bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2023.07.24.550267

Unraveling the Potential of Epicatechin Gallate in Targeting Aberrant Cardiac Ca2+ Signaling Protein

KS, J.·Jeeva, P.·Muthusamy, A.
View original source ↗Export metadata ↓

Abstract

The cardiovascular sarcoplasmic reticulum (SR) calcium (Ca2+) ATPase is an imperative determinant of cardiac functionality. In addition, anomalies in Ca2+ handling protein and atypical energy metabolism are inherent in heart failure (HF). Moreover, Ca2+ overload in SR leads to mitochondrial matrix Ca2+ overload, which can trigger the generation of Reactive Oxygen Species (ROS), culminating in the triggering of the Permeability Transition Pore (PTP) and Cytochrome C release, resulting in apoptosis that leads to arrhythmias and numerous disorders. Although proteins involved in the molecular mechanism of Ca2+ dysfunction regarding mitochondrial dysfunction remains elusive, this study aims to assess the major Ca2+ handling proteins which may be involved in the Ca2+ malfunction that causes mitochondrial dysfunction and predicting the most effective drug by targeting the analyzed Ca2+ handling proteins through various insilico analyses.Thirteen proteins absorbed from interaction analysis were docked with four optimal phytochemicals from Crataegus oxyacantha (COC) to identify the potential agonist/antagonist against those anomalies causing Ca2+ handling signaling proteins. Furthermore,The ADMET profile of tyramine, vitexin, epicatechin, and epicatechin gallate was acclimated to evaluate potential drugability utilizing QikProp by Schrodinger.Keeping this in view, critical molecular docking evaluations were performed using Glide (Maestro), autodock, and autodock vina.Based on the results of 156 dockings by Maestro, auto-dock, and auto-dock vina, PKA C with epicatechin gallate exhibits good interaction. Therefore, a 2000ns molecular dynamics (MD) simulation was utilized to assess the feasible phytochemical epicatechin gallate - PKA C complex binding stability utilizing Desmond. As a result, the molecular dynamics simulation study confirmed that epicatechin gallate from COC has high possibilities to inhibit the aberrant cardiac Ca2+ signaling proteins due to its conformational rigidity.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

KS, J., Jeeva, P., Muthusamy, A.. 2023-07-25. Unraveling the Potential of Epicatechin Gallate in Targeting Aberrant Cardiac Ca2+ Signaling Protein. https://doi.org/10.1101/2023.07.24.550267

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Inferring cascade drivers of VEXAS syndrome by a causal machine learning tool CauNagi

VEXAS syndrome is an adult-onset severe autoinflammatory disease caused by somatic mutations in UBA1, yet the cascade mechanisms linking primitive hematopoietic abnormalities to mature myeloid dysfunctions remain largely unknown. Identifying master regulators of a progressive disease, a black-box process, from complex transcriptomic data also remains challenging. To address this challenge, we developed CauNagi, a computational framework for prioritizing cascade candidate regulators (CCRs). CauNagi integrates a causal representation learning module derived from CausCell with an iterative deep learning backbone adapted from UNAGI; in addition, CauNagi extends these two components with a unique downstream module for CCRs analysis designed to characterize regulatory propagation across hierarchical cellular states. Mechanistically, CauNagi iteratively integrates causal disentangled representation learning with (1) disease-stage cell-state trajectory reconstruction and (2) dynamic regulatory analysis. Benchmarking on single-cell transcriptomic datasets showed that CauNagi preserved cell-type structure in idiopathic pulmonary fibrosis (IPF) and enriched known acute myeloid leukemia(AML)-associated genes among its top-ranked global regulators. When applied to VEXAS syndrome, CauNagi readily revealed inflammatory responses, endoplasmic reticulum stress, and myeloid bias, consistent with the disease features. Furthermore, the CCRs analysis module of CauNagi assisted us in identifying 36 causal drivers, with SPI1, NFKB1, STAT3, and FOS prioritized as high-confidence regulatory hubs linking aberrant myeloid differentiation and inflammatory programs. These findings were further supported by an independent single-cell transcriptomic dataset from a murine VEXAS model. Overall, CauNagi provides a computationally efficient and systematic framework for identifying candidate causal regulators. Beyond hematopoietic diseases, CauNagi may also be applicable to other progressive disorders for which multistage single-cell transcriptomic datasets are available. CauNagi is available at https://github.com/steamed-stuffed-bun/CauNagi.

bioinformatics↗
bioRxiv2026

Inferential boundaries of age prediction: why prediction does not establish biological age measurement

Chronological-age clocks reconstruct age from biological measurements, yet their outputs are interpreted as biological age, gaps as ageing acceleration and intervention-associated decreases as rejuvenation. We show that age supervision identifies an age-task statistic, not a biological-age construct, and establish how this distinction changes biomarker construction and validation. Even at the population optimum, the same observable distribution and age-prediction performance admit incompatible biological-age interpretations. Resolving this ambiguity requires assumptions or evidence beyond the age task. Squared-error age loss penalizes within-age output dispersion without defining its biological direction. Given age and background, a gap re-expresses the compressed score; exact age recovery eliminates it even when heterogeneity remains in the measurements. Shared biological covariance permits genuine prognostic value without establishing construct identity. After allogeneic haematopoietic stem-cell transplantation, recipient-blood scores showed excess donor-lineage affiliation under a score-pairing null. In NHANES, age-trained scores improved held-out five-year mortality prediction beyond age and background, yet direct modelling of source measurements and mortality supervision at matched scalar capacity yielded further gains. The intended biological object must therefore guide study design, measurement selection and representation; validation must establish the claimed measurement relation rather than rely on age-prediction success alone.

bioinformatics↗
bioRxiv2026

DisenTE: Sparse Pattern-Context Modeling for Interpretable Translation-Efficiency Matrix Completion

Partially observed object-by-context matrices arise across data-rich science, where dominant object effects can obscure smaller but informative context-dependent variation. We study this problem in a translation-efficiency atlas of 9,494 5' UTRs across 78 cellular and tissue contexts. We present DisenTE, a sequence-conditioned neural model that combines separate sequence and context branches with a sparse low-rank pattern-context channel. Each module pairs a sequence-derived activation with context-specific deployment weights, forming a dictionary whose sequence and context components can be examined separately. Under five-fold within-panel entry masking, DisenTE achieves a UTR-centered residual Spearman correlation of 0.641 +/- 0.005, compared with 0.304 +/- 0.003 for the strongest reference model. The learned dictionary retains 11 of 20 candidate modules. CTM 6 has the largest overlap with an external TOP set and a cap-proximal pyrimidine pattern; CTMs 5 and 7 also overlap the set but have purine-containing consensuses. The evidence supports CTM 6 as a TOP sequence anchor and CTMs 5 and 7 as TOP-set-associated factors. On this dataset, DisenTE improves completion over the evaluated references and provides module-level summaries of its fitted context-dependent variation.

bioinformatics↗