Studying the Protein Thermostabilities and Folding Rates by the Interaction Energy Network in Solvent DOI
Jun Liao,

Mincong Wu,

F. Meng

et al.

Journal of Computational Chemistry, Journal Year: 2025, Volume and Issue: 46(11)

Published: April 18, 2025

ABSTRACT Residue interaction networks determine various characteristics of proteins, such as the folding rate, thermostability, and allosteric process. The interactions between residues can be described by distances or energies. former is simple but less rigorous. latter complicated more precise, especially when considering solvent effect. In this work, we apply an existing energy decomposition method based on Poisson–Boltzmann equation solver. calculation accelerated GPU for higher performance. four formal applications, constructed (IE) network shows good results. First, it found that protein rate has a stronger correlation with energy‐based contact order than distance‐based order. Pearson coefficient (PCC) 0.839 versus 0.784 dataset non‐two‐state proteins. Second, find most thermophilic proteins have lower IEs mesophilic IE in acts indicator to evaluate thermostabilities Third, use predict key formation insulin dimer. Most are agreement findings previous alanine‐scanning experiments. Lastly, propose novel (called APFN) pathway network. gives same CheY nuclear magnetic resonance spectroscopy On whole, been demonstrated reliable describing embedded structures.

Language: Английский

Identification and Understanding of Allostery Hotspots in Proteins: Integration of Deep Mutational Scanning and Multi-faceted Computational Analyses DOI
Qiang Cui

Journal of Molecular Biology, Journal Year: 2025, Volume and Issue: unknown, P. 168998 - 168998

Published: Feb. 1, 2025

Language: Английский

Citations

0
The Evolving Landscape of Protein Allostery: From Computational and Experimental Perspectives DOI

E. Srinivasan,

Grigor Arakelov, Nikolay V. Dokholyan

et al.

Journal of Molecular Biology, Journal Year: 2025, Volume and Issue: unknown, P. 169060 - 169060

Published: March 1, 2025

Language: Английский

Citations

0
Enzyme Enhancement Through Computational Stability Design Targeting NMR-Determined Catalytic Hotspots DOI
Luis I. Gutierrez-Rus, Eva Vos, David Pantoja‐Uceda

et al.

Journal of the American Chemical Society, Journal Year: 2025, Volume and Issue: unknown

Published: March 19, 2025

Enzymes are the quintessential green catalysts, but realizing their full potential for biotechnology typically requires improvement of biomolecular properties. Catalysis enhancement, however, is often accompanied by impaired stability. Here, we show how interplay between activity and stability in enzyme optimization can be efficiently addressed coupling two recently proposed methodologies guiding directed evolution. We first identify catalytic hotspots from chemical shift perturbations induced transition-state-analogue binding then use computational/phylogenetic design (FuncLib) to predict stabilizing combinations mutations at sets such hotspots. test this approach on a previously designed de novo Kemp eliminase, which already highly optimized terms both Most tested variants displayed substantially increased denaturation temperatures purification yields. Notably, our most efficient engineered variant shows ∼3-fold enhancement (kcat ∼ 1700 s-1, kcat/KM 4.3 × 105 M-1 s-1) an heavily starting variant, resulting proficient proton-abstraction eliminase date, with efficiency par naturally occurring enzymes. Molecular simulations pinpoint origin as being due progressive elimination catalytically inefficient substrate conformation that present original design. Remarkably, interaction network analysis identifies significant fraction hotspots, thus providing computational tool useful even natural-enzyme engineering. Overall, work showcases power dynamically guided engineering principle obtaining novel biocatalysts tailored physicochemical properties, toward anthropogenic reactions.

Language: Английский

Citations

0
Studying the Protein Thermostabilities and Folding Rates by the Interaction Energy Network in Solvent DOI
Jun Liao,

Mincong Wu,

F. Meng

et al.

Journal of Computational Chemistry, Journal Year: 2025, Volume and Issue: 46(11)

Published: April 18, 2025

ABSTRACT Residue interaction networks determine various characteristics of proteins, such as the folding rate, thermostability, and allosteric process. The interactions between residues can be described by distances or energies. former is simple but less rigorous. latter complicated more precise, especially when considering solvent effect. In this work, we apply an existing energy decomposition method based on Poisson–Boltzmann equation solver. calculation accelerated GPU for higher performance. four formal applications, constructed (IE) network shows good results. First, it found that protein rate has a stronger correlation with energy‐based contact order than distance‐based order. Pearson coefficient (PCC) 0.839 versus 0.784 dataset non‐two‐state proteins. Second, find most thermophilic proteins have lower IEs mesophilic IE in acts indicator to evaluate thermostabilities Third, use predict key formation insulin dimer. Most are agreement findings previous alanine‐scanning experiments. Lastly, propose novel (called APFN) pathway network. gives same CheY nuclear magnetic resonance spectroscopy On whole, been demonstrated reliable describing embedded structures.

Language: Английский

Citations

0