## 4 Discussion  
### General aspects  
The aims of this approach as defined in the introduction were the proposition of a generalized methodology that can be extended and customized by the user for different macromolecules and applications. In the results section we showed that the performance and precision / coverage of the approach is comparable to common methods available freely today. The trade-off between performance and precision / coverage can be adjusted using the n-gram length. The described chain to string translators are independent from an initial precomputation of the secondary structure elements. With the dataset CATH_1258 derived from the 35% filtered CATH it becomes clear, that the approach works well when the sequence of the proteins is not entirely similar. Moreover, because this approach is implemented as open source in the framework LaJolla, it can be easily extended with novel translators that abstract the macromolecular structure in different ways such as suite codes or protein building blocks. It has to be pointed out that the results presented for both proteins and RNA were achieved by the backbone information alone. It is safe to assume that on both sides the accuracy of the approach could be improved by including sequence-specific information. In the case of RNA, this could be for instance the isostericity matrices of Leontis and Westhof [45]. The principal performance bottleneck is the refinement step where the biomolecules have to be read from the hard disk and superimposed in 3D. Almost 80% of the time currently used for search are input / output operations. It is possible to tackle this problem from many sides. The implementation of a caching infrastructure that stores frequently used structures in memory so that subsequent hard disk reads are redirected to memory would be the first logical step. Another possibility is to store specially prepared files that only contain atoms used for 3D refinement, which would reduce the file size that has to be read. Defining a threshold of how many matching n-grams between query and target at least have to be found to carry out the expensive 3D alignment has the potential to eliminate impossible  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline PDB-ID & chain & resolution & tRNA type & complex with & N hits & RMSD & TM4 Score & percentage aligned residues \\ \hline  
& A & 3,00 & 683A\_Mate & 705 downloaded & 98 & 1,72 & 0.44 & 40.72 \\
2 nzg & B & 3,50 & 683A\_Mate & LeuRS & 69 & 1,85 & 0.42 & 40.74 \\
2 nzg & F & 3,50 & 683A\_Mate & 705 ribosome & 98 & 1,80 & 0.46 & 44.24 \\
2 nz+d8 & W & 3,80 & 683A\_Mate & 705 ribosome & 96 & 1,86 & 0.46 & 44.87 \\
3 nzn & A & 3,00 & 683A\_Mate & 705 ribosome & 93 & 1,75 & 0.45 & 41.92 \\
4 nzn & A & 2,50 & 683A\_Mate & uncomplexed & 100 & 1,70 & 0.52 & 49.00 \\ \hline \end{tabular}
\end{table}
Table 2: tRNA search result table (part 2).  
alignments beforehand. Although the method was not planned to be used as tool for multiple alignment it can be used for this purpose, by the simple fact that the query structure is never translated / rotated. Subsequently, all target structures are superimposed in a multiple alignment fashion (Figure 6).