BEER-SHEVA, Israel (Press Release)—Researchers at Ben-Gurion University of the Negev (BGU)—with collaborators from the La Jolla Institute for Immunology (LJI)—have developed a new computational method for designing “super-peptides” capable of triggering an immune response in people with vastly different genetic backgrounds.
The discovery could lead to more effective vaccines for cancer and global infectious diseases. Details of the method were published in the prestigious journal PNAS.
The immune system operates through presentation. T cells do not recognize an invading pathogen directly; rather, they recognize infected or cancerous cells only when fragments of proteins from those cells—peptides—are displayed on the cell surface by molecules known as HLA.
HLA molecules are the most diverse region of the human genome, with more than 22,000 known genetic variants. Each person has a unique set of HLA molecules, and each molecule tends to bind only a limited group of peptides. This creates a longstanding challenge in designing T-cell-based vaccines: a peptide that provokes a strong immune response in one person may be completely invisible to another.
The study was led by Prof. Tomer Hertz of BGU’s Faculty of Health Sciences, together with students Elinor Peer and Liel Cohen-Lavi and partners from LJI.
The method, nicknamed “Super HLA,” searches for needles in a biological haystack: Peptides composed of nine amino acids that are capable of binding simultaneously to many different HLA variants across multiple HLA supertypes. The computational framework is based on a probabilistic model (a Markov chain) and machine-learning-based binding prediction algorithms. It also filters out peptides that resemble human self-proteins or are difficult to synthesize together.
From an initial pool of more than 190,000 candidate peptides, 100 were selected, synthesized, and tested experimentally at LJI.
Twenty-four peptides were experimentally validated as true “super binders.” Of these, 21 bound to four or more HLA supertypes, and one peptide bound to nine out of twelve possible supertypes. By comparison, existing global medical databases (such as the IEDB) currently document only a few dozen peptides with such broad binding capability.
The findings suggest that super binders are not actually rare—they simply had not previously been identified using the right computational tools.
The algorithm enables the design of peptides with broad immunological coverage across genetically diverse populations, a prerequisite for truly effective vaccines, especially for global infectious diseases, cancer antigens, and populations underrepresented in current reference databases.
Prof. Hertz says the seeds of the idea were planted during a conversation about 15 years ago with his colleague Dr. Chen Yanover of the KI Research Institute, when both were postdoctoral researchers at the Fred Hutchinson Cancer Center in Seattle.
“Good ideas,” Prof. Hertz says, “are worth continuing to dig into, even long after the first sketch was drawn on the whiteboard.”
The method paves the way for designing vaccines that will be effective across genetically diverse populations, including minority groups that are insufficiently represented in existing databases. The approach is especially relevant for the development of personalized cancer vaccines as well as vaccines against global pandemics, where broad and rapid immune coverage is essential.
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Preceding provided by Ben-Gurion University of the Negev