Predicting small ligand binding sites in proteins using backbone structure

Motivation: Specific non-covalent binding of metal ions and ligands, such as nucleotides and cofactors, is essential for the function of many proteins. Computational methods are useful for predicting the location of such binding sites when experimental information is lacking. Methods that use structural information, when available, are particularly promising since they can potentially identify non-contiguous binding motifs that cannot be found using only the amino acid sequence. Furthermore, a prediction method that can utilize low-resolution models is advantageous because high-resolution structures are available for only a relatively small fraction of proteins

Collaborative annotation of 3D crystallographic models

This paper describes the AnnoCryst system-a tool that was designed to enable authenticated collaborators to share online discussions about 3D crystallographic structures through the asynchronous attachment, storage, and retrieval of annotations. Annotations are personal comments, interpretations, questions, assessments, or references that can be attached to files, data, digital objects, or Web pages. The AnnoCryst system enables annotations to be attached to 3D crystallographic models retrieved from either private local repositories (e.g., Fedora) or public online databases (e.g., Protein Data Bank or Inorganic Crystal Structure Database) via a Web browser. The system uses the Jmol plugin for viewing and manipulating the 3D crystal structures but extends Jmol by providing an additional interface through which annotations can be created, attached, stored, searched, browsed, and retrieved. The annotations are stored on a standardized Web annotation server (Annotea), which has been extended to support 3D macromolecular structures. Finally, the system is embedded within a security framework that is capable of authenticating users and restricting access only to trusted colleagues