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Introduction: The PICK1 PDZ domain has been identified as a potential drug target forneurological disorders. After many years of effort, a few inhibitors, such as TAT-C5 and mPD5,have been discovered experimentally to bind to the PDZ domain with a relatively high bindingaffinity. With the rapid growth of computational research, there is an urgent need for more efficientcomputational methods to design viable ligands that target proteins.Method: Recently, a newly developed program called AfDesign (part of ColabDesign) at https://github.com/sokrypton/ColabDesign), an open-source software built on AlphaFold, has beensuggested to be capable of generating ligands that bind to targeted proteins, thus potentially facilitatingthe ligand development process. To evaluate the performance of this program, we exploredits ability to target the PICK1 PDZ domain, given our current understanding of it. We found thatthe designated length of the ligand and the number of recycles play vital roles in generating ligandswith optimal properties.Results: Utilizing AfDesign with a sequence length of 5 for the ligand produced the highest comparableligands to that of prior identified ligands. Moreover, these designed ligands displayed significantlylower binding energy compared to manually created sequences.Conclusion: This work demonstrated that AfDesign can potentially be a powerful tool to facilitatethe exploration of the ligand space for the purpose of targeting PDZ domains. 

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a growing class of natural products biosynthesized from a genetically encoded precursor peptide. The enzymes that install the post-translational modifications on these peptides have the potential to be useful catalysts in the production of natural-product-like compounds and can install non-proteogenic amino acids in peptides and proteins. However, engineering these enzymes has been somewhat limited, due in part to limited structural information on enzymes in the same families that nonetheless exhibit different substrate selectivities. Despite AlphaFold2’s superior performance in single-chain protein structure prediction, its multimer version lacks accuracy and requires high-end GPUs, which are not typically available to most research groups. Additionally, the default parameters of AlphaFold2 may not be optimal for predicting complex structures like RiPP biosynthetic enzymes, due to their dynamic binding and substrate-modifying mechanisms. This study assessed the efficacy of the structure prediction program ColabFold (a variant of AlphaFold2) in modeling RiPP biosynthetic enzymes in both monomeric and dimeric forms. After extensive benchmarking, it was found that there were no statistically significant differences in the accuracy of the predicted structures, regardless of the various possible prediction parameters that were examined, and that with the default parameters, ColabFold was able to produce accurate models. We then generated additional structural predictions for select RiPP biosynthetic enzymes from multiple protein families and biosynthetic pathways. Our findings can serve as a reference for future enzyme engineering complemented by AlphaFold-related tools.