protein–protein interaction (PPI) prediction ‐ major role in biological processessupport vector machines (SVM) ‐ supervised learning method for classification, function approximation, signal processing and regression analysisPPIs prediction, based on whole genome analysis...
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 ...
3.2.2. Elimination of Genes through PPI In this step, the protein–protein interactions of each gene ontological group are obtained, and the genes that did not participate in the PPI interactions are identified. These genes are referred to as isolated interaction genes (IIG) and were treated as...
We first present the results of our model on two protein–protein interaction (PPI) datasets before discussing this particular scenario. 3.1. The Orthogonal Dataset To train and evaluate our model, we use two datasets. The first one, commonly called the MaSIF dataset, was used to evaluate the...
By utilizing the protein sequence information, all of these models can predict the interaction between proteins. Fourteen PPI datasets are extracted and utilized to compare the prediction performance of all these methods. The experimental results show that hyperbolic graph neural networks tend to have ...
Figure 1. Principle of PUB method in living cell and workflow for quantification of PPI. In vivo interaction (A) of proteins X and Y results in site-specific biotinylation of the biotin acceptor peptide (BAP) by wild-type humanized biotin ligase (BirA). Biotinylated protein can be detected ...
MultiBacMam Bimolecular Fluorescence Complementation (BiFC) tool-kit identifies new small-molecule inhibitors of the CDK5-p25 protein-protein interaction (PPI). Sci. Rep. 2018, 8, 5083. [Google Scholar] [CrossRef] Doyle, T.B.; Muntean, B.S.; Ejendal, K.F.; Hayes, M.P.; Soto-...
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...
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...
The inputs of AtomNet used vectorized 3D grids placed over the protein–ligand interaction interface, with each grid cell storing a value describing the presence of some basic structural features, varying from a simple enumeration of atom types to more complex descriptors. Its network topology was ...