Exploration of Ca⁺² Binding Affinity to DEAD-Box Helicase through Computational Approaches


  • Shehryar Iqbal Department of National Center of Bioinformatics, Quaid-i-Azam University, 44000 Islamabad, Pakistan
  • Zafar Iqbal Department of Computer Science, MY University, Islamabad, 44000, Pakistan
  • Hamid Hussain Department of Computer Science, MY University, Islamabad, 44000, Pakistan
  • Usman Mauvia Department of Computer Science, Comsats University, Islamabad, 44000, Pakistan
  • Muhamamd Sajid Rehman Department of Computer Science, Qurtuba University of Sci. & IT, Peshawar, 25000, Pakistan
  • Ali Raza Department of Computer Science, MY University, Islamabad, 44000, Pakistan




CPVT, Calmodulin (CALM), in_silico analysis, P68(Pep-IQ), pathological impact, conformational changes, Neuropathological disorder


Catecholaminergic polymorphic ventricular tachycardia (CPVT) is an occasional catastrophic fatal autosomal dominant or recessive inherited disease that affects an estimated ≈1-5000/10000 people including children, adolescents and young adults, which may cause syncope, abrupt cardiac death during exercise and emotional state. Calmodulin (CALM) functions as a messenger protein of intracellular Ca+2 signaling in cardiomyocytes that transmits complex Ca+2 ions to the proteins involved in cardiac contraction, and its activation is also facilitated by the binding of Ca+2 ions. CALM structure contains 4 EF-hands, each EF-hand holds a single Ca+2 ion (designated as, CA149, CA150, CA151 and CA152). In this study, we performed detailed in_silico analysis of normal and mutated (ASN53ILE) CALM structures to characterize their Ca+2 binding abilities. In CALM-ASN53ILE-Pep-IQ complex, we observed a binding shift of P68(Pep-IQ) as compared to CALM-WT. The root mean square deviation was in the range of 0.4-1 nm for all the systems, while root mean square fluctuation values were in the range of 0.3-0.6 nm for bound versus unbound proteins. Hydrogen-bond profiling was significantly different between CALM-WT and CALM-ASN53ILE over the course of simulation. We observed an introduction of β1 and β2-segment between α1- α2 and α3- α4 along with the movement of C-terminal approximately to 1800 in the apo-CALM-ASN53ILE. Thus, we propose that, ASN53ILE has a pathological impact in the progression of CPVT due to structural and conformational changes in CALM and its binding affinity towards P68(Pep-IQ). The current study may constitute a valuable starting point for CPVT therapeutics through the involvement of CALM-ASN53ILE for designing novel inhibitors to cope with neuropathological disorder.


A. R. Pérez‐Riera, R. Barbosa‐Barros, M. P. de Rezende Barbosa, R. Daminello‐Raimundo, A. A. de Lucca Jr, and L. C. de Abreu. (2018). Catecholaminergic polymorphic ventricular tachycardia, an update. Ann. Noninvasive Electrocardiol., 23(4), e12512.

D. Reid, M. Tynan, L. Braidwood, and G. Fitzgerald. (1975). Bidirectional tachycardia in a child. A study using His bundle electrography. Heart, 37(3), 339-344.

K. Kistamas, R. Veress, B. Horvath, T. Banyasz, P. P. Nanasi, and D. A. Eisner. (2020). Calcium handling defects and cardiac arrhythmia syndromes. Front. Pharmacol., 11, 72.

F. Van Petegem. (2012). Ryanodine receptors: structure and function. J. Biol. Chem., 287(38), 31624-31632.

J. T. Lanner, D. K. Georgiou, A. D. Joshi, and S. L. Hamilton. (2010). Ryanodine receptors: structure, expression, molecular details, and function in calcium release. Cold Spring Harb. Perspect. Biol., 2(11), a003996.

W. J. Chazin and C. N. Johnson. (2020). Calmodulin mutations associated with heart arrhythmia: a status report. Int. J. Mol. Sci., 21(4), 1418.

M. Ben-Johny and D. T. Yue. (2014). Calmodulin regulation (calmodulation) of voltage-gated calcium channels. J. Gen. Physiol., 143(6), 679-692.

K. Kontula, P. J. Laitinen, A. Lehtonen, L. Toivonen, M. Viitasalo, and H. Swan. (2005). Catecholaminergic polymorphic ventricular tachycardia: recent mechanistic insights. Cardiovasc. Res., 67(3), 379-387.

M. Nyegaard et al. (2012). Mutations in calmodulin cause ventricular tachycardia and sudden cardiac death. The American Journal of Human Genetics, 91(4), 703-712.

T.-Y. Dai et al. (2014). P68 RNA helicase as a molecular target for cancer therapy. J. Exp. Clin. Cancer Res., 33(1), 1-8.

R. Janknecht. (2010). Multi-talented DEAD-box proteins and potential tumor promoters: p68 RNA helicase (DDX5) and its paralog, p72 RNA helicase (DDX17). American Journal of Translational Research, 2(3), 223.

H. Wang, X. Gao, J. J. Yang, and Z.-R. Liu. (2013). Interaction between p68 RNA helicase and Ca2+-calmodulin promotes cell migration and metastasis. Nature Communications, 4(1), 1354.

S. F. Altschul et al. (1997). Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Research, 25(17), 3389-3402.

B. Webb and A. Sali. (2017). Protein structure modeling with MODELLER. Functional Genomics: Methods and Protocols, 39-54.

E. F. Pettersen et al. (2004). UCSF Chimera—A visualization system for exploratory research and analysis. Journal of Computational Chemistry, 25(13),1605-1612.

Y. Wardi. (1988). A stochastic steepest-descent algorithm. Journal of Optimization Theory and Applications, 59, 307-323.

Y. Dai, J. Han, G. Liu, D. Sun, H. Yin, and Y.-x. Yuan. (2000). Convergence properties of nonlinear conjugate gradient methods. SIAM Journal on Optimization, 10(2), 345-358.

E. C. Meng, E. F. Pettersen, G. S. Couch, C. C. Huang, and T. E. Ferrin. (2006). Tools for integrated sequence-structure analysis with UCSF Chimera. BMC Bioinformatics, 7, 1-10.

V. B. Chen et al. (2010). MolProbity: All-atom structure validation for macromolecular crystallography. Acta Crystallogr. Sect. D. Biol. Crystallogr., 66(1), 12-21.

C. Zhang, G. Vasmatzis, J. L. Cornette, and C. DeLisi. (1997). Determination of atomic desolvation energies from the structures of crystallized proteins. Journal of Molecular Biology, 267(3), 707-726.

D. Kozakov, R. Brenke, S. R. Comeau, and S. Vajda. (2006). PIPER: An FFT‐based protein docking program with pairwise potentials. Proteins: Structure, Function, and Bioinformatics, 65(2), 392-406.

D. Kozakov et al. (2013). How good is automated protein docking? Proteins: Structure, Function, and Bioinformatics, 81(12), 2159-2166.

M. J. Abraham et al. (2015). GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX, 1, 19-25.

I. McDonald. (1972). NpT-ensemble Monte Carlo calculations for binary liquid mixtures. Molecular Physics, 23(1), 41-58.