scholarly journals ATP Converts Aβ42 Oligomer into Off-Pathway Species by Making Contact with Its Backbone Atoms Using Hydrophobic Adenosine

2019 ◽  
Vol 123 (46) ◽  
pp. 9922-9933
Author(s):  
Ikuo Kurisaki ◽  
Shigenori Tanaka
Keyword(s):  
Backbone Atoms

KORP: knowledge-based 6D potential for fast protein and loop modeling

2019 ◽  
Vol 35 (17) ◽  
pp. 3013-3019 ◽  
Author(s):  
José Ramón López-Blanco ◽  
Pablo Chacón
Keyword(s):  
Structure Prediction ◽  
Protein Structures ◽  
Joint Probability ◽  
Protein Modeling ◽  
Supplementary Information ◽  
Joint Probability Distribution ◽  
Loop Modeling ◽  
Statistical Potentials ◽  
Knowledge Based ◽  
Backbone Atoms

Abstract Motivation Knowledge-based statistical potentials constitute a simpler and easier alternative to physics-based potentials in many applications, including folding, docking and protein modeling. Here, to improve the effectiveness of the current approximations, we attempt to capture the six-dimensional nature of residue–residue interactions from known protein structures using a simple backbone-based representation. Results We have developed KORP, a knowledge-based pairwise potential for proteins that depends on the relative position and orientation between residues. Using a minimalist representation of only three backbone atoms per residue, KORP utilizes a six-dimensional joint probability distribution to outperform state-of-the-art statistical potentials for native structure recognition and best model selection in recent critical assessment of protein structure prediction and loop-modeling benchmarks. Compared with the existing methods, our side-chain independent potential has a lower complexity and better efficiency. The superior accuracy and robustness of KORP represent a promising advance for protein modeling and refinement applications that require a fast but highly discriminative energy function. Availability and implementation http://chaconlab.org/modeling/korp. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Using Protein Homology Models for Structure-Based Studies: Approaches to Model Refinement

2006 ◽  
Vol 6 ◽  
pp. 1542-1554 ◽  
Author(s):  
V. Kairys ◽  
M.K. Gilson ◽  
Miguel Xavier Fernandes
Keyword(s):  
Experimental Data ◽  
Homology Model ◽  
Target Protein ◽  
Protein Homology ◽  
Model Refinement ◽  
Sequence Identity ◽  
Computational Methodology ◽  
Backbone Atoms ◽  
Homology Models ◽  
Template Protein

Homology modeling is a computational methodology to assign a 3-D structure to a target protein when experimental data are not available. The methodology uses another protein with a known structure that shares some sequence identity with the target as a template. The crudest approach is to thread the target protein backbone atoms over the backbone atoms of the template protein, but necessary refinement methods are needed to produce realistic models. In this mini-review anchored within the scope of drug design, we show the validity of using homology models of proteins in the discovery of binders for potential therapeutic targets. We also report several different approaches to homology model refinement, going from very simple to the most elaborate. Results show that refinement approaches are system dependent and that more elaborate methodologies do not always correlate with better performances from built homology models.

scholarly journals Aminoacyl-tRNA Substrate and Enzyme Backbone Atoms Contribute to Translational Quality Control by YbaK

2012 ◽  
Vol 117 (16) ◽  
pp. 4521-4527 ◽  
Author(s):  
Sandeep Kumar ◽  
Mom Das ◽  
Christopher M. Hadad ◽  
Karin Musier-Forsyth
Keyword(s):  
Quality Control ◽  
Backbone Atoms ◽  
Trna Substrate

Polyphosphazenes: New Polymers with Inorganic Backbone Atoms

Science ◽  
1976 ◽  
Vol 193 (4259) ◽  
pp. 1214-1219 ◽  
Author(s):  
H. R. Allcock
Keyword(s):  
Backbone Atoms

scholarly journals Anisotropic Hydrolysis Susceptibility in Deformed Polydimethylsiloxanes

2019 ◽  
Author(s):  
Matthew Kroonblawd ◽  
Nir Goldman ◽  
James Lewicki
Keyword(s):  
Density Functional ◽  
Degrees Of Freedom ◽  
Tight Binding ◽  
Hydrolytic Degradation ◽  
Chain Scission ◽  
Functional Theory ◽  
Chain Mobility ◽  
Mechanical Deformations ◽  
Macro Scale ◽  
Backbone Atoms

<div>Chemical reactions involving the polydimethylsiloxane (PDMS) backbone can induce significant network rearrangements and ultimately degrade macro-scale mechanical properties of silicone components. Using two levels of quantum chemical theory, we identify a possible electronic driver for chemical susceptibility in strained PDMS chains and explore the complicated interplay between hydrolytic chain scissioning reactions, mechanical deformations of the backbone, water attack vector, and chain mobility. Density functional theory (DFT) calculations reveal that susceptibility to hydrolysis varies significantly with the vector for water attacks on silicon backbone atoms, which matches strain-induced anisotropic changes in the backbone electronic structure. Efficient semiempirical density functional tight binding (DFTB) calculations are shown to reproduce DFT predictions for select reaction pathways and facilitate more exhaustive explorations of configuration space. We show that concerted strains of the backbone must occur over at least few monomer units to significantly increase hydrolysis susceptibility. In addition, we observe that sustaining tension across multiple monomer lengths by constraining molecular degrees of freedom further enhances hydrolysis susceptibility, leading to barrierless scission reactions for less substantial backbone deformations than otherwise. We then compute chain scission probabilities as functions of the backbone degrees of freedom, revealing complicated configurational inter-dependencies that impact the likelihood for hydrolytic degradation. The trends identified in our study suggest simple physical descriptions for the synergistic coupling between local mechanical deformation and environmental moisture in hydrolytic degradation of silicones.</div>

scholarly journals Protein structure prediction using AI and quantum computers

2021 ◽  
Author(s):  
Ben Geoffrey A S
Keyword(s):  
Machine Learning ◽  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Machine Learning Algorithms ◽  
Quantum Computers ◽  
Data Set ◽  
Backbone Atoms ◽  
Protein Contact Maps

This work seeks to combine the combined advantage of leveraging these emerging areas of Artificial Intelligence and quantum computing in applying it to solve the specific biological problem of protein structure prediction using Quantum Machine Learning algorithms. The CASP dataset from ProteinNet was downloaded which is a standardized data set for machine learning of protein structure. Its large and standardized dataset of PDB entries contains the coordinates of the backbone atoms, corresponding to the sequential chain of N, C_alpha, and C' atoms. This dataset was used to train a quantum-classical hybrid Keras deep neural network model to predict the structure of the proteins. To visually qualify the quality of the predicted versus the actual protein structure, protein contact maps were generated with the experimental and predicted protein structure data and qualified. Therefore this model is recommended for the use of protein structure prediction using AI leveraging the power of quantum computers. The code is provided in the following Github repository https://github.com/bengeof/Protein-structure-prediction-using-AI-and-quantum-computers.

scholarly journals Evolutionary couplings detect side-chain interactions

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7280 ◽  
Author(s):  
Adam J. Hockenberry ◽  
Claus O. Wilke
Keyword(s):  
De Novo ◽  
Protein Structures ◽  
Side Chain ◽  
Sequence Alignments ◽  
Practical Applications ◽  
Residue Contacts ◽  
Coupling Algorithms ◽  
Backbone Atoms ◽  
Evolutionary Coupling ◽  
Binding Interfaces

Patterns of amino acid covariation in large protein sequence alignments can inform the prediction of de novo protein structures, binding interfaces, and mutational effects. While algorithms that detect these so-called evolutionary couplings between residues have proven useful for practical applications, less is known about how and why these methods perform so well, and what insights into biological processes can be gained from their application. Evolutionary coupling algorithms are commonly benchmarked by comparison to true structural contacts derived from solved protein structures. However, the methods used to determine true structural contacts are not standardized and different definitions of structural contacts may have important consequences for interpreting the results from evolutionary coupling analyses and understanding their overall utility. Here, we show that evolutionary coupling analyses are significantly more likely to identify structural contacts between side-chain atoms than between backbone atoms. We use both simulations and empirical analyses to highlight that purely backbone-based definitions of true residue–residue contacts (i.e., based on the distance between Cα atoms) may underestimate the accuracy of evolutionary coupling algorithms by as much as 40% and that a commonly used reference point (Cβ atoms) underestimates the accuracy by 10–15%. These findings show that co-evolutionary outcomes differ according to which atoms participate in residue–residue interactions and suggest that accounting for different interaction types may lead to further improvements to contact-prediction methods.

scholarly journals Steric vs Electronic Effects: A New Look into Stability of Diastereomers, Conformers and Constitutional Isomers

2021 ◽  
Author(s):  
Sopanant Datta ◽  
Taweetham Limpanuparb
Keyword(s):  
Steric Effect ◽  
Electronic Effect ◽  
Electronic Effects ◽  
Gauche Conformation ◽  
Prevalence Data ◽  
The Stability ◽  
Stable Forms ◽  
The Right ◽  
Backbone Atoms ◽  
Meta Isomer

<div>A quantum chemical investigation of the stability of compounds with identical formulas was carried out on 23 classes of halogenated compounds made of H, F, Cl, Br, I, C, N, P, O and S atoms. The prevalence of formula in which its Z configuration, gauche conformation and meta isomer are the most stable forms is calculated and discussed. The prevalence data shows that in compounds made of carbon backbones, the electronic effect is weaker than the steric effect. The electronic factor is more important as the backbone atoms are replaced with atoms on the right and upper part of the periodic table.</div>

scholarly journals Steric vs Electronic Effects: A New Look into Stability of Diastereomers, Conformers and Constitutional Isomers

2021 ◽  
Author(s):  
Sopanant Datta ◽  
Taweetham Limpanuparb
Keyword(s):  
Steric Effect ◽  
Electronic Effect ◽  
Electronic Effects ◽  
Gauche Conformation ◽  
Prevalence Data ◽  
The Stability ◽  
Stable Forms ◽  
The Right ◽  
Backbone Atoms ◽  
Meta Isomer

<div>A quantum chemical investigation of the stability of compounds with identical formulas was carried out on 23 classes of halogenated compounds made of H, F, Cl, Br, I, C, N, P, O and S atoms. The prevalence of formula in which its Z configuration, gauche conformation and meta isomer are the most stable forms is calculated and discussed. The prevalence data shows that in compounds made of carbon backbones, the electronic effect is weaker than the steric effect. The electronic factor is more important as the backbone atoms are replaced with atoms on the right and upper part of the periodic table.</div>

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