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Extrapolating the effect of deleterious nsSNPs in the binding adaptability of flavopiridol with CDK7 protein: a
C George Priya Doss1, N Nagasundaram, Chiranjib Chakraborty
1Medical Biotechnology Division, Centre for Nanobiotechnology, School of Biosciences and Technology, VIT University, Vellore 632014, Tamil Nadu 632014, India. georgecp77@yahoo.co.in
Background:
Recent reports suggest the role of nonsynonymous single nucleotide polymorphisms (nsSNPs) in cyclin-dependent kinase 7 (CDK7) gene associated with defect in the DNA repair mechanism that may contribute to cancer risk. Among the various inhibitors developed so far, flavopiridol proved to be a potential antitumor drug in the phase-III clinical trial for chronic lymphocytic leukemia. Here, we described a theoretical assessment for the discovery of new drugs or drug targets in CDK7 protein owing to the changes caused by deleterious nsSNPs.
Methods:
Three nsSNPs (I63R, H135R, and T285M) were predicted to have functional impact on protein function by SIFT, PolyPhen2, I-Mutant3, PANTHER, SNPs&GO, PhD-SNP, and screening for non-acceptable polymorphisms (SNAP). Furthermore, we analyzed the native and proposed mutant models in atomic level 10 ns simulation using the molecular dynamics (MD) approach. Finally, with the aid of Autodock 4.0 and PatchDock, we analyzed the binding efficacy of flavopiridol with CDK7 protein with respect to the deleterious mutations.
Results:
By comparing the results of all seven prediction tools, three nsSNPs (I63R, H135R, and T285M) were predicted to have functional impact on the protein function. The results of protein stability analysis inferred that I63R and H135R exhibited less deviation in root mean square deviation in comparison with the native and T285M protein. The flexibility of all the three mutant models of CDK7 protein is diverse in comparison with the native protein. Following to that, docking study revealed the change in the active site residues and decrease in the binding affinity of flavopiridol with mutant proteins.
Conclusion:
This theoretical approach is entirely based on computational methods, which has the ability to identify the disease-related SNPs in complex disorders by contrasting their costs and capabilities with those of the experimental methods. The identification of disease related SNPs by computational methods has the potential to create personalized tools for the diagnosis, prognosis, and treatment of diseases.
Lay Abstract:
Cell cycle regulatory protein, CDK7, is linked with DNA repair mechanism which can contribute to cancer risk. The main aim of this study is to extrapolate the relationship between the nsSNPs and their effects in drug-binding capability. In this work, we propose a new methodology which (1) efficiently identified the deleterious nsSNPs that tend to have functional effect on protein function upon mutation by computational tools, (2) analyze d the native protein and proposed mutant models in atomic level using MD approach, and (3) investigated the protein-ligand interactions to analyze the binding ability by docking analysis. This theoretical approach is entirely based on computational methods, which has the ability to identify the disease-related SNPs in complex disorders by contrasting their costs and capabilities with those of the experimental methods. Overall, this approach has the potential to create personalized tools for the diagnosis, prognosis, and treatment of diseases.
Insights
Nonsynonymous single nucleotide polymorphisms (nsSNPs) in the cyclin-dependent kinase 7 (CDK7) gene may increase cancer risk. Computational analysis revealed that specific nsSNPs decrease flavopiridol
Area of Science:
- Genomics and Bioinformatics
- Molecular Biology
- Computational Chemistry
Background:
- Nonsynonymous single nucleotide polymorphisms (nsSNPs) in the cyclin-dependent kinase 7 (CDK7) gene are implicated in DNA repair defects and cancer risk.
- Flavopiridol, a CDK inhibitor, has shown potential as an antitumor drug, particularly in chronic lymphocytic leukemia.
Purpose of the Study:
- To theoretically assess the impact of deleterious nsSNPs in CDK7 on drug-binding capabilities.
- To identify potential new drug targets or drug candidates by analyzing mutations in CDK7.
Main Methods:
- Utilized multiple computational tools (SIFT, PolyPhen2, etc.) to predict the functional impact of nsSNPs.
- Performed molecular dynamics (MD) simulations to analyze protein stability and flexibility of native and mutant CDK7 models.
- Employed docking analysis (Autodock 4.0, PatchDock) to evaluate flavopiridol binding affinity with wild-type and mutant CDK7 proteins.
Main Results:
- Identified three nsSNPs (I63R, H135R, T285M) predicted to functionally impact CDK7 protein.
- MD simulations showed varied flexibility in mutant models; I63R and H135R exhibited less deviation than T285M.
- Docking studies revealed altered active site residues and reduced binding affinity of flavopiridol to mutant CDK7 proteins.
Conclusions:
- Computational methods offer a cost-effective approach to identify disease-related SNPs in complex disorders.
- This in silico strategy can aid in developing personalized diagnostic, prognostic, and therapeutic tools for diseases linked to genetic variations.
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