BACKGROUND: D-Allulose is one of the most well-known rare sugars widely used in food, cosmetics, and pharmaceutical industries. The most popular method for D-allulose production is the conversion from D-fructose catalyzed by D-allulose 3-epimerase (DAEase). To address the general problem of low catalytic efficiency and poor thermostability of wild-type DAEase, D-allulose biosensor was adopted in this study to develop a convenient and efficient method for high-throughput screening of DAEase variants.
RESULTS: The catalytic activity and thermostability of DAEase from Caballeronia insecticola were simultaneously improved by semi-rational molecular modification. Compared with the wild-type enzyme, DAEaseS37N/F157Y variant exhibited 14.7% improvement in the catalytic activity and the half-time value (t1/2) at 65°C increased from 1.60 to 27.56 h by 17.23-fold. To our delight, the conversion rate of D-allulose was 33.6% from 500-g L-1 D-fructose in 1 h by Bacillus subtilis WB800 whole cells expressing this DAEase variant. Furthermore, the practicability of cell immobilization was evaluated and more than 80% relative activity of the immobilized cells was maintained from the second to seventh cycle.
CONCLUSIONS: All these results indicated that the DAEaseS37N/F157Y variant would be a potential candidate for the industrial production of D-allulose.

While many computational methods accurately predict destabilizing mutations, identifying stabilizing mutations has remained a challenge, because of their relative rarity. We tested ΔΔG0 predictions from computational predictors such as Rosetta, ThermoMPNN, RaSP, and DeepDDG, using 82 mutants of the bacterial toxin CcdB as a test case. On this dataset, the best computational predictor is ThermoMPNN, which identifies stabilizing mutations with a precision of 68%. However, the average increase in Tm for these predicted mutations was only 1°C for CcdB, and predictions were poorer for a more challenging target, influenza neuraminidase. Using data from multiple previously described yeast surface display libraries and in vitro thermal stability measurements, we trained logistic regression models to identify stabilizing mutations with a precision of 90% and an average increase in Tm of 3°C for CcdB. When such libraries contain a population of mutants with significantly enhanced binding relative to the corresponding wild type, there is no benefit in using computational predictors. It is then possible to predict stabilizing mutations without any training, simply by examining the distribution of mutational binding scores. This avoids laborious steps of in vitro expression, purification, and stability characterization. When this is not the case, combining data from computational predictors with high-throughput experimental binding data enhances the prediction of stabilizing mutations. However, this requires training on stability data measured in vitro with known stabilized mutants. It is thus feasible to predict stabilizing mutations rapidly and accurately for any system of interest that can be subjected to a binding selection or screen.

KCNE1 encodes a 129-residue cardiac potassium channel (IKs) subunit. KCNE1 variants are associated with long QT syndrome and atrial fibrillation. However, most variants have insufficient evidence of clinical consequences and thus limited clinical utility.
In this study, we leveraged the power of variant effect mapping, which couples saturation mutagenesis with high-throughput sequencing, to ascertain the function of thousands of protein-coding KCNE1 variants.
We comprehensively assayed KCNE1 variant cell surface expression (2554/2709 possible single-amino-acid variants) and function (2534 variants). Our study identified 470 loss- or partial loss-of-surface expression and 574 loss- or partial loss-of-function variants. Of the 574 loss- or partial loss-of-function variants, 152 (26.5%) had reduced cell surface expression, indicating that most functionally deleterious variants affect channel gating. Nonsense variants at residues 56-104 generally had WT-like trafficking scores but decreased functional scores, indicating that the latter half of the protein is dispensable for protein trafficking but essential for channel function. 22 of the 30 KCNE1 residues (73%) highly intolerant of variation (with > 70% loss-of-function variants) were in predicted close contact with binding partners KCNQ1 or calmodulin. Our functional assay data were consistent with gold standard electrophysiological data (ρ =  - 0.64), population and patient cohorts (32/38 presumed benign or pathogenic variants with consistent scores), and computational predictors (ρ =  - 0.62). Our data provide moderate-strength evidence for the American College of Medical Genetics/Association of Molecular Pathology functional criteria for benign and pathogenic variants.
Comprehensive variant effect maps of KCNE1 can both provide insight into I Ks channel biology and help reclassify variants of uncertain significance.

Yogurt usually contains 5% to 7% sugar and 3% to 5% lactose. As β-galactosidases can hydrolyze lactose and improve sweetness, they have the potential to produce lactose-free (LF) and no-sugar-added (NSA) yogurt. In this study, the β-galactosidase AoBgal35A from Aspergillus oryzae was engineered by site-saturation mutagenesis. Results of 19 variants of T955 residue showed that the lactose hydrolysis rate of T955R-AoBgal35A was up to 90.7%, which is much higher than the 78.5% of the wild type. Moreover, the optimal pH of T955R-AoBgal35A was shifted from pH 4.5 to pH 5.5, and the optimal temperature decreased from 60°C to 50°C. The mutant T955R-AoBgal35A was successfully expressed in Komagataella pastoris, which produced extracellularly 4,528 U/mL of β-galactosidase activity. The mutant T955R-AoBgal35A was used to produce LF yogurt. The Streptococcus thermophilus count of LF yogurt increased from 7.9 to 9.5 log cfu/g, which is significantly higher than that of the control group (8.9 log cfu/g). The residual lactose content of LF yogurt was 0.13%, meeting the requirements of the national standard in China for the \"lactose-free\" label (<0.5%). Furthermore, sugar in yogurt was replaced by whey powder to produce LF-NSA yogurt. The optimal addition content of whey powder was 7.5%. The texture, water-holding capacity, and titratable acidity of LF and LF-NSA yogurt achieved good shelf life stability. Therefore, this study provides an insight for technological implications of β-galactosidases in the dairy industry.

Phloretin is widely found in fruit and shows various biological activities. Here, we demonstrate the dimethylallylation, geranylation, and farnesylation, particularly the first dimethylallylation at the nonaromatic carbon of phloretin (1) by the fungal prenyltransferase AnaPT and its mutants. F265 was identified as a key amino acid residue related to dimethylallylation at the nonaromatic carbon of phloretin. Mutants AnaPT_F265D, AnaPT_F265G, AnaPT_F265P, AnaPT_F265C, and AnaPT_F265Y were discovered to generally increase prenylation activity toward 1. AnaPT_F265G catalyzes the O-geranylation selectively at the C-2\' hydroxyl group, which involves an intramolecular hydrogen bond with the carbonyl group of 1. Seven products, 1D5, 1D7-1D9, 1G2, 1G4, and 1F2, have not been reported prior to this study. Twelve compounds, 1D3-1D9, 1G1-1G3, and 1F1-1F2, exhibited potential inhibitory effects on α-glucosidase with IC50 values ranging from 11.45 ± 0.87 to 193.80 ± 6.52 μg/mL. Among them, 1G1 with an IC50 value of 11.45 ± 0.87 μg/mL was the most potential α-glucosidase inhibitor, which is about 30 times stronger than the positive control acarbose with an IC50 value of 346.63 ± 15.65 μg/mL.

Phage display is an important technology to study protein-protein interaction and protein evolution, with applications in basic science and applied biotechnology, such as drug discovery and the development of targeted therapies. However, in order to be successful during a phage display screening, it is paramount to have good phage libraries. Here, we described detailed procedures to generate peptide phage display libraries with high diversity and billions of transformants.

Glucose oxidase (EC 1.1.3.4, GOD) is a widely used industrial enzyme. To construct a GOD-hyperproducing Pichia pastoris strain, combinatorial strategies have been applied to improve GOD activity, synthesis, and secretion. First, wild-type GOD was subjected to saturation mutagenesis to obtain an improved variant, MGOD1 (V20W/T30S), with 1.7-fold higher kcat /KM . Subsequently, efficient signal peptides were screened, and the copy number of MGOD1 was optimized to generate a high-producing strain, 8GM1, containing eight copies of AOX1 promoter-GAS1 signal peptide-MGOD1 expression cassette. Finally, the vesicle trafficking of 8GM1 was engineered to obtain the hyperproducing strain G1EeSe co-expressing the trafficking components EES and SEC. 22, and the EES gene (PAS_chr3_0685) was found to facilitate both protein secretion and production for the first time. Using these strategies, GOD secretion was enhanced 65.2-fold. In the 5-L bioreactor, conventional fed-batch fermentation without any process optimization resulted in up to 7223.0 U/mL extracellular GOD activity (3.3-fold higher than the highest level reported to date), with almost only GOD in the fermentation supernatant at a protein concentration of 30.7 g/L. Therefore, a GOD hyperproducing strain for industrial applications was developed, and this successful case can provide a valuable reference for the construction of high-producing strains for other industrial enzymes.

Thermostable proteins find their use in numerous biomedical and biotechnological applications. However, the computational design of stable proteins often results in single-point mutations with a limited effect on protein stability. However, the construction of stable multiple-point mutants can prove difficult due to the possibility of antagonistic effects between individual mutations. FireProt protocol enables the automated computational design of highly stable multiple-point mutants. FireProt 2.0 builds on top of the previously published FireProt web, retaining the original functionality and expanding it with several new stabilization strategies. FireProt 2.0 integrates the AlphaFold database and the homology modeling for structure prediction, enabling calculations starting from a sequence. Multiple-point designs are constructed using the Bron-Kerbosch algorithm minimizing the antagonistic effect between the individual mutations. Users can newly limit the FireProt calculation to a set of user-defined mutations, run a saturation mutagenesis of the whole protein or select rigidifying mutations based on B-factors. Evolution-based back-to-consensus strategy is complemented by ancestral sequence reconstruction. FireProt 2.0 is significantly faster and a reworked graphical user interface broadens the tool\'s availability even to users with older hardware. FireProt 2.0 is freely available at http://loschmidt.chemi.muni.cz/fireprotweb.

Protein mutagenesis is essential for unveiling the molecular mechanisms underlying protein function in health, disease, and evolution. In the past decade, deep mutational scanning methods have evolved to support the functional analysis of nearly all possible single-amino acid changes in a protein of interest. While historically these methods were developed in lower organisms such as E. coli and yeast, recent technological advancements have resulted in the increased use of mammalian cells, particularly for studying proteins involved in human disease. These advancements will aid significantly in the classification and interpretation of variants of unknown significance, which are being discovered at large scale due to the current surge in the use of whole-genome sequencing in clinical contexts. Here, we explore the experimental aspects of deep mutational scanning studies in mammalian cells and report the different methods used in each step of the workflow, ultimately providing a useful guide toward the design of such studies.

Interpretation of disease-causing genetic variants remains a challenge in human genetics. Current costs and complexity of deep mutational scanning methods hamper crowd-sourcing approaches toward genome-wide resolution of variants in disease-related genes. Our framework, Saturation Mutagenesis-Reinforced Functional assays (SMuRF), addresses these issues by offering simple and cost-effective saturation mutagenesis, as well as streamlining functional assays to enhance the interpretation of unresolved variants. Applying SMuRF to neuromuscular disease genes FKRP and LARGE1, we generated functional scores for all possible coding single nucleotide variants, which aid in resolving clinically reported variants of uncertain significance. SMuRF also demonstrates utility in predicting disease severity, resolving critical structural regions, and providing training datasets for the development of computational predictors. Our approach opens new directions for enabling variant-to-function insights for disease genes in a manner that is broadly useful for crowd-sourcing implementation across standard research laboratories.