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Abstract In this study, adsorption of volatile organic compounds (VOCs) (here just gasoline vapor) by activated carbon- modified UIO-66 was investigated. First, activated carbon prepared from mesquite grain (ACPMG) and then UIO/ACPMG nanohybrid was synthesized by the solvothermal method. In following, the effect of main key parameters which effect on the surface and adsorption capacity such as the ratio of ACPMG to UIO-66 was studied. Physiochemical changes of as- synthesized samples were investigated by TGA, HR-TEM, PSD, SEM, EDX/MAP, BET, FT-IR, XRD, and XPS. It was found the UIO/ACPMG20% nanohybrid had the highest adsorption capacity (391.304 mg/g) for VOCs compared with the other samples, while the adsorption capacity of UIO-66, UIO/ACPMG10% nanohybrid, and UIO/ACPMG30% nanohybrid was 298.871, 309.523, and 320 mg/g respectively. UIO/ACPMG20% nanohybrid desorbed 285.71 mg/g of the adsorbed gasoline, which is an excellent result in desorption. So, the sample of UIO/ACPMG20% nanohybrid was selected as the optimum nano-adsorbent. In other hand, all the nano-adsorbent showed a rapid kinetic behavior for gasoline vapor adsorption and the maximum time for reaching a high adsorption capacity approximately was obtained in 20 min. Density functional theory calculations also performed to understand the adsorption characteristics of gasoline vapor on activated carbon-modified UIO-66.

Abstract A series of 4-ferrcenylbutyl carboxylate esters with different alkyl chain length (C2-C4) of carboxylic acids were synthesized using Fe3O4@SiO2@(CH2)3-Im-bisEthylFc[I] nanoparticles as catalyst and have been characterized with FT-IR, 1H NMR, and 13C NMR. Ferrocenyl-based esters were used as corrosion inhibitors of mild steel in the 1M HCl solution as corrosive media. The corrosion inhibition efficiency of the synthesized ferrocenyl-based esters has been assessed by electrochemical impedance spectroscopy (EIS), potentiodynamic polarization (PDP), atomic force microscopy (AFM), and scanning electron microscopy (SEM). The 4-ferrocenylbutyl propionate showed a more effective corrosion inhibition behavior among the studied esters with 96% efficiency after immersion in the corrosive media for 2 weeks. The corrosion inhibition mechanism is dominated by formation of passive layer of inhibitor on the surface of the mild steel by adsorption. Moreover, the adsorption characteristics of 4-butylferrcenyl carboxylate esters on mild steel were thoroughly explored using density functional theory calculations. It was found that the Fe atoms located around the C impurity in the mild steel are the most efficient and active sites to adsorb 4-butylferrcenyl carboxylate esters.

This study examines the ability of Ca-decorated C48B12 fullerene (Ca/C48B12) to adsorb and store carbon dioxide (CO2) molecules through density functional theory calculations. The most stable structure of the Ca/C48B12 is achived by placing a Ca atom on the center of each C4B2 ring. To address the issue of clustering, six Ca atoms were favourably decorated on the C48B12 fullerene. Additionally, up to five CO2 molecules can be adsorbed on each decorated Ca atom, with an average adsorption energy of −0.44 eV and a gravimetric density of 58 %. Furthermore, the Ca/C48B12 fullerene exhibits high selectivity in separating CO2 from natural gas mixtures containing CO2/H2, CO2/N2, and CO2/CH4. These findings suggest that Ca/C48B12 fullerene holds potential as an effective solution for CO2 storage and separation in real-world applications.

MIL-101(Cr), a class of metal-organic framework, is a potential candidate for CO2 capture applications because of its high capacity of adsorption and separation capability. However, the intrinsic microporous structure of this nanomaterial poses limitations on its adsorption kinetics. Techniques employed to enhance its adsorption kinetics often adversely impact its adsorption capacity at equilibrium. Herein, as a new approach, we prepared amine-functionalized FAC@MIL-101(Cr) composites with adjustable micro-mesoporous structure and tunable nitrogen content by embedding different ratios of amine-functionalized activated carbon throughout the framework of MIL-101(Cr). This led to a simultaneous improvement in both kinetics and adsorption capacity for CO2. The best adsorbent, FAC-6@MIL-101(Cr), has excellent textural properties with a high surface area (1763.1 m2.g−1), great pore volume (1.29 cm3.g−1), and suitable nitrogen content (4.7 wt%). The adsorption analysis revealed that the modification of MIL-101(Cr) improved its CO2 adsorption capacity from 3.21 to 5.27 mmol/g under standard conditions of 1 bar and 25 °C. Furthermore, the FAC-6@MIL-101(Cr) adsorbent demonstrated fast CO2 adsorption kinetics (three times more relative to the pure MIL-101(Cr)), high CO2/N2 selectivity, and remarkable cyclic stability. The results confirmed that hybridization enhanced the polarizability of FAC@MIL-101(Cr) samples, causing more robust CO2-adsorbent surface interactions. Simultaneously, the existence of mesopores in the structure facilitated the transport of CO2 into the interior pores, resulting in a more efficient contact of CO2 molecules with all of the amine sites and a faster adsorption rate as well as more efficient regeneration. According to density functional theory (DFT) calculations, hybridization process induces significant changes in composites’ electronic structure, enhancing their capacity to interact with CO2 molecules more effectively. On the other hand, DFT calculations confirm that N2 molecule is less activated on the FAC@MIL-101(Cr) as evidenced by calculated small adsorption energy and charge-transfer values.

Modular proteins are regulatory proteins that carry out more than one function. These proteins upregulate or downregulate a biochemical cascade to establish homeostasis in cells. To switch the function or alter the efficiency (based on cellular needs), these proteins require different facilitators that bind to a site different from the catalytic (active/orthosteric) site, aka ‘allosteric site’, and fine-tune their function. These facilitators (or effectors) are allosteric modulators. In this Review, we have discussed the allostery, characterized them based on their mechanisms, and discussed how allostery plays an important role in the activity modulation and function fine-tuning of proteins. Recently there is an emergence in the discovery of allosteric drugs. We have also emphasized the role, significance, and future of allostery in therapeutic applications.

In this study, we synthesized and employed the amine impregnated activated carbon as an efficacious adsorbent for uptaking gasoline vapor. For this regard, anthracite as activated carbon source and hexamethylenetetramine (HMTA) as amine were selected and utilized. Physiochemical characterization of made sorbents were evaluated and investigated using SEM, FESEM, BET, FTIR, XRD, zeta potential, and elemental analysis. The synthesized sorbents provided an excellent textural features as compared with the literature and other activated carbon based sorbents and impregnated with amine. Our findings also suggested that in addition to high surface area (up to 2150 m2 / g), the micro- meso pores created (Vmeso / V micro = 0.79 Cm 3 / g) surface chemistry may significantly affect the gasoline sorption capacity, which here the role of mesoporous is further highlighted. V meso for amine impregnated sample and free activated carbon was 0.89 and 0.31 Cm 3 / g, respectively. According to the results, the prepared sorbents have a potential capability in uptaking gasoline vapor and with line this, we report a high sorption capacity of 572.56 mg / g. After, four cycles used the sorbent had a high durability and about 99.11% of the initial uptake was maintained. Taking together the synthesized adsorbents as an activated carbon provided an excellent and unique features and enhanced gasoline uptake, therefore its applicability in uptaking gasoline vapor can be substantially considered.

Ionic liquids (IL) and aqueous ionic liquids (aIL) are attractive (co-)solvents for green industrial processes involving biocatalysts, but often reduce enzyme activity. Experimental and computational methods are applied to predict favorable substitution sites and, most often, subsequent site-directed surface charge modifications are introduced to enhance enzyme resistance towards aIL. However, almost no studies evaluate the prediction precision with random mutagenesis or the application of simple data-driven filtering processes. Here, we systematically and rigorously evaluated the performance of 22 previously described structure-based approaches to increase enzyme resistance to aIL based on an experimental complete site-saturation mutagenesis library of Bacillus subtilis Lipase A (BsLipA) screened against four aIL. We show that, surprisingly, most of the approaches yield low gain-in-precision (GiP) values, particularly for predicting relevant positions: 14 approaches perform worse than random mutagenesis. Encouragingly, exploiting experimental information on the thermostability of BsLipA or structural weak spots of BsLipA predicted by rigidity theory yields GiP = 3.03 and 2.39 for relevant variants and GiP = 1.61 and 1.41 for relevant positions. Combining five simple-to-compute physicochemical and evolutionary properties substantially increases the precision of predicting relevant variants and positions, yielding GiP = 3.35 and 1.29. Finally, combining these properties with predictions of structural weak spots identified by rigidity theory additionally improves GiP for relevant variants up to 4-fold to ∼10 and sustains or increases GiP for relevant positions, resulting in a prediction precision of ∼90% compared to ∼9% in random mutagenesis. This combination should be applicable to other enzyme systems for guiding protein engineering approaches towards improved aIL resistance.

Abstract An active site is normally located inside enzymes, hence substrates should go through a tunnel to access the active site. Tunnel engineering is a powerful strategy for refining the catalytic properties of enzymes. Here, P450BsβHI (Q85H/V170I) derived from hydroxylase P450Bsβ from Bacillus subtilis was chosen as the study model, which is reported as a potential decarboxylase. However, this enzyme showed low decarboxylase activity towards long-chain fatty acids. Here, a tunnel engineering campaign was performed for modulating the substrate preference and improving the decarboxylation activity of P450BsβHI. The finally obtained BsβHI-F79A variant had a 15.2-fold improved conversion for palmitic acid; BsβHI-F173V variant had a 3.9-fold improved conversion for pentadecanoic acid. The study demonstrates how the substrate preference can be modulated by tunnel engineering strategy.

Histamine is a biogenic amine found in fish-derived and fermented food products with physiological relevance since its concentration is proportional to food spoilage and health risk for sensitive consumers. There are various analytical methods for histamine quantification from food samples; however, a simple and quick enzymatic detection and quantification method is highly desirable. Histamine dehydrogenase (HDH) is a candidate for enzymatic histamine detection; however, other biogenic amines can change its activity or produce false positive results with an observed substrate inhibition at higher concentrations. In this work, we studied the effect of site saturation mutagenesis in Rhizobium sp. Histamine Dehydrogenase (Rsp HDH) in nine amino acid positions selected through structural alignment analysis, substrate docking, and proximity to the proposed histamine-binding site. The resulting libraries were screened for histamine and agmatine activity. Variants from two libraries (positions 72 and 110) showed improved histamine/agmatine activity ratio, decreased substrate inhibition, and maintained thermal resistance. In addition, activity characterization of the identified Phe72Thr and Asn110Val HDH variants showed a clear substrate inhibition curve for histamine and modified kinetic parameters. The observed maximum velocity (Vmax) increased for variant Phe72Thr at the cost of an increased value for the Michaelis–Menten constant (Km) for histamine. The increased Km value, decreased substrate inhibition, and biogenic amine interference observed for variant Phe72Thr support a tradeoff between substrate affinity and substrate inhibition in the catalytic mechanism of HDHs. Considering this tradeoff for future enzyme engineering of HDH could lead to breakthroughs in performance increases and understanding of this enzyme class.

Cellulases are industrially important enzymes, e.g., in the production of bioethanol, in pulp and paper industry, feedstock, and textile. Thermostability is often a prerequisite for high process stability and improving thermostability without affecting specific activities at lower temperatures is challenging and often time-consuming. Protein engineering strategies that combine experimental and computational are emerging in order to reduce experimental screening efforts and speed up enzyme engineering campaigns. Constraint Network Analysis (CNA) is a promising computational method that identifies beneficial positions in enzymes to improve thermostability. In this study, we compare CNA and directed evolution in the identification of beneficial positions in order to evaluate the potential of CNA in protein engineering campaigns (e.g., in the identification phase of KnowVolution). We engineered the industrially relevant endoglucanase EGLII from Penicillium verruculosum towards increased thermostability. From the CNA approach, six variants were obtained with an up to 2-fold improvement in thermostability. The overall experimental burden was reduced to 40% utilizing the CNA method in comparison to directed evolution. On a variant level, the success rate was similar for both strategies, with 0.27% and 0.18% improved variants in the epPCR and CNA-guided library, respectively. In essence, CNA is an effective method for identification of positions that improve thermostability.

Ionic liquids (IL) and aqueous ionic liquids (aIL) are attractive (co–)solvents for biocatalysis due to their unique properties. On the other hand, the incubation of enzymes in IL or aIL often reduces enzyme activity. Recent studies proposed various aIL-induced effects to explain the reduction, classified as direct effects, e.g., local dehydration or competitive inhibition, and indirect effects, e.g., structural perturbations or disturbed catalytic site integrity. However, the molecular origin of indirect effects has largely remained elusive. Here we show by multi-μs long molecular dynamics simulations, free energy computations, and rigidity analyses that aIL favorably interact with specific residues of Bacillus subtilis Lipase A (BsLipA) and modify the local structural stability of this model enzyme by inducing long-range perturbations of noncovalent interactions. The perturbations percolate over neighboring residues and eventually affect the catalytic site and the buried protein core. Validation against a complete experimental site saturation mutagenesis library of BsLipA (3620 variants) reveals that the residues of the perturbation pathways are distinguished sequence positions where substitutions highly likely yield significantly improved residual activity. Our results demonstrate that identifying these perturbation pathways and specific IL ion-residue interactions there effectively predicts focused variant libraries with improved aIL tolerance.

Protein engineering campaigns are driven by the demand for superior enzyme performance under non-natural process conditions, such as elevated temperature or non-neutral pH, to achieve utmost efficiency and conserve limited resources. Phytases are industrial relevant feed enzymes that contribute to the overall phosphorus (P) management by catalyzing the stepwise phosphate hydrolysis from phytate, which is the main phosphorus storage in plants. Phosphorus is referred to as a critical disappearing nutrient, emphasizing the urgent need to implement strategies for a sustainable circular use and recovery of P from renewable resources. Engineered phytases already contribute today to an efficient phosphorus mobilization in the feeding industry and might pave the way to a circular P-bioeconomy. To date, a bottleneck in its application is the drastically reduced hydrolysis on lower phosphorylated reaction intermediates (lower inositol phosphates, ≤InsP4) and their subsequent accumulation. Here, we report the first KnowVolution campaign of the E. coli phytase toward improved hydrolysis on InsP4 and InsP3. As a prerequisite prior to evolution, a suitable screening setup was established and three isomers Ins(2,4,5)P3, Ins(2,3,4,5)P4 and Ins(1,2,5,6)P4 were generated through enzymatic hydrolysis of InsP6 and subsequent purification by HPLC. Screening of epPCR libraries identified clones with improved hydrolysis on Ins(1,2,5,6)P4 carrying substitutions involved in substrate binding and orientation. Saturation of seven positions and screening of, in total, 10,000 clones generated a dataset of 46 variants on their activity on all three isomers. This dataset was used for training, testing, and inferring models for machine learning guided recombination. The PyPEF method used allowed the prediction of recombinants from the identified substitutions, which were analyzed by reverse engineering to gain molecular understanding. Six variants with improved InsP4 hydrolysis of >2.5 were identified, of which variant T23L/K24S had a 3.7-fold improved relative activity on Ins(2,3,4,5)P4 and concomitantly shows a 2.7-fold improved hydrolysis of Ins(2,4,5)P3. Reported substitutions are the first published Ec phy variants with improved hydrolysis on InsP4 and InsP3.

Recently, the study of chitinases has become an important target of numerous research projects due to their potential for applications, such as biocontrol pest agents. Plant chitinases from carnivorous plants of the genus Drosera are most aggressive against a wide range of phytopathogens. However, low solubility or insolubility of the target protein hampered application of chitinases as biofungicides. To obtain plant chitinase from carnivorous plants of the genus Drosera in soluble form in E.coli expression strains, three different approaches including dialysis, rapid dilution, and refolding on Ni-NTA agarose to renaturation were tested. The developed « Rapid dilution » protocol with renaturation buffer supplemented by 10% glycerol and 2M arginine in combination with the redox pair of reduced/oxidized glutathione, increased the yield of active soluble protein to 9.5 mg per 1 g of wet biomass. A structure-based removal of free cysteines in the core domain based on homology modeling of the structure was carried out in order to improve the soluble of chitinase. One improved chitinase variant (C191A/C231S/C286T) was identified which shows improved expression and solubility in E. coli expression systems compared to wild type. Computational analyzes of the wild-type and the improved variant revealed overall higher fluctuations of the structure while maintaining a global protein stability. It was shown that free cysteines on the surface of the protein globule which are not involved in the formation of inner disulfide bonds contribute to the insolubility of chitinase from Drosera capensis. The functional characteristics showed that chitinase exhibits high activity against colloidal chitin (360 units/g) and high fungicidal properties of recombinant chitinases against Parastagonospora nodorum. Latter highlights the application of chitinase from D. capensis as a promising enzyme for the control of fungal pathogens in agriculture.

In the postgenomic age, rapid growth in the number of sequence-known proteins has been accompanied by much slower growth in the number of structure-known proteins (as a result of experimental limitations), and a widening gap between the two is evident. Because protein function is linked to protein structure, successful prediction of protein structure is of significant importance in protein function identification. Foreknowledge of protein structural class can help improve protein structure prediction with significant medical and pharmaceutical implications. Thus, a fast, suitable, reliable, and reasonable computational method for protein structural class prediction has become pivotal in bioinformatics. Here, we review recent efforts in protein structural class prediction from protein sequence, with particular attention paid to new feature descriptors, which extract information from protein sequence, and the use of machine learning algorithms in both feature selection and the construction of new classification models. These new feature descriptors include amino acid composition, sequence order, physicochemical properties, multiprofile Bayes, and secondary structure-based features. Machine learning methods, such as artificial neural networks (ANNs), support vector machine (SVM), K-nearest neighbor (KNN), random forest, deep learning, and examples of their application are discussed in detail. We also present our view on possible future directions, challenges, and opportunities for the applications of machine learning algorithms for prediction of protein structural classes.

The novel diastereomeric sulfonylamide derivatives 4a–h were synthesized by multi-component reactions between (l)-α-amino acids, 1, aromatic aldehydes, 2 and p-toluenesulfonylmethyl isocyanide, 3 in water/methanol using nanosilica as a catalyst. The one-pot green reactions were carried out at room temperature with a quantitative yield in water/methanol. The yield of the reactions and products was determined and discussed. The reusablity of the catalyst is discussed.

Light-oxygen-voltage (LOV) domains are ubiquitous photosensory modules found in proteins from bacteria, archaea and eukaryotes. Engineered versions of LOV domains have found widespread use in fluorescence microscopy and optogenetics, with improved versions being continuously developed. Many of the engineering efforts focused on the thermal stabilization of LOV domains. Recently, we described a naturally thermostable LOV domain from Chloroflexus aggregans. Here we show that the discovered protein can be further stabilized using proline substitution. We tested the effects of three mutations, and found that the melting temperature of the A95P mutant is raised by approximately 2 °C, whereas mutations A56P and A58P are neutral. To further evaluate the effects of mutations, we crystallized the variants A56P and A95P, while the variant A58P did not crystallize. The obtained crystal structures do not reveal any alterations in the proteins other than the introduced mutations. Molecular dynamics simulations showed that mutation A58P alters the structure of the respective loop (Aβ-Bβ), but does not change the general structure of the protein. We conclude that proline substitution is a viable strategy for the stabilization of the Chloroflexus aggregans LOV domain. Since the sequences and structures of the LOV domains are overall well-conserved, the effects of the reported mutations may be transferable to other proteins belonging to this family.

In recent years, noncovalent interactions involving group-14 elements of the periodic table acting as a Lewis acid center (or tetrel-bonding interactions) have attracted considerable attention due to their potential applications in supramolecular chemistry, material science and so on. The aim of the present study is to characterize the geometry, strength and bonding properties of strong tetrel-bond interactions in some charge-assisted tetrel-bonded complexes. Ab initio calculations are performed, and the results are supported by the quantum theory of atoms in molecules (QTAIM) and natural bond orbital (NBO) approaches. The interaction energies of the anionic tetrel-bonded complexes formed between XF3M molecule (X=F, CN; M=Si, Ge and Sn) and A− anions (A−=F−, Cl−, Br−, CN−, NC− and N3−) vary between −16.35 and −96.30 kcal/mol. The M atom in these complexes is generally characterized by pentavalency, i.e., is hypervalent. Moreover, the QTAIM analysis confirms that the anionic tetrel-bonding interaction in these systems could be classified as a strong interaction with some covalent character. On the other hand, it is found that the tetrel-bond interactions in cationic tetrel-bonded [p-NH3(C6H4)MH3]+···Z and [p-NH3(C6F4)MH3]+···Z complexes (M=Si, Ge, Sn and Z=NH3, NH2CH3, NH2OH and NH2NH2) are characterized by a strong orbital interaction between the filled lone-pair orbital of the Lewis base and empty BD*M-C orbital of the Lewis base. The substitution of the F atoms in the benzene ring provides a strong orbital interaction, and hence improved tetrel-bond interaction. For all charge-assisted tetrel-bonded complexes, it is seen that the formation of tetrel-bond interaction is accompanied bysignificant electron density redistribution over the interacting subunits. Finally, we provide some experimental evidence for the existence of such charge-assisted tetrel-bond interactions in crystalline phase.

The functionalization of polymer surfaces by polymer-binding peptides offers tremendous opportunities for directed immobilization of enzymes, bioactive peptides, and antigens. The application of polymer-binding peptides as adhesion promoters requires reliable and stable binding under process conditions. Molecular modes of interactions between material surfaces, peptides, and solvent are often not understood to an extent that enables (semi-) rational design of polymer-binding peptides, hindering the full exploitation of their potential. Knowledge-gaining directed evolution (KnowVolution) is an efficient protein engineering strategy that facilitates tailoring protein properties to application demands through a combination of directed evolution and computational guided protein design. A single round of KnowVolution was performed to gain molecular insights into liquid chromatography peak I peptide, 47 aa (LCI)-binding to polypropylene (PP) in the presence of the competing surfactant Triton X-100. KnowVolution yielded a total of 8 key positions (D19, S27, Y29, D31, G35, I40, E42, and D45), which govern PP-binding in the presence of Triton X-100. The recombination of two of the identified amino acid substitutions (Y29R and G35R; variant KR-2) yielded a 5.4 ± 0.5-fold stronger PP-binding peptide compared to LCI WT in the presence of Triton X-100 (1 mM). The LCI variant KR-2 shows a maximum binding capacity of 8.8 ± 0.1 pmol/cm2 on PP in the presence of Triton X-100 (up to 1 mM). The KnowVolution approach enables the development of polymer-binding peptides, which efficiently coat and functionalize PP surfaces and withstand surfactant concentrations that are commonly used, such as in household detergents.

Aim: to assess physiological and operative severity score for the enumeration of mortality (POSSUM) scoring system and compare it with European system for cardiac operative risk evaluation (EuroSCORE) scores in patients who underwent cardiac surgery from two hospitals in the southwestern region of Iran. Methods: in this retrospective study, total of all 1420 patients who were admitted for elective cardiac surgery at our centers, from 2007 to 2012, were scored using the POSSUM and EuroSCORE systems. Results: the overall mortality rate was 0.87%. Among the risk factors, history of diabetes, smoking, respiratory disease, and myocardial infarction, were significantly affect the mortality rate. Therefore, of these risk factors, only the hemoglobin was significantly correlated with the morbidity rate. The predictive accuracy of mortality equations was 74.5%. The lower predictive accuracy of mortality equations was 67.8% was observed using EuroSCORE. Conclusion: although results are statistically significant, but the analysis have never intended to affect the decision to operate, and this decision must be based on clinical expertise, because of the need to standardize data collection and stratify the risks involved in operations, scoring systems such as POSSUM should be used prospectively. However, if analyzed correctly, POSSUM is a good predictor of mortality in patients undergoing cardiac surgery. Key words: cardiac surgery, physiological and operative severity score for the enumeration of mortality (POSSUM), European system for cardiac operative risk evaluation (EuroSCORE).