scholarly journals Inferring strengths of protein-protein interactions from experimental data using linear programming

2003 ◽  
Vol 19 (Suppl 2) ◽  
pp. ii58-ii65 ◽  
Author(s):  
M. Hayashida ◽  
N. Ueda ◽  
T. Akutsu
Keyword(s):  
Experimental Data ◽  
Linear Programming ◽  
Protein Interactions ◽  
Protein Protein Interactions

scholarly journals Estimating the Directional Flexibility of Proteins from Equilibrium Thermal Fluctuations

2020 ◽  
Author(s):  
SANJOY PAUL ◽  
Ravindra Venkatramani
Keyword(s):  
Experimental Data ◽  
Single Molecule ◽  
Protein Interactions ◽  
Thermal Fluctuations ◽  
Globular Proteins ◽  
Mechanical Anisotropy ◽  
Elastic Network Model ◽  
Protein Protein Interactions ◽  
Elastic Network ◽  
Spring Constants

<b><i>In this manuscript, we demonstrate the abilities of atomistic MD trajectories to estimate the directional spring constants of proteins. The MD-derived spring constants are cross-correlated with single-molecule force spectropcopy (SMFS) experimental data for 5 different globular proteins. We employ the framework to predict the mechanical anisotropy of ubiquitin and associated changes in the anisotropy with functionally relevant protein-protein interactions. Finally, we use the MD-based framework to benchmark and improve computationally inexpensive and scalable elastic network model (ENM) based methods to estimate protein directional flexibility.</i></b>

scholarly journals ProfPPIdb: pairs of physical protein-protein interactions predicted for entire proteomes

2018 ◽  
Author(s):  
Linh Tran ◽  
Tobias Hamp ◽  
Burkhard Rost
Keyword(s):  
Experimental Data ◽  
Protein Interactions ◽  
Web Application ◽  
Domain Knowledge ◽  
Prediction Method ◽  
Model Organisms ◽  
Evolutionary Information ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Link Type

AbstractMotivationProtein-protein interactions (PPIs) play a key role in many cellular processes. Most annotations of PPIs mix experimental and computational data. The mix optimizes coverage, but obfuscates the annotation origin. Some resources excel at focusing on reliable experimental data. Here, we focused on new pairs of interacting proteins for several model organisms based solely on sequence-based prediction methods.ResultsWe extracted reliable experimental data about which proteins interact (binary) for eight diverse model organisms from public databases, namely from Escherichia coli, Schizosaccharomyces pombe, Plasmodium falciparum, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, Rattus norvegicus, Arabidopsis thaliana, and for the previously used Homo sapiens and Saccharomyces cerevisiae. Those data were the base to develop a PPI prediction method for each model organism. The method used evolutionary information through a profile-kernel Support Vector Machine (SVM). With the resulting eight models, we predicted all possible protein pairs in each organism and made the top predictions available through a web application. Almost all of the PPIs made available were predicted between proteins that have not been observed in any interaction, in particular for less well-studied organisms. Thus, our work complements existing resources and is particularly helpful for designing experiments because of its uniqueness. Experimental annotations and computational predictions are strongly influenced by the fact that some proteins have many partners and others few. To optimize machine learning, recent methods explicitly ignored such a network-structure and rely either on domain knowledge or sequence-only methods. Our approach is independent of domain-knowledge and leverages evolutionary information. The database interface representing our results is accessible from https://rostlab.org/services/ppipair/. The data can also be downloaded from https://figshare.com/collections/ProfPPI-DB/4141784.

scholarly journals Estimating the Directional Flexibility of Proteins from Equilibrium Thermal Fluctuations

2020 ◽  
Author(s):  
SANJOY PAUL ◽  
Ravindra Venkatramani
Keyword(s):  
Experimental Data ◽  
Single Molecule ◽  
Protein Interactions ◽  
Thermal Fluctuations ◽  
Globular Proteins ◽  
Mechanical Anisotropy ◽  
Elastic Network Model ◽  
Protein Protein Interactions ◽  
Elastic Network ◽  
Spring Constants

<b><i>In this manuscript, we demonstrate the abilities of atomistic MD trajectories to estimate the directional spring constants of proteins. The MD-derived spring constants are cross-correlated with single-molecule force spectropcopy (SMFS) experimental data for 5 different globular proteins. We employ the framework to predict the mechanical anisotropy of ubiquitin and associated changes in the anisotropy with functionally relevant protein-protein interactions. Finally, we use the MD-based framework to benchmark and improve computationally inexpensive and scalable elastic network model (ENM) based methods to estimate protein directional flexibility.</i></b>

scholarly journals High-resolution protein fragment interactions using AVA-Seq on a human reference set

2021 ◽  
Author(s):  
Stephanie Ramadan ◽  
Jovana Aleksic ◽  
Nayra M Al-Thani ◽  
Yasmin A Mohamoud ◽  
David E Hill ◽  
...  
Keyword(s):  
Experimental Data ◽  
Protein Interactions ◽  
Protein Function ◽  
Gold Standard ◽  
Protein Protein Interactions ◽  
Protein Fragment ◽  
Human Protein Interaction ◽  
Reference Set ◽  
Tumor Protein P53

Protein-protein interactions (PPIs) are important in understanding numerous aspects of protein function. Here, the recently developed all-vs-all sequencing (AVA-Seq) approach to determine protein-protein interactions was tested on a gold-standard human protein interaction set (hsPRS-v2). Initially, these data were interpreted strictly from a binary PPI perspective to compare AVA-Seq to other binary PPI methods tested on the same hsPRS-v2. AVA-Seq recovered 20 of 47 (43%) binary PPIs from this reference set comparing favorably with other methods. The same experimental data allowed for the determination of >500 known and novel PPIs including interactions between wildtype fragments of tumor protein p53 and minichromosomal maintenance complex proteins 2, and 5 (MCM2 and MCM5) that could be of interest in human disease. Additional results gave a better understanding of why interactions might be missed using AVA-Seq and aide future PPI experimental design for maximum recovery of information.

scholarly journals SynSysNet: integration of experimental data on synaptic protein–protein interactions with drug-target relations

2012 ◽  
Vol 41 (D1) ◽  
pp. D834-D840 ◽  
Author(s):  
Joachim von Eichborn ◽  
Mathias Dunkel ◽  
Björn O. Gohlke ◽  
Sarah C. Preissner ◽  
Michael F. Hoffmann ◽  
...  
Keyword(s):  
Experimental Data ◽  
Protein Interactions ◽  
Drug Target ◽  
Synaptic Protein ◽  
Protein Protein Interactions

scholarly journals Evolution of Protein-protein Interaction Networks in Duplication-Divergence Model

2012 ◽  
Vol 22 (1) ◽  
pp. 7-14
Author(s):  
Bui Phuong Thuy ◽  
Trinh Xuan Hoang
Keyword(s):  
Experimental Data ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Interaction Network ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Protein Protein Interactions ◽  
Scale Free ◽  
Protein Protein Interaction ◽  
Living Organisms

Protein interacts with one another resulting in complex functions in living organisms. Like many other real-world networks, the networks of protein-protein interactions possess a certain degree of ordering, such as the scale-free property. The latter means that the probability $P$ to find a protein that interacts with $k$ other proteins follows a power law, $P(k) \sim k^{-\gamma}$. Protein interaction networks (PINs) have been studied by using a stochastic model, the duplication-divergence model, which is based on mechanisms of gene duplication and divergence during evolution. In this work, we show that this model can be used to fit experimental data on the PIN of yeast Saccharomyces cerevisae at two different time instances simultaneously. Our study shows that the evolution of PIN given by model is consistent with growing experimental data over time, and that the scale-free property of protein interaction network is robust against random deletion of interactions.

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