AI-ACCELERATED DRUG DISCOVERY

B-cell linker protein

Explore its Potential with AI-Driven Innovation
Predicted by Alphafold

B-cell linker 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 B-cell linker 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 B-cell linker 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 B-cell linker 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 B-cell linker 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 B-cell linker 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 B-cell linker 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.

B-cell linker protein

partner:

Reaxense

upacc:

Q8WV28

UPID:

BLNK_HUMAN

Alternative names:

B-cell adapter containing a SH2 domain protein; B-cell adapter containing a Src homology 2 domain protein; Cytoplasmic adapter protein; Src homology 2 domain-containing leukocyte protein of 65 kDa

Alternative UPACC:

Q8WV28; O75498; O75499; Q2MD49

Background:

The B-cell linker protein, known for its pivotal role in B-cell development and function, acts as a central connector downstream of the B-cell receptor. It facilitates signaling pathways crucial for B-cell maturation, including ERK/EPHB2, MAP kinase p38, and JNK activation. This protein is instrumental in NF-kappa-B and NFAT activation, PLCG1 and PLCG2 activation, and Ca(2+) mobilization, highlighting its significance in immune response regulation.

Therapeutic significance:

Linked to Agammaglobulinemia 4, an autosomal recessive condition characterized by severe B-cell development blockage, the B-cell linker protein's dysfunction underscores its therapeutic potential. Understanding its role could pave the way for innovative treatments targeting primary immunodeficiencies, offering hope for patients with compromised immune systems.

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