On the Use of low-resolution Data to Improve Structure Prediction of Proteins and Protein Complexes

2009 ◽  
Vol 5 (11) ◽  
pp. 3129-3137 ◽  
Author(s):  
Marco D’Abramo ◽  
Tim Meyer ◽  
Pau Bernadó ◽  
Carles Pons ◽  
Juan Fernández Recio ◽  
...  
Keyword(s):  
Structure Prediction ◽  
Protein Complexes ◽  
Low Resolution ◽  
Resolution Data

scholarly journals Sampling native-like structures of RNA-protein complexes through Rosetta folding and docking

2018 ◽  
Author(s):  
Kalli Kappel ◽  
Rhiju Das
Keyword(s):  
Conformational Changes ◽  
Posttranscriptional Regulation ◽  
De Novo ◽  
Protein Complexes ◽  
Regulation Of Gene Expression ◽  
Low Resolution ◽  
Human Telomerase ◽  
Nucleotide Resolution ◽  
Rna Protein Complexes ◽  
Resolution Data

AbstractRNA-protein complexes underlie numerous cellular processes including translation, splicing, and posttranscriptional regulation of gene expression. The structures of these complexes are crucial to their functions but often elude high-resolution structure determination. Computational methods are needed that can integrate low-resolution data for RNA-protein complexes while modeling de novo the large conformational changes of RNA components upon complex formation. To address this challenge, we describe a Rosetta method called RNP-denovo to simultaneously fold and dock RNA to a protein surface. On a benchmark set of structurally diverse RNA-protein complexes that are not solvable with prior strategies, this fold-and-dock method consistently sampled native-like structures with better than nucleotide resolution. We revisited three past blind modeling challenges in which previous methods gave poor results: human telomerase, an RNA methyltransferase with a ribosomal RNA domain, and the spliceosome. When coupled with the same sparse FRET, cross-linking, and functional data used in previous work, RNP-denovo gave models with significantly improved accuracy. These results open a route to computationally modeling global folds of RNA-protein complexes from low-resolution data.

Proteins and direct methods

1991 ◽  
Vol 197 (3-4) ◽  
Author(s):  
Fan Hai-fu ◽  
Hao Quan ◽  
M. M. Woolfson
Keyword(s):  
First Principles ◽  
Figure Of Merit ◽  
Direct Methods ◽  
Large Structure ◽  
Low Resolution ◽  
Figures Of Merit ◽  
Large Structures ◽  
Resolution Data

AbstractConventional direct methods, which work so well for small structures, are less successful for macromolecules. Where it has been demonstrated that a solution might be found using direct methods it is then found that the usual figures of merit are unable to distinguish the few good sets of phases from the large number of sets generated. The reasons for the difficulties with very large structures are considered from a first-principles approach taking into account both the factors of having a large number of atoms and low resolution data. A proposal is made for trying to recognize good phase sets by taking a large structure as a sum of a number of smaller structures for each of which a conventional figure of merit can be applied.

scholarly journals FOLDING ELASTIC TRANSMEMBRANE HELICES TO FIT IN A LOW-RESOLUTION IMAGE BY ELECTRON MICROSCOPY

2011 ◽  
Vol 09 (supp01) ◽  
pp. 37-50 ◽  
Author(s):  
YUTAKA UENO ◽  
KAZUNORI KAWASAKI ◽  
OSAMU SAITO ◽  
MASAFUMI ARAI ◽  
MAKIKO SUWA
Keyword(s):  
Membrane Proteins ◽  
Structure Prediction ◽  
Molecular Model ◽  
Imaging Techniques ◽  
Transmembrane Helix ◽  
Prediction Method ◽  
Molecular Shape ◽  
Coarse Grained ◽  
Transmembrane Helices ◽  
Low Resolution

Structure prediction of membrane proteins could be constrained and thereby improved by introducing data of the observed molecular shape. We studied a coarse-grained molecular model that relied on residue-based dummy atoms to fold the transmembrane helices of a protein in the observed molecular shape. Based on the inter-residue potential, the α-helices were folded to contact each other in a simulated annealing protocol to search optimized conformation. Fitting the model into a three-dimensional volume was tested for proteins with known structures and resulted in a fairly reasonable arrangement of helices. In addition, the constraint to the packing transmembrane helix with the two-dimensional region was tested and found to work as a very similar folding guide. The obtained models nicely represented α-helices with the desired slight bend. Our structure prediction method for membrane proteins well demonstrated reasonable folding results using a low-resolution structural constraint introduced from recent cell-surface imaging techniques.

scholarly journals Bioinformatics Tools and Benchmarks for Computational Docking and 3D Structure Prediction of RNA-Protein Complexes

Genes ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 432 ◽  
Author(s):  
Chandran Nithin ◽  
Pritha Ghosh ◽  
Janusz Bujnicki
Keyword(s):  
Rna Splicing ◽  
Structure Prediction ◽  
Protein Complexes ◽  
3D Structure ◽  
Structural Models ◽  
Computational Prediction ◽  
Computational Docking ◽  
3D Structure Prediction ◽  
Rna Protein Complexes

RNA-protein (RNP) interactions play essential roles in many biological processes, such as regulation of co-transcriptional and post-transcriptional gene expression, RNA splicing, transport, storage and stabilization, as well as protein synthesis. An increasing number of RNP structures would aid in a better understanding of these processes. However, due to the technical difficulties associated with experimental determination of macromolecular structures by high-resolution methods, studies on RNP recognition and complex formation present significant challenges. As an alternative, computational prediction of RNP interactions can be carried out. Structural models obtained by theoretical predictive methods are, in general, less reliable compared to models based on experimental measurements but they can be sufficiently accurate to be used as a basis for to formulating functional hypotheses. In this article, we present an overview of computational methods for 3D structure prediction of RNP complexes. We discuss currently available methods for macromolecular docking and for scoring 3D structural models of RNP complexes in particular. Additionally, we also review benchmarks that have been developed to assess the accuracy of these methods.

scholarly journals Long Slit Echelle Spectroscopy of Supernova Remnants in M33

1988 ◽  
Vol 101 ◽  
pp. 193-196
Author(s):  
William P. Blair ◽  
You-Hua Chu ◽  
Robert C. Kennicutt
Keyword(s):  
Supernova Remnants ◽  
Low Resolution ◽  
Low Velocity ◽  
Line Intensities ◽  
Echelle Spectroscopy ◽  
Resolution Data

AbstractWe have obtained long slit echelle spectroscopy for 10 of the brightest supernova remnants in M33 using the KPNO 4 m telescope. The profiles at Hα indicate bulk motions in the range 100–350 km s−1 in these remnants. Nearly all of the objects show signs of contamination by low velocity H II emission at some level. This affects the line intensities measured from low resolution data and may affect diameter measurements of these remnants.

Sign in / Sign up

Export Citation Format

Share Document