mutLBSgeneDB

mutLBSgeneDB catalogs somatic mutations at ligand binding sites of proteins and integrates multi-omic and structural annotations to support analyses of protein stability, drug interactions, and resistance mechanisms in cancer.

Key Features:

• Extensive Mutation Data: Curated information on over 2,300 mutLBSgenes with approximately 12,000 somatic mutations at around 10,000 ligand binding sites across 16 cancer types.
• Targetable Gene Selection: Identification and categorization of 744 targetable_mutLBSgenes by integrating kinases, transcription factors, pharmacological genes, and cancer driver genes.
• Integrated Data Analysis: Integration of genetic, genomic, transcriptomic, proteomic, network, and functional data to assess differential gene expression networks, drug response correlations with gene expression, and effects on protein stability.
• Binding Affinity Calculations: Calculated binding affinities for 20 selected genes comparing wild-type and mutant protein forms.
• Comprehensive Annotations: Seven annotation categories covering Gene Summary; Mutated Information; Protein Structure-Related Information; Differential Gene Expression and Gene-Gene Network; Phenotype Information; Pharmacological Information; and Conservation Information.

Scientific Applications:

• Functional genomics and structural biology: Analyze how LBS mutations affect protein function, stability, and ligand interactions.
• Drug development and pharmacology: Assess how LBS mutations alter drug binding and inform potential mechanisms of drug resistance.
• Therapeutic target identification: Prioritize potential cancer drug targets using mutation distribution and targetable_mutLBSgenes.
• Pharmacogenomics and drug-response analysis: Correlate gene expression with drug response to inform pharmacogenomic hypotheses.
• Network and expression analysis: Explore differential gene expression and gene-gene network changes associated with LBS mutations.

Methodology:

Curated somatic mutations at ligand binding sites across 16 cancer types; integrated genetic, genomic, transcriptomic, proteomic, network, and functional data; calculated binding affinities for 20 selected genes comparing wild-type and mutant forms; and identified 744 targetable_mutLBSgenes by integrating kinases, transcription factors, pharmacological genes, and cancer driver genes.