Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article 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).

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9868583PMC
http://dx.doi.org/10.3389/fchem.2022.1090643DOI Listing

Publication Analysis

Top Keywords

protein-protein interactions
8
drug discovery
8
compounds registered
8
active inactive
8
inactive compounds
8
compounds
7
ppi
6
dlip-ppi library
4
library integrated
4
integrated chemical
4

Similar Publications

Integrative profiling of lung cancer biomarkers EGFR, ALK, KRAS, and PD-1 with emphasis on nanomaterials-assisted immunomodulation and targeted therapy.

Front Immunol

September 2025

Department of Thoracic Surgery, Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, Guangdong, China.

Background: Lung cancer remains the leading cause of cancer-related mortality globally, primarily due to late-stage diagnosis, molecular heterogeneity, and therapy resistance. Key biomarkers such as EGFR, ALK, KRAS, and PD-1 have revolutionized precision oncology; however, comprehensive structural and clinical validation of these targets is crucial to enhance therapeutic efficacy.

Methods: Protein sequences for EGFR, ALK, KRAS, and PD-1 were retrieved from UniProt and modeled using SWISS-MODEL to generate high-confidence 3D structures.

View Article and Find Full Text PDF

Purpose: This study aimed to conduct functional proteomics across breast cancer subtypes with bioinformatics analyses.

Methods: Candidate proteins were identified using nanoscale liquid chromatography with tandem mass spectrometry (NanoLC-MS/MS) from core needle biopsy samples of early stage (0-III) breast cancers, followed by external validation with public domain gene-expression datasets (TCGA TARGET GTEx and TCGA BRCA).

Results: Seventeen proteins demonstrated significantly differential expression and protein-protein interaction (PPI) found the strong networks including COL2A1, COL11A1, COL6A1, COL6A2, THBS1 and LUM.

View Article and Find Full Text PDF

Background: Synaptic dysfunction and synapse loss occur in Alzheimer's disease (AD). The current study aimed to identify synaptic-related genes with diagnostic potential for AD.

Methods: Differentially expressed genes (DEGs) were overlapped with phenotype-associated module selected through weighted gene co-expression network analysis (WGCNA), and synaptic-related genes.

View Article and Find Full Text PDF

Background: Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease. However, the biological role of mitochondrial metabolism (MM) in COPD remains poorly understood. This study aimed to explore the underlying mechanisms of MM in COPD using bioinformatics methods.

View Article and Find Full Text PDF

Predicting Antibody-Antigen (Ab-Ag) docking and structure-based design represent significant long-term and therapeutically important challenges in computational biology. We present SAGERank, a general, configurable deep learning framework for antibody design using Graph Sample and Aggregate Networks. SAGERank successfully predicted the majority of epitopes in a cancer target dataset.

View Article and Find Full Text PDF