Reply to “Comment on 'Computational Model for Predicting Experimental RNA and DNA Nearest-Neighbor Free Energy Rankings'”

2012 ◽  
Vol 116 (28) ◽  
pp. 8333-8334 ◽  
Author(s):  
Charles A. Johnson ◽  
Richard J. Bloomingdale ◽  
Vikram E. Ponnusamy ◽  
Conor A. Tillinghast ◽  
Brent M. Znosko ◽  
...  
Keyword(s):  
Free Energy ◽  
Computational Model ◽  
Nearest Neighbor ◽  
Rna And Dna

scholarly journals Computational Model for Predicting Experimental RNA and DNA Nearest-Neighbor Free Energy Rankings

2011 ◽  
Vol 115 (29) ◽  
pp. 9244-9251 ◽  
Author(s):  
Charles A. Johnson ◽  
Richard J. Bloomingdale ◽  
Vikram E. Ponnusamy ◽  
Conor A. Tillinghast ◽  
Brent M. Znosko ◽  
...  
Keyword(s):  
Free Energy ◽  
Computational Model ◽  
Nearest Neighbor ◽  
Rna And Dna

lncRNATargets: A platform for lncRNA target prediction based on nucleic acid thermodynamics

2016 ◽  
Vol 14 (04) ◽  
pp. 1650016 ◽  
Author(s):  
Ruifeng Hu ◽  
Xiaobo Sun
Keyword(s):  
Free Energy ◽  
Nucleic Acid ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Binding Free Energy ◽  
Target Prediction ◽  
Easy Access ◽  
Web Based ◽  
Size Limitation ◽  
User Friendly

Many studies have supported that long noncoding RNAs (lncRNAs) perform various functions in various critical biological processes. Advanced experimental and computational technologies allow access to more information on lncRNAs. Determining the functions and action mechanisms of these RNAs on a large scale is urgently needed. We provided lncRNATargets, which is a web-based platform for lncRNA target prediction based on nucleic acid thermodynamics. The nearest-neighbor (NN) model was used to calculate binging-free energy. The main principle of NN model for nucleic acid assumes that identity and orientation of neighbor base pairs determine stability of a given base pair. lncRNATargets features the following options: setting of a specific temperature that allow use not only for human but also for other animals or plants; processing all lncRNAs in high throughput without RNA size limitation that is superior to any other existing tool; and web-based, user-friendly interface, and colored result displays that allow easy access for nonskilled computer operators and provide better understanding of results. This technique could provide accurate calculation on the binding-free energy of lncRNA-target dimers to predict if these structures are well targeted together. lncRNATargets provides high accuracy calculations, and this user-friendly program is available for free at http://www.herbbol.org:8001/lrt/ .

Computational model of excitable cell indicates ATP free energy dynamics in response to calcium oscillations are undampened by cytosolic ATP buffers

2006 ◽  
Vol 153 (5) ◽  
pp. 405 ◽  
Author(s):  
R.G.P.M. van Stiphout ◽  
N.A.W. van Riel ◽  
P.J. Verhoog ◽  
P.A.J. Hilbers ◽  
K. Nicolay ◽  
...  
Keyword(s):  
Free Energy ◽  
Computational Model ◽  
Calcium Oscillations ◽  
Excitable Cell ◽  
Energy Dynamics

Insights to the Binding of a Selective Adenosine A3 Receptor Antagonist using Molecular Dynamics Simulations, Binding Free Energy Calculations and Mutagenesis

2019 ◽  
Author(s):  
Panagiotis Lagarias ◽  
Kerry Barkan ◽  
Eva Tzortzini ◽  
Eleni Vrontaki ◽  
Margarita Stampelou ◽  
...  
Keyword(s):  
Free Energy ◽  
Computational Model ◽  
Molecular Mechanics ◽  
Surface Area ◽  
Binding Free Energy ◽  
Md Simulations ◽  
Free Energy Calculations ◽  
Binding Energies ◽  
Energy Calculations ◽  
Binding Free Energy Calculations

<p>Adenosine A<sub>3 </sub>receptor (A<sub>3</sub>R), is a promising drug target against cancer cell proliferation. Currently there is no experimentally determined structure of A<sub>3</sub>R. Here, we have investigate a computational model, previously applied successfully for agonists binding to A<sub>3</sub>R, using molecular dynamic (MD) simulations, Molecular Mechanics-Poisson Boltzmann Surface Area (MM-PBSA) and Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) binding free energy calculations. Extensive computations were performed to explore the binding profile of O4-{[3-(2,6-dichlorophenyl)-5-methylisoxazol-4-yl]carbonyl}-2-methyl-1,3-thiazole-4-carbohydroximamide (K18) to A<sub>3</sub>R. K18 is a new specific and competitive antagonist at the orthosteric binding site of A<sub>3</sub>R, discovered using virtual screening and characterized pharmacologically in our previous studies. The most plausible binding conformation for the dichlorophenyl group of K18 inside the A<sub>3</sub>R is oriented towards trans-membrane helices (TM) 5 and 6, according to the MM-PBSA and MM-GBSA binding free energy calculations, and by the previous results obtained by mutating residues of TM5, TM6 to alanine which reduce antagonist potency. The results from 14 site-directed mutagenesis experiments were interpreted using MD simulations and MM-GBSA calculations which show that the relative binding free energies of the mutant A<sub>3</sub>R - K18 complexes compare to the WT A<sub>3</sub>R are in agreement with the effect of the mutations, i.e. the reduction, maintenance or increase of antagonist potency. We show that when the residues V169<sup>5.30</sup>, M177<sup>5.38</sup>, I249<sup>6.54</sup> involved in direct interactions with K18 are mutated to alanine, the mutant A<sub>3</sub>R - K18 complexes reduce potency, increase the RMSD value of K18 inside the binding area and the MM-GBSA binding free energy compared to the WT A<sub>3</sub>R complex. Our computational model shows that other mutant A<sub>3</sub>R complexes with K18, including directly interacting residues, i.e. F168<sup>5.29</sup>A, L246<sup>6.51</sup>A, N250<sup>6.55</sup>A complexes with K18 are not stable. In these complexes of A<sub>3</sub>R mutated in directly interacting residues one or more of the interactions between K18 and these residues are lost. In agreement with the experiments, the computations show that, M174<sup>5.35</sup> a residue which does not make direct interactions with K18 is critical for K18 binding. A striking results is that the mutation of residue V169<sup>5.30</sup> to glutamic acid maintained antagonistic potency. This effect is in agreement with the binding free energy calculations and it is suggested that is due to K18 re-orientation but also to the plasticity of A<sub>3</sub>R binding area. The mutation of direct interacting L90<sup>3.32</sup> in the low region and the non-directly interacting L264<sup>7.35</sup> to alanine in the middle region increases the antagonistic potency, suggesting that chemical modifications of K18 can be applied to augment antagonistic potency. The calculated binding energies Δ<i>G</i><sub>eff</sub> values of K18 against mutant A<sub>3</sub>Rs displayed very good correlation with experimental potencies (pA<sub>2</sub> values). These results further approve the computational model for the description of K18 binding with critical residues of the orthosteric binding area which can have implications for the design of more effective antagonists based on the structure of K18.</p>

scholarly journals The 2-D Cluster Variation Method: Topography Illustrations and Their Enthalpy Parameter Correlations

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 319
Author(s):  
Alianna J. Maren
Keyword(s):  
Free Energy ◽  
Nearest Neighbor ◽  
Activation Enthalpy ◽  
Cluster Variation Method ◽  
Variation Method ◽  
Energy Minimum ◽  
Dimensional Parameter ◽  
Free Energy Minimum ◽  
Interaction Enthalpy ◽  
Cluster Variation

One of the biggest challenges in characterizing 2-D image topographies is finding a low-dimensional parameter set that can succinctly describe, not so much image patterns themselves, but the nature of these patterns. The 2-D cluster variation method (CVM), introduced by Kikuchi in 1951, can characterize very local image pattern distributions using configuration variables, identifying nearest-neighbor, next-nearest-neighbor, and triplet configurations. Using the 2-D CVM, we can characterize 2-D topographies using just two parameters; the activation enthalpy (ε0) and the interaction enthalpy (ε1). Two different initial topographies (“scale-free-like” and “extreme rich club-like”) were each computationally brought to a CVM free energy minimum, for the case where the activation enthalpy was zero and different values were used for the interaction enthalpy. The results are: (1) the computational configuration variable results differ significantly from the analytically-predicted values well before ε1 approaches the known divergence as ε1→0.881, (2) the range of potentially useful parameter values, favoring clustering of like-with-like units, is limited to the region where ε0<3 and ε1<0.25, and (3) the topographies in the systems that are brought to a free energy minimum show interesting visual features, such as extended “spider legs” connecting previously unconnected “islands,” and as well as evolution of “peninsulas” in what were previously solid masses.

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