Molecular interactions of c-ABL mutants in complex with imatinib/nilotinib: a computational study using linear

Elen Gomes Pereira1, Miguel Angelo Martins Moreira, Ernesto Raúl Caffarena

  • 1Laboratório Nacional de Computação Científica, LNCC, Av. Getúlio Vargas, 333, Petrópolis, RJ, Brazil, 25651-075.

Insights

Computational simulations using the linear interaction energy (LIE) method effectively predicted drug resistance in chronic myeloid leukemia (CML). This approach revealed how mutations and water molecule interactions affect tyrosine kinase inhibitor efficacy.

Area of Science:

  • Biochemistry
  • Computational Chemistry
  • Pharmacology

Background:

  • Imatinib is effective for chronic myeloid leukemia (CML), but resistance emerges due to BCR-ABL gene mutations.
  • Tyrosine kinase inhibitors are developed to overcome imatinib resistance.
  • Computational simulations are valuable for understanding drug-protein interactions.

Purpose of the Study:

  • To assess the feasibility of molecular dynamics and the linear interaction energy (LIE) method for studying drug resistance in CML.
  • To comparatively analyze the interaction of imatinib and nilotinib with wild-type c-ABL and its mutants.
  • To investigate the role of water molecule behavior in c-ABL mutants and its link to drug resistance.

Main Methods:

  • Comparative molecular dynamics simulations using the semi-empirical linear interaction energy (LIE) method.
  • Analysis of interactions between imatinib/nilotinib and wild-type c-ABL protein along with 12 mutants.
  • Investigation of water molecule dynamics around specific residues (Glu286, Lys271, Asp381) in c-ABL.

Main Results:

  • The LIE method accurately predicted the superior inhibitory activity of nilotinib over imatinib.
  • LIE successfully predicted resistance to inhibition in c-ABL mutants.
  • Increased water molecule turnover around specific residues in c-ABL mutants correlated with imatinib resistance.

Conclusions:

  • Molecular dynamics simulations with the LIE method are suitable for predicting drug efficacy and resistance in CML.
  • Understanding water molecule dynamics offers insights into the mechanisms of drug resistance.
  • This computational approach aids in the development of more effective tyrosine kinase inhibitors for CML treatment.

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