Efficient Sampling of Protein Conformational Space Using Fast Loop Building and Batch Minimization on Highly Parallel Computers
By Michael D. Tyka, K. Jung, David Baker. 2012
43 citations recorded by the source corpus.
All‐atom sampling is a critical and compute‐intensive end stage to protein structural modeling. Because of the vast size and extreme ruggedness of conformational space, even close to the native structure, the high‐resolution sampling problem is almost as difficult as predicting the rough fold of a protein. Here, we present a combination of new algorithms that considerably speed up the exploration of very rugged conformational landscapes and are capable of finding heretofore hidden low‐energy states. The algorithm is based on a hierarchical workflow and can be parallelized on supercomputers with up to 128,000 compute cores with near perfect efficiency. Such scaling behavior is notable, as with Moore's law continuing only in the number of cores per chip, parallelizability is a critical property of new algorithms. Using the enhanced sampling power, we have uncovered previously invisible deficiencies in the Rosetta force field and created an extensive decoy training set for optimizing and testing force fields. © 2012 Wiley Periodicals, Inc.
Subfields: Materials Chemistry, Molecular Biology, Spectroscopy
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Materials Chemistry
- David Baker
- Jeffrey J. Gray
- Tanja Kortemme
- Frank DiMaio
- Hahnbeom Park
- Jens Meiler
- David E. Kim
- Sergey Ovchinnikov
In Molecular Biology
- David Baker
- Jeffrey J. Gray
- Tanja Kortemme
- Jens Meiler
- Xingjie Pan
- Frank DiMaio
- Hahnbeom Park
- Rhiju Das
In Spectroscopy
- David Baker
- Sergey Ovchinnikov
- Frank DiMaio
- Hahnbeom Park
- Santrupti Nerli
- Nikolaos G. Sgourakis
- David E. Kim
- Brian D. Weitzner
Citations in the corpus
- Macromolecular modeling and design in Rosetta: recent methods and frameworks (2020, methodology): “LoopHashKIC 214 addresses this problem and uses the Rosetta LoopHash algorithm 215 to efficiently query a database of loop conformations based on rigid-body transforms between the first and last loop residues.”
- Protein homology model refinement by large-scale energy optimization (2018, methodology): “The major stumbling block was that without restraints on the input structure or experimental data to guide the search, inaccuracies in the energy function would cause structures to drift away rather than toward the native structure (33).”
- Algorithms for Protein Design (2016, methodology): “in the FastRelax [45] and BatchRelax [71] algorithms, which improve the speed of these methods by”
- Improvements to Robotics-Inspired Conformational Sampling in Rosetta (2013, result): “Similar scoring problems have also been observed with other recent sampling improvements [36].”
- Essentials of de novo protein design: Methods and applications (2018, background): “60 For connecting helices of parametrically generated helical bundles, Boyken et al. similarly generated a database of naturally occurring fragments connecting pairs of helices through loops of five or less residues, and those loops with low-RMSD alignment between its two open (C-and N-terminal)…”
- Relaxation of backbone bond geometry improves protein energy landscape modeling (2014, methodology): “For 10 of the targets from the original test set, we used the large-scale parallel loop hash (PLS) sampling procedure 11 starting from 200 input structures per target.”