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Understanding protein complex formation is crucial in drug design and development of therapeutic proteins such as antibodies. However, proteins can attach to millions of different combinations of each other and current docking solutions are used to predict these interactions can be very slow. Faster and more accurate solutions are needed to streamline the process.
In a pre-print Published earlier this year, a new machine-learning model – EquiDock – was introduced that could quickly predict how the two proteins will interact. Unlike other approaches, the model does not rely on heavy candidate sampling and was shown to reach predictions up to 80-500 times faster than popular docking software.
To learn more about EquiDock and how artificial intelligence (AI) methods are advancing the field of structural proteomics, Technology Networks talked to co-lead author of the paper, Octavian-Eugen Ganeaa postdoctoral researcher in the MIT Computer Science and Artificial Intelligence Laboratory.
Molly Campbell (MC): For our readers that may be unfamiliar, please describe your current research focus in proteomics?
Octavian Ganea (OG): My research uses AI (especially, deep learning) to model aspects of molecules that are important in various applications such as drug discovery.
Proteins are most involved in the biological processes in our bodies. Two or more proteins interact with different functions and form larger machines, that is, complexes. They also bind to smaller molecules such as those found in drugs. These processes change the biological functions of individual proteins, for example an ideal drug would inhibit a cancer-causing protein by attaching specific parts of its surface. I am interested in using deep learning to model these interactions and to assist and speed up the research of chemists and biologists by providing better and faster computational tools.
MC: How are AI-based methods advancing the field of proteomics and structural structural proteomics?
OG: Biological processes are inherently very complex and have their own mysteries, even for domain experts. For example, to understand how interacting proteins attach to each other, humans or computers have to try out all possible attachment combinations in order to find the most plausible one. Intuitively, having two three-dimensional objects with very irregular surfaces, one has to rotate them and try to dock them in all possible ways until one can find two complementary regions on both surfaces that would match very well in terms of their geometric and chemical patterns. General Chat Chat Lounge This is a very time-consuming process for both manual approaches and computational ones. Moreover, biologists are interested in discovering new interactions across a very large set of proteins such as the ~ 20,000-sized human proteome. This is important, for example, for discovering unexpected side-effects of new treatments. Such a problem now becomes similar to an extremely large 3D puzzle where one has to simultaneously scan for matching pieces, as well as understand how each single pairwise attachment happens by trying out all possible combinations and rotations.
MC: Can you explain how you created EquiDock?
OG: EquiDock takes the 3D structures of two proteins and directly identifies areas that are likely to interact, which would otherwise be a complex problem even for a biology expert. Discovering this information is then sufficient for understanding how to rotate and orient the two proteins in their attached positions. EquiDock learns to capture complex docking patterns from a large set of ~ 41,000 protein structures using a geometrically constrained model with thousands of parameters that are dynamically adjusted and adjusted until they solve the task very well.
MC: What are the potential applications of EquiDock?
OG: As already mentioned, EquiDock can enable fast computational scanning of drug side effects. It goes along with large-scale virtual screening of drugs and other types of molecules (eg, antibodies, nanobodies, peptides). This is needed in order to significantly reduce an astronomical search space that would otherwise be infeasible for all our current experimental capabilities (even world-wide aggregated). A fast protein-protein docking method such as EquiDock combined with a fast protein structure prediction model (such as AlphaFold2 developed by DeepMind) would help in drug design, protein engineering, antibody generation, or understanding of a drug’s mechanism of action, among many other exciting applications. Critically needed in our search for better disease treatments.
Octavian Ganea was speaking to Molly Campbell, Senior Science Writer for Technology Networks.