advances-in-protein-chemistry

entire set of comprehensive and pre-computed structural alignments are obtained with the VAST algorithm [36] where as
the visualization tools for 3D structure and structure/sequence alignment are provided by the molecular graphics viewer
Cn3D [37].
As on April 29, 2013, there are 89,571 structure records total which includes 22,084 proteins, 715 DNA and 508 RNA
molecules only. Additionally, MMDB also consist of 2571 protein-DNA complexes, 1113 protein-RNA complexes and 116
protein-DNA-RNA complexes, in addition to more than 60,000 proteins bound to chemicals.
2.2.2.3 ModBase
ModBase is a database of annotated homology based protein structure models. Models in ModBase are generated as
an automated software pipeline for comparative protein structure modeling, known as ModPipe [3] which mostly rely on
modules of Modeller [38]. For fold assignment and target–template alignment, ModPipe uses sequence–sequence [39]
sequence–profile [40,41] and profile–profile [40,42] methods by using an E-value cut-off of 1.0 to augment the possibility
of identifying the finest available template structure. 10 models are generated [38] for each target–template alignment, and
the model with the top Discrete Optimized Protein Energy (DOPE) statistical potential [43] score is selected and further
assessed by numerous additional quality criteria: (i) target–template sequence identity, (ii) GA341 score [44] (iii) Z-DOPE
score [43] (iv) ModPipe Quality Score (MPQS) and (v) TSVMod score [45].
Because of the rapid growth of the public sequence databases, models in ModBase are structured in data sets that are
useful for specific projects. Currently, ModBase includes about 27,288,148 models and 4,332,658 unique sequences modeled
for more than 50 complete genomes [46]. ModBase can be queried through its web interface by querying with UniprotKB
[47] and GI [48] identifiers, gene names, annotation keywords, PDB codes [49] data set names, organism names, sequence
similarity to the modeled sequences (BLAST [41]) and model-specific criteria such as model reliability, model size and
target–template sequence identity. Additionally, the coordinate and alignment files can also be retrieved as text files [50].
2.2.2.4 SWISS-MODEL Repository
SWISS-MODEL Repository is a database of 3D protein structure models constructed by using the SWISS-MODEL
homology-modelling pipeline based on protein sequences from the UniProt database [47]. The SWISS-MODEL pipeline
integrates various steps like: template selection, target sequence and template structure alignment, model building, energy
minimization and/or refinement and model quality assessment [51] Model target sequences are individually identified by
their md5 cryptographic hash of the full length raw amino acid sequence which permits the redundancy in protein sequence
databases entries to be reduced, and in turn assists cross-referencing with databases by means of different accession codes.
The current SWISS-MODEL Repository release contains 3143784 model entries for 2286870 unique sequences in the
UniProt database (2013_02).
The database could be queried for particular proteins by using diverse database accession codes (e.g. UniProt AC and
ID, GenBank, IPI, Refseq) or directly by means of the protein amino acid sequence. For a particular query protein, a
graphical outline demonstrating the segments for which models or experimental structures are available is shown. SWISS-
MODEL Repository users can review the quality of the models in the database; search for alternative template structures,
and construct models interactively by the use of SWISS-MODEL Workspace [52]. Repository is updated on a regular basis
to reflect the growth of the sequence and structure databases.
3. 3D Structure Prediction
3.1 A brief history of molecular modelling
The first homology based model dates back to 1969 when a wire and plastic models of bonds and atoms of α-lactalbumin
was constructed by using the coordinates of a hen’s egg-white lysozyme and adjusting, physically, those amino acids that
did not match the structure [53]. The two proteins exhibited 39% of sequence identity. Afterwards, the crystal structure of
lysozyme was used to generate a model for α-lactalbumin [54]. These models were created by taking the existing coordinates
of the well-known structure, and mutating side chains that were not identical in the protein to be modeled. This approach to
protein modeling is still used at present with substantial success, particularly when the proteins share a considerable degree
of sequence similarity [55].
McLachlan and Shotton [56] used the structures of mammalian chymotrypsin and elastase, and modeled the structure
of α-lytic proteinase of the fungus Myxobacter 495. The modeling was not easy as the sequence similarity between the
target and the template was only about 18%. Subsequently, the crystal structure of α-lytic proteinase was determined and
compared with the homology model [57]. Although the domains of the model were constructed accurately, it was found
that misalignment of the sequences led to local errors.
The modeling of variable regions was introduced in proteins on the basis of equivalent regions from homologous
proteins of known structures [58,59]. Therefore, in order to construct the homology models of various serine proteases,
structures of trypsin, chymotrypsin and elastase were superimposed, and it was found many equivalent Cα atoms were
within 1.0Å of one another. The regions comprising of the amino acids of these Cα atoms were described as structurally
conserved regions (SCRs). All the other remaining sites correspond to structurally variable or loop regions (VR) where
the insertions/deletions were located. The backbone of SCRs and VRs was generated from the fragments of known serine
proteases, where as the side chains were modeled based on the conformation found at the equivalent locations for those
identical side chains in the well-known structures.
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