Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2011 Jul 29:12:313.
doi: 10.1186/1471-2105-12-313.

Comprehensive, atomic-level characterization of structurally characterized protein-protein interactions: the PICCOLO database

Affiliations

Comprehensive, atomic-level characterization of structurally characterized protein-protein interactions: the PICCOLO database

George R Bickerton et al. BMC Bioinformatics. .

Abstract

Background: Structural studies are increasingly providing huge amounts of information on multi-protein assemblies. Although a complete understanding of cellular processes will be dependent on an explicit characterization of the intermolecular interactions that underlie these assemblies and mediate molecular recognition, these are not well described by standard representations.

Results: Here we present PICCOLO, a comprehensive relational database capturing the details of structurally characterized protein-protein interactions. Interactions are described at the level of interacting pairs of atoms, residues and polypeptide chains, with the physico-chemical nature of the interactions being characterized. Distance and angle terms are used to distinguish 12 different interaction types, including van der Waals contacts, hydrogen bonds and hydrophobic contacts. The explicit aim of PICCOLO is to underpin large-scale analyses of the properties of protein-protein interfaces. This is exemplified by an analysis of residue propensity and interface contact preferences derived from a much larger data set than previously reported. However, PICCOLO also supports detailed inspection of particular systems of interest.

Conclusions: The current PICCOLO database comprises more than 260 million interacting atom pairs from 38,202 protein complexes. A web interface for the database is available at http://www-cryst.bioc.cam.ac.uk/piccolo.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Residue propensities for protein-protein interfaces. The propensity of each of the 20 canonical residues for the four different structural environments suggests a highly environment-dependent distribution. Grey bars indicate the overall observed frequency across all environments (Bi in Equation 3). Coloured bars indicate the environment dependent residue frequency (Eei in Equation 4). Coloured bars higher than their respective gray bars indicate the normalized environment-dependent propensity (Rei in Equation 5) is greater than 1. Residues are ordered by decreasing hydrophobicity from left to right. Inset pie chart indicates the underlying proportion of each of the four residue environments.
Figure 2
Figure 2
Contact preference matrix for intermolecular residue-residue interactions. Colours represent the log ratio of the solvent accessibility normalized observed to expected residue frequencies, L(i, j).
Figure 3
Figure 3
Complex of human somatotropin and the prolactin receptor (PDB entry 1bp3). Residues in the interface core are shown in orange, interface periphery in dark red, non-interface exposed surface in light blue and buried protein core in dark blue. Interaction types are coloured as follows: hydrogen bonds in dark blue; water mediated hydrogen bonds in light blue; π-cation interactions in pink; ionic interactions in pink; hydrophobic contacts in yellow; and van der Waals in red. Figure prepared using PyMOL [66].

Similar articles

Cited by

References

    1. Gavin AC, Bosche M, Krause R, Grandi P, Marzioch M, Bauer A, Schultz J, Rick JM, Michon AM, Cruciat CM. et al.Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature. 2002;415:141–147. doi: 10.1038/415141a. - DOI - PubMed
    1. Wells J, McClendon C. Reaching for high-hanging fruit in drug discovery at protein-protein interfaces. Nature. 2007;450:1001–1009. doi: 10.1038/nature06526. - DOI - PubMed
    1. Thorn KS, Bogan AA. ASEdb: a database of alanine mutations and their effects on the free energy of binding in protein interactions. Bioinformatics. 2001;17:284–285. doi: 10.1093/bioinformatics/17.3.284. - DOI - PubMed
    1. Shoemaker B, Panchenko A. Deciphering Protein-Protein Interactions. Part II. Computational Methods to Predict Protein and Domain Interaction Partners. PLoS computational biology. 2007;3:e43. doi: 10.1371/journal.pcbi.0030043. - DOI - PMC - PubMed
    1. Valencia A, Pazos F. Computational methods for the prediction of protein interactions. Curr Opin Struct Biol. 2002;12:368–373. doi: 10.1016/S0959-440X(02)00333-0. - DOI - PubMed

Publication types

LinkOut - more resources