scholarly journals Generalized protein structure prediction based on combination of fold-recognition with de novo folding and evaluation of models

2005 ◽  
Vol 61 (S7) ◽  
pp. 84-90 ◽  
Author(s):  
Andrzej Koliński ◽  
Janusz M. Bujnicki
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
De Novo ◽  
Fold Recognition ◽  
De Novo Folding

scholarly journals Building a Better Fragment Library for De Novo Protein Structure Prediction

PLoS ONE ◽  
2015 ◽  
Vol 10 (4) ◽  
pp. e0123998 ◽  
Author(s):  
Saulo H. P. de Oliveira ◽  
Jiye Shi ◽  
Charlotte M. Deane
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
De Novo ◽  
Fragment Library

scholarly journals Sampling Bottlenecks in De novo Protein Structure Prediction

2009 ◽  
Vol 393 (1) ◽  
pp. 249-260 ◽  
Author(s):  
David E. Kim ◽  
Ben Blum ◽  
Philip Bradley ◽  
David Baker
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
De Novo

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.

A Novel Conformation Generation Framework for De novo Protein Structure Prediction Using Hydrophobic-Polar Model

2016 ◽  
Vol 11 (3) ◽  
pp. 149-155
Author(s):  
Sandhya P.N. Dubey ◽  
N. Gopalakrishna Kini ◽  
M. Sathish Kumar ◽  
S. Balaji ◽  
M.P. Sumana Bha ◽  
...  
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
De Novo ◽  
Polar Model ◽  
Conformation Generation

scholarly journals De novo protein structure prediction : Optimization of potential function

2003 ◽  
Vol 43 (supplement) ◽  
pp. S51
Author(s):  
Y. Fujitsuka ◽  
G. Chikenji ◽  
S. Takada
Keyword(s):  
Protein Structure ◽  
Potential Function ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
De Novo
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