Focused On-demand Library for Myosin-binding protein C, slow-type

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

The focused library is created on demand with the latest virtual screening and parameter assessment technology, supported by the Receptor.AI drug discovery platform. This method is more effective than traditional methods and results in higher-quality compounds with better activity, selectivity, and safety.

Our selection of compounds is from a large virtual library of over 60 billion molecules. The production and distribution of these compounds are managed by our partner Reaxense.

In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best docking poses, affinity scores, and activity scores, providing a comprehensive overview.

We use our state-of-the-art dedicated workflow for designing focused libraries.

 Fig. 1. The sreening workflow of Receptor.AI

Our strategy employs molecular simulations to explore an extensive range of proteins, capturing their dynamics both individually and within complexes with other proteins. Through ensemble virtual screening, we address proteins' conformational mobility, uncovering key binding sites at both functional regions and remote allosteric locations. This comprehensive investigation ensures a thorough assessment of all potential mechanisms of action, with the goal of discovering innovative therapeutic targets and lead molecules across across diverse biological functions.

Our library distinguishes itself through several key aspects:

  • The Receptor.AI platform integrates all available data about the target protein, including past experiments, literature data, known ligands, structural information and more. This consolidated approach maximises the probability of prioritising highly relevant compounds.
  • The platform uses sophisticated molecular simulations to identify possible binding sites so that the compounds in the focused library are suitable for discovering allosteric inhibitors and the binders for cryptic pockets.
  • The platform integrates over 50 highly customisable AI models, which are thoroughly tested and validated on a multitude of commercial drug discovery programs and research projects. It is designed to be efficient, reliable and accurate. All this power is utilised when producing the focused libraries.
  • In addition to producing the focused libraries, Receptor.AI provides services and end-to-end solutions at every stage of preclinical drug discovery. The pricing model is success-based, which reduces your risks and leverages the mutual benefits of the project's success.







Alternative names:

C-protein, skeletal muscle slow isoform

Alternative UPACC:

Q00872; B4DKR5; B7Z8G8; B7ZL02; B7ZL09; B7ZL10; E7ESM5; E7EWS6; G3XAE8; Q15497; Q17RR7; Q569K7; Q86T48; Q86TC8; Q8N3L2


Myosin-binding protein C, slow-type, also known as C-protein, skeletal muscle slow isoform, plays a pivotal role in muscle contraction. It is a thick filament-associated protein located in the crossbridge region of vertebrate striated muscle a bands, binding to both myosin and actin. This protein modulates the activity of actin-activated myosin ATPase, potentially influencing muscle contraction or serving a structural role.

Therapeutic significance:

Linked to diseases such as Arthrogryposis, distal, 1B, Lethal congenital contracture syndrome 4, and Congenital myopathy 16, Myosin-binding protein C, slow-type's genetic variants underscore its clinical importance. Understanding its role could open doors to potential therapeutic strategies, offering hope for targeted interventions in these muscular disorders.

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