Estimating the size of the human interactome

By M. P. Stumpf, Thomas Thorne, Eric de Silva, Ronald Stewart, H. An, Michael Lappe, C. Wiuf. Proceedings of the National Academy of Sciences of the United States of America, 2008

789 citations recorded by the source corpus.

Subfields: Aging, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9b403693903d460a333dcc81c7ad6e33da86acec

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Citations in the corpus

Template-based structure modeling of protein-protein interactions (2013, background): “Moreover, while the estimated size of the human interactome ranges from ~130,000 [6] to ~650,000 [7], interactome databases currently contain only ~41,000 binary interactions between human proteins, and many of th em may be in error because of the inherent limitations of high-throughput experimenta ”
Tissue-specific subnetworks and characteristics of publicly available human protein interaction databases (2011, positive): “With the availability of the resulting large datasets, the challenge now lies in the generation PPIs within a cell and between cells at any given moment in . i c . oup . o m b i o i human proteome is estimated to encompass 130000–650000 (Stumpf , 2008; Venkatesan , 2009).”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, methodology): “These methods also allow the extrapolation of the partially available network data to the total dataset (Wiuf et al., 2006; Stumpf et al., 2008).”
Literature-curated protein interaction datasets (2009, background): “Whether user impressions were formed from statements made by database authors 18–21 or not, belief that database entries accurately correspond to high-quality, direct physical interactions is widespread 6,72 .”
Integrated interactions database: tissue-specific view of the human and model organism interactomes (2015, background): “The human interactome, estimated at up to 650,000 PPIs (13), may be less than one-third complete.”
A proteome-scale map of the human interactome network. (2014, background): “In 2013 this corresponded to 11,045 high-quality protein pairs (Lit-BM-13), more than an order of magnitude below current estimates of the number of PPIs in the full human interactome (Stumpf et al., 2008; Venkatesan et al., 2009).”
CPORT: A Consensus Interface Predictor and Its Performance in Prediction-Driven Docking with HADDOCK (2011, neutral): “least one order of magnitude [1].”
Recent advances in the development of protein–protein interactions modulators: mechanisms and clinical trials (2020, background): “It is estimated that there are about 130,000–650,000 types of PPIs in the human interactome.(4,8,20) Although the number of protein complexes exceeds that of enzymes and receptors, designing a small molecule to bind to a PPI interface is challenging because of the reasons below.”
An empirical framework for binary interactome mapping (2008, neutral): “Therefore the distributions provided as inputs for the Monte Carlo simulations (see below) in this case are given by Iobs = Beta(52,138) , f- = 1 – Beta(15,29) and f+ = Beta(4,183).”
Negatome 2.0: a database of non-interacting proteins derived by literature mining, manual annotation and protein structure analysis (2013, neutral): “For example, as of this writing the IntAct database (1) lists 48 669 interactions in the human cell, while the total number of human interactions has been estimated to be around 650 000 (2).”
Missing and spurious interactions and the reconstruction of complex networks (2009, background): “Additionally, different methods result in networks that have different topological properties [3], and the coverage of real interactomes is very limited: 80% of the interactome of yeast [3] and 99.7% of the human interactome [4, 5] are still unknown.”
Link Prediction in Complex Networks: A Survey (2010, background): “d/or laboratorial experiments, which are usually very costly. Our knowledge of these networks is very limited, for example, 80% of the molecular interactions in cells of Yeast [10] and 99.7% of human [11,12] are still unknown. Instead of blindly checking all possible interactions, to predict based o”