SCOPPI, the structural classification of protein-protein interfaces, is a comprehensive database that classifies and annotates domain interactions derived from all known protein structures. SCOPPI applies SCOP domain definitions and a distance criterion to determine inter-domain interfaces. Using a novel method based on multiple sequence and structural alignments of SCOP families, SCOPPI presents a comprehensive geometrical classification of domain interfaces. Various interface characteristics such as number, type and position of interacting amino acids, conservation, interface size, and permanent or transient nature of the interaction are further provided. Proteins in SCOPPI are annotated with Gene Ontology terms, and the ontology can be used to quickly browse SCOPPI. Screenshots are available for every interface and its participating domains. Here, we describe contents and features of the web-based user interface as well as the underlying methods used to generate SCOPPI's data. In addition, we present a number of examples where SCOPPI becomes a useful tool to analyze viral mimicry of human interface binding sites, gene fusion events, conservation of interface residues and diversity of interface localizations. SCOPPI is available at http://www.scoppi.org.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1347461PMC
http://dx.doi.org/10.1093/nar/gkj099DOI Listing

Publication Analysis

Top Keywords

scoppi
8
scoppi structural
8
structural classification
8
classification protein-protein
8
protein-protein interfaces
8
conservation interface
8
interface
7
interfaces
4
interfaces scoppi
4
interfaces comprehensive
4

Similar Publications

SARS-CoV-2 Pandemic Impact on Pediatric Emergency Rooms: A Multicenter Study.

Int J Environ Res Public Health

November 2020

Department of Maternal, Infantile and Urological Sciences, Sapienza University of Rome, 00161 Rome, Italy.

From 9 March to 3 May 2020, lockdown was declared in Italy due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Our aim was to evaluate how the SARS-CoV-2 pandemic and related preventive strategies affected pediatric emergency rooms (ERs) during this period. We performed a retrospective cohort multicenter study, comparing the lockdown period to the corresponding period in 2019.

View Article and Find Full Text PDF

Background: Acute asthma attack is a frequent condition in children. It is one of the most common reasons for emergency department (ED) visit and hospitalization. Appropriate care is fundamental, considering both the high prevalence of asthma in children, and its life-threatening risks.

View Article and Find Full Text PDF

iWRAP: An interface threading approach with application to prediction of cancer-related protein-protein interactions.

J Mol Biol

February 2011

Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA.

Current homology modeling methods for predicting protein-protein interactions (PPIs) have difficulty in the "twilight zone" (<40%) of sequence identities. Threading methods extend coverage further into the twilight zone by aligning primary sequences for a pair of proteins to a best-fit template complex to predict an entire three-dimensional structure. We introduce a threading approach, iWRAP, which focuses only on the protein interface.

View Article and Find Full Text PDF

Optimal contact map alignment of protein-protein interfaces.

Bioinformatics

October 2008

Computer Science and Artificial Intelligence Laboratory, MIT, MIT, Cambridge, USA.

The long-standing problem of constructing protein structure alignments is of central importance in computational biology. The main goal is to provide an alignment of residue correspondences, in order to identify homologous residues across chains. A critical next step of this is the alignment of protein complexes and their interfaces.

View Article and Find Full Text PDF

Predicting protein interaction interfaces and protein complexes are two important related problems. For interface prediction, there are a number of tools, such as PPI-Pred, PPISP, PINUP, Promate, and SPPIDER, which predict enzyme-inhibitor interfaces with success rates of 23% to 55% and other interfaces with 10% to 28% on a benchmark dataset of 62 complexes. Here, we develop, metaPPI, a meta server for interface prediction.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!