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439 papers
The findings indicate that multimodal phenotyping, coupled with explainable machine learning, offers a practical and biologically interpretable decision-support approach for chia genotype classification.
In vitro simulated digestion demonstrated its characteristic intestinal sustained-release behavior, which provides preliminary in vitro evidence that may favor enhanced intestinal absorption of α-linolenic acid (ALA); however, further cellular or animal trials are required to quantitatively validate its actual in vivo bioavailability improvement.
How PilC/PilY1 can be retained on the fiber tip under enormous tensile loads generated during mechanical shear and T4P retraction is explained and how diverse T4P systems employ β-strand addition to license fiber assembly is demonstrated.
A structure dynamics-based design strategy is reported that transforms OB into a selective antibacterial agent through rational modification of the nitrophenyl moiety, and establishes a 'kinetic sensor' strategy for achieving species selectivity.
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A hydrogel microsphere-based CHA method that integrates CHA signal amplification with the solid-phase carrier of hydrogel microspheres for the detection of miRNAs in blood samples with favorable application potential for the detection of miRNA biomarkers in complex biological samples with a simple, low-cost, enzyme-free mode is reported.
Suboptimal vitamin D status is highly prevalent among IBD patients in Misurata, Libya, with disease activity acting as the primary driver of vitamin D depletion regardless of patient gender, underscoring the critical need for continuous assessment and the implementation of effective supplementation strategies to improve therapeutic outcomes across all IBD cohorts.
Repeat proteins fold through pathways that are strongly shaped by local energetics, making them highly sensitive to mutations. The ankyrin repeat (AR) domain of IκBα is a cooperative folding unit in which the first four repeats (AR1–AR4) are stable, while the last two are destabilized. Despite this simple modular architecture, IκBα follows a complex folding trajectory involving high-energy intermediates. Here, we investigate how sequence variations modulate folding pathways using coarse-grained AWSEM simulations combined with the Energy Landscape Visualization Method (ELViM) and local frustration analysis. We compare the wild type (WT) with two consensus-designed variants: V93L, which accelerates folding, and L131V, which destabilizes and slows down folding of the protein in vitro. Our results show that WT and V93L share a similar folding funnel, though V93L folds more directly into the native state. In contrast, L131V reshapes the landscape by stabilizing non-native kinetic trap with minimally frustrated contacts. Reversing the L131V mutation allows the protein to reach the native conformation, whereas maintaining it confines the protein to misfolded states that can only be escaped at very high temperatures, without productive folding. These findings highlight how subtle sequence changes can tune frustration, modulate kinetic trapping, and control folding efficiency in repeat proteins.
Temporal Wave Function Collapse Dynamics explores the theoretical underpinnings of the collapse of temporal wave functions – fundamental units of information within complex systems such as neural networks and protein folding – as a dynamic process. This paper posits that collapse isn't a discrete event but rather a continuous evolution driven by a set of differential equations that capture the interplay between system state, external stimuli, and feedback loops. We propose a novel differential equation system that models this collapse, emphasizing the generation of new, potentially transformative states. This research aims to advance our understanding of complex system behavior by providing a framework for modeling this fundamental process.
Thoracic aortic aneurysms and dissections (TAAD) are life-threatening conditions linked to gain-of-function mutations in cGMP-dependent protein kinase I (PKG I), a central regulator of vascular smooth muscle signaling. Among these, substitutions at Val 234 have been associated with kinase overactivation and early-onset disease, despite this residue being distal from the active site and cGMP binding regions. Using NMR, molecular dynamics simulations, and complementary kinase and binding assays, we show that Val 234 acts as a crucial allosteric hub of PKG autoinhibition. TAAD-associated variants at Val 234 disrupt PKG regulation through two distinct yet complementary allosteric mechanisms: by biasing the kinase toward active conformations that increase sensitivity to cGMP and by decreasing the folding stability of the regulatory domain, thereby weakening inhibitory contacts independently of cGMP control. Despite these differences, both mechanisms result in excessive PKG signaling at basal and intermediate cGMP levels, while maximal activity remains unaltered. Together, these findings explain how distal mutations can unpredictably rewire kinase allostery and drive pathogenic vascular signaling.
Dynamic Symmetry Field Analysis (DSFA) is a novel technique for unraveling the intricate patterns within complex systems, specifically focusing on identifying and quantifying 'dynamic symmetry fields.' This research explores the potential of employing a new mathematical operator to analyze the evolution of these fields, revealing underlying instabilities and potential for change. The core mechanism leverages the concept of a dynamically evolving symmetry function, allowing for the detection of critical points and deviations from expected behavior. This paper details the mathematical framework, initial results, and potential implications of DSFA for a range of applications, including protein folding, fluid dynamics, and complex mechanical systems. The investigation emphasizes a shift from passive observation to active detection of dynamic structural features, offering a potentially transformative approach to understanding the behavior of such systems.
Overview A prospectively frozen benchmark suite for leakage-safe multi-omic integration. It evaluates predictive performance, modality utility, null behavior, failure handling, and compute cost without making claims of clinical utility or causal biological inference. Included datasets Synthetic controls: paired null and planted-signal families for binary classification and continuous regression. DepMap/CCLE: transcriptomics, copy number, LC-MS metabolomics, and PRMT5 dependency across 644 cell lines. TCGA BRCA: transcriptomics, copy number, and RPPA protein abundance for ductal-versus-lobular classification across 783 tumors. TCGA LGG, KIRC, and UCEC: transcriptomics and copy number for IDH status, pathological stage, and histology endpoints across 507, 507, and 500 tumors, respectively. Design and controls All real cohorts were fixed after source and endpoint eligibility checks and before method performance was observed. The design uses shared group-aware partitions, training-only preprocessing, five outer folds repeated three times for real cohorts, 40 independent synthetic replicates, ten target permutations per real cohort, 5,000 paired group bootstraps, and Holm adjustment across the five primary real-dataset contrasts. Literature-anchored controls are evaluated independently of method ranking. Failed, unfavorable, discordant, and indeterminate outcomes remain reportable. Reproducibility The archive contains the frozen protocol, immutable source registry, download and validation code, group-aware partitions, fixed comparator implementations, statistical aggregation, schemas, environment pins, and fault-injection tests. Raw molecular matrices, participant-level data, local paths, and benchmark results are excluded. Deviations Deviation 1 - Aggregation target normalization. Final aggregation converts read-only NumPy memory-mapped target vectors to base NumPy arrays before metric and bootstrap validation. This preserves all values, ordering, dtypes, datasets, endpoints, partitions, methods, predictions, thresholds, statistical procedures, and frozen randomization streams. The correction has no scientific impact on the estimand. All definitive work units are regenerated under the corrected implementation identity; no prior run outputs are reused.
Overview A prospectively frozen benchmark suite for leakage-safe multi-omic integration. It evaluates predictive performance, modality utility, null behavior, failure handling, and compute cost without making claims of clinical utility or causal biological inference. Included datasets Synthetic controls: paired null and planted-signal families for binary classification and continuous regression. DepMap/CCLE: transcriptomics, copy number, LC-MS metabolomics, and PRMT5 dependency across 644 cell lines. TCGA BRCA: transcriptomics, copy number, and RPPA protein abundance for ductal-versus-lobular classification across 783 tumors. TCGA LGG, KIRC, and UCEC: transcriptomics and copy number for IDH status, pathological stage, and histology endpoints across 507, 507, and 500 tumors, respectively. Design and controls All real cohorts were fixed after source and endpoint eligibility checks and before method performance was observed. The design uses shared group-aware partitions, training-only preprocessing, five outer folds repeated three times for real cohorts, 40 independent synthetic replicates, ten target permutations per real cohort, 5,000 paired group bootstraps, and Holm adjustment across the five primary real-dataset contrasts. Literature-anchored controls are evaluated independently of method ranking. Failed, unfavorable, discordant, and indeterminate outcomes remain reportable. Reproducibility The archive contains the frozen protocol, immutable source registry, download and validation code, group-aware partitions, fixed comparator implementations, statistical aggregation, schemas, environment pins, and fault-injection tests. Raw molecular matrices, participant-level data, local paths, and benchmark results are excluded. No deviations are registered at deposit.
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.
Scientists are using AlphaFold to strengthen a photosynthesis enzyme for resilient, heat-tolerant crops.
Explore how AlphaFold has accelerated science and fueled a global wave of biological discovery.
AlphaFold has revealed the structure of a key protein behind heart disease
