Web-based Resources

There are several online resources that can assist in the various stages of structure production listed below. Tools in the PSI Structural Biology Knowledgebase can also help with the analysis of protein sequences and structures.

Surface entropy reduction for crystallization prediction (SERp) server

Integrated Center for Structure and Function Innovation

A server is available at http://nihserver.mbi.ucla.edu/SER/ that can suggest mutations to render a protein sequence more likely to crystallize, based on the concept of Surface Entropy Reduction from Derewenda (2004).



TOPSAN: Wiki-based collaborative annotation

Joint Center for Structural Genomics

The JCSG has developed The Open Protein Structure Annotation Network (TOPSAN), a wiki-based community project to collect, share, and distribute information about protein structures determined at PSI centers.



Target annotation using the Protein Sequence Comparative Analysis System (PSCA)

Joint Center for Structural Genomics

Access to target annotations can be accomplished through the PSCA system.



The CATH classification revisited

Midwest Center for Structural Genomics

The latest version of CATH (class, architecture, topology, homology) (version 3.2), released in July 2008 (http://www.cathdb.info), contains 114,215 domains, 2178 Homologous superfamilies and 1110 fold groups. We have assigned 20,330 new domains, 87 new homologous superfamilies and 26 new folds since CATH release version 3.1. A total of 28,064 new domains have been assigned since our NAR 2007 database publication (CATH version 3.0).



The PSIPRED Protein Structure Prediction Server

Developed by researchers working in the Bioinformatics Unit at University College London, the popular PSIPRED server allows users to perform secondary structure prediction, transmembrane topology prediction and protein fold recognition.



The Pfam protein families database

Pfam is a comprehensive collection of protein domains and families, represented as multiple sequence alignments and as profile hidden Markov models. Pfam data are freely accessible via the web and are available for download in a variety of forms.



XANNpred: Neural nets that predict the propensity of a protein to yield diffraction-quality crystals

XANNpred estimates the propensity of a protein to progress through the various experimental stages (including soluble expression) required to produce diffraction-quality crystals.