CABS-flex as a de novo structure prediction tool for cyclic peptides

Cyclic peptides have become an attractive class of molecules in drug design due to their diverse biological activities and increased stability. However, their design and synthesis can be challenging due to their complex three-dimensional structures. To overcome this challenge, modeling approaches have been developed to predict their structures and assist in the design of new cyclic peptides with desired properties. One such approach is CABS-flex, a de novo structure prediction tool that has been specifically developed for cyclic peptides. Despite the recent breakthroughs in the field of protein structure prediction, including AlphaFold, there is still a need to improve and learn from existing tools like CABS-flex. This presentation will highlight the importance of cyclic peptides in drug design, the benefits of using modeling approaches like CABS-flex, and the ongoing efforts to improve and optimize structure prediction methods.