Computationally Revealing Protein Targets for Metal-based Drugs

Overview

Objectives/Goals Knowing the structural properties and cellular distributions of metal-protein interactions is useful for metal-based drug design. Cysteine (Cys) residues are reactive towards metals and conserved in protein evolution. Thus, Cys-rich domains (protein structures) are ideal targets of metal-based drugs. With the advance of technology, computational tools like molecular-dynamics (MD) simulations and application protocol interfaces (APIs) have provided an affordable way to gain insight into the nature of metal-protein interactions. My objective is to computationally discover the cellular distributions and structural characteristics of Cys-rich protein domains to reveal effective targets for metal-based drugs. Methods/Materials I applied MD-simulations to metal-Cys protein models from PDB database. Through simulations, I created a scientific standard of what defines a Cys-cluster by finding the average distance between Cys-residues. I applied the standard to an algorithm I developed using the concept of "k-means clustering" so that I can identify Cys-clusters in any protein model. I applied this algorithm to protein models in the CATH database to find which have Cys-clusters. I used statistical analysis to determine the Cys-models# structures and correlated the models' ID to the SubCellLoc database to find their locations. I created a database that effectively combines information about Cys-domains and their structure and location. Results I identified 11,406 Cys-rich structures from 173,207 domains and investigated their location and structure. By visual verification of the clusters, I found that my algorithm can accurately identify Cys-domains. Cys-domains are closely related to the structures of Arc repressor-like domains, four helix bundle, and zinc finger, and have functions like metal homeostasis and proteolysis, implying a critical role in metal-protein interactions. Metallodrug development can be enhanced as scientists know where to target drugs and what structure/ligands the drug should have to best bind to Cys-clusters. Conclusions/Discussion Through my algorithm to identify clusters, examination of dynamic protein structures, and data-correlation to discover their structure and localizations, I have computationally explored these domains, and their structure and localizations may uncover the great potential of metallodrug targets that are particularly sensitive to metals and expedite drug development.

Competition history

• CSEF 2016 Biochemistry/ Molecular Biology · Entry S0527

Resources

• CSEF project summary ↗

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