Saturday, May 17, 2025
5/17/2025
Structure-based in-silico prediction of peptide interactions. - Interactions between peptides and their substrates play a central role in many cellular processes and computational methods, such as automated docking, are used to gain a mechanistic understanding of these interactions. While docking small molecules works well, peptide-docking remains a challenging, relatively new, and fast evolving field with direct and high translational impact. We have been at the forefront of developing physics-based peptide-docking methods. Emerging deep learning methods (DL) such as AlphaFold are deeply impacting structural biology and have recently been shown to perform well for predicting protein-peptide interactions. We propose to further develop and enhance cutting-edge approaches for the prediction of peptide interactions with their binding partners, with a special focus on expanding coverage of the chemical space of building blocks used to synthesize therapeutic peptides. To this end, we will extend our state-of-the-art peptide docking software program AutoDock CrankPep to support non-standard amino-acids, and additional peptide cyclization modes. We will also develop novel scoring functions specifically designed for peptide docking. Furthermore, physics-based and DL-based peptide docking methods are complementary and we have evidence that they can be synergistically combined to leverage their respective strengths. These combined docking strategies will further extend the range of biological questions and biomedical targets for which peptide docking can be applied. Finally, we will extend peptide-docking methods for predicting peptide-peptide interactions. Such a method will have a significant impact on the development of peptide-based therapeutic approaches. We will continue to collaborate with experimental biologists as the biological systems they study challenge our software tools and drive their development and extension with new methods. Furthermore, these collaborations provide us with data beyond the traditional benchmark datasets for validating and refining our computational methods and predictions. These collaborations span from translational applications such as the development of vaccines against the flue or molecules used to prevent brain damage in stroke patients, to pioneering the use of computational structural biology approaches to study small Open Reading Frame (smORFs) encoded polypeptides, an exciting emerging new frontier in biology. We have a long-standing track record of promoting diversity in our research team, of implementing best practices in software development, and producing and distributing production-grade, open-source software, widely used by large and growing and diverse community. The software tools resulting from this project will be developed and distributed using the same principles, supporting the research of many medicinal chemists and biologist and extending their use to an even wider community. These software programs will support the preclinical design of peptide-based therapeutic molecules and approaches, thereby supporting the advancement of biomedical research as well as fundamental aspects of structural biology.
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