page_title: Protein fragment library
section_title: Construction
hierarchical_section_title: Protein fragment library / Construction
caption_attribution_description: English: Fragment clustering.
context_page_description: Protein backbone fragment libraries have been used successfully in a variety of structural biology applications, including homology modeling, de novo structure prediction, and structure determination. By reducing the complexity of the search space, these fragment libraries enable more rapid search of conformational space, leading to more efficient and accurate models.
context_section_description: Libraries of these fragments are constructed from an analysis of the Protein Data Bank (PDB). First, a representative subset of the PDB is chosen which should cover a diverse array of structures, preferably at a good resolution. Then, for each structure, every set of n consecutive residues is taken as a sample fragment. The samples are then clustered into k groups, based upon how similar they are to each other in spatial configuration, using algorithms such as k-means clustering. The parameters n and k are chosen according to the application (see discussion on complexity below). The centroids of the clusters are then taken to represent the fragment. Further optimization can be performed to ensure that the centroid possesses ideal bond geometry, as it was derived by averaging other geometries. 
  Because the fragments are derived from structures that exist in nature, the segment of backbone they represent will have realistic bonding geometries. This helps avoid having to explore the full space of conformation angles, much of which would lead to unrealistic geometries.
  The clustering above can be performed without regard to the identities of the residues, or it can be residue-specific. That is, for any given input sequence of amino acids, a clustering can be derived using only samples found in the PDB with the same sequence in the k-mer fragment. This requires more computational work than deriving a sequence-independent fragment library but can potentially produce more accurate models. Conversely, a larger sample set is required, and one may not achieve full coverage.
caption: Clustering of similar fragments. Centroid is shown in blue.
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