N-terminal kinase-like protein

N-terminal kinase-like protein - Focused Library Design

Available from Reaxense

This protein is integrated into the Receptor.AI ecosystem as a prospective target with high therapeutic potential. We performed a comprehensive characterization of N-terminal kinase-like protein including:

1. LLM-powered literature research

Our custom-tailored LLM extracted and formalized all relevant information about the protein from a large set of structured and unstructured data sources and stored it in the form of a Knowledge Graph. This comprehensive analysis allowed us to gain insight into N-terminal kinase-like protein therapeutic significance, existing small molecule ligands, relevant off-targets, and protein-protein interactions.

Fig. 1. Preliminary target research workflow

2. AI-Driven Conformational Ensemble Generation

Starting from the initial protein structure, we employed advanced AI algorithms to predict alternative functional states of N-terminal kinase-like protein, including large-scale conformational changes along "soft" collective coordinates. Through molecular simulations with AI-enhanced sampling and trajectory clustering, we explored the broad conformational space of the protein and identified its representative structures. Utilizing diffusion-based AI models and active learning AutoML, we generated a statistically robust ensemble of equilibrium protein conformations that capture the receptor's full dynamic behavior, providing a robust foundation for accurate structure-based drug design.

Fig. 2. AI-powered molecular dynamics simulations workflow

3. Binding pockets identification and characterization

We employed the AI-based pocket prediction module to discover orthosteric, allosteric, hidden, and cryptic binding pockets on the protein’s surface. Our technique integrates the LLM-driven literature search and structure-aware ensemble-based pocket detection algorithm that utilizes previously established protein dynamics. Tentative pockets are then subject to AI scoring and ranking with simultaneous detection of false positives. In the final step, the AI model assesses the druggability of each pocket enabling a comprehensive selection of the most promising pockets for further targeting.

Fig. 3. AI-based binding pocket detection workflow

4. AI-Powered Virtual Screening

Our ecosystem is equipped to perform AI-driven virtual screening on N-terminal kinase-like protein. With access to a vast chemical space and cutting-edge AI docking algorithms, we can rapidly and reliably predict the most promising, novel, diverse, potent, and safe small molecule ligands of N-terminal kinase-like protein. This approach allows us to achieve an excellent hit rate and to identify compounds ready for advanced lead discovery and optimization.

Fig. 4. The screening workflow of Receptor.AI

Receptor.AI, in partnership with Reaxense, developed a next-generation technology for on-demand focused library design to enable extensive target exploration.

The focused library for N-terminal kinase-like protein includes a list of the most effective modulators, each annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Furthermore, each compound is shown with its optimal docking poses, affinity scores, and activity scores, offering a detailed summary.

N-terminal kinase-like protein

partner:

Reaxense

upacc:

Q96KG9

UPID:

SCYL1_HUMAN

Alternative names:

Coated vesicle-associated kinase of 90 kDa; SCY1-like protein 1; Telomerase regulation-associated protein; Telomerase transcriptional element-interacting factor; Teratoma-associated tyrosine kinase

Alternative UPACC:

Q96KG9; A6NJF1; Q96G50; Q96KG8; Q96KH1; Q9HAW5; Q9HBL3; Q9NR53

Background:

The N-terminal kinase-like protein, known by alternative names such as Coated vesicle-associated kinase of 90 kDa and SCY1-like protein 1, plays a pivotal role in cellular processes. It regulates COPI-mediated retrograde protein traffic between the Golgi apparatus and the endoplasmic reticulum, crucial for maintaining Golgi apparatus morphology. Despite lacking detectable kinase activity in vitro, its isoform 6 acts as a transcriptional activator, influencing the beta-polymerase and TERT promoter regions.

Therapeutic significance:

Spinocerebellar ataxia, autosomal recessive, 21 (SCAR21), characterized by cerebellar atrophy, ataxia, liver failure, and peripheral neuropathy, is linked to variants affecting this protein. Understanding the role of N-terminal kinase-like protein could open doors to potential therapeutic strategies for SCAR21 and related cerebellar disorders.