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Updated: May 22, 2026

A Method for Screening and Validation of Resistant Mutations Against Kinase Inhibitors
Published on: December 7, 2014
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.
Abstract:
In spite of the effectiveness of Imatinib for chronic myeloid leukemia (CML) treatment, resistance has repeatedly been reported and is associated with point mutations in the BCR-ABL chimeric gene. To overcome this resistance, several inhibitors of BCR-ABL tyrosine kinase activity were developed. In this context, computational simulations have become a powerful tool for understanding drug-protein interactions. Herein, we report a comparative molecular dynamics analysis of the interaction between two tyrosine kinase inhibitors (imatinib or nilotinib) against wild type c-ABL protein and 12 mutants, using the semi-empirical linear interaction energy (LIE) method, to assess the feasibility of this approach for studying resistance against the inhibitory activity of these drugs. In addition, to understand the structural changes that are associated with resistance, we describe the behavior of water molecules that interact simultaneously with specific residues (Glu286, Lys271 and Asp381) of c-ABL (wild type or mutant) and their relationship with drug resistance. Experimental IC50 values for the interaction between imatinib, wild type c-ABL, and 12 mutants were used to obtain the proper LIE coefficients (α, β and γ) to estimate the free energy of the binding of imatinib with wild-type and mutant proteins, and values were extrapolated for the analysis of the nilotinib/c-ABL interaction. Our results indicate that LIE was suitable to predict the superior inhibitory activity of nilotinib and the resistance to inhibition that was observed in c-ABL mutants. Additionally, for c-ABL mutants, the observed number of water molecules being turned over while interacting with amino acids Glu286, Lys271 and Asp381 was associated with resistance to imatinib, resulting in a less effective inhibition of the kinase activity.
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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