Analysis of predicted interaction networks in context of expression correlation in various conditions reveals the dynamic changes associated with a number of biological processes. Full-size image (31K) View Within Article Figure 1 The performance of machine learning methods on gold standard data using ...
Here, genes that are strongly correlated with each other are obtained from protein–protein interaction networks. However, the genes that survive after the PPI-based elimination are not the only essential genes that may cause the disease. There are still some noisy genes that may affect ...
Retinitis Pigmentosa; retinal degeneration; protein interaction network (PIN); protein–protein interaction (PPI); complex networks; network medicine; bioinformatics; STRING 1. Introduction Retinitis Pigmentosa (RP) is one of the monogenic human retinal dystrophies [1]. It is an inherited retinal ...
Enzymatically catalyzed proximity labeling is an alternative to immunoprecipitation and biochemical fractionation for the proteomic analysis of macromolecular complexes and protein interaction networks [19]. In this method, ligation enzymes are expressed in cells as conjugates with proteins of interest. For ...
It is also possible that SASA descriptors are fuzzier than interaction fingerprints or scoring functions and as such less sensitive to possible docking errors likely to occur due to the plasticity of most PPI interfaces. Thus, they can be interesting to combine with such approaches while important...
(CqMV1), in someChenopodium quinoaaccessions. In this study, we analyzed the mitochondrial proteome from leaves of quinoa, infected and not infected by CqMV1. Furthermore, by protein–protein interaction and co-expression network models, we provided a system perspective of how CqMV1 affects ...
Durrant and McCammon proposed two models using neural networks, NNScore 1.0 and NNScore 2.0 [107,108]. NNScore 1.0 employed a simple neural network composed of only one hidden layer with five neurons to classify the active and inactive compounds based on 194 features including both interaction ...
2.1. Analysis of the LsrK-HPr PPI Site The PPI site was difficult to target by small molecules because of its large interaction area and dispersion of key amino acid residues [38]. So, we first analyzed whether the LsrK/HPr PPI site was suitable for the binding of small-molecule inhibito...
The performances of the different graph neural networks for PPI prediction are compared using fourteen datasets. The experimental results demonstrate that graph neural network-based models are powerful for solving the protein–protein interaction prediction tasks, no matter the size of these PPI datasets...
The water solubility of protein increases during the protein–protein interaction process, based on the higher activity of electrostatic repulsion more than hydrophobic interactions between the polymers of proteins. Hydrophobic interactions in an aqueous solution can also enhance PPI. Different types of pro...