Free Energy Calculations on the Two Drug Binding Sites in the M2 Proton Channel

2011 ◽  
Vol 133 (28) ◽  
pp. 10817-10825 ◽  
Author(s):  
Ruo-Xu Gu ◽  
Limin Angela Liu ◽  
Dong-Qing Wei ◽  
Jian-Guo Du ◽  
Lei Liu ◽  
...  
Keyword(s):  
Free Energy ◽  
Binding Sites ◽  
Free Energy Calculations ◽  
Drug Binding ◽  
Proton Channel ◽  
Energy Calculations ◽  
Drug Binding Sites ◽  
M2 Proton Channel

Interpreting Thermodynamic Profiles of Aminoadamantane Compounds Inhibiting the M2 Proton Channel of Influenza A by Free Energy Calculations

2016 ◽  
Vol 56 (1) ◽  
pp. 110-126 ◽  
Author(s):  
Nadine Homeyer ◽  
Harris Ioannidis ◽  
Felix Kolarov ◽  
Günter Gauglitz ◽  
Christos Zikos ◽  
...  
Keyword(s):  
Free Energy ◽  
Influenza A ◽  
Free Energy Calculations ◽  
Proton Channel ◽  
Energy Calculations ◽  
M2 Proton Channel

Energetic analysis of the two controversial drug binding sites of the M2 proton channel in influenza A virus

2009 ◽  
Vol 259 (1) ◽  
pp. 159-164 ◽  
Author(s):  
Qi-Shi Du ◽  
Ri-Bo Huang ◽  
Cheng-Hua Wang ◽  
Xiao-Ming Li ◽  
Kuo-Chen Chou
Keyword(s):  
Influenza A Virus ◽  
Binding Sites ◽  
Influenza A ◽  
Drug Binding ◽  
Proton Channel ◽  
Energetic Analysis ◽  
Drug Binding Sites ◽  
M2 Proton Channel

Applications of rare event dynamics on the free energy calculations for membrane protein systems

2013 ◽  
Vol 91 (9) ◽  
pp. 769-774 ◽  
Author(s):  
Yun-Kun Wang ◽  
Dong-Qing Wei ◽  
Ruo-Xu Gu ◽  
Huai-Meng Fan ◽  
Jakob Ulmschneider
Keyword(s):  
Free Energy ◽  
Rare Event ◽  
Free Energy Calculations ◽  
Drug Binding ◽  
Proton Channel ◽  
Active Peptides ◽  
Biochemical Simulation ◽  
Dynamics Simulations ◽  
Event Dynamics ◽  
Energy Information

Techniques of rare event dynamics were reviewed, including string methods, which will be implemented with the biochemical simulation packages. The existing methods were applied to study biological systems with relevance to drug design and drug metabolism. The rare event dynamics simulations were performed to understand the kinetic and thermodynamic free energy information on the drug binding sites in the M2 proton channel, the free energy of insertion and association of membrane proteins and membrane active peptides. Results give a theoretical framework to interpret and reconcile existing and often conflicting opinions.

scholarly journals Is structure based drug design ready for selectivity optimization?

2020 ◽  
Author(s):  
Steven K. Albanese ◽  
John D. Chodera ◽  
Andrea Volkamer ◽  
Simon Keng ◽  
Robert Abel ◽  
...  
Keyword(s):  
Free Energy ◽  
Systematic Error ◽  
Small Molecule ◽  
Binding Sites ◽  
Statistical Error ◽  
Systematic Errors ◽  
Free Energy Calculations ◽  
Energy Calculation ◽  
Structure Based Drug Design ◽  
Energy Calculations

AbstractAlchemical free energy calculations are now widely used to drive or maintain potency in small molecule lead optimization with a roughly 1 kcal/mol accuracy. Despite this, the potential to use free energy calculations to drive optimization of compound selectivity among two similar targets has been relatively unexplored in published studies. In the most optimistic scenario, the similarity of binding sites might lead to a fortuitous cancellation of errors and allow selectivity to be predicted more accurately than affinity. Here, we assess the accuracy with which selectivity can be predicted in the context of small molecule kinase inhibitors, considering the very similar binding sites of human kinases CDK2 and CDK9, as well as another series of ligands attempting to achieve selectivity between the more distantly related kinases CDK2 and ERK2. Using a Bayesian analysis approach, we separate systematic from statistical error and quantify the correlation in systematic errors between selectivity targets. We find that, in the CDK2/CDK9 case, a high correlation in systematic errors suggests free energy calculations can have significant impact in aiding chemists in achieving selectivity, while in more distantly related kinases (CDK2/ERK2), the correlation in systematic error suggests fortuitous cancellation may even occur between systems that are not as closely related. In both cases, the correlation in systematic error suggests that longer simulations are beneficial to properly balance statistical error with systematic error to take full advantage of the increase in apparent free energy calculation accuracy in selectivity prediction.

scholarly journals Multiple binding modes of ibuprofen in human serum albumin identified by absolute binding free energy calculations

2016 ◽  
Author(s):  
Stefania Evoli ◽  
David L. Mobley ◽  
Rita Guzzi ◽  
Bruno Rizzuti
Keyword(s):  
Free Energy ◽  
Human Serum Albumin ◽  
Serum Albumin ◽  
Human Serum ◽  
Binding Sites ◽  
Free Energy Calculations ◽  
Binding Modes ◽  
Energy Calculations ◽  
Multiple Binding Sites ◽  
Multiple Binding

AbstractHuman serum albumin possesses multiple binding sites and transports a wide range of ligands that include the anti-inflammatory drug ibuprofen. A complete map of the binding sites of ibuprofen in albumin is difficult to obtain in traditional experiments, because of the structural adaptability of this protein in accommodating small ligands. In this work, we provide a set of predictions covering the geometry, affinity of binding and protonation state for the pharmaceutically most active form (S– isomer) of ibuprofen to albumin, by using absolute binding free energy calculations in combination with classical molecular dynamics (MD) simulations and molecular docking. The most favorable binding modes correctly reproduce several experimentally identified binding locations, which include the two Sudlow’s drug sites (DS2 and DS1) and the fatty acid binding sites 6 and 2 (FA6 and FA2). Previously unknown details of the binding conformations were revealed for some of them, and formerly undetected binding modes were found in other protein sites. The calculated binding affinities exhibit trends which seem to agree with the available experimental data, and drastically degrade when the ligand is modeled in a protonated (neutral) state, indicating that ibuprofen associates with albumin preferentially in its charged form. These findings provide a detailed description of the binding of ibuprofen, help to explain a wide range of results reported in the literature in the last decades, and demonstrate the possibility of using simulation methods to predict ligand binding to albumin.Graphical abstractFocusAlchemical free energy methods can identify favored binding modes of a ligand within a large protein with multiple binding sitesHighlightsHuman serum albumin binds the anti-inflammatory drug ibuprofen in multiple sitesAlchemical free energy calculations predicted favored binding modes of ibuprofenBound geometry, affinity and protonation state of the ligand were determinedSimulations identified a number of previously undetected binding sites for ibuprofenFree energy methods can be used to study large proteins with multiple binding sites

Automated Assessment of Binding Affinity via Alchemical Free Energy Calculations

2020 ◽  
Author(s):  
Maximilian Kuhn ◽  
Stuart Firth-Clark ◽  
Paolo Tosco ◽  
Antonia S. J. S. Mey ◽  
Mark Mackey ◽  
...  
Keyword(s):  
Free Energy ◽  
Protein Structures ◽  
Free Energy Calculations ◽  
Distinct Advantage ◽  
Binding Affinities ◽  
Structure Based Drug Design ◽  
Energy Calculations ◽  
Kendall’S Τ ◽  
Experimental Values ◽  
Better Than

Free energy calculations have seen increased usage in structure-based drug design. Despite the rising interest, automation of the complex calculations and subsequent analysis of their results are still hampered by the restricted choice of available tools. In this work, an application for automated setup and processing of free energy calculations is presented. Several sanity checks for assessing the reliability of the calculations were implemented, constituting a distinct advantage over existing open-source tools. The underlying workflow is built on top of the software Sire, SOMD, BioSimSpace and OpenMM and uses the AMBER14SB and GAFF2.1 force fields. It was validated on two datasets originally composed by Schrödinger, consisting of 14 protein structures and 220 ligands. Predicted binding affinities were in good agreement with experimental values. For the larger dataset the average correlation coefficient Rp was 0.70 ± 0.05 and average Kendall’s τ was 0.53 ± 0.05 which is broadly comparable to or better than previously reported results using other methods. <br>

Sign in / Sign up

Export Citation Format

Share Document