Critical assessment of side-chain conformational space sampling procedures designed for quantifying the effect of side-chain environment

2003 ◽  
Vol 24 (15) ◽  
pp. 1950-1961 ◽  
Author(s):  
R. Gautier ◽  
P. Tufféry
Keyword(s):  
Critical Assessment ◽  
Conformational Space ◽  
Side Chain ◽  
Sampling Procedures ◽  
Conformational Space Sampling

scholarly journals Enhanced Conformational Space Sampling Improves the Prediction of Chemical Shifts in Proteins

2010 ◽  
Vol 132 (4) ◽  
pp. 1220-1221 ◽  
Author(s):  
Phineus R. L. Markwick ◽  
Carla F. Cervantes ◽  
Barrett L. Abel ◽  
Elizabeth A. Komives ◽  
Martin Blackledge ◽  
...  
Keyword(s):  
Chemical Shifts ◽  
Conformational Space ◽  
Conformational Space Sampling

scholarly journals Glycine in Water Favors the Polyproline II State

Biomolecules ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 1121 ◽  
Author(s):  
Brian Andrews ◽  
Shuting Zhang ◽  
Reinhard Schweitzer-Stenner ◽  
Brigita Urbanc
Keyword(s):  
Amino Acid ◽  
Spectroscopic Data ◽  
Heavy Atom ◽  
Conformational Space ◽  
Side Chain ◽  
Amino Acid Residues ◽  
Glycine Residue ◽  
Polyproline Ii ◽  
Conformational Preferences ◽  
Central Residue

Conformational preferences of amino acid residues in water are determined by the backbone and side-chain properties. Alanine is known for its high polyproline II (pPII) propensity. The question of relative contributions of the backbone and side chain to the conformational preferences of alanine and other amino acid residues in water is not fully resolved. Because glycine lacks a heavy-atom side chain, glycine-based peptides can be used to examine to which extent the backbone properties affect the conformational space. Here, we use published spectroscopic data for the central glycine residue of cationic triglycine in water to demonstrate that its conformational space is dominated by the pPII state. We assess three commonly used molecular dynamics (MD) force fields with respect to their ability to capture the conformational preferences of the central glycine residue in triglycine. We show that pPII is the mesostate that enables the functional backbone groups of the central residue to form the most hydrogen bonds with water. Our results indicate that the pPII propensity of the central glycine in GGG is comparable to that of alanine in GAG, implying that the water-backbone hydrogen bonding is responsible for the high pPII content of these residues.

scholarly journals Theoretical study of the potential energy surface of N-formyl-L-cysteine-amide by using a genetic algorithm multi niche crowding

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Genetic Algorithm ◽  
Energy Surface ◽  
Energy Analysis ◽  
Theoretical Study ◽  
Molecular Structures ◽  
Conformational Space ◽  
Side Chain ◽  
Am1 Method ◽  
The Subject ◽  
Semi Empirical

The generation of molecular structures constituting the conformational space of trans-N-For-L-Cys-NH2 was accomplished using the genetic algorithm MNC coupled to the semi-empirical AM1 method, AM1/GA-MNC. The structural and energy analysis of the obtained conformational space E=E(,ψ) locates 7 regions or minima ɣL, ɣD, L, D, L, ɛD and ɛL. The combination of these folds to structuring modes adopted by the side chain CH2-SH has allowed us to identify 27 stable geometric structures. The regions corresponding to helical folds αD and αL are not favorable for the system that is the subject of current study.

Conformational Space of a Polyphilic Molecule with a Fluorophilic Side Chain Integrated in a DPPC Bilayer

2017 ◽  
Vol 38 (9) ◽  
pp. 576-583 ◽  
Author(s):  
Guido F. von Rudorff ◽  
Tobias Watermann ◽  
Xiang-Yang Guo ◽  
Daniel Sebastiani
Keyword(s):  
Conformational Space ◽  
Side Chain ◽  
Dppc Bilayer

scholarly journals Flex ddG: Rosetta ensemble-based estimation of changes in protein-protein binding affinity upon mutation

2017 ◽  
Author(s):  
Kyle A. Barlow ◽  
Shane O Conchúir ◽  
Samuel Thompson ◽  
Pooja Suresh ◽  
James E. Lucas ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Energy Function ◽  
Large Scale ◽  
Additive Model ◽  
Conformational Space ◽  
Side Chain ◽  
Free Energies ◽  
Protein Protein Interactions ◽  
Conformational Plasticity ◽  
Macromolecular Modeling

AbstractComputationally modeling changes in binding free energies upon mutation (interface ΔΔG) allows large-scale prediction and perturbation of protein-protein interactions. Additionally, methods that consider and sample relevant conformational plasticity should be able to achieve higher prediction accuracy over methods that do not. To test this hypothesis, we developed a method within the Rosetta macromolecular modeling suite (flex ddG) that samples conformational diversity using “backrub” to generate an ensemble of models, then applying torsion minimization, side chain repacking and averaging across this ensemble to estimate interface ΔΔG values. We tested our method on a curated benchmark set of 1240 mutants, and found the method outperformed existing methods that sampled conformational space to a lesser degree. We observed considerable improvements with flex ddG over existing methods on the subset of small side chain to large side chain mutations, as well as for multiple simultaneous non-alanine mutations, stabilizing mutations, and mutations in antibody-antigen interfaces. Finally, we applied a generalized additive model (GAM) approach to the Rosetta energy function; the resulting non-linear reweighting model improved agreement with experimentally determined interface DDG values, but also highlights the necessity of future energy function improvements.

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