Large-Scale Free Energy Calculations on a Computational Metal–Organic Frameworks Database: Toward Synthetic Likelihood Predictions

2020 ◽  
Vol 32 (19) ◽  
pp. 8106-8119
Author(s):  
Ryther Anderson ◽  
Diego A. Gómez-Gualdrón
Keyword(s):  
Free Energy ◽  
Large Scale ◽  
Metal Organic Frameworks ◽  
Free Energy Calculations ◽  
Energy Calculations ◽  
Scale Free ◽  
Metal Organic

Large-Scale Free Energy Calculations on a Computational MOF Database: Toward Synthetic Likelihood Predictions

2020 ◽  
Author(s):  
Ryther Anderson ◽  
Diego Gómez-Gualdrón
Keyword(s):  
Free Energy ◽  
Force Field ◽  
Large Scale ◽  
Free Energy Calculations ◽  
Free Energies ◽  
Scale Free ◽  
Computational Screening ◽  
Synthetic Accessibility ◽  
Isomorphic Series ◽  
Metal Organic

Metal-organic frameworks (MOFs) have captivated the research community due to a modular crystal structure that is tailorable for many applications. However, with millions of possible MOFs to be considered, it is challenging to identify the ideal MOF for the application of choice. Although computational screening of MOF databases has provided a fast way to evaluate MOF properties, validation experiments on predicted “exceptional” MOFs are not common due to uncertainties on the synthetic likelihood of computationally constructed MOFs, hence hindering material discovery. Aiming to leverage the perspective provided by large datasets, here we created and screened a topologically diverse database of 8,500 MOFs to interrogate whether thermodynamic stability metrics such as free energy could be used to generally predict the synthetic likelihood of computationally constructed MOFs. To this end, we first evaluated the suitability of two methods and three force fields to calculate free energies in MOFs at large scale, settling on the Frenkel-Ladd path thermodynamic integration method and the UFF4MOF force field. Upon defining a relative free energy, Δ<sub>LM</sub>F<sub>FL</sub>, that corrects for some force field artifacts specific to MOF nodes, we found that previously synthesized MOFs tended to cluster in a region below Δ<sub>LM</sub>F<sub>FL</sub> = 4.4 kJ/mol per atom, suggesting a general first filter to discriminate between synthetically likely and unlikely MOFs. However, a second filter is needed when several MOF isomorphs are below the Δ<sub>LM</sub>F<sub>FL</sub> threshold. In 84% of the cases, the synthetically accessible MOF within an isomorphic series presented the lowest predicted free energy. The present; work suggests that crystal free energies could be key to understanding synthetic likelihood for MOFs in computational databases (and MOFs in general), and that the thermodynamics stability of the fully assembled MOF often determines synthetic accessibility.

Large-Scale Free Energy Calculations on a Computational MOF Database: Toward Synthetic Likelihood Predictions

2020 ◽  
Author(s):  
Ryther Anderson ◽  
Diego Gómez-Gualdrón
Keyword(s):  
Free Energy ◽  
Force Field ◽  
Large Scale ◽  
Free Energy Calculations ◽  
Free Energies ◽  
Scale Free ◽  
Computational Screening ◽  
Synthetic Accessibility ◽  
Isomorphic Series ◽  
Metal Organic

Metal-organic frameworks (MOFs) have captivated the research community due to a modular crystal structure that is tailorable for many applications. However, with millions of possible MOFs to be considered, it is challenging to identify the ideal MOF for the application of choice. Although computational screening of MOF databases has provided a fast way to evaluate MOF properties, validation experiments on predicted “exceptional” MOFs are not common due to uncertainties on the synthetic likelihood of computationally constructed MOFs, hence hindering material discovery. Aiming to leverage the perspective provided by large datasets, here we created and screened a topologically diverse database of 8,500 MOFs to interrogate whether thermodynamic stability metrics such as free energy could be used to generally predict the synthetic likelihood of computationally constructed MOFs. To this end, we first evaluated the suitability of two methods and three force fields to calculate free energies in MOFs at large scale, settling on the Frenkel-Ladd path thermodynamic integration method and the UFF4MOF force field. Upon defining a relative free energy, Δ<sub>LM</sub>F<sub>FL</sub>, that corrects for some force field artifacts specific to MOF nodes, we found that previously synthesized MOFs tended to cluster in a region below Δ<sub>LM</sub>F<sub>FL</sub> = 4.4 kJ/mol per atom, suggesting a general first filter to discriminate between synthetically likely and unlikely MOFs. However, a second filter is needed when several MOF isomorphs are below the Δ<sub>LM</sub>F<sub>FL</sub> threshold. In 84% of the cases, the synthetically accessible MOF within an isomorphic series presented the lowest predicted free energy. The present; work suggests that crystal free energies could be key to understanding synthetic likelihood for MOFs in computational databases (and MOFs in general), and that the thermodynamics stability of the fully assembled MOF often determines synthetic accessibility.

scholarly journals Large scale free energy calculations for blind predictions of protein–ligand binding: the D3R Grand Challenge 2015

2016 ◽  
Vol 30 (9) ◽  
pp. 743-751 ◽  
Author(s):  
Nanjie Deng ◽  
William F. Flynn ◽  
Junchao Xia ◽  
R. S. K. Vijayan ◽  
Baofeng Zhang ◽  
...  
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Large Scale ◽  
Free Energy Calculations ◽  
Grand Challenge ◽  
Energy Calculations ◽  
Scale Free ◽  
D3r Grand Challenge 2015

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.

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.

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects

2020 ◽  
Vol 60 (11) ◽  
pp. 5457-5474 ◽  
Author(s):  
Christina E. M. Schindler ◽  
Hannah Baumann ◽  
Andreas Blum ◽  
Dietrich Böse ◽  
Hans-Peter Buchstaller ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Large Scale ◽  
Active Drug ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
Energy Calculations ◽  
Large Scale Assessment ◽  
Scale Assessment ◽  
Binding Free Energy Calculations

scholarly journals Large Scale Study of Ligand-Protein Relative Binding Free Energy Calculations: Actionable Predictions from Statistically Robust Protocols

2021 ◽  
Author(s):  
Agastya P Bhati ◽  
Peter V. Coveney
Keyword(s):  
Free Energy ◽  
Large Scale ◽  
Binding Free Energy ◽  
Personalised Medicine ◽  
Free Energy Calculations ◽  
Enhanced Sampling ◽  
Normal Behaviour ◽  
Energy Calculations ◽  
Relative Binding ◽  
Speed Accuracy

The accurate and reliable prediction of protein-ligand binding affinities can play a central role in the drug discovery process as well as in personalised medicine. Of considerable importance during lead optimisation are the alchemical free energy methods that furnish estimation of relative binding free energies (RBFE) of similar molecules. Recent advances in these methods have increased their speed, accuracy and precision. This is evident from the increasing number of retrospective as well as prospective studies employing them. However, such methods still have limited applicability in real-world scenarios due to a number of important yet unresolved issues. Here, we report the findings from a large dataset comprising over 500 ligand transformations spanning over 300 ligands binding to a diverse set of 14 different protein targets which furnish statistically robust results on the accuracy, precision and reproducibility of RBFE calculations. We use ensemble-based methods which are the only way to provide reliable uncertainty quantification given that the underlying molecular dynamics is chaotic. These are implemented using TIES (Thermodynamic Integration with Enhanced Sampling) but are equally applicable to free energy perturbation calculations for which we expect essentially very similar results. Results achieve chemical accuracy in all cases. Ensemble simulations also furnish information on the statistical distributions of the free energy calculations which exhibit non-normal behaviour. We find that the “enhanced sampling” method known as replica exchange with solute tempering degrades RBFE predictions. We also report definitively on numerous associated alchemical factors including the choice of ligand charge method, flexibility in ligand structure and the size of the alchemical region including the number of atoms involved in transforming one ligand into another. Our findings provide a key set of recommendations that should be adopted for the reliable application of RBFE methods.

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.

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