scholarly journals Exhaustive exploration of the conformational landscape of small cyclic peptides using a robotics approach

2018 ◽  
Author(s):  
Maud Jusot ◽  
Dirk Stratmann ◽  
Marc Vaisset ◽  
Jacques Chomilier ◽  
Juan Cortés
Keyword(s):  
Structure Prediction ◽  
Cyclic Peptides ◽  
Structural Characteristics ◽  
Chemical Properties ◽  
Computational Design ◽  
Complete Characterization ◽  
Conformational Space ◽  
Drug Candidate ◽  
Implicit Solvent ◽  
Dynamics Simulations

Small cyclic peptides represent a promising class of therapeutic molecules with unique chemical properties. However, the poor knowledge of their structural characteristics makes their computational design and structure prediction a real challenge. In order to better describe their conformational space, we developed a method, named EGSCyP, for the exhaustive exploration of the energy landscape of small head-to-tail cyclic peptides. The method can be summarized by (i) a global exploration of the conformational space based on a mechanistic representation of the peptide and the use of robotics-based algorithms to deal with the closure constraint, (ii) an all-atom refinement of the obtained conformations. EGSCyP can handle D-form residues and N-methylations. Two strategies for the side-chains placement were implemented and compared. To validate our approach, we applied it to a set of three variants of cyclic RGDFV pentapeptides, including the drug candidate Cilengitide. A comparative analysis was made with respect to replica exchange molecular dynamics simulations in implicit solvent. It results that the EGSCyP method provides a very complete characterization of the conformational space of small cyclic pentapeptides.

scholarly journals Ensemble completeness in conformer sampling: the case of small macrocycles

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Lea Seep ◽  
Anne Bonin ◽  
Katharina Meier ◽  
Holger Diedam ◽  
Andreas H. Göller
Keyword(s):  
Computational Study ◽  
Principal Component ◽  
Conformational Space ◽  
Implicit Solvent ◽  
Surface Areas ◽  
Solvent Models ◽  
Charge States ◽  
Dynamics Simulations ◽  
Intramolecular Hydrogen ◽  
The University

AbstractIn this study we compare the three algorithms for the generation of conformer ensembles Biovia BEST, Schrödinger Prime macrocycle sampling (PMM) and Conformator (CONF) form the University of Hamburg, with ensembles derived for exhaustive molecular dynamics simulations applied to a dataset of 7 small macrocycles in two charge states and three solvents. Ensemble completeness is a prerequisite to allow for the selection of relevant diverse conformers for many applications in computational chemistry. We apply conformation maps using principal component analysis based on ring torsions. Our major finding critical for all applications of conformer ensembles in any computational study is that maps derived from MD with explicit solvent are significantly distinct between macrocycles, charge states and solvents, whereas the maps for post-optimized conformers using implicit solvent models from all generator algorithms are very similar independent of the solvent. We apply three metrics for the quantification of the relative covered ensemble space, namely cluster overlap, variance statistics, and a novel metric, Mahalanobis distance, showing that post-optimized MD ensembles cover a significantly larger conformational space than the generator ensembles, with the ranking PMM > BEST >> CONF. Furthermore, we find that the distributions of 3D polar surface areas are very similar for all macrocycles independent of charge state and solvent, except for the smaller and more strained compound 7, and that there is also no obvious correlation between 3D PSA and intramolecular hydrogen bond count distributions.

scholarly journals In silico/computational analysis of mevalonate pyrophosphate decarboxylase gene families in Campanulids

2021 ◽  
Vol 16 (1) ◽  
pp. 1022-1036
Author(s):  
Minghui Cui ◽  
Limei Lin ◽  
Hongyu Guo ◽  
Duoduo Zhang ◽  
Jie Zhang ◽  
...  
Keyword(s):  
Medicinal Plants ◽  
Adaptive Evolution ◽  
Structure Prediction ◽  
Structural Characteristics ◽  
Chemical Properties ◽  
Gene Families ◽  
Chromosome 1 ◽  
Plant Metabolism ◽  
Chromosome 2 ◽  
Systematic Analysis

Abstract Mevalonate pyrophosphate decarboxylase (MPD) is a key enzyme in terpenoid biosynthesis. MPD plays an important role in the upstream regulation of secondary plant metabolism. However, studies on the MPD gene are relatively very few despite its importance in plant metabolism. Currently, no systematic analysis has been conducted on the MPD gene in plants under the order Apiales, which comprises important medicinal plants such as Panax ginseng and Panax notoginseng. This study sought to explore the structural characteristics of the MPD gene and the effect of adaptive evolution on the gene by comparing and analyzing MPD gene sequences of different campanulids species. For that, phylogenetic and adaptive evolution analyses were carried out using sequences for 11 Campanulids species. MPD sequence characteristics of each species were then analyzed, and the collinearity analysis of the genes was performed. As a result, a total of 21 MPD proteins were identified in 11 Campanulids species through BLAST analysis. Phylogenetic analysis, physical and chemical properties prediction, gene family analysis, and gene structure prediction showed that the MPD gene has undergone purifying selection and exhibited highly conserved structure. Analysis of physicochemical properties further showed that the MPD protein was a hydrophilic protein without a transmembrane region. Moreover, collinearity analysis in Apiales showed that MPD gene on chromosome 2 of D. carota and chromosome 1 of C. sativum were collinear. The findings showed that MPD gene is highly conserved. This may be a common characteristic of all essential enzymes in the biosynthesis pathways of medicinal plants. Notably, MPD gene is significantly affected by environmental factors which subsequently modulate its expression. The current study’s findings provide a basis for follow-up studies on MPD gene and key enzymes in other medicinal plants.

scholarly journals Energy and Reactivity Profile and Proton Affinity Analysis of Rimegepant with Special Reference to its Potential Activity against SARS-Cov-2 Virus Proteins using Molecular Dynamics

2021 ◽  
Author(s):  
T Pooventhiran ◽  
Ephraim Felix Marondedze ◽  
Penny Poomani Govender ◽  
Utsab Bhattacharyya ◽  
D Jagadeeswara Rao ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Hydrophobic Interactions ◽  
Chemical Properties ◽  
Basis Set ◽  
Basis Sets ◽  
Implicit Solvent ◽  
B3lyp Functional ◽  
Potential Activity ◽  
Dynamics Simulations ◽  
Physical And Chemical

Abstract Rimegepant is a new medicine developed for the management of chronic headache due to migraine. This manuscript is an attempt to study the various structural, physical and chemical properties of the molecules. The molecule was optimised using B3LYP functional with 6-311G+(2d,p) basis set. Excited state properties of the compound were studied using CAM-B3LYP functional with same basis sets using IEFPCM model in methanol for the implicit solvent atmosphere. The various electronic descriptors helped to identify the reactivity behaviour and stability. The compound is found to possess good nonlinear optical properties in gas phase. The various intramolecular electronic delocalisations and non-covalent interactions were analysed and explained. As the compound contain several heterocyclic nitrogen atoms, they have potential proton abstraction features, which was analysed energetically. The most important result from this study is from the molecular docking analysis which indicates that rimegepant binds irreversibly with three established SARS-CoV-2 proteins with ID 6LU7, 6M03 and 6W63 with docking scores − 9.2988, -8.3629 and − 9.5421 kcal/mol respectively. Further assessment of docked complexes with molecular dynamics simulations revealed that hydrophobic interactions, water bridges and π – π interactions play a signification role in stabilising the ligand within the binding region of respective proteins. MMGBSA free energies further demonstrated that rimegepant is more stable when complexed with 6LU7 among the selected PDB models. As the pharmacology and pharmacokinetics of this molecule are already established, rimegepant can be considered as an ideal candidate with potential for use in the treatment of COVID patients after clinical studies.

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>

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.

Quantitative structure-activity relationship model, molecular docking simulation and computational design of some novel compounds against DNA gyrase receptor.

2020 ◽  
Vol 16 ◽  
Author(s):  
Shola Elijah Adeniji
Keyword(s):  
Biological Activity ◽  
Molecular Docking ◽  
Binding Energy ◽  
Biological Activities ◽  
Docking Simulation ◽  
Dna Gyrase ◽  
Computational Design ◽  
Multi Drug Resistance ◽  
Drug Candidate ◽  
Standard Drug

Introduction: Mycobacterium tuberculosis has instigated a serious challenge toward the effective treatment of tuberculosis. The reoccurrence of the resistant strains of the disease to accessible drugs/medications has mandate for the development of more effective anti-tubercular agents with efficient activities. Time expended and costs in discovering and synthesizing new hypothetical drugs with improved biological activity have been a major challenge toward the treatment of multi-drug resistance strain M. tuberculosis (TB). Meanwhile, to solve the problem stated, a new approach i.e. QSAR which establish connection between novel drugs with a better biological against M. tuberculosis is adopted. Methods: The anti-tubercular model established in this study to forecast the biological activities of some anti-tubercular compounds selected and to design new hypothetical drugs is subjective to the molecular descriptors; MATS7s, SM1_DzZ, SpMin4_Bhv, TDB3v and RDF70v. Ligand-receptor interactions between quinoline derivatives and the receptor (DNA gyrase) was carried out using molecular docking technique by employing the PyRx virtual screening software and discovery studio visualizer software. Furthermore, docking study indicates that compounds 20 of the derivatives with promising biological activity have the utmost binding energy of -17.79 kcal/mol. Results: Meanwhile, the interaction of the standard drug; isoniazid with the target enzyme was observed with the binding energy -14.6 kcal/mol which was significantly lesser than the binding energy of the ligand (compound 20).Therefore, compound 20 served as a template structure to designed compounds with more efficient activities. Among the compounds designed; compounds 20p was observed with better anti-tubercular activities with more prominent binding affinities of -24.3kcal/mol. Conclusion: The presumption of this research aid the medicinal chemists and pharmacist to design and synthesis a novel drug candidate against the tuberculosis. Moreover, in-vitro and in-vivo test could be carried out to validate the computational results.

scholarly journals Computational Design of Radical Recognition Assay with the Possible Application of Cyclopropyl Vinyl Sulfides as Tunable Sensors

2021 ◽  
Vol 22 (14) ◽  
pp. 7637
Author(s):  
Liliya T. Sahharova ◽  
Evgeniy G. Gordeev ◽  
Dmitry B. Eremin ◽  
Valentine P. Ananikov
Keyword(s):  
Uv Light ◽  
Computational Design ◽  
Energy Profiles ◽  
Vinyl Sulfides ◽  
Tunable Sensors ◽  
Radical Generation ◽  
Metal Catalyzed ◽  
Dynamics Simulations ◽  
Simulations And Modeling ◽  
Insight Into

The processes involving the capture of free radicals were explored by performing DFT molecular dynamics simulations and modeling of reaction energy profiles. We describe the idea of a radical recognition assay, where not only the presence of a radical but also the nature/reactivity of a radical may be assessed. The idea is to utilize a set of radical-sensitive molecules as tunable sensors, followed by insight into the studied radical species based on the observed reactivity/selectivity. We utilize this approach for selective recognition of common radicals—alkyl, phenyl, and iodine. By matching quantum chemical calculations with experimental data, we show that components of a system react differently with the studied radicals. Possible radical generation processes were studied involving model reactions under UV light and metal-catalyzed conditions.

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