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Graphic abstract There have been recent renewed interests in the importance of suicide enzyme inhibition. The principal objective of this review is to investigate all types of suicide inhibitions for natural enzymes, artificial biocatalysts as well as therapeutic potential of enzyme suicide inhibition. It is discussed the suicide inhibition beneficial in drug design and treatments and non-beneficial achievements for some industrial enzymes such as HRP peroxidase enzyme. The design of biomimetic artificial enzymes explained to prevent inhibition by protecting the active site via environmental conditions. Suicide enzyme inhibition development can be the key mechanism against sever diseases such as SARS. In this report, suicide enzyme inactivation classes are classified based on target enzyme groups via their substrates. Suicide substrate deceives enzyme by its shape similarity to normal substrate and after interaction with enzyme, its suicidal functional group appears (e.g., Trojan horse).

Hemoproteins play important roles in physiological activities in animals and plants. The research on the structures and properties of hemoproteins can better understand the relationship between structure and function of hemoproteins and expand the application fields and scopes of hemoproteins. In this report, the peroxidase activity, stability and structure changes of cytochrome c (Cyt c) and quench treatment Cyt c at different pH values and different quenching temperatures were studied by UV–Vis spectroscopy, circular dichroism, Fourier transform infrared spectroscopy and electrochemical methods, respectively. The results indicated: 1. The optimal pH value and temperature for Cyt c quench treatment were pH 9 and 85 °C (marked as Q-Cyt c), respectively, in 50 mM sodium phosphate buffer solution (PBS). 2. The Michaelis–Menten constant Km, maximum catalytic rate Vmax and catalytic rate constant Kcat of Q-Cyt c were calculated to be 3.8 μM, 0.42 μM·s−1 and 0.085 s−1, respectively, in 50 mM pH 9 PBS. The catalytic efficiency of Q-Cyt c was 0.022 μM−1 s−1, which was 30% of the catalytic efficiency of horseradish peroxidase (0.0742 μM−1 s−1). 3. Compared with natural Cyt c, while the overall structure of Q-Cyt c became looser, its hydrophobicity had changed. At the same time, the degree of exposure and hydrophobicity of the heme structure decreased, respectively. 4. The content of Random-Coil structure of Q-Cyt c would restore to its state at room temperature, while the other secondary structures maintained their structures at high temperature (below denaturation temperature) before quenching. 5. The formal potential of Q-Cyt c was greater than that of Cyt c, the hydrophobicity of Q-Cyt c was enhanced compared with Cyt c, which might lead to the decreased stability and improved catalytic activity of Q-Cyt c. 6. Q-Cyt c could be used in industrial decolorization and biosensor construction. 7. Quench treatment may be an effective method to transform Cyt c from a classical redox protein to a peroxidase like enzyme.

Based on a biomimetic point of view, a new artificial enzyme model was designed to imitate the biocatalytic behavior of native-chloroperoxidase hemoenzyme. Transition metals catalyzed C–H chlorination and represented the C–H activation process. Cysteine–metal complexes of Mn(II), Fe(III), Co(II), Ni(II), Cu(II) and Zn(II) were prepared and encapsulated in a sodium dodecyl sulfate (SDS)/dodecyl trimethyl ammonium bromide (DTAB) vesicular mixture to imitate a native-chloroperoxidase model at pH 3. Current biocatalysts also behaved as a catalase at pH 3 and as a peroxidase at pH 1. Surprisingly, “Cu(II)–cysteine/SDS/DTAB” nanozyme indicated the maximum chloroperoxidase, catalase and peroxidase activity or efficacy among other mentioned intermediate metal complexes including manganese (2+), cobalt (2+), zinc (2+), iron (3+) and nickel (2+). The performed structural analysis by transmission electron microscopy and dynamic light scattering illustrated different nanozyme shapes and surface charges at distinct pHs. Zeta potential of colloids indicated the presence of Cu(II)–cysteine active site inside the nanozyme. Differential scanning calorimetry shows different stabilities and fluidities of nanozyme at different pHs. FTIR spectroscopy was utilized to investigate the structural complexation of Cu(II)–cysteine active essence. The present study demonstrated that “the sulfur” moiety of l-cysteine is ligated to copper ion at pH 1 and 3 in order to form Cu(II)–cysteine center as a multi-functional active site. Accordingly, the current study presents the preparation of an efficient pseudo-vesicular Cu(II)–cysteine supramolecular system with pH-switchable multi-functional enzymatic ability.

In this study, we focused on the interference of methylparaben (MP), as the most commonly used paraben, on hemoglobin (Hb) fructation, generating reactive oxygen species (ROS) and resulting lipid peroxidation (LPO). We showed the enhancement of ROS generation and heme degradation during hemoglobin fructation in the presence of MP using some biophysical techniques such as chemiluminescence and fluorescence spectroscopies, respectively. Lipid peroxidation was observed using thiobarbituric acid reactive substances (TBARS) assay, which correlated with ROS production. Also, the results showed that by increasing the levels of fructation, especially in diabetic-like groups, ROS production was increased augmenting the intensity of LPO. This was accompanied by the elevation of malondialdehyde (MDA) production. Thus, MP has an adverse impact on hemoglobin by enhancing advance glycation end products (AGEs) and ROS generation upon its fructation, which results in MDA generation through enhanced LPO. This kind of research emphasises the importance of preservatives’ application revision especially methylparaben in various industries and the necessity of special attention to the consumption of these products by diabetic patients.

In this work, four types of data mining methods, namely adaptive neuro-fuzzy inference system, artificial neural network—multilayer perceptron algorithm (ANN-MLP), artificial neural network—radial basis function algorithm (ANN-RBF), and group method of data handling (GMDH) have been used to predict the enhancement of the relative thermal conductivity of a wide range of nanofluids with different base fluids and nanoparticles. The total number of experimental data used in this work is 483 from 18 different nanofluids. The input parameters are thermal conductivity of base fluid and nanoparticles, volume fraction percent, the average size of nanoparticles, and temperature. Although the results showed that all four models are in relatively good agreement with experimental data, the ANFIS method is the best. The average absolute relative deviations (AARD%) between the experimental data and those of obtained using ANFIS, ANN-MLP, ANN-RBF, and GMDH methods were calculated as 2.7, 2.8, 4.2, and 4.3, respectively, for the test sets and as 1.1, 2.4, 3.9, and 4.5, respectively, for the training sets. Comparison between the predictions of the proposed ANN-MLP, ANN-RBF, ANFIS, and GMDH models and those predicted by traditional models, namely Maxwell and Bruggeman models showed that much better agreements can be obtained using the four models especially ANFIS model. Accordingly, the ANFIS method can able us to predict the relative thermal conductivity of new nanofluids in different conditions with good accuracy.

The “inversion recovery experiment” is used as a tool in nuclear magnetic resonance spectroscopy for characterization and identification of basic important amino acids. This methodology is based on determination of “longitudinal relaxation time (T 1)”of Carbon-13 of these molecular structures which relates to assignment of each carbon atom of amino acids. Not only the chemical shifts and frequencies of carbon atoms are different, but also the relaxation times of them in scale of seconds or less are different, without much overlapping. Due to larger shift effects in 13C NMR spectra and larger paramagnetic origin for carbon-13 and other differences, it has made us to rely on 13C nucleus as main clue in this work rather than 1H nucleus. This procedure has helped us to identify the amino acids in terms of both “frequency” and “time” of relaxation for each carbon atom simultaneously. Applying “average linkage” as an agglomerative clustering method to the feature vectors extracted from NMR spectra of amino acids, a hierarchical clustering is provided. The obtained clusters reveal notable relationships between amino acids in a same cluster. After a time gap, the proposed clusters of amino acids which have similarities and differences with traditional grouping of amino acids provide a new perspective on amino acids characterization and related studies such as defining descriptors for proteins and peptides based on their sequence information.