In Silico Screening, Molecular Dynamics Simulation and Binding Free Energy Identify Single-Point Mutations That...

Area of Science:

• Genetics
• Molecular Biology
• Bioinformatics

Background:

• The p53 protein is a critical tumor suppressor.
• Early identification of harmful p53 mutations is vital for public health.
• Nonsynonymous single nucleotide polymorphisms (nsSNPs) can alter protein function.

Purpose of the Study:

• To computationally identify potentially deleterious p53 nsSNPs.
• To assess the impact of these mutations on p53 stability, structure, dynamics, and DNA binding.
• To evaluate the potential human health risks associated with identified p53 variants.

Main Methods:

• Utilized four computational prediction tools: SIFT, Polyphen-2, PhD-SNP, and MutPred2.
• Employed sequence-based and machine-learning algorithms for nsSNP identification.
• Conducted molecular dynamics (MD) simulations to analyze protein structure and flexibility.
• Quantified DNA-binding affinity of wild-type and mutant p53 variants.

Main Results:

• Identified three potentially detrimental p53 variants: C141Y, C238S, and L265P.
• MD simulations showed increased structural flexibility in all three variants, particularly C141Y and L265P.
• DNA-binding affinity decreased significantly for all variants (1.8-9.7 times lower than wild-type).
• The L265P mutation showed a notable ten-fold reduction in DNA-binding affinity.

Conclusions:

• The identified p53 variants (C141Y, C238S, L265P) negatively impact protein stability and DNA binding.
• The L265P mutation presents a substantial potential risk due to its severe reduction in DNA-binding affinity.
• Findings have implications for cancer surveillance and the development of targeted anticancer therapies.