Spermidine as an inhibiting agent of the protein of myotonic dystrophy type 1 (MBNL‐1) using computational chemistry

Authors

  • Carlos Chuquimango Centro de Investigacion en Ingenieria Molecular, Vicerrectorado de Investigacion, Universidad Catolica de Santa Maria, Arequipa, Peru

DOI:

https://doi.org/10.21754/tecnia.v27i1.129

Keywords:

inhibiting agent, myotonic dystrophy type 1, spermidine, quantum mechanics, molecular mechanics, computational chemistry

Abstract

Spermidine is a polyamine compound found in fruits, vegetables, meats and milk products. It is also known for its role as a modulator of functions for DNA, RNA, nucleotide triphosphates and proteins. Some recent experimental studies have shown that it has an inhibitory function on the protein related to the pathogenesis of myotonic dystrophy type 1 (MBNL‐1), providing some hope in relation to this disease presently without cure. However these studies have not provided sufficient information to explain its inhibitory role. In the present work, the existing interaction between spermidine and the MBNL‐1 protein has been confirmed by computational chemistry. The quantum mechanics analysis was carried out with the CAM‐B3LYP density functional, using a TZVP basis set. For the molecular mechanics, the OPLS‐AA force field was used, followed immediately by the solvation of the MBNL‐1 protein, which stabilized at less than 0.3 nm. As an NVT canonical ensemble (constant volume, temperature and number of moles) was used, the process can simulate what is happening at the physiological temperature of a human body. Furthermore, a Ramachandran analysis allowed the validation of the structure, which was preserved through time (100 ns), at a 98.4 % confidence level. It was therefore demonstrated that the in silico interaction between spermidine and the MBNL‐1 protein was similar to the reported interactions in vivo and in vitro.

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Published

2017-06-01

How to Cite

[1]
C. Chuquimango, “Spermidine as an inhibiting agent of the protein of myotonic dystrophy type 1 (MBNL‐1) using computational chemistry”, TEC, vol. 27, no. 1, pp. 79–84, Jun. 2017.

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