SUPFAM is a database that consists of clusters of potentially related homologous protein domain families, with and without three-dimensional structural information, forming superfamilies. The present release (Release 3.0) of SUPFAM uses homologous families in Pfam (Version 23.0) and SCOP (Release 1.69) which are examples of sequence -alignment and structure classification databases respectively. The two steps involved in setting up of SUPFAM database are * Relating Pfam and SCOP families using a new profile-profile alignment algorithm AlignHUSH. This results in identifying many Pfam families which could be related to a family or superfamily of known structural information. * An all-against-all match among Pfam families with yet unknown structure resulting in identification of related Pfam families forming new potential superfamilies. The SUPFAM database can be used in either the Browse mode or Search mode. In Browse mode you can browse through the Superfamilies, Pfam families or SCOP families. In each of these modes you will be presented with a full list which can be easily browsed. In Search mode, you can search for Pfam families, SCOP families or Superfamilies based on keywords or SCOP/Pfam identifiers of families and superfamilies.

SUPERFAMILY is a library of profile hidden Markov models that represent all proteins of known structure. The library is based on the SCOP classification of proteins: each model corresponds to a SCOP domain and aims to represent the entire SCOP superfamily that the domain belongs to. SUPERFAMILY is based at the University of Bristol, UK.

CATH Domain Classification List (latest release) - protein structural domains classified into CATH hierarchy.

This report introduces the MuLiMs-MCoMPAs software (acronym for Multi-Linear Maps based on N-Metric and Contact Matrices of 3D Protein and Amino-acid weightings), designed to compute tensor-based 3D protein structural descriptors by applying two- and three-linear algebraic forms. Moreover, these descriptors contemplate generalizing components such as novel 3D protein structural representations, (dis)similarity metrics, and multimetrics to extract geometrical related information between two and three amino acids, weighting schemes based on amino acid properties, matrix normalization procedures that consider simple-stochastic and mutual probability transformations, topological and geometrical cutoffs, amino acid, and group-based MD calculations, and aggregation operators for merging amino acidic and group MDs. The MuLiMs-MCoMPAs software, which belongs to the ToMoCoMD-CAMPS suite, was developed in Java (version 1.8) using the Chemistry Development Kit (CDK) (version 1.4.19) and the Jmol libraries. This software implemented a divide-and-conquer strategy to parallelize the computation of the indices as well as modules for data preprocessing and batch computing functionalities. Furthermore, it consists of two components: (i) a desktop-graphical user interface (GUI) and (ii) an API library. The relevance of this novel approach is demonstrated through two analyses that considered Shannon’s entropy-based variability and a principal component analysis. These studies showed that the MuLiMs-MCoMPAs’ three-linear descriptor family contains higher informational entropy than several other descriptors generated with available computation tools. Moreover, the MuLiMs-MCoMPAs indices capture additional orthogonal information to the one codified by the available calculation approaches. As a result, two sets of suggested theoretical configurations that contain 13648 two-linear indices and 20263 three-linear indices are available for download at tomocomd.com. Furthermore, as a demonstration of the applicability and easy integration of the MuLiMs library into a QSAR-based expert system, a software application (ProStAF) was generated to predict SCOP protein structural classes and folding rate. It can thus be anticipated that the MuLiMs-MCoMPAs framework will turn into a valuable contribution to the chem- and bioinformatics research fields.

Supporting info to Silva, P.J. (2007) "Assessing the reliability of sequence similarities detected through hydrophobic sequence analysis" Proteins: Structure, Function and Bioinformatics, 70, 1588-1594.

HCA_db.zip contains the complete HCA patterns database, which is also available at :http://homepage.ufp.pt/pedros/HCA_db HCA_analyze (source code + Win executable).Syntax: HCA_analyze input_fileinput_file is an alignment file in PIR format, generated by ClustalW with the appropriate matrix (hca.txt , also present in the distribution). The aligned sequences may have a size up to 2800 aa. The program outputs a tab separated, human-readable, file ("results.txt") which can be easily imported into common spreadsheet software for further analysis. ClustalW can be downloaded from the European Bioinformatics Institute. HCA_analyze_multiple_aligns (source code + Win executable).Syntax: HCA_analyze_multiple_align input_file resultsinput_file is an alignment file in PIR format, generated by ClustalW with the appropriate matrix (hca.txt , also present in the distribution). The program outputs a tab separated, human-readable, file (results) which can be easily imported into common spreadsheet software for further analysis. Two further output files are created: "Distinct_HCA_patterns.txt" lists all sequences with less than 60% HCA similarity (relative to each other), and "minima.txt" includes the characteristics of the most divergent sequences (based on HCA score, charged aminoacid similarity and proline distribution).

SCOP (Win executable)Syntax: SCOP input_file output_fileinput_file should be an output file generated by HCA_analyze. The program outputs the SCOP_class of each protein present in the original input file. The program assumes that PDB codes were used to name the original alignment files analyzed by HCA_analyze. E.g. an alignment of a sequence to chain E of PDB structure 1ABF should have been named 1abf_e.aln. The program REQUIRES lower-case PDB names, and reports the SCOP class according to SCOP release 1.73 (November 2007)

Automated comparisons vs. PDB90.Includes HCA_analyze and SCOP (Win executables), as well as every PDB sequence with less than 90 % similarity to other PDB sequences (as of November 2008). Also included: a simple batch file that automatizes the task of performing alignment of a query sequence vs. every PDB90 sequence, followed by HCA analysis and SCOP class attribution. Before runnig this batch file, the query sequence to analyze must be placed by the user in a new file called test.txt. DO NOT change the .bat file. Last updated on January, 22nd, 2009. Comparison of models generated through this method with the best CASP models and experimentally-derived structures.Comparison of models generated through this method with experimentally-derived structures.PDB coordinates of the model of amyloid beta peptide described in the paper.