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 Protein structure prediction and design in a biologically-realistic implicit membrane

2019 ◽  
Author(s):  
Rebecca F. Alford ◽  
Patrick J. Fleming ◽  
Karen G. Fleming ◽  
Jeffrey J. Gray
Keyword(s):  
Protein Structure ◽  
Amino Acid ◽  
Membrane Proteins ◽  
Membrane Protein ◽  
Protein Structure Prediction ◽  
Protein Design ◽  
Structure Prediction ◽  
De Novo ◽  
Computational Design ◽  
Amino Acid Distribution

ABSTRACTProtein design is a powerful tool for elucidating mechanisms of function and engineering new therapeutics and nanotechnologies. While soluble protein design has advanced, membrane protein design remains challenging due to difficulties in modeling the lipid bilayer. In this work, we developed an implicit approach that captures the anisotropic structure, shape of water-filled pores, and nanoscale dimensions of membranes with different lipid compositions. The model improves performance in computational bench-marks against experimental targets including prediction of protein orientations in the bilayer, ΔΔG calculations, native structure dis-crimination, and native sequence recovery. When applied to de novo protein design, this approach designs sequences with an amino acid distribution near the native amino acid distribution in membrane proteins, overcoming a critical flaw in previous membrane models that were prone to generating leucine-rich designs. Further, the proteins designed in the new membrane model exhibit native-like features including interfacial aromatic side chains, hydrophobic lengths compatible with bilayer thickness, and polar pores. Our method advances high-resolution membrane protein structure prediction and design toward tackling key biological questions and engineering challenges.Significance StatementMembrane proteins participate in many life processes including transport, signaling, and catalysis. They constitute over 30% of all proteins and are targets for over 60% of pharmaceuticals. Computational design tools for membrane proteins will transform the interrogation of basic science questions such as membrane protein thermodynamics and the pipeline for engineering new therapeutics and nanotechnologies. Existing tools are either too expensive to compute or rely on manual design strategies. In this work, we developed a fast and accurate method for membrane protein design. The tool is available to the public and will accelerate the experimental design pipeline for membrane proteins.

scholarly journals Tight Turns of Outer Membrane Proteins: An Analysis of Sequence, Structure, and Hydrogen Bonding

2018 ◽  
Author(s):  
Meghan Whitney Franklin ◽  
Joanna S.G. Slusky
Keyword(s):  
Hydrogen Bonding ◽  
Membrane Proteins ◽  
Outer Membrane ◽  
Soluble Protein ◽  
Structure Prediction ◽  
Outer Membrane Proteins ◽  
Sequence Structure ◽  
Structural Class ◽  
Future Design ◽  
Extracellular Loops

I.AbstractAs a structural class, tight turns can control molecular recognition, enzymatic activity, and nucleation of folding. They have been extensively characterized in soluble proteins but have not been characterized in outer membrane proteins (OMPs), where they also support critical functions. We clustered the 4-6 residue tight turns of 110 OMPs to characterize the phi/psi angles, sequence, and hydrogen bonding of these structures. We find significant differences between reports of soluble protein tight turns and OMP tight turns. Since OMP strands are less twisted than soluble strands they favor different turn structures types. Moreover, the membrane localization of OMPs yields different sequence hallmarks for their tight turns relative to soluble protein turns. We also characterize the differences in phi/psi angles, sequence, and hydrogen bonding between OMP extracellular loops and OMP periplasmic turns. As previously noted, the extracellular loops tend to be much longer than the periplasmic turns. We find that this difference in length is due to the broader distribution of lengths of the extracellular loops not a large difference in the median length. Extracellular loops also tend to have more charged residues as predicted by the charge-out rule. Finally, in all OMP tight turns, hydrogen bonding between the sidechain and backbone two to four residues away plays an important role. These bonds preferentially use an Asp, Asn, Ser or Thr residue in a beta or pro phi/psi conformation. We anticipate that this study will be applicable to future design and structure prediction of OMPs.

scholarly journals High-resolution structure prediction ofβ-barrel membrane proteins

2018 ◽  
Vol 115 (7) ◽  
pp. 1511-1516 ◽  
Author(s):  
Wei Tian ◽  
Meishan Lin ◽  
Ke Tang ◽  
Jie Liang ◽  
Hammad Naveed
Keyword(s):  
Membrane Proteins ◽  
Structure Prediction ◽  
Prediction Accuracy ◽  
Main Chain ◽  
Structural Prediction ◽  
Nmr Structures ◽  
Genome Wide ◽  
High Resolution Structure ◽  
The Difference ◽  
Resolution Structure

β-Barrel membrane proteins (βMPs) play important roles, but knowledge of their structures is limited. We have developed a method to predict their 3D structures. We predict strand registers and construct transmembrane (TM) domains of βMPs accurately, including proteins for which no prediction has been attempted before. Our method also accurately predicts structures from protein families with a limited number of sequences and proteins with novel folds. An average main-chain rmsd of 3.48 Å is achieved between predicted and experimentally resolved structures of TM domains, which is a significant improvement (>3 Å) over a recent study. For βMPs with NMR structures, the deviation between predictions and experimentally solved structures is similar to the difference among the NMR structures, indicating excellent prediction accuracy. Moreover, we can now accurately model the extended β-barrels and loops in non-TM domains, increasing the overall coverage of structure prediction by>30%. Our method is general and can be applied to genome-wide structural prediction of βMPs.

scholarly journals Interplay between hydrophobicity and the positive-inside rule in determining membrane-protein topology

2016 ◽  
Vol 113 (37) ◽  
pp. 10340-10345 ◽  
Author(s):  
Assaf Elazar ◽  
Jonathan Jacob Weinstein ◽  
Jaime Prilusky ◽  
Sarel Jacob Fleishman
Keyword(s):  
Membrane Proteins ◽  
Membrane Protein ◽  
Structure Prediction ◽  
In Vitro Selection ◽  
Prediction Algorithm ◽  
Systematic Analysis ◽  
Protein Topology ◽  
Functional Sites ◽  
Membrane Protein Topology

The energetics of membrane-protein interactions determine protein topology and structure: hydrophobicity drives the insertion of helical segments into the membrane, and positive charges orient the protein with respect to the membrane plane according to the positive-inside rule. Until recently, however, quantifying these contributions met with difficulty, precluding systematic analysis of the energetic basis for membrane-protein topology. We recently developed the dsTβL method, which uses deep sequencing and in vitro selection of segments inserted into the bacterial plasma membrane to infer insertion-energy profiles for each amino acid residue across the membrane, and quantified the insertion contribution from hydrophobicity and the positive-inside rule. Here, we present a topology-prediction algorithm called TopGraph, which is based on a sequence search for minimum dsTβL insertion energy. Whereas the average insertion energy assigned by previous experimental scales was positive (unfavorable), the average assigned by TopGraph in a nonredundant set is −6.9 kcal/mol. By quantifying contributions from both hydrophobicity and the positive-inside rule we further find that in about half of large membrane proteins polar segments are inserted into the membrane to position more positive charges in the cytoplasm, suggesting an interplay between these two energy contributions. Because membrane-embedded polar residues are crucial for substrate binding and conformational change, the results implicate the positive-inside rule in determining the architectures of membrane-protein functional sites. This insight may aid structure prediction, engineering, and design of membrane proteins. TopGraph is available online (topgraph.weizmann.ac.il).

scholarly journals 3P002 Structure Prediction of Membrane Proteins by the Fragment Assembly Method

2005 ◽  
Vol 45 (supplement) ◽  
pp. S204
Author(s):  
W. Jin ◽  
T. Miki ◽  
S. Takada
Keyword(s):  
Membrane Proteins ◽  
Structure Prediction ◽  
Fragment Assembly ◽  
Assembly Method

scholarly journals Enhancing Structure Prediction and Design of Soluble and Membrane Proteins with Explicit Solvent-Protein Interactions

Structure ◽  
2017 ◽  
Vol 25 (11) ◽  
pp. 1758-1770.e8 ◽  
Author(s):  
Jason K. Lai ◽  
Joaquin Ambia ◽  
Yumeng Wang ◽  
Patrick Barth
Keyword(s):  
Membrane Proteins ◽  
Protein Interactions ◽  
Structure Prediction ◽  
Explicit Solvent
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