Reinforcing thermostability and pH robustness of exo-inulinase facilitated by ReverseTag/ReverseCatcher tagging system.

Enhancing protein stability is pivotal in the field of protein engineering. Protein self-cyclization using peptide a tagging system has emerged as an effective strategy for augmenting the thermostability of target proteins. In this study, we utilized a novel peptide tagging system, ReverseTag/ReverseCatcher, which leverages intramolecular ester bond formation. Initially, we employed GFP as a model to validate the feasibility of cyclization mediated by ReverseTag/ReverseCatcher in improving the protein thermostability. Cyclized GFP (cGFP) retained 30 % of its relative fluorescence after a 30-min incubation at 100 °C, while both GFP and linear GFP (lGFP) completely lost their fluorescence within 5 min. Additionally, we applied this method to exo-inulinase (EXINU), resulting in a variant named cyclized EXINU (cEXINU). The T50 and t1/2 values of cEXINU exhibited significant enhancements of 10 °C and 10 min, respectively, compared to EXINU. Furthermore, post-cyclization, EXINU demonstrated a broad operational pH range from 5 to 10 with sustained catalytic activity, and cEXINU maintained a half-life of 960 min at pH 5 and 9. Molecular dynamics simulations were conducted to elucidate the mechanisms underlying the enhanced thermostability and pH robustness of EXINU following cyclization. This study highlights that cyclization substanitially enhances the stability of both highly stable protein GFP and low-stable protein EXINU, mediated by the ReverseTag/ReverseCatcher tagging system. The ReverseTag/ReverseCatcher tagging system proves to be a potent conjugation method, with potential applications in improving thermostability, pH robustness, and other areas of protein engineering.

GATSol, an enhanced predictor of protein solubility through the synergy of 3D structure graph and large language modeling.

BACKGROUND: Protein solubility is a critically important physicochemical property closely related to protein expression. For example, it is one of the main factors to be considered in the design and production of antibody drugs and a prerequisite for realizing various protein functions. Although several solubility prediction models have emerged in recent years, many of these models are limited to capturing information embedded in one-dimensional amino acid sequences, resulting in unsatisfactory predictive performance. RESULTS: In this study, we introduce a novel Graph Attention network-based protein Solubility model, GATSol, which represents the 3D structure of proteins as a protein graph. In addition to the node features of amino acids extracted by the state-of-the-art protein large language model, GATSol utilizes amino acid distance maps generated using the latest AlphaFold technology. Rigorous testing on independent eSOL and the Saccharomyces cerevisiae test datasets has shown that GATSol outperforms most recently introduced models, especially with respect to the coefficient of determination R2, which reaches 0.517 and 0.424, respectively. It outperforms the current state-of-the-art GraphSol by 18.4% on the S. cerevisiae_test set. CONCLUSIONS: GATSol captures 3D dimensional features of proteins by building protein graphs, which significantly improves the accuracy of protein solubility prediction. Recent advances in protein structure modeling allow our method to incorporate spatial structure features extracted from predicted structures into the model by relying only on the input of protein sequences, which simplifies the entire graph neural network prediction process, making it more user-friendly and efficient. As a result, GATSol may help prioritize highly soluble proteins, ultimately reducing the cost and effort of experimental work. The source code and data of the GATSol model are freely available at https://github.com/binbinbinv/GATSol .

How alginate lyase produces quasi-monodisperse oligosaccharides: A normal-mode-based docking and molecular dynamics simulation study.

Oligosaccharide degradation products of alginate (AOS) hold significant potential in diverse fields, including pharmaceuticals, health foods, textiles, and agricultural production. Enzymatic alginate degradation is appealing due to its mild conditions, predictable activity, high yields, and controllability. However, the alginate degradation often results in a complex mixture of oligosaccharides, necessitating costly purification to isolate highly active oligosaccharides with a specific degree of polymerization (DP). Addressing this, our study centers on the alginate lyase AlyB from Vibrio Splendidus OU02, which uniquely breaks down alginate into mono-distributed trisaccharides. This enzyme features a polysaccharide lyase family 7 domain (PL-7) and a CBM32 carbohydrate-binding module connected by a helical structure. Through normal-mode-based docking and all-atom molecular simulations, we demonstrate that AlyB's substrate and product specificities are influenced by the spatial conformation of the catalytic pocket and the flexibility of its structure. The helically attached CBM is pivotal in releasing trisaccharides, which is crucial for avoiding further degradation. This study sheds light on AlyB's specificity and efficiency and contributes to the evolving field of enzyme design for producing targeted oligosaccharides, with significant implications for various bioindustries.