A mutation-induced drug resistance database (MdrDB).

Area of Science:

• Biochemistry
• Computational Biology
• Pharmacology

Background:

• Mutation-induced drug resistance poses a significant clinical challenge, diminishing drug efficacy due to protein structural changes.
• Understanding mutation effects on protein-ligand binding affinities is critical for developing new drugs and therapies.
• Existing research is hindered by the lack of large-scale, high-quality databases for drug resistance data.

Purpose of the Study:

• To develop MdrDB, the largest integrated database of its kind, to address the limitations in current drug resistance research.
• To expand existing drug resistance data by integrating information on drug sensitivity and cell line mutations.
• To provide a comprehensive resource for studying mutation-induced drug resistance.

Main Methods:

• Integrated data from seven publicly available datasets, including Genomics of Drug Sensitivity in Cancer and DepMap.
• Compiled MdrDB with 100,537 samples, 240 proteins, 5119 PDB structures, 2503 mutations, and 440 drugs.
• Included 3D structures of wild type and mutant protein-ligand complexes, binding affinity changes (ΔΔG), and biochemical features for each sample.

Main Results:

• MdrDB is the largest integrated database for mutation-induced drug resistance.
• The database significantly expands available data on drug sensitivity and cell line mutations.
• Experimental results show MdrDB enhances machine learning model performance in predicting ΔΔG.

Conclusions:

• MdrDB is a comprehensive resource that advances the understanding of mutation-induced drug resistance.
• The database can accelerate the discovery of novel therapeutic chemicals.
• MdrDB facilitates research into protein structural changes affecting drug efficacy.