**10. Rd-HMMer limitations**

232 Bioinformatics

scores, statistical significance and the alignment may guide your template selection. However, if the Rd.HMM of a template candidate gives a negative score, and still you decide to use it, do not trust the Rd.HMM alignment without further improvement

g. Finally, if you use the ROSETTA suite or the ROBETTA server to produce your models, these structures are expected to have a ROSETTA-like bias, *i.e.* their Rd.HMM scores will increase and a good model with this bias is expected to have a ratio of HMMer score to sequence length close to one. While in models produced with other software a Rd.HMM score ratio of 0.3 is acceptable, in a ROSETTA produced model this score is low and may reflect important flaws. Look at the alignment carefully, as recommended

**Figure 5.** Comparison of the yeast α-glucosidase model produced included in the publication by Brindis et al (Brindis et al., 2011), with the X-ray solved structure of its homologue, the yeast isomaltase (Yamamoto et al., 2010). The isomaltase is shown as blue cartoons and the α-glucosidase cartoons are

As an example of the advantages of Rd.HMM, we refer to two cases of recent success. Brindis and coworkers (Brindis et al., 2011) analyzed the effects of a natural product on αglucosidase. This work reports a model for the budding yeast α-glucosidase used to analyze the molecular grounds for the (Z)-3-butylidenephthalide inhibitory action. In the preparation of the model, Rd.HMM allowed to detect a threading problem (insertion/deletion pair) in one β-strand in the core of the model. While the sheet was slid only a few Å from its position, the contact with neighboring strands completely distorted

colored according to the amino acid rmsd from isomaltase, ranging from very low (blue) to

using other tools, as it may be seriously flawed.

in the previous section.

intermediate (white) to high (red).

Since most Rd.HMM limitations have been mentioned. We only summarize them here:

