Computational analysis of protein-protein interactions for bench biologists 2-8 September, Berlin Protein Interaction Databases Francesca Diella

Computational analysis of protein-protein interactions for bench biologists 2-8 September, Berlin Protein interactions determine the outcome of most cellular processes therefore, identifying and characterizing protein–protein interactions and their networks is essential for understanding the molecular mechanisms of biological processes

Computational analysis of protein-protein interactions for bench biologists 2-8 September, Berlin Types of protein interactions: A)Direct (physical) interaction via the formation of an interaction complex, more or less stable depending of the affinity of the interaction B)Indirect (just functional) interaction via a variety of genetic dependencies, transcriptional regulation mechanisms

Computational analysis of protein-protein interactions for bench biologists 2-8 September, Berlin Common Methods for Identifying Protein Interactions MethodsType of interaction Yeast two-hybrid (Y2H) Tandem affinity purification (TAP) Physical interactions (binary) Physical interactions Co-ImmunoprecipitationPhysical interactions Affinity purification–MSPhysical interactions (complex) Phage displayPhysical interactions (complex) X-ray crystallography, NMR spectroscopyPhysical interactions Synthetic lethalityGenetic Interaction (Functional association) DNA microarray/ Gene expressionFunctional association

Computational analysis of protein-protein interactions for bench biologists 2-8 September, Berlin Schematic Representations of some interaction detection Methods Shoemaker, 2007

Computational analysis of protein-protein interactions for bench biologists 2-8 September, Berlin Experimental data (LTP, HTP) Literature Databases

Computational analysis of protein-protein interactions for bench biologists 2-8 September, Berlin Protein Interaction Databases IntAct BioGRID MINT / DIP HPRD STRING MIPS Additional links to Interaction Databases can be found at:

Computational analysis of protein-protein interactions for bench biologists 2-8 September, Berlin Databases specific for certain diseases or organisms only NCBI’s HIV-1, Human Protein Interaction DB Tair (Arabidopsis) DroID (Drosophila) SPIDEr (Saccharomyces)

Computational analysis of protein-protein interactions for bench biologists 2-8 September, Berlin Intact

Computational analysis of protein-protein interactions for bench biologists 2-8 September, Berlin BioGrid

Computational analysis of protein-protein interactions for bench biologists 2-8 September, Berlin MINT

Computational analysis of protein-protein interactions for bench biologists 2-8 September, Berlin MINT (2)

Computational analysis of protein-protein interactions for bench biologists 2-8 September, Berlin Classification of PPI databases, based on the methods used to collect or generate the data DBs of experimental data, collected either through manual curation, computational extraction, or direct deposit by the authors, such as DIP, MINT, BioGRID and IntAct. DBs which store predicted PPI, such as PIPs and HomoMINT. Portals that provides unified access to a variety of protein interaction databases, such as STRING.

Computational analysis of protein-protein interactions for bench biologists 2-8 September, Berlin Annotation policy Curated data are generally of higher quality, but more expensive to produce Automated/electronic (text mining) Manual Curation

Computational analysis of protein-protein interactions for bench biologists 2-8 September, Berlin Standardization is a necessary requirement to allow exchange of data among various sources The Molecular Interactions workgroup is concentrating on: – improving the annotation and representation of molecular interaction data wherever it is published, be this in journal articles, authors web-sites or public domain databases – improving the accessibility of molecular interaction data to the user community. By using a common standard, data can be downloaded from multiple sources and easily combined using a single parser PMID:

Computational analysis of protein-protein interactions for bench biologists 2-8 September, Berlin Confidence scoring of interactions It is important to assess the quality of individual interactions reported in the DBs LTP versus HTP Interaction scores have been introduced User have to be critical, e.g. proteins that have different localization patterns are unlikely to interact

Computational analysis of protein-protein interactions for bench biologists 2-8 September, Berlin CoIP, Pull-down Low-throughput TAP High-throughput 2-hybrid, peptide chip High-throughput 2-hybrid, X-ray crystallography Low-throughput Contribution of different experimental setups to cumulative evidence and score for direct interaction. X-ray crystallography + 2-hybrid+ Co-IP+GST-pull down Andrew Chatr-aryamontri

Computational analysis of protein-protein interactions for bench biologists 2-8 September, Berlin Score (2) The MINT score takes into account all the experimental evidence associated with the interaction detection method. The score is calculated as a function of the cumulative evidence (x) according to the formula: The IntAct MI score is based on the manual annotation of every instance of a binary interaction (A-B). First all instances of the A-B interacting pair are clustered by accession number. The score takes in account also the interaction detection method and the interaction type. Additionally the number of publications the interaction has appeared in are counted. 1 represents an interaction which have the highest confidence. a determines the initial slope of the curve and is chosen (a=1.6) so that the function has a suitable dynamic range and only well supported interactions obtain a value close to 1. (PMID: )