scholarly journals Effective protein model structure refinement by loop modeling and overall relaxation

2015 ◽  
Vol 84 ◽  
pp. 293-301 ◽  
Author(s):  
Gyu Rie Lee ◽  
Lim Heo ◽  
Chaok Seok
Keyword(s):  
Structure Refinement ◽  
Model Structure ◽  
Loop Modeling ◽  
Protein Model

scholarly journals When good ligands go bad

2014 ◽  
Vol 70 (a1) ◽  
pp. C1269-C1269
Author(s):  
Ethan Merritt
Keyword(s):  
Task Force ◽  
Structure Refinement ◽  
Binding Pocket ◽  
Software Packages ◽  
Protein Model ◽  
Protein Components ◽  
First Time ◽  
Standard Software ◽  
New Protein

"Tools for validating structural models of proteins are relatively mature and widely implemented. New protein crystallographers are introduced early on to the importance of monitoring conformance with expected φ/ψ values, favored rotamers, and local stereochemistry. The protein model is validated by the PDB at the time of deposition using criteria that are also available in the standard software packages used to refine the model being deposited. By contrast, crystallographers are typically much less familiar with procedures to validate key non-protein components of the model – cofactors, substrates, inhibitors, etc. It has been estimated that as many as a third of all ligands in the PDB exhibit preventable errors of some sort, ranging from minor deviations in expected bond angles to wholly implausible placement in the binding pocket. Following recommendations from the wwPDB Validation Task Force, the PDB recently began validating ligand geometry as an integral part of deposition processing. This means that many crystallographers will soon receive for the first time a ""grade"" on the quality of ligands in the structure they have just deposited. Some will be surprised, as I was following my first PDB deposition of 2014, at how easily bad ligand geometry can slip through the cracks in supposedly robust structure refinement protocols that their lab has used for many years. I will illustrate use of current tools for generating ligand restraints to guide model refinement. One is the jligand+coot+cprodrg pipeline integrated into the CCP4 suite. Another is the Grade web server provided as a community resource by Global Phasing Ltd. Furthermore I will show examples from recent in-house refinements of how things can still go wrong even if you do use these tools, and how we recovered. The new PDB deposition checks may expose errors in your ligand descriptions after the fact. This presentation may help you avoid introducing those errors in the first place."

scholarly journals Developing a Fully-glycosylated Full-length SARS-CoV-2 Spike Protein Model in a Viral Membrane

2020 ◽  
Author(s):  
Hyeonuk Woo ◽  
Sang-Jun Park ◽  
Yeol Kyo Choi ◽  
Taeyong Park ◽  
Maham Tanveer ◽  
...  
Keyword(s):  
Modeling And Simulation ◽  
Structure Prediction ◽  
De Novo ◽  
Protein Structures ◽  
Full Length ◽  
Loop Modeling ◽  
S Protein ◽  
Viral Membrane ◽  
Protein Model ◽  
Dynamics Simulations

ABSTRACTThis technical study describes all-atom modeling and simulation of a fully-glycosylated full-length SARS-CoV-2 spike (S) protein in a viral membrane. First, starting from PDB:6VSB and 6VXX, full-length S protein structures were modeled using template-based modeling, de-novo protein structure prediction, and loop modeling techniques in GALAXY modeling suite. Then, using the recently-determined most occupied glycoforms, 22 N-glycans and 1 O-glycan of each monomer were modeled using Glycan Reader & Modeler in CHARMM-GUI. These fully-glycosylated full-length S protein model structures were assessed and further refined against the low-resolution data in their respective experimental maps using ISOLDE. We then used CHARMM-GUI Membrane Builder to place the S proteins in a viral membrane and performed all-atom molecular dynamics simulations. All structures are available in CHARMM-GUI COVID-19 Archive (http://www.charmm-gui.org/docs/archive/covid19), so researchers can use these models to carry out innovative and novel modeling and simulation research for the prevention and treatment of COVID-19.

Monte-Carlo Estimations for the Precision of Iterative Structure Refinement in QHREM

1996 ◽  
Vol 54 ◽  
pp. 100-101
Author(s):  
G. Möbus
Keyword(s):  
Monte Carlo ◽  
Structure Refinement ◽  
Atomic Radius ◽  
Crystal Defects ◽  
Model Structure ◽  
Simulated Image ◽  
High Resolution Tem ◽  
Experimental Image ◽  
Retrieval Problem

1.Introduction:Quantitative high-resolution TEM (QHREM) serves for the determination of structures of crystal defects. The structure retrieval problem is usually considered to be solved when a simulated image calculated from a model structure matches with high quality to the experimental image. Often, however, surprisingly large (and continuous) shifts of individual atomic columns are possible before the simulation looses its similarity to the experiment. Therefore, we propose printing structure retrieval results as “atomic ball pictures” with the radius of the ball as a circle of confidence (replacing the chemical atomic radius otherwise used for these pictures). Statistical methods are necessary to derive the proper radius of the circles which are element and site specific : Monte-Carlo based strategies are highly suitable for this purpose. As a model system we use two bulk sections from a micrograph of a Cu/Sapphire interface (Fig. 1). For interface properties see.

Phase determination and structure refinement by density and shift functions

1970 ◽  
Vol 26 (2) ◽  
pp. 230-234 ◽  
Author(s):  
H. Jagodzinski ◽  
D. Philipp
Keyword(s):  
Crystal Structure ◽  
Structure Refinement ◽  
Three Dimensional ◽  
Complete Information ◽  
Shift Function ◽  
Model Structure ◽  
Phase Determination ◽  
One Dimensional ◽  
Structure Factors ◽  
The Right

Any crystal structure may be described in terms of a sublattice of points, each of which represents a certain fraction of the electron density. Multiplying this sublattice by a density function f(x) and applying a shift function s(x), which brings the atoms into the right positions, the correct crystal structure can be given in many different ways. It is shown that the shift function s(x) yields phase relations between the structure factors F(h), which may be evaluated directly, if the coefficients of the Fourier representation of s(x) converge rapidly. This behaviour is demonstrated for the case of a one-dimensional acentric model structure consisting of 50 atoms. Complete information on the structure may be obtained by routine methods with the aid of 5 given phases of the structure factor. This procedure may also be applied to three-dimensional structures, if the corresponding computer programs are available.

scholarly journals Protein model structure evaluation using the solvation free energy of folding.

1990 ◽  
Vol 87 (8) ◽  
pp. 3240-3243 ◽  
Author(s):  
L. Chiche ◽  
L. M. Gregoret ◽  
F. E. Cohen ◽  
P. A. Kollman
Keyword(s):  
Free Energy ◽  
Solvation Free Energy ◽  
Model Structure ◽  
Protein Model ◽  
Structure Evaluation

scholarly journals Assessment of protein model structure accuracy estimation in CASP13: Challenges in the era of deep learning

2019 ◽  
Vol 87 (12) ◽  
pp. 1351-1360 ◽  
Author(s):  
Jonghun Won ◽  
Minkyung Baek ◽  
Bohdan Monastyrskyy ◽  
Andriy Kryshtafovych ◽  
Chaok Seok
Keyword(s):  
Deep Learning ◽  
Model Structure ◽  
Accuracy Estimation ◽  
Protein Model ◽  
Structure Accuracy
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