Drug resistance in cancer: molecular evolution and compensatory proliferation

Mutations that lead to drug resistance limit the efficacy of antibiotics, antiviral drugs, targeted cancer therapies, and other treatments. Accurately calculating protein–drug binding affinity changes upon mutations in the drug target is of high interest as this can yield a better understanding into how such mutations drive drug‐resistance, especially when the mutation in question does not directly interfere with binding of the drug. The main aim of this article is to provide an up‐to‐date reference on the computational tools that are available for the calculation of Gibbs energy (free energy) changes upon mutation, their strengths, and limitations. The methods that are discussed include free energy calculations (free energy perturbation, thermodynamic integration, multistate Bennett acceptance ratio), analysis of molecular dynamics simulations (linear interaction energy, molecular mechanics [MM]/Poisson–Boltzmann solvated area, and MM/generalized Born solvated area), and methods that involve quantum mechanical calculations (including QM/MM). The possibility to use machine learning is also introduced. Given that the benefit of accurately calculating binding affinity changes upon mutation depends on comparing calculated values with experimental measurements, a brief survey on experimental methods and observables is provided. Examples of computational studies that go beyond calculating the Gibbs energy changes are given. Factors that need to be addressed by the computational chemist and potential pitfalls are discussed at length.This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Free Energy Methods Molecular and Statistical Mechanics > Molecular Interactions

Muscle contraction and a range of critical cellular functions rely on force-producing interactions between myosin motors and actin filaments, powered by turnover of adenosine triphosphate (ATP). The relationship between release of the ATP hydrolysis product ortophosphate (Pi) from the myosin active site and the force-generating structural change, the power-stroke, remains enigmatic despite its central role in energy transduction. Here, we present a model with multistep Pi-release that unifies current conflicting views while also revealing additional complexities of potential functional importance. The model is based on our evidence from kinetics, molecular modelling and single molecule fluorescence studies of Pi binding outside the active site. It is also consistent with high-speed atomic force microscopy movies of single myosin II molecules without Pi at the active site, showing consecutive snapshots of pre- and post-power stroke conformations. In addition to revealing critical features of energy transduction by actomyosin, the results suggest enzymatic mechanisms of potentially general relevance.

Inhibitors that bind competitively to the ATP binding pocket in the kinase domain of the oncogenic fusion protein BCR–Abl1 are used successfully in targeted therapy of chronic myeloid leukemia (CML). Such inhibitors provided the first proof of concept that kinase inhibition can succeed in a clinical setting. However, emergence of drug resistance and dose-dependent toxicities limit the effectiveness of these drugs. Therefore, treatment with a combination of drugs without overlapping resistance mechanisms appears to be an appropriate strategy. In the present work, we explore the effectiveness of combination therapies of the recently developed allosteric inhibitor asciminib with the ATP-competitive inhibitors nilotinib and dasatinib in inhibiting the BCR–Abl1 kinase activity in CML cell lines. Through these experiments, we demonstrate that asciminib significantly enhances the inhibition activity of nilotinib, but not of dasatinib. Exploring molecular mechanisms for such allosteric enhancement via systematic computational investigation incorporating molecular dynamics, metadynamics simulations, and density functional theory calculations, we found two distinct contributions. First, binding of asciminib triggers conformational changes in the inactive state of the protein, thereby making the activation process less favorable by ∼4 kcal/mol. Second, the binding of asciminib decreases the binding free energies of nilotinib by ∼3 and ∼7 kcal/mol for the wildtype and T315I-mutated protein, respectively, suggesting the possibility of reducing nilotinib dosage and lowering risk of developing resistance in the treatment of CML.

Amyloid-related diseases are a group of illnesses in which an abnormal accumulation of proteins into fibrillar structures is evident. Results from a wide range of studies, ranging from identification of amyloid-β dimers in the brain to biophysical characterization of the interactions between amyloidogenic peptides and lipid membranes during fibril growth shed light on the initial events which take place during amyloid aggregation. Accounts of fibril disaggregation and formation of globular aggregates due to interactions with lipids or fatty acids further demonstrate the complexity of the aggregation process and the difficulty to treat amyloid-related diseases. There is an inherent difficulty in generalizing from studies of aggregation in vitro, but the involvement of too many cellular components limits the ability to follow amyloid aggregation in a cellular (or extracellular) context. Fortunately, the development of experimental methods to generate stable globular aggregates suggests new means of studying the molecular events associated with amyloid aggregation. Furthermore, simulation studies enable deeper understanding of the experimental results and provide useful predictions that can be tested in the laboratory. Computer simulations can nowadays provide molecular or even atomistic details that are experimentally not available or very difficult to obtain. In the present review, recent developments on modelling and experiments of amyloid aggregation are reviewed, and an integrative account on how isolated interactions (as observed in vitro and in silico) combine during the course of amyloid-related diseases is presented. Finally, it is argued that an integrative approach is necessary to get a better understanding of the protein aggregation process.