DLiP-PPI library: An integrated chemical database of small-to-medium-sized molecules targeting protein–protein interactions

Abstract

Protein–protein interactions (PPIs) are recognized as important targets in drug discovery. The characteristics of molecules that inhibit PPIs differ from those of small-molecule compounds. We developed a novel chemical library database system (DLiP) to design PPI inhibitors. A total of 32,647 PPI-related compounds are registered in the DLiP. It contains 15,214 newly synthesized compounds, with molecular weight ranging from 450 to 650, and 17,433 active and inactive compounds registered by extracting and integrating known compound data related to 105 PPI targets from public databases and published literature. Our analysis revealed that the compounds in this database contain unique chemical structures and have physicochemical properties suitable for binding to the protein–protein interface. In addition, advanced functions have been integrated with the web interface, which allows users to search for potential PPI inhibitor compounds based on types of protein–protein interfaces, filter results by drug-likeness indicators important for PPI targeting such as rule-of-4, and display known active and inactive compounds for each PPI target. The DLiP aids the search for new candidate molecules for PPI drug discovery and is available online (https://skb-insilico.com/dlip).

A Multimodal Deep Learning Framework for Predicting PPI-Modulator Interactions

“…We used the newly synthesized DLiP-PPI library as the virtual screening library, which contains 15,074 compounds after deduplication and is independent of the training data set of MultiPPIMI. We standardized all the candidate compounds by RDKit and predicted their probability of interaction with Keap1/Nrf2 by MultiPPIMI.…”

“…important target for the regulation of antioxidant defenses and has been relevant to a wide range of diseases including neurodegenerative, autoimmune, and metabolic disorders. 53,54 We used the newly synthesized DLiP-PPI library 35 as the virtual screening library, which contains 15,074 compounds after deduplication and is independent of the training data set of MultiPPIMI. We standardized all the candidate compounds by RDKit and predicted their probability of interaction with Keap1/Nrf2 by MultiPPIMI.…”

“…To evaluate our proposed model for PPIMI prediction, we construct a benchmark data set based on a public database DLiP, which contains active and inactive small modulators of PPI targets curated from public databases and published literature. The protein sequences of PPI partners are retrieved from the UniProt database .…”

A Multimodal Deep Learning Framework for Predicting PPI-Modulator Interactions

“…We used the newly synthesized DLiP-PPI library 35 as the virtual screening library, which contains 15,074 compounds after deduplication and is independent of the training dataset of MultiPPIMI. We standardized all the candidate compounds by RDKit, and predicted their probability of interaction with Keap1/Nrf2 by MultiPPIMI.…”

“…Datasets. To evaluate our proposed model for PPIMI prediction, we construct a benchmark dataset based on a public database DLiP 35 , which contains active and inactive small modulators of PPI targets curated from public databases and published literatures. The protein sequences of PPI partners are retrieved from the UniProt database 36 .…”