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Chemical Engineering EBooks [By. Ahmad Amino]

Biomedical engineers, pharmacologists, and materials scientists; Researchers specialising in polysaccharides, gene delivery, and tissue engineering; Chemists and chemical engineers; Postgraduate students in polymer technology, biomedical science, and biotechnology

Chemical Engineering eBooks [By. Ahmad Amino]

Recombinant human insulin was first produced in E. coli by Genentech in 1978, using a approach that required the expression of chemically synthesized cDNA encoding for the insulin A and B chains separately in E. coli[59]. After expressing independently, the two chains are purified and co-incubated under optimum reaction conditions that promoted the generation of intact and bioactive insulin by disulphide bond formation. The first commercial recombinant insulin was developed for therapeutic use in human by this two-chain combination procedure [60]. Another approach involves the expression of a single chemically synthesized cDNA encoding for human proinsulin in E. coli followed by purification and subsequent excision of C-peptide by proteolytic digestion. This approach was more efficient and convenient for large scale production of therapeutic insulin as compared to the two chain combination approach and has been used commercially since 1986 [60]. Eli Lilly followed this technology to produce Humulin, the first recombinant insulin approved in 1982, for the treatment of diabetic patients. These first generation recombinant insulins have an amino acid sequence identical to native human insulin and are preferred over animal derived insulin products [14]. However, advancement in the field of genetic engineering and development of technology to chemically synthesize genes with altered nucleotide sequence, facilitated the development of insulin analogues with altered amino acid sequence. It had been observed that native insulin in commercial preparations usually exist in oligomeric form, as zinc-containing hexamer due to very high concentration, but in blood, biologically active insulin is in monomeric form [61]. Hence, this oligomeric complex should dissociate so that insulin can be absorbed from the site of injection into the blood. Due to this, subcutaneously injected recombinant insulin usually have a slow onset with peak plasma concentration after 2 hours of injection and longer duration of action that last for 6-8 hours [62]. Hence, in order to develop a fast- acting insulin analogue, it was required to modify the amino acids residues whose side chains are involved in dimer or oligomer formation. It has been shown that amino acids residues in insulin B-chain particularly B8, 9,12, 13, 16 and 23-28 play critical role in oligomerization [63],[64]. Lispro, developed by Eli Lilly, was the first fast acting insulin analogue to obtain regulatory approval in 1996, for therapeutic use [60]. Insulin Lispro is engineered in such a way that it has similar amino acid sequence as the native insulin but has an inversion of proline-lysine sequence at position 28 and 29 of the B-chain, which resulted in reduced hydrophobic interactions and thus prevented dimer formations. For commercial production of insulin Lispro, a synthetic cDNA encoding for Lys B28- Pro B29 human proinsulin was expressed in E. coli and insulin Lispro was excised proteolytically from the proinsulin by treating with trypsin and carboxypeptidase. Another rapid-acting insulin analogue, produced in E. coli is Glulisine (Apidra) which was developed by Aventis Pharmaceuticals and approved by US regulatory authorities in 2004. Insulin Glulisine have been generated by replacing B3 asparagine by a lysine and B29 lysine replaced by glutamic acid [14].

Insects pose a great threat to plants and plants in turn, withstand to insect attack through various morphological and biochemical traits. Among the plant defensive traits, secondary metabolites play a major role against insect herbivory as they are highly dynamic. They either occur constitutively in plants or are induced in response to insect herbivory. These metabolites include sulfur- (terpenes and flavonoids) and nitrogen-containing metabolites (alkaloids, cyanogenic glucosides, and nonprotein amino acids), which are being implicated by plants against insect pests. Plant secondary metabolites either are directly toxic to insect pests or mediate signaling pathways that produce plant toxins. Further, some of the plant secondary metabolites act through antixenosis mode by developing non-preference in host plant to the insect pests. However, some plant secondary metabolites recruit natural enemies of the insect pests, thus, indirectly defending plants against insect pests. However, insects have developed adaptations to these plant secondary metabolites. In this review, important plant secondary metabolites, their mechanism of action against insect pests, counter-adaptation by insects, and promising advances and challenges are discussed.

Linear B-cell epitopes consist of peptides which can readily be used to replace antigens for immunizations and antibody production. Therefore, despite being a minority, prediction of linear B-cell epitopes have received major attention. Linear B-cell epitopes are predicted from the primary sequence of antigens using sequence-based methods. Early computational methods for the prediction of B-cell epitopes were based on simple amino acid propensity scales depicting physicochemical features of B-cellepitopes. For example, Hopp and Wood applied residue hydrophilicity calculations for B-cell epitope prediction [96, 97] on the assumption that hydrophilic regions are predominantly located on the protein surface and are potentially antigenic. We know now, however, that protein surfaces contain roughly the same number of hydrophilic and hydrophobic residues [98]. Other amino acid propensity scales introduced for B-cell epitope prediction are based on flexibility [99], surface accessibility [100], and β-turn propensity [101]. Current available bioinformatics tools to predict linear B-cell epitopes using propensity scales include PREDITOP [102] and PEOPLE [103] (Table 2). PREDITOP [102] uses a multiparametric algorithm based on hydrophilicity, accessibility, flexibility, and secondary structure properties of the amino acids. PEOPLE [103] uses the same parameters and in addition includes the assessment of β-turns. A related method to predict B-cell epitopes was introduced by Kolaskar and Tongaonkar [104], consisting on a simple antigenicity scale derived from physicochemical properties and frequencies of amino acids in experimentally determined B-cell epitopes. This index is perhaps the most popular antigenic scale for B-cell epitope prediction, and it is actually implemented by GCG [105] and EMBOSS [106] packages. Comparative evaluations of propensity scales carried out in a dataset of 85 linear B-cell epitopes showed that most propensity scales predicted between 50 and 70% of B-cell epitopes, with the β-turn scale reaching the best values [101, 107]. It has also been shown that combining the different scales does not appear to improve predictions [102, 108]. Moreover, Blythe and Flower [109] demonstrated that single-scale amino acid propensity scales are not reliable to predict epitope location.

There are several available methods to predict conformational B-cell epitopes (Table 2). The first to be introduced was CEP [118], which relied almost entirely on predicting patches of solvent-exposed residues. It was followed by DiscoTope [119], which, in addition to solvent accessibility, considered amino acid statistics and spatial information to predict conformational B-cell epitopes. An independent evaluation of these two methods using a benchmark dataset of 59 conformational epitopes revealed that they did not exceed a 40% of precision and a 46% of recall [120]. Subsequently, more methods were developed, like ElliPro [121] that aims to identify protruding regions in antigen surfaces and PEPITO [122] and SEPPA [123] that combine single physicochemical properties of amino acids and geometrical structure properties. The reported area under the curve (AUC) of these methods is around 0.7, which is indicative of a poor discrimination capacity yet better than random. Though, in an independent evaluation, SEPPA reached an AUC of 0.62 while all the mentioned methods had an AUC around 0.5 [124]. ML has also been applied to predict conformational B-cell epitopes in 3D-structures. Relevant examples include EPITOPIA [125] and EPSVR [126] which are based on naïve Bayes classifiers and support vector regressions, respectively, trained on feature vectors combining different scores. The reported AUC of these two methods is around 0.6.

This review outlines the approaches and mechanisms through which peptides and amino acids functionalize electrocatalytically active surfaces to promote or inhibit the electrochemical hydrogen evolution reaction (HER). HER is important in many electrochemical systems. For example, HER is highly desired in water electrolysis, which if driven by renewable energy could serve as a green alternative to the fossil-fuel-driven steam methane-reforming process. However, HER is often an undesired side reaction and thus limits the selectivity of promising electrochemical technologies such as electrochemical nitrogen reduction or carbon dioxide reduction. In pursuing higher product selectivity and yield in emerging and existing electrochemical systems, amino acids and short-chain peptides are promising molecules for the modification of electrochemically active surfaces. Peptides are attractive because they are highly tunable, which allows for versatility in their applications. This short review article summarizes literature that illustrates the mechanisms through which electrode-bound peptides can affect HER including via modulating surface binding and adsorbate coverage, altering the surface composition, and controlling proton transfer rates. Our goal is to motivate additional studies utilizing electrode-bound peptides to modulate electrochemical hydrogen evolution reactions.


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