scholarly journals Flexible backbone assembly and refinement of symmetrical homomeric complexes

2018 ◽  
Author(s):  
Shourya S. Roy Burman ◽  
Remy A. Yovanno ◽  
Jeffrey J. Gray
Keyword(s):  
Protein Structures ◽  
Score Function ◽  
Conformational Space ◽  
Coarse Grained ◽  
List Type ◽  
Promising Alternative ◽  
Computational Docking ◽  
Realistic Modeling ◽  
High Resolution Structure ◽  
Resolution Structure

SummarySymmetrical homomeric proteins are ubiquitous in every domain of life, and information about their structure is essential to decipher function. The size of these complexes often makes them intractable to high-resolution structure determination experiments. Computational docking algorithms offer a promising alternative for modeling large complexes with arbitrary symmetry. Accuracy of existing algorithms, however, is limited by backbone inaccuracies when using homology-modeled monomers. Here, we present Rosetta SymDock2 with a broad search of symmetrical conformational space using a six-dimensional coarse-grained score function followed by an all-atom flexible-backbone refinement, which we demonstrate to be essential for physically-realistic modeling of tightly packed complexes. In global docking of a benchmark set of complexes of different point symmetries — staring from homology-modeled monomers — we successfully dock (defined as predicting three near-native structures in the five top-scoring models) 19 out of 31 cyclic complexes and 5 out of 12 dihedral complexes.HighlightsSymDock2 is an algorithm to assemble symmetric protein structures from monomersCoarse-grained score function discriminates near-native conformationsFlexible backbone refinement is necessary to create realistic all-atom modelsResults improve six-fold and outperform other symmetric docking algorithmsGraphical Abstract

scholarly journals Efficient Flexible Backbone Protein-Protein Docking for Challenging Targets

2017 ◽  
Author(s):  
Nicholas A. Marze ◽  
Shourya S. Roy Burman ◽  
William Sheffler ◽  
Jeffrey J. Gray
Keyword(s):  
Conformational Changes ◽  
Computational Prediction ◽  
Protein Docking ◽  
Previous Method ◽  
Conformational Space ◽  
Coarse Grained ◽  
Computational Time ◽  
Success Rates ◽  
Discriminative Ability ◽  
Computational Docking

AbstractComputational prediction of protein-protein complex structures facilitates a fundamental understanding of biological mechanisms and enables therapeutics design. Binding-induced conformational changes challenge all current computational docking algorithms by exponentially increasing the conformational space to be explored. To restrict this search to relevant space, some computational docking algorithms exploit the inherent flexibility of the protein monomers to simulate conformational selection from pre-generated ensembles. As the ensemble size expands with increased protein flexibility, these methods struggle with efficiency and high false positive rates. Here, we develop and benchmark a method that efficiently samples large conformational ensembles of flexible proteins and docks them using a novel, six-dimensional, coarse-grained score function. A strong discriminative ability allows an eight-fold higher enrichment of nearnative candidate structures in the coarse-grained phase compared to a previous method. Further, the method adapts to the diversity of backbone conformations in the ensemble by modulating sampling rates. It samples 100 conformations each of the ligand and the receptor backbone while increasing computational time by only 20–80%. In a benchmark set of 88 proteins of varying degrees of flexibility, the expected success rate for blind predictions after resampling is 77% for rigid complexes, 49% for moderately flexible complexes, and 31% for highly flexible complexes. These success rates on flexible complexes are a substantial step forward from all existing methods. Additionally, for highly flexible proteins, we demonstrate that when a suitable conformer generation method exists, RosettaDock 4.0 can dock the complex successfully.SignificancePredicting binding-induced conformational plasticity in protein backbones remains a principal challenge in computational protein–protein docking. To date, there are no methods that can reliably dock proteins that undergo more than 1 Å root-mean-squared-deviation of the backbones of the interface residues upon binding. Here, we present a method that samples backbone motions and scores conformations rapidly, obtaining–for the first time–successful docking of nearly 50% of flexible target complexes with backbone conformational change up to 2.2 Å RMSD. This method will be applicable to a broader range of protein docking problems, which in turn will help us understand biomolecular assembly and protein function.

High-resolution structure determination by ALCHEMI

1983 ◽  
Vol 41 ◽  
pp. 178-181 ◽  
Author(s):  
Peter G. Self ◽  
Peter R. Buseck
Keyword(s):  
Site Occupancy ◽  
Real Space ◽  
Unit Cell ◽  
Bragg Angle ◽  
Electron Current ◽  
High Resolution Structure ◽  
Beam Incident ◽  
Crystallographic Planes ◽  
Electron Beam Incident ◽  
Resolution Structure

ALCHEMI (Atom Location by CHanneling Enhanced Microanalysis) enables the site occupancy of atoms in single crystals to be determined. In this article the fundamentals of the method for both EDS and EELS will be discussed. Unlike HRTEM, ALCHEMI does not place stringent resolution requirements on the microscope and, because EDS clearly distinguishes between elements of similar atomic number, it can offer some advantages over HRTEM. It does however, place certain constraints on the crystal. These constraints are: a) the sites of interest must lie on alternate crystallographic planes, b) the projected charge density on the alternate planes must be significantly different, and c) there must be at least one atomic species that lies solely on one of the planes.An electron beam incident on a crystal undergoes elastic scattering; in reciprocal space this is seen as a diffraction pattern and in real space this is a modulation of the electron current across the unit cell. When diffraction is strong (i.e., when the crystal is oriented near to the Bragg angle of a low-order reflection) the electron current at one point in the unit cell will differ significantly from that at another point.

Cathodoluminescence of Catalyst Crystallites

1982 ◽  
Vol 40 ◽  
pp. 664-665
Author(s):  
E.D. Boyes ◽  
P.L. Gai ◽  
D.B. Darby ◽  
C. Warwick
Keyword(s):  
Defect Density ◽  
Chemical Properties ◽  
Operating Conditions ◽  
Semiconductor Materials ◽  
Environmental Cell ◽  
High Resolution Structure ◽  
Commercially Important ◽  
Crystallographic Defects ◽  
Resolution Structure

The extended crystallographic defects introduced into some oxide catalysts under operating conditions may be a consequence and accommodation of the changes produced by the catalytic activity, rather than always being the origin of the reactivity. Operation without such defects has been established for the commercially important tellurium molybdate system. in addition it is clear that the point defect density and the electronic structure can both have a significant influence on the chemical properties and hence on the effectiveness (activity and selectivity) of the material as a catalyst. SEM/probe techniques more commonly applied to semiconductor materials, have been investigated to supplement the information obtained from in-situ environmental cell HVEM, ultra-high resolution structure imaging and more conventional AEM and EPMA chemical microanalysis.

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>

scholarly journals High-resolution structure determination by continuous-rotation data collection in MicroED

2014 ◽  
Vol 11 (9) ◽  
pp. 927-930 ◽  
Author(s):  
Brent L Nannenga ◽  
Dan Shi ◽  
Andrew G W Leslie ◽  
Tamir Gonen
Keyword(s):  
High Resolution ◽  
Data Collection ◽  
Structure Determination ◽  
Continuous Rotation ◽  
High Resolution Structure ◽  
Resolution Structure
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