Molecular similarity studies on sandalwood odour molecules: Investigations of the conformational space

1997 ◽  
Vol 128 (6-7) ◽  
pp. 609-618 ◽  
Author(s):  
B. B�sel ◽  
G. Buchbauer ◽  
P. Wei�-Greiler ◽  
P. Wolschann
Keyword(s):  
Molecular Similarity ◽  
Conformational Space ◽  
Sandalwood Odour

Conformational Calculations on Sandalwood Odour, Part XI. Molecular Similarity of Sandalwood Odour Compounds

1997 ◽  
Vol 12 (3) ◽  
pp. 141-146 ◽  
Author(s):  
Gerhard Buchbauer ◽  
Alexander Hayn ◽  
Elisabeth Liedl ◽  
Petra Weiss-Greiler ◽  
Peter Wolschann
Keyword(s):  
Molecular Similarity ◽  
Sandalwood Odour ◽  
Conformational Calculations

Hierarchy of π-Stacking Determines the Conformational Preference of Bis-Squaraines

2020 ◽  
Author(s):  
Abhishek Singh ◽  
Reman K. Singh ◽  
G Naresh Patwari
Keyword(s):  
Hydrogen Bonding ◽  
Rational Design ◽  
Biological Molecules ◽  
Conformational Space ◽  
X Ray Crystallography ◽  
Simple Strategy ◽  
Induced Folding ◽  
Π Stacking ◽  
Varying Length ◽  
Dynamics Simulations

The rational design of conformationally controlled foldable modules can lead to a deeper insight into the conformational space of complex biological molecules where non-covalent interactions such as hydrogen bonding and π-stacking are known to play a pivotal role. Squaramides are known to have excellent hydrogen bonding capabilities and hence, are ideal molecules for designing foldable modules that can mimic the secondary structures of bio-molecules. The π-stacking induced folding of bis-squaraines tethered using aliphatic primary and secondary-diamine linkers of varying length is explored with a simple strategy of invoking small perturbations involving the length linkers and degree of substitution. Solution phase NMR investigations in combination with molecular dynamics simulations suggest that bis-squaraines predominantly exist as extended conformations. Structures elucidated by X-ray crystallography confirmed a variety of folded and extended secondary conformations including hairpin turns and 𝛽-sheets which are determined by the hierarchy of π-stacking relative to N–H···O hydrogen bonds.

Hierarchy of π-Stacking Determines the Conformational Preference of Bis-Squaraines

2020 ◽  
Author(s):  
Abhishek Singh ◽  
Reman K. Singh ◽  
G Naresh Patwari
Keyword(s):  
Hydrogen Bonding ◽  
Rational Design ◽  
Biological Molecules ◽  
Conformational Space ◽  
X Ray Crystallography ◽  
Simple Strategy ◽  
Induced Folding ◽  
Π Stacking ◽  
Varying Length ◽  
Dynamics Simulations

The rational design of conformationally controlled foldable modules can lead to a deeper insight into the conformational space of complex biological molecules where non-covalent interactions such as hydrogen bonding and π-stacking are known to play a pivotal role. Squaramides are known to have excellent hydrogen bonding capabilities and hence, are ideal molecules for designing foldable modules that can mimic the secondary structures of bio-molecules. The π-stacking induced folding of bis-squaraines tethered using aliphatic primary and secondary-diamine linkers of varying length is explored with a simple strategy of invoking small perturbations involving the length linkers and degree of substitution. Solution phase NMR investigations in combination with molecular dynamics simulations suggest that bis-squaraines predominantly exist as extended conformations. Structures elucidated by X-ray crystallography confirmed a variety of folded and extended secondary conformations including hairpin turns and 𝛽-sheets which are determined by the hierarchy of π-stacking relative to N–H···O hydrogen bonds.

Improved Sampling Strategies for Protein Model Refinement based on Molecular Dynamics Simulation

2020 ◽  
Author(s):  
Lim Heo ◽  
Collin Arbour ◽  
Michael Feig
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Structure Prediction ◽  
Protein Structures ◽  
Conformational Space ◽  
Dynamics Simulation ◽  
Model Refinement ◽  
Protein Model ◽  
Lower Accuracy ◽  
Simulation Based

Protein structures provide valuable information for understanding biological processes. Protein structures can be determined by experimental methods such as X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, or cryogenic electron microscopy. As an alternative, in silico methods can be used to predict protein structures. Those methods utilize protein structure databases for structure prediction via template-based modeling or for training machine-learning models to generate predictions. Structure prediction for proteins distant from proteins with known structures often results in lower accuracy with respect to the true physiological structures. Physics-based protein model refinement methods can be applied to improve model accuracy in the predicted models. Refinement methods rely on conformational sampling around the predicted structures, and if structures closer to the native states are sampled, improvements in the model quality become possible. Molecular dynamics simulations have been especially successful for improving model qualities but although consistent refinement can be achieved, the improvements in model qualities are still moderate. To extend the refinement performance of a simulation-based protocol, we explored new schemes that focus on an optimized use of biasing functions and the application of increased simulation temperatures. In addition, we tested the use of alternative initial models so that the simulations can explore conformational space more broadly. Based on the insight of this analysis we are proposing a new refinement protocol that significantly outperformed previous state-of-the-art molecular dynamics simulation-based protocols in the benchmark tests described here. <br>

Theoretical investigation of Ramachandran plot of N-formyl-L-alanine-amide

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Genetic Algorithm ◽  
Theoretical Investigation ◽  
Energy Analysis ◽  
Empirical Method ◽  
Conformational Space ◽  
Ramachandran Plot ◽  
Global Minima ◽  
Reasonable Time ◽  
Semi Empirical

The full conformational space of N-formyl-L-alanine-amide was explored by the semi-empirical method AM1 coupled to the Multi Niche Crowding (MNC) genetic algorithm implemented in a package of programs developed in our laboratory. The structural and energy analysis of the resulting conformational space E(,ψ) exhibits 5 regions or minima ɣL, ɣD, ɛL, D and αD. The technique provides better detection of local and global minima within a reasonable time.

An Effective Cumulative Torsion Angles Model for Prediction of Protein Folding Rates

2020 ◽  
Vol 27 (4) ◽  
pp. 321-328 ◽  
Author(s):  
Yanru Li ◽  
Ying Zhang ◽  
Jun Lv
Keyword(s):  
Protein Folding ◽  
Size Structure ◽  
Conformational Space ◽  
Coefficient Of Determination ◽  
Folding Rate ◽  
Torsion Angles ◽  
Folding Rates ◽  
Protein Folding Rate ◽  
Optimal Set ◽  
Conformation Space

Background: Protein folding rate is mainly determined by the size of the conformational space to search, which in turn is dictated by factors such as size, structure and amino-acid sequence in a protein. It is important to integrate these factors effectively to form a more precisely description of conformation space. But there is no general paradigm to answer this question except some intuitions and empirical rules. Therefore, at the present stage, predictions of the folding rate can be improved through finding new factors, and some insights are given to the above question. Objective: Its purpose is to propose a new parameter that can describe the size of the conformational space to improve the prediction accuracy of protein folding rate. Method: Based on the optimal set of amino acids in a protein, an effective cumulative backbone torsion angles (CBTAeff) was proposed to describe the size of the conformational space. Linear regression model was used to predict protein folding rate with CBTAeff as a parameter. The degree of correlation was described by the coefficient of determination and the mean absolute error MAE between the predicted folding rates and experimental observations. Results: It achieved a high correlation (with the coefficient of determination of 0.70 and MAE of 1.88) between the logarithm of folding rates and the (CBTAeff)0.5 with experimental over 112 twoand multi-state folding proteins. Conclusion: The remarkable performance of our simplistic model demonstrates that CBTA based on optimal set was the major determinants of the conformation space of natural proteins.

Clustering and Sampling of the c-Met Conformational Space: A Computational Drug Discovery Study

2020 ◽  
Vol 22 (9) ◽  
pp. 635-648 ◽  
Author(s):  
Korosh Mashayekh ◽  
Shahrzad Sharifi ◽  
Tahereh Damghani ◽  
Maryam Elyasi ◽  
Mohammad S. Avestan ◽  
...  
Keyword(s):  
Critical Role ◽  
Scientific Work ◽  
Binding Mode ◽  
Docking Studies ◽  
Conformational Space ◽  
Hydrogen Bond Interactions ◽  
Docking Program ◽  
Dynamics Simulations ◽  
Computational Drug Discovery ◽  
Dynamic Ensembles

Background: c-Met kinase plays a critical role in a myriad of human cancers, and a massive scientific work was devoted to design more potent inhibitors. Objective: In this study, 16 molecular dynamics simulations of different complexes of potent c-Met inhibitors with U-shaped binding mode were carried out regarding the dynamic ensembles to design novel potent inhibitors. Methods: A cluster analysis was performed, and the most representative frame of each complex was subjected to the structure-based pharmacophore screening. The GOLD docking program investigated the interaction energy and pattern of output hits from the virtual screening. The most promising hits with the highest scoring values that showed critical interactions with c-Met were presented for ADME/Tox analysis. Results: The screening yielded 45,324 hits that all of them were subjected to the docking studies and 10 of them with the highest-scoring values having diverse structures were presented for ADME/Tox analyses. Conclusion: The results indicated that all the hits shared critical Pi-Pi stacked and hydrogen bond interactions with Tyr1230 and Met1160 respectively.

scholarly journals On the Use of the Discrete Constant pH Molecular Dynamics to Describe the Conformational Space of Peptides

Polymers ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 99
Author(s):  
Cristian Privat ◽  
Sergio Madurga ◽  
Francesc Mas ◽  
Jaime Rubio-Martínez
Keyword(s):  
Molecular Dynamics ◽  
Amino Acids ◽  
Md Simulations ◽  
Point Of View ◽  
Conformational Space ◽  
Conformational Sampling ◽  
Protonation State ◽  
Constant Ph ◽  
Biochemical Systems ◽  
Constant Ph Molecular Dynamics

Solvent pH is an important property that defines the protonation state of the amino acids and, therefore, modulates the interactions and the conformational space of the biochemical systems. Generally, this thermodynamic variable is poorly considered in Molecular Dynamics (MD) simulations. Fortunately, this lack has been overcome by means of the Constant pH Molecular Dynamics (CPHMD) methods in the recent decades. Several studies have reported promising results from these approaches that include pH in simulations but focus on the prediction of the effective pKa of the amino acids. In this work, we want to shed some light on the CPHMD method and its implementation in the AMBER suitcase from a conformational point of view. To achieve this goal, we performed CPHMD and conventional MD (CMD) simulations of six protonatable amino acids in a blocked tripeptide structure to compare the conformational sampling and energy distributions of both methods. The results reveal strengths and weaknesses of the CPHMD method in the implementation of AMBER18 version. The change of the protonation state according to the chemical environment is presumably an improvement in the accuracy of the simulations. However, the simulations of the deprotonated forms are not consistent, which is related to an inaccurate assignment of the partial charges of the backbone atoms in the CPHMD residues. Therefore, we recommend the CPHMD methods of AMBER program but pointing out the need to compare structural properties with experimental data to bring reliability to the conformational sampling of the simulations.

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