SCOWLP update: 3D classification of protein-protein, -peptide, -saccharide and -nucleic acid interactions, and structure-based binding inferences across folds

Abstract

BackgroundProtein interactions are essential for coordinating cellular functions. Proteomic studies have already elucidated a huge amount of protein-protein interactions that require detailed functional analysis. Understanding the structural basis of each individual interaction through their structural determination is necessary, yet an unfeasible task. Therefore, computational tools able to predict protein binding regions and recognition modes are required to rationalize putative molecular functions for proteins. With this aim, we previously created SCOWLP, a structural classification of protein binding regions at protein family level, based on the information obtained from high-resolution 3D protein-protein and protein-peptide complexes.DescriptionWe present here a new version of SCOWLP that has been enhanced by the inclusion of protein-nucleic acid and protein-saccharide interactions. SCOWLP takes interfacial solvent into account for a detailed characterization of protein interactions. In addition, the binding regions obtained per protein family have been enriched by the inclusion of predicted binding regions, which have been inferred from structurally related proteins across all existing folds. These inferences might become very useful to suggest novel recognition regions and compare structurally similar interfaces from different families.ConclusionsThe updated SCOWLP has new functionalities that allow both, detection and comparison of protein regions recognizing different types of ligands, which include other proteins, peptides, nucleic acids and saccharides, within a solvated environment. Currently, SCOWLP allows the analysis of predicted protein binding regions based on structure-based inferences across fold space. These predictions may have a unique potential in assisting protein docking, in providing insights into protein interaction networks, and in guiding rational engineering of protein ligands. The newly designed SCOWLP web application has an improved user-friendly interface that facilitates its usage, and is available at http://www.scowlp.org.

Comprehensive Analysis of the Human SH3 Domain Family Reveals a Wide Variety of Non-canonical Specificities

“…Protein Definition, Expression and Purification All human SH3 domain sequences predicted by InterProScan (Quevillon et al, 2005) (Perrakis et al, 1997) http://www.embl-hamburg.de/ARP/ MolProbity (Chen et al, 2010) http://molprobity.biochem.duke.edu/ Buccaneer (Cowtan, 2006) http://www.ccp4.ac.uk/ Refmac (Murshudov et al, 2011) http://www.ccp4.ac.uk/ Coot (Emsley et al, 2010) http://www2.mrc-lmb.cam.ac.uk/Personal/ pemsley/coot/ MUSI (Kim et al, 2012) http://www.kimlab.org/software/musi InterProScan (Quevillon et al, 2005) http://www.ebi.ac.uk/interpro/search/ sequence-search PROSITE (Sigrist et al, 2013) http://prosite.expasy.org/ Superfamily (Oates et al, 2015) http://supfam.org/SUPERFAMILY/ Pfam (Punta et al, 2012) http://pfam.xfam.org/ SMART (Letunic et al, 2015) http://smart.embl-heidelberg.de/ Mafft (Katoh et al, 2002) http://mafft.cbrc.jp/alignment/software/ 3DID (Stein et al, 2010) http://3did.irbbarcelona.org/ SCOWLP (Teyra et al, 2011) http://projects.biotec.tu-dresden.de/ scowlp/…”

“…This sequence alignment was used to define the standard SH3 domain numbering scheme. In addition, 3DID (Stein et al, 2010) and SCOWLP database (Teyra et al, 2011) were used to automatically extract all the SH3 domain-peptide complexes in the PDB repository (Table S3). In total, we identified 484 SH3 domain-containing PDB files, and 91 of these were for SH3 domains binding to a polypeptide chain.…”

ProtCID: A data resource for structural information on protein interactions

“…For some entries, the PDB provides additional biological assemblies derived from the Protein Interfaces, Surfaces and Assemblies (PISA) 4 server from the EBI that are predicted to be stable in solution based on calculations of thermodynamic stability of the complexes. Several servers analyze interfaces in either the asymmetric units and/or the deposited biological assemblies of PDB entries [6][7][8][9][10][11] , and some are intended to predict which interfaces may be biologically relevant by measuring conservation scores and physical features and using machine learning predictors [12][13][14] . Interactions with peptides, nucleic acids, and ligands have also been analyzed and presented in several webservers and databases 9,[15][16][17][18][19][20][21] .…”

Elucidation of the binding preferences of peptide recognition modules: SH3 and PDZ domains

“…Currently, there are annotated 179 classes of literature‐curated motifs that are interacting to 87 distinct Pfam domains. However, analysis of the PDB repository has shown that the peptide‐binding domain families are much more abundant [80,81]. For example, PepX database currently contains 119 Pfam domains structurally solved in complex to peptides, suggesting that the biologically relevant peptide‐binding domains might be larger.…”