PyDPI: Freely Available Python Package for Chemoinformatics, Bioinformatics, and Chemogenomics Studies

Abstract

The rapidly increasing amount of publicly available data in biology and chemistry enables researchers to revisit interaction problems by systematic integration and analysis of heterogeneous data. Herein, we developed a comprehensive python package to emphasize the integration of chemoinformatics and bioinformatics into a molecular informatics platform for drug discovery. PyDPI (drug-protein interaction with Python) is a powerful python toolkit for computing commonly used structural and physicochemical features of proteins and peptides from amino acid sequences, molecular descriptors of drug molecules from their topology, and protein-protein interaction and protein-ligand interaction descriptors. It computes 6 protein feature groups composed of 14 features that include 52 descriptor types and 9890 descriptors, 9 drug feature groups composed of 13 descriptor types that include 615 descriptors. In addition, it provides seven types of molecular fingerprint systems for drug molecules, including topological fingerprints, electro-topological state (E-state) fingerprints, MACCS keys, FP4 keys, atom pair fingerprints, topological torsion fingerprints, and Morgan/circular fingerprints. By combining different types of descriptors from drugs and proteins in different ways, interaction descriptors representing protein-protein or drug-protein interactions could be conveniently generated. These computed descriptors can be widely used in various fields relevant to chemoinformatics, bioinformatics, and chemogenomics. PyDPI is freely available via https://sourceforge.net/projects/pydpicao/.

Computing wide range of protein/peptide features from their sequence and structure

“…Pfeature also integrate all these descriptors and many more protein descriptors but it does not have modules for computing DNA and chemical descriptors, integrated in PyBioMed. PyDPI is feature rich python based standalone that computes 52 types of protein features from six feature groups (Cao, Liang, et al, 2013). Recently, one of the powerful package iFeature has been developed that compute a comprehensive spectrum of 18 major sequence encoding schemes that encompass 53 different types of feature descriptors.…”

A generalized machine-learning aided method for targeted identification of industrial enzymes from metagenome: a xylanase temperature dependence case study

“…Using an appropriate set of features is undoubtedly one of the most crucial elements in creating an efficient classifier. Because there is not much precise evidence regarding the most related features to thermal activity, in this study, various protein descriptors were computed using the PyDPI python package (37). The PyDPI computed protein features are 15 descriptor types, which are from six main groups.…”

In silico toxicity prediction of chemicals from EPA toxicity database by kernel fusion-based support vector machines

“…Molecular fingerprints have been widely applied to various chemical classification problems [38][39][40][41][42]. Although it divides the whole molecule into a large number of fragments, it has the potential to keep overall complexity of molecules.…”