Tugba G. Kucukkal

Tugba G. Kucukkal

*This is an algorithmically generated author record based on publicly available information.

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Tugba G. Kucukkal has a total of 7 co-authors. Here are the top co-authors by number of shared publications:


Latest Publications

  • Open Access

    Mutations in the KDM5C ARID Domain and Their Plausible Association with Syndromic Claes-Jensen-Type Disease

    by Yunhui Peng, Jimmy Suryadi, Ye Yang, Tugba G. Kucukkal, Weiguo Cao, Emil Alexov

    Mutations in KDM5C gene are linked to X-linked mental retardation, the syndromic Claes-Jensen-type disease. This study focuses on non-synonymous mutations in the KDM5C ARID domain and evaluates the effects of two disease-associated missense mutations (A77T and D87G) and three not-yet-classified missense mutations (R108W, N142S, and R179H). We predict the ARID domain’s folding and binding free energy changes due to mutations, and also study the effects of mutations on protein dynamics. Our computational results indicate that A77T and D87G mutants have minimal effect on the KDM5C ARID domain stability and DNA binding. In parallel, the change in the free energy unfolding caused by the mutants A77T and D87G were experimentally measured by urea-induced unfolding experiments and were shown to be similar to the in silico predictions. The evolutionary conservation analysis shows that the disease-associated mutations are located in a highly-conserved part of the ARID structure (N-terminal domain), indicating their importance for the KDM5C function. N-terminal residues’ high conservation suggests that either the ARID domain utilizes the N-terminal to interact with other KDM5C domains or the N-terminal is involved in some yet unknown function. The analysis indicates that, among the non-classified mutations, R108W is possibly a disease-associated mutation, while N142S and R179H are probably harmless.


  • Open Access

    Structural, Dynamical, and Energetical Consequences of Rett Syndrome Mutation R133C in MeCP2

    by Tugba G. Kucukkal, Emil Alexov

    Rett Syndrome (RTT) is a progressive neurodevelopmental disease affecting females. RTT is caused by mutations in theMECP2gene and various amino acid substitutions have been identified clinically in different domains of the multifunctional MeCP2 protein encoded by this gene. The R133C variant in the methylated-CpG-binding domain (MBD) of MeCP2 is the second most common disease-causing mutation in the MBD. Comparative molecular dynamics simulations of R133C mutant and wild-type MBD have been performed to understand the impact of the mutation on structure, dynamics, and interactions of the protein and subsequently understand the disease mechanism. Two salt bridges within the protein and two critical hydrogen bonds between the protein and DNA are lost upon the R133C mutation. The mutation was found to weaken the interaction with DNA and also cause loss of helicity within the 141-144 region. The structural, dynamical, and energetical consequences of R133C mutation were investigated in detail at the atomic resolution. Several important implications of this have been shown regarding protein stability and hydration dynamics as well as its interaction with DNA. The results are in agreement with previous experimental studies and further provide atomic level understanding of the molecular origin of RTT associated with R133C variant.


  • Open Access

    Computational and Experimental Approaches to Reveal the Effects of Single Nucleotide Polymorphisms with Respect to Disease Diagnostics

    by Tugba G. Kucukkal, Ye Yang, Susan C. Chapman, Weiguo Cao, Emil Alexov

    DNA mutations are the cause of many human diseases and they are the reason for natural differences among individuals by affecting the structure, function, interactions, and other properties of DNA and expressed proteins. The ability to predict whether a given mutation is disease-causing or harmless is of great importance for the early detection of patients with a high risk of developing a particular disease and would pave the way for personalized medicine and diagnostics. Here we review existing methods and techniques to study and predict the effects of DNA mutations from three different perspectives: in silico, in vitro and in vivo. It is emphasized that the problem is complicated and successful detection of a pathogenic mutation frequently requires a combination of several methods and a knowledge of the biological phenomena associated with the corresponding macromolecules.