Stable H-bond networks are crucial for selective CLK1 inhibition: a computational perspective

Studying the selectivity mechanism of inhibitors towards highly similar isoforms is an important task in the development of new drugs, which are designed to avoid the undesired side effects <i>in vivo</i>. CDC-like kinase isoforms (CLKs) are serine/threonine protein kinases that are involved in the phosphorylation of mRNA spliceosomes leading to the regulation of gene expression. The CLK isoforms are expressed in most human tissues and cells, but the expression levels of each isoform vary in different cells. Typically, CLK3 is expressed in male testes and sperm, by contrast, as a potential cancer treatment target, the expression level of CLK1 in testicular tissue is significantly lower than other isoforms. These differences in the tissue distribution of CLK1 and CLK3 suggest that the development of selective CLK1 inhibitors to avoid potential side effects. Here, our study is designed to reveal the selectivity mechanism of CLK1 inhibition from a computational perspective. In this study, the binding modes of known selective inhibitors towards CLK1/3 are discussed by computational methods such as protein comparison, molecular docking, binding free energy calculation, molecular dynamics simulations, alanine mutagenesis simulations, and quantum mechanical calculation. The simulations reveal selective key roles involved in CLK1/3 binding, including protein-ligand interactions, mutations, and conformational differences in key amino acid residues. This study will contribute to analyze the selectivity mechanism of CLKs inhibitors and bring insight into the development of novel selective inhibitor drugs.

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Publication Details

Journal
Frontiers in Chemistry
Published
2025-06-17
DOI
https://doi.org/10.3389/fchem.2025.1582515
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

Stable H-bond networks are crucial for selective CLK1 inhibition: a computational perspective

Baichun Hu, Haihan Liu, Na Duan, Jian Wang et al.
Frontiers in Chemistry
Computational Drug Discovery Methods
article

Stable H-bond networks are crucial for selective CLK1 inhibition: a computational perspective

Baichun Hu, Haihan Liu, Na Duan, Jian Wang, Yuzhou Huang
article en

Abstract

Studying the selectivity mechanism of inhibitors towards highly similar isoforms is an important task in the development of new drugs, which are designed to avoid the undesired side effects <i>in vivo</i>. CDC-like kinase isoforms (CLKs) are serine/threonine protein kinases that are involved in the phosphorylation of mRNA spliceosomes leading to the regulation of gene expression. The CLK isoforms are expressed in most human tissues and cells, but the expression levels of each isoform vary in different cells. Typically, CLK3 is expressed in male testes and sperm, by contrast, as a potential cancer treatment target, the expression level of CLK1 in testicular tissue is significantly lower than other isoforms. These differences in the tissue distribution of CLK1 and CLK3 suggest that the development of selective CLK1 inhibitors to avoid potential side effects. Here, our study is designed to reveal the selectivity mechanism of CLK1 inhibition from a computational perspective. In this study, the binding modes of known selective inhibitors towards CLK1/3 are discussed by computational methods such as protein comparison, molecular docking, binding free energy calculation, molecular dynamics simulations, alanine mutagenesis simulations, and quantum mechanical calculation. The simulations reveal selective key roles involved in CLK1/3 binding, including protein-ligand interactions, mutations, and conformational differences in key amino acid residues. This study will contribute to analyze the selectivity mechanism of CLKs inhibitors and bring insight into the development of novel selective inhibitor drugs.

Frontiers in ChemistryVol. 13
Shenyang Pharmaceutical University (CN), Liaoning Provincial People's Hospital (CN), China Medical University (CN)
Openalex Percentile: Top 85%
Computational Drug Discovery Methods
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