Biology subjects

ma, w.

Publications and source records attributed to ma, w..

3 recordsLinked to original sources

bioRxiv2025

Myosin modulator Aficamten inhibits force in cardiac muscle by altering myosin's biochemical activity without changing thick filament structure

BackgroundInhibiting contractility by targeting cardiac myosin is an effective treatment for patients with hypertrophic cardiomyopathy (HCM). Aficamten is a second in class myosin inhibitor with promising clinical data showing improvements in hemodynamics and symptoms in patients with HCM. While it is known that Aficamten inhibits force and cardiomyocyte contractility by stabilizing the weak pre-powerstroke conformation, effects on myosin structure and kinetics during loaded contraction are lacking. MethodsPermeabilized porcine cardiac tissue and myofibrils were used for single-molecule imaging of ATP turn over, X-ray diffraction, and mechanical measurements. Engineered heart tissues from human induced pluripotent stem cell cardiomyocytes were used to evaluate effects on force and contraction kinetics. ResultsIn contrast to Mavacamten, Aficamten does not structurally sequester myosin heads along the thick filament. Aficamten inhibits ATPase activity by shifting myosin heads from higher to slower ATPase state, with the emergence of a super slow biochemical nucleotide turnover state. This results in decreased force and calcium sensitivity without altering cross-bridge cycling. These contractile mechanical changes are comparable to Mavacamten. Our myofibril mechanical assay showed inhibition of force with accelerated relaxation. In EHTs, while Mavacamten and Aficamten inhibit cardiac twitch forces, Mavacamten reduces the activation kinetics while both result in faster relaxation. ConclusionsWe used a combination of biochemical and biomechanical assays to show that Aficamten inhibits myosin ATPase without appreciably altering myosin structure. This is different from Mavacamten that strongly affects both. While both compounds inhibit contractility, differences in mechanisms of action and kinetics of force activation and relaxation could allow use in different patient populations.

biophysics↗

bioRxiv2025

Protenix - Advancing Structure Prediction Through a Comprehensive AlphaFold3 Reproduction

In this technical report, we present Protenix, a comprehensive reproduction of AlphaFold3 (AF3), aimed at advancing the field of biomolecular structure prediction. Protenix tackles the challenges of predicting complex interactions involving proteins, ligands, and nucleic acids, while enhancing accessibility and reproducibility. Across diverse benchmarks, including PoseBusters V2, low-homology PDB sets, and CASP15 RNA, Protenix achieves state-of-the-art performance in protein-ligand, protein-protein, and protein-nucleic acid predictions. We also address limitations, such as potential memorization effects, and outline future directions for improvement. By open-sourcing Protenix, we aim to democratize advanced structure prediction tools and accelerate interdisciplinary research in computational biology and drug discovery.

bioinformatics↗

bioRxiv2020

DreamAI: algorithm for the imputation of proteomics data

Deep proteomics profiling using labeled LC-MS/MS experiments has been proven to be powerful to study complex diseases. However, due to the dynamic nature of the discovery mass spectrometry, the generated data contain a substantial fraction of missing values. This poses great challenges for data analyses, as many tools, especially those for high dimensional data, cannot deal with missing values directly. To address this problem, the NCI-CPTAC Proteogenomics DREAM Challenge was carried out to develop effective imputation algorithms for labeled LC-MS/MS proteomics data through crowd learning. The final resulting algorithm, DreamAI, is based on an ensemble of six different imputation methods. The imputation accuracy of DreamAI, as measured by Pearson correlation, is about 15%-50% greater than existing tools among less abundant proteins, which are more vulnerable to be missed in proteomics data sets. This new tool notably enhances data analysis capabilities in proteomics research.

bioinformatics↗