A novel approach for large-scale polypeptide folding based on elastic networks using continuous optimization.
We present a new computationally efficient method for large-scale polypeptide folding using coarse-grained elastic networks and gradient-based continuous optimization techniques. The folding is governed by minimization of energy based on Miyazawa-Jernigan contact potentials. Using this method we are able to substantially reduce the computation time on ordinary desktop computers for simulation of polypeptide folding starting from a fully unfolded state. We compare our results with available native state structures from Protein Data Bank (PDB) for a few de-novo proteins and two natural proteins, Ubiquitin and Lysozyme. Based on our simulations we are able to draw the energy landscape for a small de-novo protein, Chignolin. We also use two well known protein structure prediction software, MODELLER and GROMACS to compare our results. In the end, we show how a modification of normal elastic network model can lead to higher accuracy and lower time required for simulation.
Rakshit
Sourav
S
Mechanical Engineering, Indian Institute of Science, Bangalore 560012, India. srakshit@mecheng.iisc.ernet.in
Ananthasuresh
G K
GK
D000465:Algorithms; D030562:Databases, Protein; D008958:Models, Molecular; D009113:Muramidase / Q000737:chemistry; D009842:Oligopeptides / Q000737:chemistry / Q000378:metabolism; D010455:Peptides / Q000737:chemistry* / Q000378:metabolism*; D017510:Protein Folding; D017433:Protein Structure, Secondary; D013816:Thermodynamics; D025801:Ubiquitin / Q000737:chemistry
D009842:Oligopeptides; D010455:Peptides; D025801:Ubiquitin; C506175:chignolin; D009113:Muramidase