Optimizing a Ligand for Competitive Inhibition of SUMO1
Abstract
SUMO1 is a protein in the body which affects cell replication. When the DAXX peptide binds to SUMO1 via competitive inhibition, it activates SUMO1 and begins regulating mitosis [1]. The SUMO1-DAXX interaction could lead to a medicinal treatment for stopping the spread of cancer cells. We seek to create an optimized version of the DAXX peptide through combinations of point mutations. This optimized list will be a prediction of the best binding peptides for SUMO1. We conducted 100 nanosecond molecular dynamics (MD) simulations using Gromacs and extracted snapshots every nanosecond to obtain a structure set to perform mutation analysis using FoldX. Of the 520 double mutations currently analyzed with this process, there were seven predicted combinations with a significant improvement of more than 0.4 kcal/mol to binding affinity. The vast majority of the remaining combinations were found to be significantly harmful to the overall binding affinity of the DAXX Peptide. The final predictive optimized list of mutations for the DAXX peptide will be synthesized to obtain experimental binding affinity values.
Optimizing a Ligand for Competitive Inhibition of SUMO1
SUMO1 is a protein in the body which affects cell replication. When the DAXX peptide binds to SUMO1 via competitive inhibition, it activates SUMO1 and begins regulating mitosis [1]. The SUMO1-DAXX interaction could lead to a medicinal treatment for stopping the spread of cancer cells. We seek to create an optimized version of the DAXX peptide through combinations of point mutations. This optimized list will be a prediction of the best binding peptides for SUMO1. We conducted 100 nanosecond molecular dynamics (MD) simulations using Gromacs and extracted snapshots every nanosecond to obtain a structure set to perform mutation analysis using FoldX. Of the 520 double mutations currently analyzed with this process, there were seven predicted combinations with a significant improvement of more than 0.4 kcal/mol to binding affinity. The vast majority of the remaining combinations were found to be significantly harmful to the overall binding affinity of the DAXX Peptide. The final predictive optimized list of mutations for the DAXX peptide will be synthesized to obtain experimental binding affinity values.