The rapid growth of computing power and automated workflow tools has also contributed significantly in the wider application of free energy approaches in real world problems. We have recently developed an approach called thermodynamic integration with enhanced sampling (TIES)10 which utilizes the concept of ensemble simulation to yield accurate, precise, and reproducible binding affinities. TIES-PM. We apply TIES-PM to fibroblast growth factor receptor 3 (FGFR3) to investigate binding free energy changes upon protein mutations. The results show that TIES-PM with REST2 successfully captures a large conformational change and generates correct free energy differences caused by a gatekeeper mutation located in the binding pocket. Simulations without REST2 fail to overcome the energy barrier between the conformations, and hence the results are highly sensitive to the initial structures. We also discuss situations where REST2 does not improve the accuracy of predictions. 1.?Introduction Mutations enable proteins to tailor molecular recognition with small-molecule ligands and other macromolecules, and can have a major impact on drug efficacy. Rapid and accurate prediction of drug responses Guacetisal to protein mutations is vital for accomplishing the promise of personalized medicine. The use of targeted therapeutics will benefit cancer patients by matching their genetic profile to the most effective drugs available. Examples of such drugs are gefitinib and erlotinib which belong to a class of targeted cancer drugs called tyrosine kinase inhibitors. A subgroup of patients with nonsmall-cell lung cancer (NSCLC) have specific point mutations and deletions in the kinase domain name of epidermal growth factor receptor (EGFR), which are associated with gefitinib and erlotinib sensitivity. Screening for these mutations may identify patients who will have a better response to certain inhibitors. free energy calculation is one of the most powerful tools to predict the binding affinity of a drug to its target proteins. It employs all-atom molecular dynamics (MD) simulation, a physics-based approach for calculating the thermodynamic properties. The accurate prediction of the binding affinities of ligands to proteins is usually a major goal in drug discovery and personalized medicine.1 The use of methods to predict binding affinities has been largely confined to academic research until recently, primarily due to the lack of their reproducibility, as well as lack of accuracy, time to solution, and computational cost. Guacetisal Recent progress in free energy calculations, marked to some extent by the introduction of Schr?dingers Guacetisal FEP+,2 has initiated major interest in their potential power for pharmaceutical drug discovery. The improvements include new sampling protocols in order to accelerate phase space sampling,3,4 such as Hamiltonian-replica exchange (H-REMD)5 and its variants, including replica exchange with solute tempering (REST2)6 and FEP/REST.7 The replica exchange methods run multiple concurrent (parallel) simulations and occasionally swap information between replicas to improve sampling. For a given set of simulation samples, different free energy estimators8 can be applied with varying accuracy and precision, of which the multistate Bennett acceptance ratio (MBAR)9 has become increasingly popular. MBAR makes use of all microscopic says from all of the replica simulations, by reweighting them to the target Hamiltonian. The implementation of an enhanced sampling protocol such as REST26 and the use of the free energy estimator MBAR9 has been shown to improve the accuracy of the free energy calculations. The rapid growth of computing power and automated workflow tools has also contributed significantly in the wider application of free energy approaches in real world problems. We have recently developed an approach called thermodynamic integration with enhanced sampling (TIES)10 which utilizes the concept of ensemble simulation to yield accurate, precise, and reproducible binding affinities. TIES is based on one of the alchemical free energy methods, thermodynamic integration (TI), employing ensemble averages and quantification of statistical uncertainties associated with the results. 11 TIES has already been shown to perform well for a wide range of target proteins and ligands.10?13 TIES provides a route to reliable predictions of free energy differences meeting the requirements of velocity, accuracy, precision, and reliability. The results are in very good agreement with experimental data while the methods are reproducible by construction. Rabbit Polyclonal to Akt (phospho-Ser473) Variants of TIES incorporate enhanced sampling techniques REST2 and the free energy estimator MBAR.11 TIES has been shown to have a positive impact in the drug design process in the pharmaceutical domain name.12,13 Some protein mutations may fortuitously bring therapeutic benefit to some patients who use a specific drug treatment, while others may impair the ability of a drug to bind with the protein, one of the reasons for the target proteins developing drug resistance. Studying the effect of protein mutations on binding affinity is usually important for both drug Guacetisal development and for personalized medicine. The purpose of the present paper is usually to.