Cell-derived extracellular vesicles (EVs) are promising nanocarriers for therapeutic delivery platforms owing to their biocompatibility and capacity to protect and efficiently transport bioactive molecules. However, EV-based therapeutics remain constrained by inefficient cargo loading and low production yields, which limit scalable biomanufacturing. To overcome these limitations, we exploited the use of the mechanosensitive ion channel Piezo1 as a robust regulator of EV biogenesis using HEK293FT cells co-transfected with Piezo1 and the bioluminescent EV reporter PalmReNL. Activation of Piezo1 with Yoda1 (30 µM) increased PalmReNL-EV release by 3-fold, while GsMTx4 inhibited EV release by 80.7%. This effect was unaffected by removal of extracellular Ca2+ but was suppressed by intracellular Ca2+ chelation with BAPTA-AM, indicating a reliance on intracellular Ca2+ mobilisation. Small EVs (sEVs) from Piezo1-activated cells were purified by anion exchange chromatography and analysed by proteomics, identifying 48 proteins exclusively in Piezo1-induced sEVs preparations among 148 total detected, including cytoskeletal and stress-related factors, while preserving enrichment of extracellular matrix (ECM) structural components prominent in both conditions. Yoda1 treatment increased the release of both large EVs (lEVs) and sEVs, with a particularly pronounced increase in sEV production. As a proof of concept for therapeutic cargo delivery, Yoda1 stimulation increased the incorporation of exogenously expressed interleukin-10 (IL-10) into sEVs by up to 4-fold, and the bioactivity of sEV-associated IL-10 was validated using IL-10-CyCLoPs reporter cells. However, exposing Piezo1-overexpressing cells to 30 µM Yoda1 markedly delayed cell adhesion and spreading, indicating that excessive Piezo1 activation may constrain sustained production of therapeutic sEVs. Collectively, these results identify mechanotransduction as a key regulator of sEV biogenesis and underscore the need for precise temporal control, potentially achievable through ultrasound-based modulation, for the rational engineering of next-generation sEV therapeutics.
Najla A. Saleh, Rozita Shafiq, Amanda P. Cicerone et al.· Journal of Extracellular Bio...· 0 citations
Resonance-Based Probability Distribution Modeling presents a novel probabilistic modeling framework predicated on the principles of resonant frequencies and vibrational modes within complex systems. This approach aims to enhance predictive accuracy across diverse domains, including protein folding, fluid dynamics, and other systems exhibiting dynamic behavior. The core mechanism involves constructing a complex, multi-dimensional resonance function to represent system stability and predict outcomes, offering a departure from conventional statistical approaches. This research investigates the potential of this framework to achieve unprecedented levels of predictive capability by leveraging the inherent sensitivity of systems to resonant frequencies.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to protein structure prediction leveraging the principles of quantum entanglement. Traditional protein structure prediction methods are often limited by the computational complexity of simulating large biomolecular systems. We propose a framework that utilizes quantum entanglement to model the complex correlations inherent in protein folding, potentially overcoming these limitations. The core idea involves translating the amino acid sequence of a protein into a quantum state and employing quantum computation, specifically entanglement-based algorithms, to predict the protein's three-dimensional structure. The theoretical framework outlines the transformation process, the quantum algorithm design, and the methods for interpreting the results. We explore the potential advantages of this approach, focusing on its ability to capture long-range interactions and conformational flexibility that are difficult to model accurately with classical methods. The ultimate goal is to establish a new paradigm for protein structure prediction, offering improved accuracy and efficiency.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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This paper explores the application of principles from quantum biology to design novel bioinformatics algorithms. Traditional bioinformatics relies heavily on classical computational models, often neglecting the potential influence of quantum effects observed within biological systems. This research proposes a framework that leverages these quantum phenomena, specifically focusing on quantum tunneling, superposition, and entanglement, to address complex challenges in biological data analysis. The core mechanism involves simulating quantum effects during processes like protein folding and DNA sequence recognition, and utilizing quantum algorithms – such as Grover's algorithm and quantum annealing – to optimize sequence data analysis. We present a conceptual approach to formulating biological problems as quantum computing problems, offering a fundamentally new perspective for bioinformatics. The potential impact of this approach lies in developing more efficient and accurate algorithms for tasks including genomic sequence alignment, protein structure prediction, and drug discovery. This work aims to bridge the gap between quantum mechanics and biology, paving the way for a new era of bioinformatics driven by quantum principles. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Pharmacogene missense variants can disrupt protein stability, catalytic competence, or substrate handling through distinct mechanisms. General-purpose predictors estimate clinical pathogenicity as a single scalar, whereas pharmacogene interpretation requires knowing which biochemical dimension a variant perturbs, since that determines whether reduced function is substrate-dependent. Five deep mutational scanning datasets comprising 26,198 missense variants across CYP2C9, CYP2C19, and NUDT15 were assembled from MaveDB. Paired assays showed that this dimensionality dominates the data: 28% of CYP2C9 variants (1,236 of 4,421) decoupled catalytic activity from abundance, and 48% of NUDT15 variants (1,364 of 2,844) decoupled thiopurine sensitivity from stability, with CYP2C9 discordance concentrating at substrate-channel residues. AlphaMissense, a representative general-purpose pathogenicity predictor, scored these classes in line with its clinical training objective rather than the assayed biochemistry, assigning likely-benign scores to 38 of 195 stable-but-dead CYP2C9 variants and likely-pathogenic scores to 140 of 222 destabilized but thiopurine-resistant NUDT15 variants. To test whether this dimensionality is recoverable, a supervised ESM-2 sequence baseline was benchmarked against the ESM1v zero-shot ensemble and AlphaMissense under position-based 5-fold cross-validation, together with three architectural extensions: AlphaFold structural features, multi-task learning across paired assays, and contact-graph neural networks. The baseline reached Pearson r of 0.54-0.72, matching or marginally exceeding both comparators, and no extension improved upon it. Trained directly on each assay, it nonetheless recovered the paired-assay difference at r = 0.28 for CYP2C9 and 0.43 for NUDT15, separating discordant variants at AUROC 0.60 and 0.51. Pharmacogene interpretation therefore requires assay-specific, substrate-aware functional measurements rather than a single generic score.
Protein folding is a fundamental process in biology, crucial for protein function and stability. Traditional methods often rely on rigid, predefined folding rules, limiting flexibility and efficiency. This paper introduces a novel computational approach – a dynamic modeling algorithm – that adapts protein structure during folding, significantly enhancing stability. We propose a model leveraging self-adaptive mechanisms to dynamically adjust the protein's conformation, achieving a more robust and versatile folding process. This research addresses the limitations of existing methods by offering a flexible framework for predicting and controlling protein folding.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the application of non-standard geometry to computational geometry, focusing on the development of a novel method for defining and computing geometric properties for complex systems, particularly fluid dynamics and protein folding. Traditional geometric computation often struggles with the inherent complexity and self-organization of these systems, necessitating the creation of intricate geometric constructs. We propose a 'geometric language' – a system of rules and symbolic representations – that enables efficient manipulation and analysis of these complex shapes. The core mechanism involves establishing a hierarchical structure within this language, allowing for the generation of novel geometric configurations through a combination of geometric transformations and parametric modeling. This approach aims to overcome limitations in current computational geometry by offering a framework for tackling problems that are currently computationally intractable. The paper will detail the conceptualization of this language, its implementation through a set of rules and algorithms, and initial explorations into its potential for solving specific problems within fluid dynamics and protein folding. Finally, we present preliminary results demonstrating the feasibility of this approach, highlighting its potential for advancing the field of computational geometry.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the application of principles from quantum biology to design novel bioinformatics algorithms. Traditional bioinformatics relies heavily on classical computational models, often neglecting the potential influence of quantum effects observed within biological systems. This research proposes a framework that leverages these quantum phenomena, specifically focusing on quantum tunneling, superposition, and entanglement, to address complex challenges in biological data analysis. The core mechanism involves simulating quantum effects during processes like protein folding and DNA sequence recognition, and utilizing quantum algorithms – such as Grover's algorithm and quantum annealing – to optimize sequence data analysis. We present a conceptual approach to formulating biological problems as quantum computing problems, offering a fundamentally new perspective for bioinformatics. The potential impact of this approach lies in developing more efficient and accurate algorithms for tasks including genomic sequence alignment, protein structure prediction, and drug discovery. This work aims to bridge the gap between quantum mechanics and biology, paving the way for a new era of bioinformatics driven by quantum principles. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Deterministic Topological Neutralization of the Aβ42 N-Terminal Stacking Interface by a D-Enantiomer RDK Cap This repository contains a theoretical computational proof of topological interface neutralization on a custom-physics protein-fold ledger. By utilizing a deterministic, non-Cartesian discrete topology (Axiom I-X), the engine successfully isolated the Aβ42 N-terminal stacking basin and deterministically generated a D-enantiomer RDK cap that occupies the previously unfulfilled negative space, neutralizing the interface. The package includes the full Markdown manuscript, validation scripts, and thermodynamic logs from the 100,000-tick terminal co-fold. Author's Note: I am Robert J. Weber (RJW). The insights driving this computational proof stem directly from my independent interpretation of physics and the mechanics of the universe. I never dreamed of building a system that resolves protein folding, but the custom physics framework I developed has achieved exactly that. It works, and it works well. Am I absolutely sure this translates perfectly to the wet lab? Not yet—but the math is solid, the physics are grounded, and it represents the universe exactly as I see it. Some will scoff. Others will wonder: What if he is right? This work is deeply personal. My Uncle Pete died trapped in his own mind and body. I am publishing this for him. To anyone reading this—especially those in pharmaceutical research—who can take this theoretical blueprint and develop it into a working clinical cure to spare others that same fate: I am here, and I am willing to help. My compensation to see this realized will be minimal indeed. Mankind needs this to reduce the suffering, to stop the tears, and to keep the Uncle Petes everywhere smiling alongside the people who love them. God be with you today and all the tomorrows you are granted — RJW
Robert Weber· Zenodo (CERN European Organi...· 0 citations
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.