PPI-Affinity: A Web Tool for the Prediction and Optimization of Protein-Peptide and Protein-Protein Binding Affinity.

Virtual screening of protein-protein and protein-peptide interactions is a challenging task that directly impacts the processes of hit identification and hit-to-lead optimization in drug design projects involving peptide-based pharmaceuticals. Although several screening tools designed to predict the binding affinity of protein-protein complexes have been proposed, methods specifically developed to predict protein-peptide binding affinity are comparatively scarce. Frequently, predictors trained to score the affinity of small molecules are used for peptides indistinctively, despite the larger complexity and heterogeneity of interactions rendered by peptide binders.

ProtDCal-Suite: A web server for the numerical codification and functional analysis of proteins.

Computational tools for the analysis of protein data and the prediction of biological properties are essential in life sciences and biomedical research. Here, we introduce ProtDCal-Suite, a web server comprising a set of machine learning-based methods for studying proteins. The main module of ProtDCal-Suite is the ProtDCal software.

PPI-Detect: A support vector machine model for sequence-based prediction of protein-protein interactions.

The prediction of peptide-protein or protein-protein interactions (PPI) is a challenging task, especially if amino acid sequences are the only information available. Machine learning methods allow us to exploit the information content in PPI datasets. However, the numerical codification of these datasets often influences the performance of data mining approaches.