Improvements to robotics-inspired conformational sampling in Rosetta
Research output: Contribution to journal › Journal article › Research › peer-review
Standard
Improvements to robotics-inspired conformational sampling in Rosetta. / Stein, Amelie; Kortemme, Tanja.
In: PLoS ONE, Vol. 8, No. 5, e63090, 2013, p. 1-13.Research output: Contribution to journal › Journal article › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - Improvements to robotics-inspired conformational sampling in Rosetta
AU - Stein, Amelie
AU - Kortemme, Tanja
PY - 2013
Y1 - 2013
N2 - To accurately predict protein conformations in atomic detail, a computational method must be capable of sampling models sufficiently close to the native structure. All-atom sampling is difficult because of the vast number of possible conformations and extremely rugged energy landscapes. Here, we test three sampling strategies to address these difficulties: conformational diversification, intensification of torsion and omega-angle sampling and parameter annealing. We evaluate these strategies in the context of the robotics-based kinematic closure (KIC) method for local conformational sampling in Rosetta on an established benchmark set of 45 12-residue protein segments without regular secondary structure. We quantify performance as the fraction of sub-Angstrom models generated. While improvements with individual strategies are only modest, the combination of intensification and annealing strategies into a new "next-generation KIC" method yields a four-fold increase over standard KIC in the median percentage of sub-Angstrom models across the dataset. Such improvements enable progress on more difficult problems, as demonstrated on longer segments, several of which could not be accurately remodeled with previous methods. Given its improved sampling capability, next-generation KIC should allow advances in other applications such as local conformational remodeling of multiple segments simultaneously, flexible backbone sequence design, and development of more accurate energy functions.
AB - To accurately predict protein conformations in atomic detail, a computational method must be capable of sampling models sufficiently close to the native structure. All-atom sampling is difficult because of the vast number of possible conformations and extremely rugged energy landscapes. Here, we test three sampling strategies to address these difficulties: conformational diversification, intensification of torsion and omega-angle sampling and parameter annealing. We evaluate these strategies in the context of the robotics-based kinematic closure (KIC) method for local conformational sampling in Rosetta on an established benchmark set of 45 12-residue protein segments without regular secondary structure. We quantify performance as the fraction of sub-Angstrom models generated. While improvements with individual strategies are only modest, the combination of intensification and annealing strategies into a new "next-generation KIC" method yields a four-fold increase over standard KIC in the median percentage of sub-Angstrom models across the dataset. Such improvements enable progress on more difficult problems, as demonstrated on longer segments, several of which could not be accurately remodeled with previous methods. Given its improved sampling capability, next-generation KIC should allow advances in other applications such as local conformational remodeling of multiple segments simultaneously, flexible backbone sequence design, and development of more accurate energy functions.
KW - Algorithms
KW - Biomechanical Phenomena
KW - Models, Molecular
KW - Protein Conformation
KW - Proteins/chemistry
KW - Robotics
KW - Thermodynamics
U2 - 10.1371/journal.pone.0063090
DO - 10.1371/journal.pone.0063090
M3 - Journal article
C2 - 23704889
VL - 8
SP - 1
EP - 13
JO - PLoS ONE
JF - PLoS ONE
SN - 1932-6203
IS - 5
M1 - e63090
ER -
ID: 203256354