protein folding

Thermodynamic Optimisation of Protein Bioavailability via Mechanical Globule-to-Filament Restructuring and Saccharomyces spp. Matrix Carrier: In Vivo Validation Under Extreme Alimentary Deprivation (Patent UA 141234

Can the fundamental thermodynamic constraints of digestion and metabolic senescence be bypassed through pre-emptive molecular engineering? Under acute physiological exhaustion and severe nutrient starvation, standard enzymatic digestion collapses because breaking down tightly folded native globular proteins demands substantial metabolic and thermodynamic activation energy that compromised organisms simply do not possess. This empirical study presents the in vivo validation of BiomEnforcer® (Patent UA 141234) — a breakthrough bio-complex combining mechanically restructured filamentous protein matrices (MSPM) and a functional Saccharomyces spp. biological carrier. By uncoiling protein globules into linear filaments prior to ingestion, the substrate eliminates steric barriers, bypassing conventional digestive bottlenecks and acting as an immediate, low-activation-energy thermodynamic shunt directly into cellular anabolism. Key Empirical Findings: Ultra-Low Dose Bio-Regulation: Micro-dose supplementation at just 0.1% (1.0 kg/t of feed) under acute dietary protein deprivation (11.43% crude protein; 23–28% below standard requirements) and elevated chemical/toxic stress. Reversing Biological Senescence: Reactivated intensive oviposition in geriatric flocks aged 150–175 weeks (surging from near-zero baseline to sustained peaks of 80–100%) alongside active medullary bone calcium mobilisation. Systemic Somatic Anabolism: Reversed severe somatic wasting, driving significant body mass gains of +14.0% to +14.56% across stressed cohorts. The convergence of bottom-up substrate structuring (MSPM) and top-down physiological signaling (NASP) provides concrete proof-of-concept for non-invasive metabolic rescue and adaptive capacity restoration. Corporate Scientific Research & Technology Framework: https://pmtstructure.com https://omaridin.com

Title: Adaptive Quantum Simulation of Biological Systems

The simulation of complex biological systems, such as protein folding and gene regulation, presents significant challenges due to the inherent complexity and often intractable nature of these systems. Traditional computational methods struggle to capture the nuanced dynamics of biological processes, limiting our ability to understand and potentially manipulate them. This research proposes an innovative approach – adaptive quantum simulation – that leverages the principles of quantum mechanics to create dynamic, self-adjusting simulations of biological systems. We aim to develop an algorithm that continuously refines simulation parameters, automatically mimicking biological behavior to achieve unprecedented accuracy and fidelity. This work explores the potential of quantum computation to overcome limitations inherent in classical simulation techniques, offering a fundamentally new pathway for biological system modeling and analysis. This includes a detailed explanation of the algorithm's core mechanisms, potential applications, and preliminary results demonstrating its adaptability. The core claim is that this adaptive quantum simulation method will allow for a level of detail and accuracy previously unattainable through conventional computational methods.

Three-Dimensional Structural Characterization and Spatial Conformational Ensemble Analysis of the Ultra-Large Multivalent Fusion Protein Construct KH-002v003 (2,091 Amino Acid Residues)

Engineering extended macromolecular therapeutics requires comprehensive structural modeling to verify tertiary folding fidelity and domain accessibility across repetitive structural units. In this study, we present the structural characterization of KH-002v003, an ultra-large synthetic multivalent fusion protein construct expanding to 2,091 amino acid residues. Building upon earlier design iterations—including the 701 aa baseline framework and the 1,354–1,455 aa KH-002v002 architecture—this maximized construct integrates multiple variable heavy-chain nanobody (VHH) domains, tumor microenvironment-cleavable matrix metalloproteinase (MMP-2/9) linkers, pH-low insertion peptides (pHLIP), and C-terminal XTEN solubilization polymers. Structural predictions were executed via high-throughput homology modeling on SWISS-MODEL utilizing a 58-template ensemble superposition. Model 15, constructed against the Cryo-EM structure of the bispecific Fab-heavy chain complex (PDB ID: 8WGW.1.B, sequence identity 60.71%), yielded a peak global QMEANDisCo score of 0.58 ± 0.07. Superposition analysis revealed a dense, rigid central core dominated by antiparallel β-sheet frameworks flanked by dynamic, highly flexible loop regions. Stereochemical validation via MolProbity confirmed 92.16% of residues within favored Ramachandran regions. These findings confirm that ultra-large constructs exceeding 2,000 residues can maintain structural integrity and spatial independence for target engagement.

Geometric Complexity of Biological Systems

This paper explores the potential of utilizing geometric analysis as a novel approach to understanding the complexity of biological systems. Biological systems, particularly protein folding and gene regulation, exhibit intricate geometric structures. This research proposes a framework to quantify and analyze these structures by employing topological concepts, establishing a bridge between mathematics and biological research. The core claim is to develop a method for comprehensively assessing the complexity of biological systems through the lens of geometric analysis, offering a fundamentally new perspective on the study of these systems. This work will examine the application of geometric topology, specifically measures of connectivity, curvature, and torsion, to reveal underlying patterns and quantify complexity. The goal is to move beyond traditional statistical methods and offer a more insightful, quantitative understanding of biological systems.

Enhancing yeast folding capacity by genome editing unlocks quantitative and qualitative improvements in antibody surface display

Yeast surface display is a widely used platform for antibody affinity maturation; however, constraints in the yeast folding and disulfide bond formation machinery can limit correct antibody expression and bias selection outcomes, favoring variants that satisfy display constraints. This limitation might be of particular relevance when selections are based on biophysical features beyond affinity, such as aggregation, polyreactivity, or thermal stability. To overcome these constraints, we engineered the yeast display system by overexpressing key folding chaperones, yeast BiP and human protein disulfide isomerase (PDI), either through co-expression from the antibody display plasmid or via genomic editing, individually and in combination. As a proof of concept, surface display of adalimumab was significantly increased upon chaperone co-expression, with the highest improvement observed in strains with genomic integration of PDI, yielding a 2.5-fold increase in display levels. These findings were consistently reproduced across four additional antibodies using three edited strains expressing BiP, PDI, or both. To assess folding quality directly at the cell surface, we implemented two novel complementary staining strategies: with maleimide-Pacific Blue to detect unpaired thiols as a result of incomplete disulfide bond formation, and with Bis-ANS to quantify exposed hydrophobic regions. Both assays revealed a substancial reduction in free thiols and surface hydrophobicity (as only the proper hydrophobic residues are exposed) in the edited strains, consistent improved disulfide bond formation and overall folding quality relative to the parental strain. Accordingly, the PDI-edited yeast strain showed the best overall performance, improving both display quantity and quality across a panel of ten therapeutic antibodies. Enhanced display translated into improved antigen binding without altering the polyreactivity profiles of several candidates, therefore retaining native biophysical characteristics. The enhancement of the yeast folding machinery, particularly through genomic integration of PDI, substantially improves both the quantity and quality of antibody surface display. This optimized yeast display platform enables more faithful translation of antibody biophysical features, supporting its application in antibody workflows, including selections based on biophysical properties.

Hydrophobic Interaction as the Result of Interfacial Frustration

The hydrophobic effect is one of the most consequential organizing principles in molecular biology, laying the foundation for explanation of concepts like protein folding, molecular recognition, membrane self-assembly and allosteric communication. Its conceptual description remains dependent primarily on a framework established in the mid-twentieth century, as per which, hydrophobic association is driven primarily by the entropic release of water molecules ordered around non-polar surfaces. The framework, while thermodynamically correct, is incomplete in some ways that have become increasingly consequential as structural, dynamic and calorimetric data have been accumulated over the years. In this paper, I propose a reframing of the hydrophobic effect in terms of interfacial frustration- a continuous, geometry sensitive incompatibility between the hydrogen bond requirements of liquid water and non-polar surfaces it is forced to accommodate. This paper argues that hydrophobic association represents the resolution of this frustration, rather than the mere expulsion of constrained solvent. This reframing provides a unified mechanistic account of phenomena that the classical picture cannot explain alone, or treats as separate problems: the geometry dependence of hydrophobic association strength, the qualitative distinction between small-solute and extended-surface hydrophobicity, the thermodynamic crossover with temperature and the propagation of allosteric signals through hydrophobic protein cores. This paper also proposes that hydrophobic cores in proteins are not passive burial sites, but active and dynamic modulators of a distributed frustration landscape. Allosteric communication through these cores operates through cooperative frustration redistribution, a mechanism distinct from classical strain propagation, but consistent with the growing body of evidence that allostery frequently proceeds through changes in protein dynamics rather than average structure. This framework generates specific, experimentally testable predictions that distinguish it from both the classical entropic model and existing density functional theories of hydrophobicity

Geometric Complexity of Biological Systems

This paper explores the potential of utilizing geometric analysis as a novel approach to understanding the complexity of biological systems. Biological systems, particularly protein folding and gene regulation, exhibit intricate geometric structures. This research proposes a framework to quantify and analyze these structures by employing topological concepts, establishing a bridge between mathematics and biological research. The core claim is to develop a method for comprehensively assessing the complexity of biological systems through the lens of geometric analysis, offering a fundamentally new perspective on the study of these systems. This work will examine the application of geometric topology, specifically measures of connectivity, curvature, and torsion, to reveal underlying patterns and quantify complexity. The goal is to move beyond traditional statistical methods and offer a more insightful, quantitative understanding of biological systems.

Title: Algorithmic Quantum Simulation of Complex Dynamical Systems

Quantum simulation holds immense promise for understanding and manipulating complex dynamical systems – phenomena ranging from fluid dynamics and climate modeling to protein folding and the behavior of complex chemical reactions. However, current simulation techniques face significant limitations, particularly when dealing with high-dimensional systems. This paper introduces an algorithmic quantum simulation framework, centered on a novel 'Quantum Monte Carlo' algorithm leveraging quantum entanglement to accelerate the solution of these systems, offering a fundamentally new approach to complex dynamics analysis. The core claim is to create a class of quantum algorithms designed to efficiently simulate and analyze complex dynamical systems, with a particular focus on uncovering critical patterns and dynamics that are difficult to discern with classical methods. This work explores the potential of entanglement as a key mechanism for accelerating the simulation process and provides a foundational outline for a new generation of quantum algorithms tailored for these challenging problems.

Title: Bayesian Geometric Chaos with Adaptive Constraint Propagation

Bayesian Geometric Chaos with Adaptive Constraint Propagation represents a novel approach to modeling complex systems, particularly those exhibiting intricate dynamics and high-dimensional parameter spaces. This paper explores the integration of a variational Bayesian framework, incorporating adaptive constraint propagation, to dynamically adjust model parameters and enhance prediction accuracy. Traditional Bayesian methods often fall short in these scenarios, struggling to effectively handle non-linearity and uncertainty. Our work proposes a fundamentally adaptive self-optimizing method, moving beyond static inference to a process where the model parameters are continually refined through a learned "chaos" function, guided by observed data. This leads to improved prediction capabilities across a range of applications, including fluid dynamics and protein folding simulations. The core mechanism leverages a variational Bayesian approach, utilizing observed data to update the model's parameters, and adaptive constraint propagation, which adjusts constraint parameters to guide the learning process. We demonstrate the efficacy of this framework through a series of simulations and analysis, highlighting its potential for addressing limitations of existing Bayesian methods.

Integrated computational analysis prioritizes candidate targets and pathways linking ochratoxin A exposure to hepatocellular carcinoma

Ochratoxin A (OTA), a food-borne mycotoxin, has been implicated in hepatotoxicity and potential carcinogenic processes, yet the molecular links between OTA exposure and hepatocellular carcinoma (HCC) remain incompletely understood. This study used an integrated computational workflow to prioritize candidate targets and pathways potentially linking OTA exposure with HCC. OTA-related and HCC-related targets were collected from public databases, intersected, and subjected to functional enrichment analysis. Transcriptomic data from the GSE36376 discovery dataset were analyzed to identify differentially expressed genes, followed by LASSO and SVM-RFE feature selection, immune-cell deconvolution, molecular docking, and molecular dynamics simulation. A total of 214 overlapping OTA-HCC-associated targets were identified and were enriched in pathways related to signal transduction, apoptosis, metabolism, and immune regulation. In GSE36376, 443 differentially expressed genes were identified using p < 0.05 and |log2 fold change| > 1, and overlap analysis yielded 13 shared target genes. Five candidate targets, CYP3A4, KIFC1, AKR1C3, CA2, and TTR, were further prioritized. KIFC1 and AKR1C3 were upregulated in HCC samples, whereas CYP3A4, CA2, and TTR were downregulated. These genes showed apparent discriminatory ability within the discovery dataset, with AUC values ranging from 0.866 to 0.958. Molecular docking predicted favorable OTA-target interactions, with docking energies ranging from −7.4 to −10.8 kcal/mol. CYP3A4 showed the lowest predicted docking energy (−10.8 kcal/mol) and was further evaluated by molecular dynamics simulation, with a protein-fitted OTA RMSD of 1.435 ± 0.097 nm and complex Rg of 2.308 ± 0.010 nm during the equilibrated 20–100 ns trajectory. Overall, this study provides a reproducible hypothesis-generating framework for exploring potential metabolic, genomic-instability-related, and immune-microenvironment links between OTA exposure and HCC. Future validation in independent datasets and experimental models will be important to further assess the biological relevance of these candidate targets and pathways.

Engineering inflammation-responsive proteins through nitric oxide-caged amino acids.

Accurate and efficient prediction of protein conformations with ProtMonomer

Deep learning-based protein structure prediction methods that leverage evolutionary information from multiple sequence alignments (MSAs), exemplified by AlphaFold2, have achieved remarkable accuracy. However, existing methods still struggle to predict challenging proteins, particularly those with novel folds or limited evolutionary information, and to recover alternative conformational states. Here we show that structure prediction models trained under different MSA-depth distributions corresponding to different levels of evolutionary information exhibit complementary generalization behaviors, and that a model trained on a mixture of these distributions can combine their complementary generalization strengths. Building on this insight, we developed ProtMonomer, a deep learning framework trained on MSA-depth distributions representing a broad range of evolutionary information levels to improve structure prediction. Across benchmarks comprising CASP15 targets, non-redundant experimentally determined structures, orphan proteins, and short peptides, ProtMonomer performed comparably to or better than leading methods, including AlphaFold2 and AlphaFold3, with particularly strong performance on challenging targets. For fold-switching proteins, ProtMonomer also recovered alternative conformational states more accurately than AlphaFold2 and AlphaFold3 across diverse homologous sequence sampling strategies. In addition to improving predictive accuracy, ProtMonomer substantially reduced inference cost through an efficient architecture, enabling high-throughput applications. Together, these findings provide insights into the generalization of evolution-informed structure prediction models and support ProtMonomer as an accurate and efficient framework for protein structure prediction.