For use's convenience, the tools of three basic ranking predictors and Learning to Rank were provided:
Psi-BLAST
Description: PSI-BLAST (Position-Specific Iterative Basic Local Alignment Search Tool) derives a position-specific scoring matrix (PSSM) or profile from the multiple sequence alignment of sequences detected above a given score threshold using protein-protein BLAST.
Availability: ftp://ftp.ncbi.nlm.nih.gov/blast/executables/LATEST
HHblits
Description: HHblits is a sensitive, general purpose, iterative sequence search tool that represents both query and database sequences by HMMs. You can search HHblits databases starting with a single query sequence, a multiple sequence alignment (MSA), or an HMM. HHblits prints out a ranked list of database HMMs/MSAs and can also generate an MSA by merging the significant database HMMs/MSAs onto the query MSA.
Availability:
ftp://toolkit.genzentrum.lmu.de/pub/HH-suite/
ProtEmbed
Description: ProtEmbed learned a large-scale embedding of protein feature vectors obtained by HHsearch all-against-all comparison into a low-dimensional “semantic space” where evolutionarily related proteins are embedded in close proximity.
Availability: http://noble.gs.washington.edu/proj/protembed/
Learning to Rank
Description: Learning to rank is the application of machine learning in the construction of ranking models for information retrieval systems. Training data consists of lists of items with some partial order specified between items in each list. This order is typically induced by giving a numerical or ordinal score or a binary judgment (e.g. "relevant" or "not relevant") for each item. The ranking model's purpose is to rank, i.e. produce a permutation of items in new, unseen lists in a way which is "similar" to rankings in the training data in some sense.
Availability:
http://sourceforge.net/p/lemur/wiki/RankLib/
Supplementary material
SCOP database
