In the recent CAFA (Critical assessment of protein function annotation) paper, they compared the ability of 54 different methods to computationally predict the functions of 866 proteins from 11 different organisms.
I checked to see whether the software for any of the top-performing methods is available.
The top 6 methods in predicting 'molecular function' GO terms were:
1) Jones-UCL - this method is not available yet. I emailed the author, and he said that part of it is available as the FFPRED web server, which uses a feature-based approach to function prediction, but doesn't use the orthology and homology components of the Jones-UCL method used for CAFA. There is a paper by Cozzetto et al 2013 on the Jones-UCL CAFA method.
2) Argot2 - there is a web server available, but you can only submit 5000 sequences at once. The program is not available for download. There is also a paper by Falda et al 2012.
3) PANNZER - there is a website, and should be soon a paper, a web server and program available for download but it's not there yet.
4) ESG - there is a web server, but you can only submit 10 sequences at once. There is a paper by Chitale et al 2009.
5) BAR+ - there is a web server, but you can only submit 50 sequences at once. There are papers by Bartoli et al 2009, and Piovesan et al 2011.
6) PDCN (MULTICOM-PDCN) - there isn't a web server or software for download yet, but I emailed the authors and they told me that a web server will be available soon. There is a paper by Wang et al 2013.
These top-performing methods had F-measures (a measure of prediction accuracy, that can have a maximum of 1) of about 0.54-0.60, compared to about 0.4 for BLAST.
The top 6 methods in predicting 'biological process' GO terms were almost the same:
4) PDCN (MULTICON-PDCN)
6) Rost Lab - there is a paper by Hamp et al 2013. The software can be downloaded as the program 'Metastudent' from the website.
These methods had F-measures of about 0.37 to 0.4, compared to about 0.27 for BLAST.
Thanks to James Cotton and Adam Reid for bringing CAFA to my attention.