A sequence-derived thermodynamic framework is introduced that maps proteins according to the relative contributions of backbone conformational entropy and solvent entropy to elastic recoil, suggesting that elastic function is far more widespread than currently recognized.
Rhea P. Desai, D. Pople, Shreyas Jain et al.· bioRxiv· 0 citations
In male mice with established two-hit HFpEF, autotaxin inhibition improved diastolic indices and reduced fibrosis, hypertrophy and cardiac myeloid accumulation and the autotaxin–LPA axis is nominated as a tractable therapeutic target.
Rajesh Chaudhary, Andrew Robbins, A. Singh et al.· bioRxiv· 0 citations
Background/Objectives: The rapid emergence of SARS-CoV-2 variants that evade neutralizing antibodies underscores the need for new antiviral biologics that integrate precise molecular design with scalable, cost-effective manufacturing. Computationally designed miniproteins targeting the spike protein’s receptor-binding...
Jinhwan Shin, Eu-min Kim, Jun-Hong Jang et al.· Pharmaceutics· 0 citations
This preprint introduces the molecular and biochemical extension of The Bell TowerArchitecture. By taking the asymptotic limit (ε → 0) of the discrete lattice Z^3, we define acontinuous rigid field Φ(z) governed by Weierstrass infinite products and Mittag-Leffler expansions. This geometric framework replaces traditiona...
Silvio Gabbianelli· Zenodo (CERN European Organi...· 0 citations
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This paper introduces an innovative framework for analyzing and optimizing fractal networks, leveraging the concept of 'Symmetry Metric' to guide network design. We propose a novel method for quantifying network structure using a mathematical definition of self-similarity and employing this metric to optimize network p...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Temporal Topology Optimization (TTO) presents a novel approach to biological system optimization, moving beyond static parameterization to model the dynamic evolution of topological states. This paper introduces a system leveraging network theory and evolutionary dynamics to predict emergent behaviors within biological...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
The findings reveal efflux pump-specific and strain-specific adaptations to antibiotic stress, positioning CraA as a major determinant of chloramphenicol resistance, while RND transporters such as AdeABC and AdeIJK may also be associated with broader stress adaptation processes, including redox and metal homeostasis, i...
W. Foong, Xin-Xin Xiang, Wen-Jun He et al.· mSystems· 1 citation
This paper investigates the algorithmic complexity of parallel simulations of complex systems, aiming to develop a novel algorithm that significantly reduces computational costs while maintaining accuracy. Complex systems, such as protein folding and climate modeling, demand substantial computational resources. Existin...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Protein folding is a fundamental biological process, and achieving accurate and stable folding is critical for protein function. Current methods often struggle to preserve the intricate network of interactions within the protein structure, leading to misfolded proteins and disease. This paper introduces a novel Dynamic...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
The platform as an analytically characterized, peptide-level EBV serology tool for translational research is supported, indicating that platform-level seropositivity reflects EBV exposure rather than an obvious MS-specific signal.
Milene Peterson, J. Hunter, Z. Gatalica et al.· Frontiers in Immunology· 0 citations
It is of utmost importance to investigate the effect of carbon-based nanostructures on plants in order to fully unlock their potential for enhancing productivity and stress tolerance. In this study, a comprehensive assessment of the effect of pre-sowing treatment of wheat seeds with fullerenol (PHF [C60(OH)24], 0.1 mg/...
A. N. Deryabin, Kseniya Zhukova, Nataliya Naraikina et al.· Plants· 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.