Modeller 9v8 release

I just received this e-mail about the modeller software for protein structure comparative modeling and alignment. It's not open source, but I remember that many people depended on it. How is the situation now?

The new version of Modeller, 9v8, is now available for download! Please see the download page at http://salilab.org/modeller/ for more information.

If you have a license key for Modeller 8 or 9, there is no need to reregister for Modeller 9v8 - the same license key will work. (It won't do any harm to reregister if you want to, though!)

9v8 is primarily a bugfix release relative to the last public release (9v7). Major user-visible changes include:Silicon Graphics (IRIX) binaries are no longer provided. If you wish to use Modeller on an IRIX machine, please use the older 9v7 version.

• Residues treated as BLK no longer have to be HETATM residues; they will be marked as ATOM if so defined in restyp.lib or in the template  PDB (for example, DNA or RNA residues). automodel.nonstd_restraints()  now builds restraints on both HETATM and BLK residues.
• Bonds between BLK residues are now detected by a simple distance criterion and are restrained more strongly than other distances by automodel.nonstd_restraints().
• A new method Chain.join() allows chain breaks between chains to be removed.
• Files compressed with 'xz' can now be read, provided that the 'xz' binary is available on your system.
• The allow_alternates option to alignment.append() will now allow G in the alignment sequence to match any non-standard residue type in the PDB file which maps to G. This is useful if the alignment sequence is 'cleaned' as by sequence_db.read().

See the Modeller manual for a full change log: http://salilab.org/modeller/9v8/manual/node38.html

If you encounter bugs in Modeller 9v8, please see http://salilab.org/modeller/9v8/manual/node10.html for information on how to report them.

Recent CUDA applications

Taking a look at Nvidia's CUDA zone I found some recent additions - besides important ones that I already knew like the Smith-Waterman and the phylogenetic likelihood algoritihms. Here is a list of these new programs and libraries that take advantage of GPUs:

1. Expectation Maximization algorithm for Gaussian Mixture Models
2. Multilevel algorithm for MDS (multidimensional scaling)
3. Multiclass classifier based on SVMs (support vector machines)
4. GPU computing in the R statistical environment (BLAS lowlevel routines and dist(), hclust(), cor(), and granger.test() functions)
5. Smith-Waterman algorithm for sequence database search
6. Quicksort library
7. Feature finding algorithm for mass spectrometry
8. Smith-Waterman algorithm for sequence alignment
9. MDR (multifactor dimensionality reduction) algorithm for detecting genetic epistatic interactions (white paper)
   

The last reference is just a two-page summary, and the 8th reference is behind a paywall - I couldn't access it myself - but the slides are available here.

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