Extrapolating the effect of deleterious nsSNPs in the binding adaptability of flavopiridol with CDK7 protein: a molecular dynamics approach

Abstract

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.

Figure 1

Outline of proposed protocol for nsSNPs analysis. This protocol explains the different steps followed in nsSNP analysis via experimental (grey color) and computational methods. Box displayed in orange color indicates the effectiveness of computational over experimental methods.

Figure 2

Change in the surrounding amino acid residues in CDK7 protein by the substitution of deleterious amino acid. (A) The native type isoleucine residue (green) at position 63 and the surrounding residues. Substitution of I63 residue with arginine (red) brings more surrounding residues in contact at position 63. (B) The native type histidine residue (green) at position 135 and its surrounding amino acid residues. Substitution of arginine (red) at position 135 brings more amino acids in the surrounding region. (C) Native type residue threonine (green) at position 285 and its surrounding amino acid residues. Substitution of methionine (red) at position 285 brings two more residues val192 and met196 within the 4 A⁰ surrounding.

Figure 3

Interaction of flavopiridol with native and mutant models of CDK7 protein. (A) Flavopiridol binds deeply with native CDK7 protein and makes contact with 12 amino acid residues. (B) Substitution of I63 with arginine reduced the binding affinity of flavopiridol in mutant model I63R. (C) Substitution of H135 with arginine, results in weak interaction of ligand flavopiridol with H135R model. (D) Flavopiridol binds shallowly on the surface of mutant model T285M and the number of amino acid contact become reduced.

Figure 4

Backbone RMSD of wild type and mutant structure of CDK7 protein. The ordinate is RMSD (nm), and the abscissa is time (ps). Black, red, green, and blue lines indicate native, I63R, H135R, and T285M mutant structures, respectively.

Figure 5

Carbon alpha RMSF of wild type and mutant structure of CDK7 protein. The ordinate is RMSF (nm), and the abscissa represents the residues. Black, red, green, and blue lines indicate the native, I63R, H135R, and T285M mutant structures, respectively.

Figure 6

Number of hydrogen bond formed in wild type and mutant structure of CDK7 protein. The ordinate is the number of hydrogen bond and the abscissa is time (ps). Black, red, green, and blue lines indicate the native, I63R, H135R, and T285M mutant structures, respectively.