Unveiling a new cardiac myosin inhibitor through AI-driven virtual screening

In a recent study published in Nature Communication, researchers employed artificial intelligence (AI)-driven virtual screening to discover 'F10', a novel cardiac-specific myosin inhibitor for potential heart disease and heart failure therapies.

Study: Discovery of a novel cardiac-specific myosin modulator using artificial intelligence-based virtual screening. Image Credit: PopTika/Shutterstock.comStudy: Discovery of a novel cardiac-specific myosin modulator using artificial intelligence-based virtual screening. Image Credit: PopTika/Shutterstock.com


The activation of the cardiac myofilaments by Ca2+ undergo contraction-relaxation cycles, during which the power strokes generated by the attachment and detachment of myosin heads from thick filaments to actin thins lead to muscle movement or Myosin availability during this procedure is complicated by its interaction with proteins such as titin and cardiac myosin binding protein-C (cMyBP-C).

Myosin can also form a ‘super relaxed state’ (SRX) that minimizes Adenosine Triphosphatase (ATPase) activity for better energy efficiency.

Dysfunctioning of this structure could lead to the development of cardiovascular disease and indicates an opportunity for designing a new therapy for heart failure that targets dysmiosin functions.

Unlike traditional treatments focusing on symptoms, myosin modulators directly address underlying causes, potentially with fewer side effects.

Further research is needed to deepen understanding of the intricate interplay between cardiac myosin's structural and functional states and to optimize novel myosin modulators like 'F10' for more effective and side-effect-minimized treatments of heart disease and heart failure.

About the study 

In this study, the researchers employed an AI-based virtual high throughput screening (VHTS) method, utilizing Atomwise's AtomNet® platform, to assess a curated library of over 4 million small molecules.

Beta-cardiac myosin from humans was the major focus of these molecules for testing their efficacy towards the Omecamtiv Mercarbil binding site.

The 200 top-selected molecules out of this vast collection satisfied Lipinski’s Rule of Five and were thus selected as drug-like substances. Myosin modulators were identified by testing these selected compounds using biochemical assay.

The biochemical assays were made using bovine cardiac myosin S1 and rabbit skeletal F-actin. These test compounds were mixed separately with an enzyme mixture of lactate dehydrogenase, bovine cardiac myosin S1, and pyruvate kinase in a single black 96-well half-area plate.

Similarly, the assay plates had negative control (Dimethyl Sulfoxide (DMSO) only), and positive control (Blebbistatin). A substrate mix was used to initiate these reactions, and their extent was determined through NADH intensity measurements at different time instances.

This process allowed for the identification of compounds that modulate the ATPase activity of cardiac myosin.

Additionally, demembranated myofibrils prepared from bovine ventricles were used further to assess the ATPase activity of the selected compounds. These myofibrils were tested in an identical assay setup, providing insights into the compounds' effects on steady-state myofibrillar ATPase activity.

This comprehensive approach, combining AI-driven screening with biochemical validation, enabled researchers to identify new cardiac myosin modulators with potential therapeutic applications effectively.

Study results 

In the present study, researchers utilized AI to screen a virtual library of approximately four million compounds for potential cardiac myosin modulators. This method developed a new compound called F10, significantly inhibiting ATPase activity in cardiac myosin.

F10 was selected after analyzing its interaction with the Omecamtiv Mecarbil binding site on human β-cardiac myosin, considering factors like hydrogen bond donors and acceptors and hydrophobic characteristics.

However, the additional assessment revealed that 10 μmol L−1 of F10 inhibited the ATPase activity in bovine cardiac myosins by about 44%. The dose-response analysis showed it was effective at 21 μmol L−1(IC50).

This compound did not resemble known myosin effectors and appeared to be a novel chemical scaffold. Intriguingly, F10 significantly decreased the maximal rate of ATP hydrolysis without affecting the myosin S1's affinity for F-actin.

This specificity was further underscored by F10's differential impact on ATPase activity across various myosin isoforms in different muscle types, highlighting its specificity for cardiac myosin.

The present study also investigated the mechanism of the F10’s inhibition. In this case, single nucleotide turnover experiments showed that the release of nucleotides from cardiac myosin was slowed down due to the effect created by F10 in stabilizing the SRX state of myosin.

The structural effect of F10 on demmembranated rat ventricular trabeculae, which decreased maximal active isometric tension and changed the orientation of myosin heads, implied that these proteins were stabilized in the OFF state.

In addition, they noted that F10 reduced the left ventricular systolic pressure of the Langendorff-perfused rat hearts almost immediately but did not cause any change in the heart rate or the coronary perfusion. Notably, the effects of F10 were reversible and faster in onset and offset compared to Mavacamten, another myosin inhibitor.

The study's structure-activity relationship analysis provided insights into F10's binding and inhibitory mechanism. Computational docking suggested multiple possible interactions of F10 within the myosin motor domain.

Moreover, variations in F10's chemical structure led to differences in inhibitory activity, reinforcing the idea of the OM binding site as a target for developing myosin modulators.

This research is a testament to the potential of AI in drug discovery, especially for cardiac myosin modulators.

The findings introduce F10 as a novel cardiac myosin inhibitor and open avenues for developing new therapeutic agents targeting cardiac myosin for heart disease treatment.

The study highlights the utility of AI in identifying novel compounds and provides a framework for future exploration in this field.

Journal reference:
Vijay Kumar Malesu

Written by

Vijay Kumar Malesu

Vijay holds a Ph.D. in Biotechnology and possesses a deep passion for microbiology. His academic journey has allowed him to delve deeper into understanding the intricate world of microorganisms. Through his research and studies, he has gained expertise in various aspects of microbiology, which includes microbial genetics, microbial physiology, and microbial ecology. Vijay has six years of scientific research experience at renowned research institutes such as the Indian Council for Agricultural Research and KIIT University. He has worked on diverse projects in microbiology, biopolymers, and drug delivery. His contributions to these areas have provided him with a comprehensive understanding of the subject matter and the ability to tackle complex research challenges.    


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