Facet Selectivity of Binding on Quartz Surfaces: Free Energy Calculations of Amino-Acid Analogue Adsorption

2012 ◽  
Vol 116 (4) ◽  
pp. 2933-2945 ◽  
Author(s):  
Louise B. Wright ◽  
Tiffany R. Walsh
Keyword(s):  
Free Energy ◽  
Amino Acid ◽  
Free Energy Calculations ◽  
Amino Acid Analogue ◽  
Energy Calculations ◽  
Acid Analogue

scholarly journals Microscopic Characterization of the Chloride Permeation Pathway in the Human Excitatory Amino Acid Transporter 1 (EAAT1)

2021 ◽  
Author(s):  
Shashank Pant ◽  
Qianyi Wu ◽  
Renae M Ryan ◽  
Emad Tajkhorshid
Keyword(s):  
Free Energy ◽  
Amino Acid ◽  
Conformational Changes ◽  
Large Scale ◽  
Excitatory Amino Acid ◽  
Free Energy Calculations ◽  
Amino Acid Transporters ◽  
Coupled Transport ◽  
Energy Calculations ◽  
Transport Cycle

Excitatory amino acid transporters (EAATs) are glutamate transporters that belong to the solute carrier 1A (SLC1A) family. They couple glutamate transport to the co-transport of three sodium (Na+) ions and one proton (H+) and the counter-transport of one potassium (K+) ion. In addition to this coupled transport, binding of substrate and Na+ ions to EAATs activates a thermodynamically uncoupled chloride (Cl-) conductance. Structures of SLC1A family members have revealed that these transporters use a twisting elevator mechanism of transport, where a mobile transport domain carries substrate and coupled ions across the membrane, while a static scaffold domain anchors the transporter in the membrane. We have recently demonstrated that the uncoupled Cl- conductance is activated by the formation of an aqueous pore at the domain interface during the transport cycle in archaeal GltPh. However, a pathway for the uncoupled Cl- conductance has not been reported for the EAATs and it is unclear if such a pathway is conserved. Here, we employ all-atom molecular dynamics (MD) simulations combined with enhanced sampling, free-energy calculations, and experimental mutagenesis to approximate large-scale conformational changes during the transport process and identified a Cl- conducting conformation in human EAAT1. We were able to extensively sample the large-scale structural transitions, allowing us to capture an intermediate conformation formed during the transport cycle with a continuous aqueous pore at the domain interface. The free-energy calculations performed for the conduction of Cl- and Na+ ions through the captured conformation, highlight the presence of two hydrophobic gates which control the selective movement of Cl- through the aqueous pathway. Overall, our findings provide insights into the mechanism by which a human glutamate transporter can support the dual functions of active transport and passive Cl- permeation and confirming the commonality of this mechanism in different members of the SLC1A family.

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>

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

Sign in / Sign up

Export Citation Format

Share Document