Allosteric Antibodies and Machine Learning: A Transformative Force in Drug Discovery
The emerging paradigm of allosteric antibodies, in concert with advancements in artificial intelligence (AI), provides new opportunities for therapeutic innovation. [1] The field of allosteric antibody design offers unique opportunities for manipulating protein function, opening new avenues for treating diseases previously considered “undruggable” and a new level of precision in treating complex diseases such as cancer and neurodegenerative disorders. This new field also challenges the intellectual property protection of investments in inventions that span unique mechanisms, the integration of AI and biotechnology, and novel diagnostic and therapeutic applications.
What Are Allosteric Antibodies?
Traditional antibody therapies often target a protein’s active site, directly competing with natural ligands or substrates. On the other hand, allosteric antibodies bind to sites that are different from the active site, often referred to as allosteric sites. The binding of an antibody to an allosteric site can trigger conformational changes within the protein that alter its activity or interactions with other molecules. This capability allows the allosteric antibodies to change the biological activity of the protein in predictable ways beyond simply targeting active sites and ligand binding, offering innovative applications in therapeutic strategies and drug discovery.
The Role of Machine Learning in the Development of Allosteric Antibodies
Advancements in machine learning and computational structural biology have significantly aided the development of allosteric antibodies by identifying allosterically active regions in target proteins and designing antibodies that can bind allosteric targets and achieve desired conformation changes in the target proteins. The design of allosteric antibodies requires consideration of the “full conformational dynamics of both the target protein and the antibody”[1] to identify antibodies that achieve the desired change in the target protein. This requires analysis of complex and large data sets that machine learning is particularly well suited to help with.
Applications of Allosteric Antibodies
Allosteric antibodies promise to redefine not only therapeutics but also drug discovery generally by providing:
- New drugs for previously “undruggable” diseases
- Support screening of small molecules
- Chaperones for structural studies
The development of allosteric antibodies should be closely monitored due to their potential for driving therapeutic innovation and the unique challenges in protecting investments in this area, which arise from the intersection of machine learning and biotechnology.
[1] Fournier, Léxane, et al., Allosteric antibodies: a novel paradigm in drug discovery, Trends in Pharmacological Sciences, Volume 0, Issue 0
Apart from their application as therapeutics, allosteric antibodies were also developed as tools to support HTS [high-throughput screening”] of small molecules. The identification of allosteric networks and communications is progressing along with the development of modern computational methods. The remaining challenge would be the directed design of allosteric antibodies. As such, the design of allosteric antibodies must be approached with meticulous consideration of the full conformational dynamics of both the target protein and the antibody.
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Allostery, often considered as the second secret of life, is emerging as the not-so-secret modulatory activity of antibodies.
