Structural Comparison for Identifying Protein Hotspots Using PhiDsc Method

Document Type : Original Article


1 Department of Computer and Data Sciences, Shahid Beheshti University, Tehran, Iran

2 School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran


Introduction: Somatic mutations in cancer are caused by a complex interaction of many starting and driving factors that work together to create a unique mutational landscape. During tumor growth, the controlled cellular environment restricts the alteration of only a few pathways. As a result, tumors that originate from various cell types frequently display similar genetic alterations. A noteworthy development in recent times is the increased detection of hotspot mutant residues located within particular genes. PhiDsc (Protein Functional Mutation Identification by 3D Structure Comparison), an innovative statistical technique developed for the purpose of detecting functional mutations in proteins that are prone to aberrations, is introduced in this study with a specific focus on the RAS and RHO protein families.
Materials and Methods: By combining 3D structural alignment and recurrence data, PhiDsc determines whether mutated residues within a protein family have the potential to be functionally significant. The protein relationships within families were determined using UniProtKB, and the structural alignment of similar proteins in three dimensions was executed using DALI. The RCSB Protein Data Bank was consulted for the protein structures. The extraction of mutational data for the pertinent proteins was performed using BioMuta. The 3D hotspot database was utilized to identify mutational hotspots within the protein families under investigation. PhiDsc is accessible for free at hobzy987/PhiDsc-DALI.
Results: The PhiDsc method successfully found both known and unknown mutational hotspots and changed residues in the RAS and RHO protein families. These changes are functionally important because they happen in or near active regions and domains that are important for protein-protein interactions.
Conclusions: PhiDsc, an innovative statistical method, effectively detects functional mutations in frequently aberrant genes through the selective targeting of altered residues located in protein families that are highly probable to have functional significance. The present study showcased the ability of PhiDsc to identify mutations that impact the development and advancement of cancer, with a specific focus on the RAS and RHO protein families.