AI-ACCELERATED DRUG DISCOVERY
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 Palmitoyl-protein thioesterase 1 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 Palmitoyl-protein thioesterase 1 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 Palmitoyl-protein thioesterase 1, 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 Palmitoyl-protein thioesterase 1. 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 Palmitoyl-protein thioesterase 1. 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 Palmitoyl-protein thioesterase 1 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.
Palmitoyl-protein thioesterase 1
partner:
Reaxense
upacc:
P50897
UPID:
PPT1_HUMAN
Alternative names:
Palmitoyl-protein hydrolase 1
Alternative UPACC:
P50897; B4DY24; Q6FGQ4
Background:
Palmitoyl-protein thioesterase 1 (PPT1), also known as Palmitoyl-protein hydrolase 1, plays a crucial role in lysosomal degradation by removing thioester-linked fatty acyl groups such as palmitate from modified cysteine residues in proteins or peptides. It shows preference for acyl chain lengths of 14 to 18 carbons. This enzyme's activity is vital for the maintenance of cellular homeostasis and the prevention of lysosomal storage diseases.
Therapeutic significance:
PPT1 is directly implicated in Ceroid lipofuscinosis, neuronal, 1, a form of neuronal ceroid lipofuscinosis characterized by seizures, dementia, visual loss, and cerebral atrophy. The disease's progression is marked by the accumulation of autofluorescent liposomal material, with PPT1 variants affecting its onset. Understanding the role of PPT1 could lead to novel therapeutic strategies for this debilitating condition.
