Exploring the cause of drug resistance by the detrimental missense mutations in KIT receptor: computational approach

Secondary Resistant Mutations to Small Molecule Inhibitors in Cancer Cells

Secondary resistant mutations in cancer cells arise in response to certain small molecule inhibitors. These mutations inevitably cause recurrence and often progression to a more aggressive form. Resistant mutations may manifest in various forms. For example, some mutations decrease or abrogate the affinity of the drug for the protein. Others restore the function of the enzyme even in the presence of the inhibitor. In some cases, resistance is acquired through activation of a parallel pathway which bypasses the function of the drug targeted pathway. The Catalogue of Somatic Mutations in Cancer (COSMIC) produced a compendium of resistant mutations to small molecule inhibitors reported in the literature. Here, we build on these data and provide a comprehensive review of resistant mutations in cancers. We also discuss mechanistic parallels of resistance.

In Silico Analysis of SNPs in PARK2 and PINK1 Genes That Potentially Cause Autosomal Recessive Parkinson Disease

Introduction. Parkinson's disease (PD) is a common neurodegenerative disorder. Mutations in PINK1 are the second most common agents causing autosomal recessive, early onset PD. We aimed to identify the pathogenic SNPs in PARK2 and PINK1 using in silico prediction software and their effect on the structure, function, and regulation of the proteins. Materials and Methods. We carried out in silico prediction of structural effect of each SNP using different bioinformatics tools to predict substitution influence on protein structure and function. Result. Twenty-one SNPs in PARK2 gene were found to affect transcription factor binding activity. 185 SNPs were found to affect splicing. Ten SNPs were found to affect the miRNA binding site. Two SNPs rs55961220 and rs56092260 affected the structure, function, and stability of Parkin protein. In PINK1 gene only one SNP (rs7349186) was found to affect the structure, function, and stability of the PINK1 protein. Ten SNPs were found to affect the microRNA binding site. Conclusion. Better understanding of Parkinson's disease caused by mutations in PARK2 and PINK1 genes was achieved using in silico prediction. Further studies should be conducted with a special consideration of the ethnic diversity of the different populations.

Insight on Mutation-Induced Resistance from Molecular Dynamics Simulations of the Native and Mutated CSF-1R and KIT

The receptors tyrosine kinases (RTKs) for the colony stimulating factor-1, CSF-1R, and for the stem cell factor, SCFR or KIT, are important mediators of signal transduction. The abnormal function of these receptors, promoted by gain-of-function mutations, leads to their constitutive activation, associated with cancer or other proliferative diseases. A secondary effect of the mutations is the alteration of receptors' sensitivity to tyrosine kinase inhibitors, compromising effectiveness of these molecules in clinical treatment. In particular, the mutation V560G in KIT increases its sensitivity to Imatinib, while the D816V in KIT, and D802V in CSF-1R, triggers resistance to the drug. We analyzed the Imatinib binding affinity to the native and mutated KIT (mutations V560G, S628N and D816V) and CSF-1R (mutation D802V) by using molecular dynamics simulations and energy calculations of Imatinib•target complexes. Further, we evaluated the sensitivity of the studied KIT receptors to Imatinib by measuring the inhibition of KIT phosphorylation. Our study showed that (i) the binding free energy of Imatinib to the targets is highly correlated with their experimentally measured sensitivity; (ii) the electrostatic interactions are a decisive factor affecting the binding energy; (iii) the most deleterious impact to the Imatinib sensitivity is promoted by D802V (CSF-1R) and D816V (KIT) mutations; (iv) the role of the juxtamembrane region, JMR, in the imatinib binding is accessory. These findings contribute to a better description of the mutation-induced effects alternating the targets sensitivity to Imatinib.

Computational identification of significant missense mutations in AKT1 gene

The AKT1 gene is of supreme importance in cell signaling and human cancer. In the present study, we aim to understand the phenotype variations that were believed to have the highest impact in AKT1 gene by different computational approaches. The analysis was initiated with SIFT tool followed by PolyPhen 2.0, I-Mutant 2.0, and SNPs&GO tools with the aid of 22 nonsynonymous (nsSNPs) retrieved from dbSNP. A total of five AKT1 variants such as E17K, E17S, E319G, L357P, and P388T are found to exert deleterious effects on the protein structure and function. Furthermore, the molecular docking study indicates the lesser binding affinity of inhibitor with the mutant structure than the native type. In addition, root mean square deviation and hydrogen bond details were also analyzed in the 10 ns molecular dynamics simulation study. These computational evidences suggested that E17K, E17S, E319G, L357P, and P388T variants of AKT1 could destabilize the protein networks, thus causing functional deviations of protein to some extent. Moreover, the findings strongly indicate that screening for AKT1, E17K, E17S, E319G, L357P, and P388T variants may be useful for disease molecular diagnosis and also to design the potential AKT inhibitors.

Successful treatment of mast cell activation syndrome with sunitinib

Mast cell (MC) activation syndrome (MCAS) is a recently recognized, likely prevalent collection of heterogeneous illnesses of inappropriate MC activation with little to no MC neoplasia likely driven by heterogeneous patterns of constitutively activating mutations in MC regulatory elements including various tyrosine kinases (TKs, dominantly KIT). MCAS typically presents as chronic multisystem polymorbidity of generally inflammatory ± allergic theme. As with indolent systemic mastocytosis (SM), treatment of MCAS focuses more against MC mediators than MC neoplasia, but some cases prove refractory even to the TK inhibitor (TKI) imatinib reported useful both in uncommon SM cases not bearing SM's usual imatinib-resistant KIT-D816V mutation and in some cases of MCAS (which rarely bears KIT-D816V). Most allergy is principally a MC activation phenomenon and sunitinib is a multitargeted TKI shown helpful in controlling a murine model of oral allergy syndrome. We present the first report of use of sunitinib in life-threatening MCAS refractory to multiple agents including imatinib achieving immediate, complete, sustained, non-toxic remission suggesting a new option for treatment of aggressive MC disease.

Computational analysis of the binding specificity of Gleevec to Abl, c-Kit, Lck, and c-Src tyrosine kinases

Gleevec, a well-known cancer therapeutic agent, is an effective inhibitor of several tyrosine kinases, including Abl and c-Kit, but displays less potency to inhibit closely homologous tyrosine kinases, such as Lck and c-Src. Because many structural features of the binding site are highly conserved in these homologous kinases, the molecular determinants responsible for the binding specificity of Gleevec remain poorly understood. To address this issue, free energy perturbation molecular dynamics (FEP/MD) simulations with explicit solvent was used to compute the binding affinity of Gleevec to Abl, c-Kit, Lck, and c-Src. The results of the FEP/MD calculations are in good agreement with experiments, enabling a detailed and quantitative dissection of the absolute binding free energy in terms of various thermodynamic contributions affecting the binding specificity of Gleevec to the kinases. Dominant binding free energy contributions arises from the van der Waals dispersive interaction, compensating about two-thirds of the unfavorable free energy penalty associated with the loss of translational, rotational, and conformational freedom of the ligand upon binding. In contrast, the contributions from electrostatic and repulsive interactions nearly cancel out due to solvent effects. Furthermore, the calculations show the importance of the conformation of the kinase activation loop. Among the kinases examined, Abl provides the most favorable binding environment for Gleevec via optimal protein-ligand interactions and a small free energy cost for loss of the translational, rotational, and conformational freedom upon ligand binding. The FEP/MD calculations additionally reveal that Lck and c-Src provide similar nonbinding interactions with the bound-Gleevec, but the former pays less entropic penalty for the ligand losing its translational, rotational, and conformational motions to bind, examining the empirically observed differential binding affinities of Gleevec between the two Src-family kinases.

Bioinformatics and variability in drug response: a protein structural perspective

Marketed drugs frequently perform worse in clinical practice than in the clinical trials on which their approval is based. Many therapeutic compounds are ineffective for a large subpopulation of patients to whom they are prescribed; worse, a significant fraction of patients experience adverse effects more severe than anticipated. The unacceptable risk-benefit profile for many drugs mandates a paradigm shift towards personalized medicine. However, prior to adoption of patient-specific approaches, it is useful to understand the molecular details underlying variable drug response among diverse patient populations. Over the past decade, progress in structural genomics led to an explosion of available three-dimensional structures of drug target proteins while efforts in pharmacogenetics offered insights into polymorphisms correlated with differential therapeutic outcomes. Together these advances provide the opportunity to examine how altered protein structures arising from genetic differences affect protein-drug interactions and, ultimately, drug response. In this review, we first summarize structural characteristics of protein targets and common mechanisms of drug interactions. Next, we describe the impact of coding mutations on protein structures and drug response. Finally, we highlight tools for analysing protein structures and protein-drug interactions and discuss their application for understanding altered drug responses associated with protein structural variants.

Analyzing effects of naturally occurring missense mutations

Single-point mutation in genome, for example, single-nucleotide polymorphism (SNP) or rare genetic mutation, is the change of a single nucleotide for another in the genome sequence. Some of them will produce an amino acid substitution in the corresponding protein sequence (missense mutations); others will not. This paper focuses on genetic mutations resulting in a change in the amino acid sequence of the corresponding protein and how to assess their effects on protein wild-type characteristics. The existing methods and approaches for predicting the effects of mutation on protein stability, structure, and dynamics are outlined and discussed with respect to their underlying principles. Available resources, either as stand-alone applications or webservers, are pointed out as well. It is emphasized that understanding the molecular mechanisms behind these effects due to these missense mutations is of critical importance for detecting disease-causing mutations. The paper provides several examples of the application of 3D structure-based methods to model the effects of protein stability and protein-protein interactions caused by missense mutations as well.