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The set of chemicals producible and usable by metabolic pathways must have evolved in parallel with the enzymes that catalyze them. One implication of this common historical path should be a correspondence between the innovation steps that gradually added new metabolic reactions to the biosphere-level biochemical toolkit, and the gradual sequence changes that must have slowly shaped the corresponding enzyme structures. However, global signatures of a long-term co-evolution have not been identified. Here we search for such signatures by computing correlations between inter-reaction distances on a metabolic network, and sequence distances of the corresponding enzyme proteins. We perform our calculations using the set of all known metabolic reactions, available from the KEGG database. Reaction-reaction distance on the metabolic network is computed as the length of the shortest path on a projection of the metabolic network, in which nodes are reactions and edges indicate whether two reactions share a common metabolite, after removal of cofactors. Estimating the distance between enzyme sequences in a meaningful way requires some special care: for each enzyme commission (EC) number, we select from KEGG a consensus set of protein sequences using the cluster of orthologous groups of proteins (COG) database. We define the evolutionary distance between protein sequences as an asymmetric transition probability between two enzymes, derived from the corresponding pair-wise BLAST scores. By comparing the distances between sequences to the minimal distances on the metabolic reaction graph, we find a small but statistically significant correlation between the two measures. This suggests that the evolutionary walk in enzyme sequence space has locally mirrored, to some extent, the gradual expansion of metabolism.

The sun is the only source of renewable energy available to us, if geothermal energy is not taken into account. In the form of radiation (UV light, visible light, infrared light, Section 1.1) it sends us annually 178,000 terawatts (1 TW = 10¹² W; unit of power 1 W = 1 J s^–1 = 859.85 calories per hour), that is to say 15,000 times the energy consumed annually by humanity. Only 0.1% of the solar energy received by planet Earth is converted into plant biomass, i.e. 100 × 10⁹ tons per year which corresponds to ca. 180 × 10⁹ tons per year of CO₂ captured from the atmosphere. This CO₂ returns to the biosphere after the death of the plants. Consumption of fossil carbon emits ca. 35 × 10⁹ tons of CO₂ yearly. Biomass is the material produced by all living organisms (plants, animals, microorganisms, fungi)…

Nowadays, textile processing based on biotechnology have gained importance in view of stringent environmental and industrial safety conditions. The use of protease enzymes on protein fibers to improve some physical and mechanical properties is particularly interesting.

In this research, wool yarns were first treated with different concentrations of protease enzymes in water solution including 1%, 2%, 4% and 6% o.w.f. for 60 minutes. The dyeing process was then carried out on the treated yarns with pistachio hulls (50% o.w.f.). Some of physical, mechanical and colorimetric properties of treated wool yarns were discussed. Tensile strength of treated yarns was decreased due to enzyme treatment and it continued to decrease with an increase in enzyme concentration in solution. The lightness was decreased for the samples treated with enzyme. The wash and light fastness properties of samples were measured according to ISO 105-CO5 and Daylight ISO 105-BO1. The washing fastness properties of treated samples were not changed. In the case of light fastness properties, it was increased a little for 4% and 6% enzyme treated samples.

Hemicellulose from dissolving pulp alkali extraction liquid was used as material for furfural production catalyzed by ZSM-5. Reaction temperature have significantly effect on furfural production. The optimal conditions for furfural production were 180 ºC, 1.5 g ZSM-5, 30 mL toluene for 2 h. Under the optimal conditions, furfural yield was 40.18%. When alkali extraction hemicellulose was hydrolyzed by enzyme (xylanase dosage 0.50 mL/g, 50ºC, 18 h) prior to ZSM-5 hydrolysis for furfural formation. The maximum furfural yield of 74.68% was achieved under the best reaction conditions. Furfural yield was effectively improved by enzyme hydrolysis of hemicellulose with high molecular weight.

Acinetobacter sp. SFA 10 was isolated from river water and found to remove ammonium at 5 °C. The ammonium removal rate was 2.05 mg NH₄⁺-N/l/h when the initial ammonium concentration was about 5 mg/l. When the initial ammonium concentration was higher than 45 mg/L, the ammonium removal rate of Acinetobacter sp. SFA 10 decreased. With the ammonium removing by Acinetobacter sp. SFA 10, NH₂OH was produced without nitrite or nitrate accumulation. Ammonia monooxygenase (AMO) and hydroxylamine oxidase (HAO) were the key enzyme for nitrification. In Acinetobacter sp. SFA 10 cells, amo andhao genes were amplified and about 600bp. Nitrate reductase gene (napA), nitrite and nitrate reductase enzymes were not detected in Acinetobacter sp. SFA 10. The results in this study showed that Acinetobacter sp. SFA 10 oxidized ammonium to NH₂OH, then convert NH₂OH to nitrate. Acinetobacter sp. SFA 10 didn't have the ability of denitrification.

Enzymes are protein molecules that play a crucial role in various biological processes in living organisms. They function as catalysts in biological reactions such as digestion, metabolism, DNA replication and other physiological processes. Furthermore, enzymes are widely used in food production, pharmaceuticals and biofuel production. In these industries, they accelerate desired chemical reactions as biocatalysts. Therefore, applying computational methods and data-driven algorithms to predict enzyme properties is essential. Over the past decade, deep learning has made remarkable advancements in science and technology. Deep learning is a subset of machine learning algorithms that rely on artificial neural networks. These algorithms can be employed for supervised, semi-supervised and unsupervised learning. Here, to provide an update on the current literature, we provide an overview of various deep learning algorithms and recent advancements in their application to enzyme science. These applications can generally be categorized into diverse subjects: function prediction, enzyme kinetic parameters prediction, enzyme-substrate identification, condition optimization, thermophilic property prediction, enzyme catalytic site prediction and enzyme design. In conclusion, we discuss the convergence of enzyme science and deep learning, highlighting the potential opportunities and challenges.