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 sub...
Volodymyr Naumenko· Zenodo (CERN European Organi...· 0 citations
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 abilit...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 constr...
Khiem Le· Zenodo (CERN European Organi...· 0 citations
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 stru...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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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.
E. Garcia-Calvo, H. Dorison, G. Mazón et al.· Microbial Cell Factories· 0 citations
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 es...
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 stru...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 dea...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 propag...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
An integrated computational workflow was used to prioritize candidate targets and pathways potentially linking OTA exposure with HCC and provides a reproducible hypothesis-generating framework for exploring potential metabolic, genomic-instability-related, and immune-microenvironment links between OTA exposure and HCC.
This work reports a protein engineering strategy that enables selective restoration of protein function in nitric oxide-rich environments and establishes nitric oxide-triggered chemical reactivation of proteins as a generalizable method for post-translational control of protein function.
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...
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.
