Identifying selective PDHK inhibitors using coupled tensor matrix completion and experimental validation

Abstract

Abstract Drug discovery often involves targeting specific members within a family of similar proteins. For example, pyruvate dehydrogenase kinase (PDHK) exists as four isozymes, which exhibit varying expression patterns across multiple tissues. Different PDHK isozymes have been implicated in conditions such as cancer, heart failure, and diabetes, suggesting that targeting them with inhibitors may offer therapeutic benefits. However, simultaneous inhibition of all four PDHK isozymes has the potential to be counterproductive, or poorly tolerated, highlighting a need for isoform-selective PDHK inhibitors. Despite multiple prior reports of PDHK inhibitors, identifying isoform-specific inhibitors for each PDHK isozyme has proven elusive. In this work, we propose a comprehensive framework that combines a machine learning-based prediction method and biochemical testing to screen a library of novel and previously reported compounds, thereby identifying selective inhibitors for PDHK isozymes. Initially, a coupled tensor matrix completion (CTMC) approach is employed to predict compound-target interaction (CTI) and pinpoint target-specific inhibitors. Subsequently, biochemical testing is performed to validate the predicted CTIs. Utilizing this approach, we successfully identified five novel PDHK1-specific inhibitors, underscoring the reliability of this approach as a screening method in the early stages of drug discovery and hit identification.

Keywords

• PDHK
• Coupled tensor matrix completion
• Compound-target interaction
• Machine learning