech SE have developed a new method to precisely elucidate the size of all particles in such pharmaceutical products, as well as their structure and how many RNA molecules they carry inside them. The study was conducted based on lipoplex formulations, a mRNA delivering technology developed by BioNTech.
“So far, it was very difficult to measure all these size-related properties; therefore, often only average values were determined,” said Heinrich Haas, one of the leaders of the project. “With our new method, we can determine many size-related features all at once, with a single measurement and for all nanoparticles in a product. This information can be very useful to evaluate product quality.”
The method will also be applicable for the investigation of other pharmaceutical products.
“Liposomes are another type of pharmaceutical nanoparticles which have been applied since years for treatment of cancer or infectious diseases such as fungal infections,” said Peter Langguth, the project leader at Johannes Gutenberg University Mainz. “Now even generic liposome products are available on the market, and probably there will be more to come. The new method can be very useful in evaluating the quality of these generics in comparison to the originator products and will pave the way for further high-quality drug products at an even more reasonable cost.”
A two-in-one method
What makes the new method so powerful is that it couples two techniques: asymmetrical-flow field-flow fractionation (AF4) and small-angle X-ray scattering (SAXS). AF4 separates lipid-based nanoparticles from other parts of an mRNA nanomedicine and sorts them according to their size. SAXS allows scientists to determine the structure and the number of the sorted particles. To do this unequivocally, it is necessary that only one type of particles is analysed at a time, which is why combining sorting and measuring is so critical.
SAXS is one of the key techniques applied and available at EMBL Hamburg as a service for researchers from academia and industry in Europe and beyond. EMBL Hamburg’s SAXS beamline at the PETRA III synchrotron, now equipped with the AF4 device – set-up with the help of collaborators at Postnova Analytics GmbH – will open up new opportunities not only for studying pharmaceutical nanoparticles, but also for other types of research.
"The combination of these two tools can now be used in many different areas of science,” said Melissa Graewert, Staff Scientist at EMBL Hamburg. “In addition to helping create new medicines, we can also use them to understand how different-sized particles interact in complex biological systems. For example, I've now used this new setup to closely examine how very small plastic debris called nanoplastics, which pollute our waters, can be covered by binding proteins on their surface. A key question is whether this protein shielding enables nanoplastics to travel through our bloodstream, potentially reaching different organs, as they may no longer be recognised as foreign objects by our immune system.”
This work follows up on several previous collaborative studies between EMBL Hamburg, BioNTech SE, and Johannes Gutenberg University Mainz, which explored how mRNA can be better formulated and delivered into human cells. The scientists are continuing their collaborative research to further explore the application of mRNA nanomedicines.
Funding
This research was funded by the "Bundesministerium für Bildung und Forschung BMBF“ grant 05K22UM3 and by the “Deutsche Forschungsgemeinschaft DFG” as part of the collaborative research center (CRC) 1066.
About EMBL
The European Molecular Biology Laboratory (EMBL) is Europe’s life sciences laboratory. We provide leadership and coordination for the life sciences across Europe, and our world-class fundamental research seeks collaborative and interdisciplinary solutions for some of society’s biggest challenges. We provide training for students and scientists, drive the development of new technology and methods in the life sciences, and offer state-of-the-art research infrastructure for a wide range of experimental and data services.
EMBL is an intergovernmental organisation with 28 member states, one associate member, and two prospective members. At our six sites in Barcelona, Grenoble, Hamburg, Heidelberg, Hinxton near Cambridge, and Rome, we seek to better understand life in its natural context, from molecules to ecosystems.
About Johannes Gutenberg University Mainz
Johannes Gutenberg University Mainz (JGU) is a globally recognized research-driven university with around 30,000 students from over 120 nations. Its core research areas are in particle and hadron physics, the materials sciences, and translational medicine. JGU's success in Germany's Excellence Strategy program has confirmed its academic excellence: In 2018, the research network PRISMA+ (Precision Physics, Fundamental Interactions and Structure of Matter) was recognized as a Cluster of Excellence – building on its forerunner, PRISMA. Moreover, excellent placings in national and international rankings as well as numerous honors and awards demonstrate the research and teaching quality of Mainz-based researchers and academics. Further information at www.uni-mainz.de/eng
About BioNTech
Biopharmaceutical New Technologies (BioNTech) is a next generation immunotherapy company pioneering novel therapies for cancer and other serious diseases. The Company exploits a wide array of computational discovery and therapeutic drug platforms for the rapid development of novel biopharmaceuticals. Its broad portfolio of oncology product candidates includes individualized and off-the-shelf mRNA-based therapies, innovative chimeric antigen receptor (“CAR”) T cells, several protein-based therapeutics, including bispecific immune checkpoint modulators, targeted cancer antibodies and antibody-drug conjugate (“ADC”) therapeutics, as well as small molecules. Based on its deep expertise in mRNA vaccine development and in-house manufacturing capabilities, BioNTech and its collaborators are developing multiple mRNA vaccine candidates for a range of infectious diseases alongside its diverse oncology pipeline. BioNTech has established a broad set of relationships with multiple global pharmaceutical collaborators, including Duality Biologics, Fosun Pharma, Genentech, a member of the Roche Group, Genevant, Genmab, OncoC4, Regeneron, Sanofi and Pfizer.
For more information, please visit www.BioNTech.com.
EMBL Staff Scientist Melissa Graewert together with two users from the Johannes Gutenberg University Mainz are performing measurements of RNA using small-angle X-ray scattering at the EMBL beamline P12 in Hamburg.
CREDIT
Dorota Badowska/EMBL
About Postnova Analytics
Postnova is the inventor and leader in field-flow fractionation and an innovator in light scattering technology, offering instruments, software, and services used in laboratories of universities, institutions, and corporations worldwide to separate and characterize analytes in the nanometer to micrometer and kilodalton to gigadalton range. In a constantly growing number of applications around polymers and further advanced materials, nutrition, and personal care, as well as in environmental and pharmaceutical science, Postnova's offering and expertise help its customers to develop and optimize products and their production processes, to ensure product quality, to contribute to a safer and more sustainable environment, as well as to fight and cure diseases.
Information about Postnova is available at www.postnova.com.
JOURNAL
Scientific Reports
METHOD OF RESEARCH
Experimental study
SUBJECT OF RESEARCH
Not applicable
ARTICLE TITLE
Quantitative size-resolved characterisation of mRNA nanoparticles by in-line coupling of asymmetrical-flow field-flow fractionation with small angle X-ray scattering.
ARTICLE PUBLICATION DATE
22-Sep-2023
COI STATEMENT
MAG is consultant to BioSAXS GmbH PL is consultant to BioNTech SE MAG, CW, TB, JS, CB, FM, RD, RW, BK, KB, TN, TK, and HH have no competing interest.
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