Computational studies of protein–drug binding affinity changes upon mutations in the drug target

Abstract

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.

Evaluation of residue variability in a conformation‐specific context and during evolutionary sequence reconstruction narrows drug resistance selection in Abl1 tyrosine kinase

“…Patients with CML may develop resistance against drugs that were specially developed to target Abl1‐KD 8 and any new drugs may be subjected to reduced efficacy due to resistant mutations. 4 , 6 Thus, there is a need to counter drug resistance occurring during CML targeted therapy and during any disease where the risk of resistance is significant. We sought to understand Abl1 resistance susceptibility with two approaches, by systematically evaluating conformational energy plasticity due to all possible substitutions in the isolated Abl1‐KD and along its evolutionary trajectory.…”

“…Better approaches are therefore needed that take into the account possible resistance mutations during the initial stages of drug development. 4 …”

“…However, the development of new drugs more robust toward resistance mutations is inefficient due to the limitations of treatment period versus the time needed for escape mutations to appear. Better approaches are therefore needed that take into the account possible resistance mutations during the initial stages of drug development 4 …”

“…Better approaches are therefore needed that take into the account possible resistance mutations during the initial stages of drug development. 4 Loss of control mechanisms during cell growth may lead to cancer, as in chronic myeloid leukemia (CML), when the constitutively active BCR-Abl1 oncogene is expressed in the stem cells of bone marrow. 5,6 First line therapy against CML uses small molecule inhibitors as imantinib, dasatinib, nilotinib, and bosutinib that prevent ATP binding to the kinase domain (KD) of BCR-Abl1, leading to growth impairment followed by cell death.…”

Correction Schemes for Absolute Binding Free Energies Involving Lipid Bilayers

“…Though such a plasma does indeed maintain the neutrality of the simulation box, it generates a size-dependent artifact. This size-dependent artifact exists in free-energy estimates of ligand binding free-energy calculations involving changes of charge, 7 charge-perturbing protein mutations, 8 and protonation free-energy calculations. 9 …”