Decoding selectivity: computational insights into AKR1B1 and AKR1B10 inhibition

Area of Science:

• Biochemistry and Molecular Biology
• Pharmacology and Drug Design
• Computational Chemistry

Background:

• Human aldo-keto reductases (AKRs) are NADPH-dependent enzymes involved in various metabolic processes.
• AKR1B1 is a target for cancer and diabetes, but its similarity to AKR1B10 causes adverse events.
• Selective inhibition of AKR1B1 over AKR1B10 is essential for therapeutic safety and efficacy.

Purpose of the Study:

• To elucidate the molecular mechanisms underlying the selectivity of AKR1B1/10 inhibition.
• To provide insights for designing potent and highly selective AKR1B1/10 inhibitors.
• To guide the development of safer therapeutic agents for cancer and diabetes.

Main Methods:

• Sequence alignment and structural comparison of AKR1B1 and AKR1B10.
• Protein Contacts Atlas analysis.
• Molecular docking, molecular dynamics simulations, MM-GBSA calculations, alanine scanning mutagenesis, and pharmacophore modeling.
• Utilized selective inhibitors lidorestat and HAHE as references.

Main Results:

• Identified key structural and interaction differences between AKR1B1 and AKR1B10.
• Characterized the binding modes and selectivity determinants of the inhibitors.
• Provided a detailed molecular understanding of AKR1B1/10 inhibition.

Conclusions:

• Computational strategies successfully revealed the selectivity mechanisms of AKR1B1/10 inhibition.
• The findings offer substantial evidence for designing improved AKR1B1/10 inhibitors.
• This research facilitates the development of safer and more effective drugs for cancer and diabetes treatment.