This method is independent of detailed symmetric data and can "guess" the symmetry from pilin structures and sparse distance constraints which could be obtained from various experiments such as cysteine crosslinking, salt bridge charge reversal experiments, and DXMS.
In "Predicting Homogeneous Pilus Structure from Monomeric Data and Sparse Constraints," K. Xiao et al. developed a new approach to predict pseudoatomic models of pili by combining ambiguous symmetric constraints with sparse distance information obtained from experiments.
In this paper, we presented a new approach to predict pseudo atomic models of pili combining ambiguous symmetric constraints with sparse distance information obtained from experiments and based neither on electronic microscope (EM) maps nor on accurate a priori symmetric details.
Isomap is a variant of classical multidimensional scaling (MDS [17]), and like MDS, it computes a non-sparse distance matrix for all points in the workspace to find a lower-dimensional representation of the data.
Thus, we show that the CDK9/Cyclin-T1 canplex can be modeled as a single conformer, based on sparse CXMS distance restraints.
The accumulation of equations of restraint into the normal matrix should also be optimised, however, because the design matrix rows are so sparse (a distance restraint, for example, only involves up to six parameters), the current implementation accumulating the restraints one by one is very efficient and BLAS routines are not used.
Sparse long-distance connections, conceivably via nonlinear phenomena such as "contraction dynamics", may have disproportionate effects relative to the far more dense intrinsic and intermediate-distance connections (Wang and Slotine 2005).
This large number of features degrades the performance of supervised and unsupervised learning algorithms because the feature space becomes sparse and the distance between the samples becomes less precise.
Here, we introduce RosettaEPR, which has been designed to improve de novo high-resolution protein structure prediction using sparse SDSL-EPR distance data.
Using a set of sparse pairwise atomic distance constraints (such as those obtained from chemical cross-linking, FRET, or dipolar EPR experiments), the algorithm performs an exhaustive search of secondary structure element packing conformations distributed throughout the entire conformational space.
Recent achievements in advanced sample preparation strategies, and the incorporation of alternative restraints, such as residual dipolar couplings,[ 1] and paramagnetic data[ 2] have overcome some of these limitations and provide a toolbox that can complement and, in some favourable cases, replace sparse NOE-based distance data.
