Previously, a method for computationally structuring patent databases as a basis for an automated design-by-analogy tool was introduced.
Computationally, structure 5 b (diastereomer #6 in the computational studies) has the smallest errors for C and H chemical shifts and correspondingly the highest DP4 probability.
We suggest a framework for utilizing logical independence computationally by structuring a propositional logic database around a directed acyclic graph.
The structures for AH-I, AH-III, and MH-V were determined using powder X-ray diffraction data (Supporting Information) using computationally generated structures to generate the starting structure (AH-III) and to locate the proton positions (AH-I and MH-V).
This allowed us to determine the AH- B and AH- C° structures using PXRD data and the computationally generated structures as starting models for Rietveld refinements.
A thorough empirical evaluation on computationally predicted structures of biological sequences and artificially generated RNA structures as well as on empirically modelled structures from the biological literature shows that RNA-SSD substantially outperforms the best known algorithm for this problem, RNAinverse from the Vienna RNA Package.
A number of computational methods have been developed to fold a single RNA sequence [59], [60]; however, computationally predicted structures are often different from the true structures in vivo, because the folding of RNAs in the cell is usually associated with protein-cofactor binding and different metal ion associations.
The proposed methodology is practical and computationally efficient in structuring more effective decisions towards the optimum design of the process.
Finally, the scheme can be used to simplify computationally complex structure analysis tools by rapidly filtering out irrelevant structures.
Errors are even more frequent in computationally derived structures, which are built either by extrapolating from a homologous protein whose structure is already solved (Fiser and Sali, 2003; Ginalski, 2006) or by computer simulation (Das and Baker, 2008; Zhang and Skolnick, 2004).
Computationally predicted structures for ~1,000 nucleotides regions along the ZIKV genome, related to Fig. 2d.
