Non-isocyanate urethane linkage formation using l -lysine residues as amine sources Abstract Bio-based polyurethane materials are broadly applied in medicine as drug delivery systems. Nevertheless, their synthesis comprises the use of petroleum-based toxic amines, isocyanates and polyols, and their biocompatibility or functionalization is limited. Therefore, the use of lysine residues as amine sources to create non-isocyanate urethane (NIU) linkages was investigated. Therefore, a five-membered biscyclic carbonate (BCC) was firstly synthetized and reacted with a protected lysine, a tripeptide and a heptapeptide to confirm the urethane linkage formation with lysine moiety and to optimize reaction conditions. Afterwards, the reactions between BCC and a model protein, elastin-like protein (ELP), and β-Lactoglobulin (BLG) obtained from whey protein, respectively, were performed. The synthesized protein materials were structural, thermally and morphologically characterized to confirm the urethane linkage formation. The results demonstrate that using both simple and more complex source of amines (lysine), urethane linkages were effectively achieved. This pioneering approach opens the possibility of using proteins to develop non-isocyanate polyurethanes (NIPUs) with tailored properties.

PaleAle 5.0: prediction of protein relative solvent accessibility by deep learning Abstract Predicting the three-dimensional structure of proteins is a long-standing challenge of computational biology, as the structure (or lack of a rigid structure) is well known to determine a protein’s function. Predicting relative solvent accessibility (RSA) of amino acids within a protein is a significant step towards resolving the protein structure prediction challenge especially in cases in which structural information about a protein is not available by homology transfer. Today, arguably the core of the most powerful prediction methods for predicting RSA and other structural features of proteins is some form of deep learning, and all the state-of-the-art protein structure prediction tools rely on some machine learning algorithm. In this article we present a deep neural network architecture composed of stacks of bidirectional recurrent neural networks and convolutional layers which is capable of mining information from long-range interactions within a protein sequence and apply it to the prediction of protein RSA using a novel encoding method that we shall call “clipped”. The final system we present, PaleAle 5.0, which is available as a public server, predicts RSA into two, three and four classes at an accuracy exceeding 80% in two classes, surpassing the performances of all the other predictors we have benchmarked.

Establishment of reference values for the lysine acetylation marker N ɛ -acetyllysine in small volume human plasma samples by a multi-target LC–MS/MS method Abstract Cardiovascular disease (CVD) and chronic kidney disease (CKD) constitute substantial burdens for public health. The identification and validation of risk markers for CVD and CKD in epidemiological studies requires frequent adaption of existing analytical methods as well as development of new methods. In this study, an analytical procedure to simultaneously quantify ten endogenous biomarkers for CVD and CKD is described. An easy-to-handle sample preparation requiring only 20 µL of human plasma is followed by liquid chromatography coupled to tandem mass spectrometry (LC–MS/MS). The method was successfully validated according to established guidelines meeting required criteria for accuracy, precision, recovery, linearity, selectivity, and limits of quantification. The scalability of the method for application in larger cohorts was assessed using a set of plasma samples from healthy volunteers (n = 391) providing first reference values for the recently established biomarker Nɛ-acetyllysine (Nɛ-AcLys). Other biomarkers analyzed were creatinine, β-aminoisobutyric acid (β-AIB), carnitine, 1-methylnicotinamide (1-MNA), citrulline, symmetric dimethylarginine (SDMA), asymmetric dimethylarginine (ADMA), homoarginine (hArg), and ornithine. All obtained results are within reference values specified elsewhere. Overall, these results demonstrate the suitability of the method for simultaneous quantification of ten endogenous biomarkers for CVD and CKD in plasma samples from larger cohorts and allow validation of Nɛ-AcLys as a biomarker in large cohorts.

Cell death and mitochondrial dysfunction induced by the dietary non-proteinogenic amino acid l -azetidine-2-carboxylic acid (Aze) Abstract In addition to the 20 protein amino acids that are vital to human health, hundreds of naturally occurring amino acids, known as non-proteinogenic amino acids (NPAAs), exist and can enter the human food chain. Some NPAAs are toxic through their ability to mimic protein amino acids and this property is utilised by NPAA-containing plants to inhibit the growth of other plants or kill herbivores. The NPAA l-azetidine-2-carboxylic acid (Aze) enters the food chain through the use of sugar beet (Beta vulgaris) by-products as feed in the livestock industry and may also be found in sugar beet by-product fibre supplements. Aze mimics the protein amino acid l-proline and readily misincorporates into proteins. In light of this, we examined the toxicity of Aze to mammalian cells in vitro. We showed decreased viability in Aze-exposed cells with both apoptotic and necrotic cell death. This was accompanied by alterations in endosomal–lysosomal activity, changes to mitochondrial morphology and a significant decline in mitochondrial function. In summary, the results show that Aze exposure can lead to deleterious effects on human neuron-like cells and highlight the importance of monitoring human Aze consumption via the food chain.

Automated feature engineering improves prediction of protein–protein interactions Abstract Over the last decade, various machine learning (ML) and statistical approaches for protein–protein interaction (PPI) predictions have been developed to help annotating functional interactions among proteins, essential for our system-level understanding of life. Efficient ML approaches require informative and non-redundant features. In this paper, we introduce novel types of expert-crafted sequence, evolutionary and graph features and apply automatic feature engineering to further expand feature space to improve predictive modeling. The two-step automatic feature-engineering process encompasses the hybrid method for feature generation and unsupervised feature selection, followed by supervised feature selection through a genetic algorithm (GA). The optimization of both steps allows the feature-engineering procedure to operate on a large transformed feature space with no considerable computational cost and to efficiently provide newly engineered features. Based on GA and correlation filtering, we developed a stacking algorithm GA-STACK for automatic ensembling of different ML algorithms to improve prediction performance. We introduced a unified method, HP-GAS, for the prediction of human PPIs, which incorporates GA-STACK and rests on both expert-crafted and 40% of newly engineered features. The extensive cross validation and comparison with the state-of-the-art methods showed that HP-GAS represents currently the most efficient method for proteome-wide forecasting of protein interactions, with prediction efficacy of 0.93 AUC and 0.85 accuracy. We implemented the HP-GAS method as a free standalone application which is a time-efficient and easy-to-use tool. HP-GAS software with supplementary data can be downloaded from: http://www.vinca.rs/180/tools/HP-GAS.php.

Components of the GABAergic signaling in the peripheral cholinergic synapses of vertebrates: a review Abstract Gamma-aminobutyric acid (GABA) is the main inhibitory neurotransmitter in the mammalian central nervous system. Since the 1970s, many studies have focused on the role of GABA in the mammalian peripheral nervous system, and particularly in the cholinergic synapses. In this review, we present current findings for the cholinergic neurons of vegetative ganglia as well as for the neurons innervating smooth and striated muscles. Synaptic contacts formed by these neurons contain GABA and the enzyme, glutamic acid decarboxylase, which catalyzes the synthesis of GABA from glutamate. Newly formed GABA is released in the cholinergic synapses and mostly all the peripheral cholinergic synaptic contacts contain iono- and metabotropic GABA receptors. Although the underlying molecular mechanism of the release is not well understood, still, it is speculated that GABA is released by a vesicular and/or non-vesicular way via reversal of the GABA transporter. We also review the signaling role of GABA in the peripheral cholinergic synapses by modulating acetylcholine release, but its exact physiological function remains to be elucidated.

Quantitative sequence-activity modeling of ACE peptide originated from milk using ACC–QTMS amino acid indices Abstract Up to now, numerous peptides/hydrolysates derived from casein and whey protein have shown angiotensin-I-converting enzyme (ACE) inhibitory. In this research, quantum topological molecular similarity (QTMS) indices of amino acids were utilized in quantitative sequence-activity modeling (QSAM) to predict the activity of a set of milk-driven peptides with ACE inhibition. Since the derived peptides have not the same number of residues, we overcame this issue by auto cross covariance (ACC) methodology. Then, some QSAMs were built to predict the pIC50 value of ACE peptides derived from Bovine Casein and Whey. The model established an acceptable relationship between the selected variables and the pIC50 of the peptides. To estimate the performance of the developed models, casein and whey proteins from human, goat, bovine and sheep were virtually broken by trypsin and chymotrypsin enzymes and the ACE activity of the resultant virtual peptides were predicted and some new ACE peptides were proposed.

Synthesis, in vitro and cellular antioxidant activity evaluation of novel peptides derived from Saccharomyces cerevisiae protein hydrolysate: structure–function relationship Abstract The relationship between structure and function of primary antioxidant peptide, YR-10 (YGKPVAVPAR) was considered by synthesizing three analogues including YHR-10 (YGKHVAVHAR), GA-8 (GKPVAVPA) and PAR-3 (PAR). Antioxidant activity was determined through in vitro and cellular assays. Substitution of Pro with His in the structure of YR-10 led to significant (P < 0.05) higher ABTS radical scavenging and ferric reducing activity. Following in silico simulated gastrointestinal digestion, Tyr and Arg were omitted, respectively, from N and C-terminal positions and resulted in decreasing DPPH, ABTS radical scavenging, and ferric reducing activities. PAR-3 showed the best inhibitory activity on linoleic acid oxidation. Pretreatment of Caco-2 cells with YR-10, YHR-10, and GA-8 (1000 µM) before exposure to H2O2 (160 µM) resulted in 34.10%, 39.66% and 29.159% reduction in malondialdehyde and 53.52%, 17.02% and 24.71% reduction in protein carbonyl levels. The peptide pretreatment reduced catalase level in cells and PAR-3 exhibited the most protective effects on the viability of cells exposed to oxidative stress.

Regulation of arginine biosynthesis, catabolism and transport in Escherichia coli Abstract Already very early, the study of microbial arginine biosynthesis and its regulation contributed significantly to the development of new ideas and concepts. Hence, the term “repression” was proposed by Vogel (The chemical basis of heredity, The John Hopkins Press, Baltimore, 1957) (in opposition to induction) to describe the relative decrease in acetylornithinase production in Escherichia coli cells upon arginine supplementation, whereas the term “regulon” was coined by Maas and Clark (J Mol Biol 8:365–370, 1964) for the ensemble of arginine biosynthetic genes dispersed over the E. coli chromosome but all subjected to regulation by the trans-acting argR gene product. Since then, unraveling of the molecular mechanisms controlling arginine biosynthesis, catabolism, and transport in and out the cell, have revealed moonlighting activities of enzymes and transcriptional regulators that generate unexpected interconnections between at first sight totally unrelated cellular processes, and have continued to replenish scientific knowledge and stimulated creative thinking. Furthermore, arginine is much more than just a common amino acid for protein synthesis. It may also be used as sole source of nitrogen by E. coli and a source of nitrogen, carbon and energy by many other bacteria. It is a substrate for the synthesis of polyamines, and important for the extreme acid resistance of E. coli. Furthermore, the guanidino group of arginine is well suited to engage in multiple interactions involving hydrogen bonds and ionic interactions with proteins and nucleic acids. Here, we combine major historical discoveries with current state of the art knowledge on arginine biosynthesis, catabolism and transport, and especially the regulation of these processes in E. coli, with reference to other microorganisms.

Surfactin application for a short period (10/20 s) increases the surface wettability of sound dentin Abstract The aim of this study was to evaluate the effect of spreading the lipopeptide surfactin, for short time (10/20 s), on dentin wettability. Study groups were surfactin: 2.8; 1.4; 0.7; 0.35; and 0.175 mg/mL and a control group that received no treatment. Dentin discs (4 mm height) were prepared and polished with 600-grit SiC paper. Contact angle determinations were carried out after microbrush spreading of surfactin on dentin specimens for, respectively, 10 and 20 s. Excess liquid was removed, and after 60 s, the specimens were analyzed in a goniometer using the sessile drop method to measure the contact angle. Results were analyzed by two-way ANOVA (concentration × time) and t student, with α = 0.05. Lower contact angles were obtained for surfactin (0.7 mg/mL) spread for 10 s. However, no statistical difference was observed for surfactin (2.8 mg/mL) applied during 20 s. Higher contact angles were observed for surfactin (0.7 mg/mL) spread for 20 s. In conclusion, dentin wettability is dependent on spreading time and surfactin concentration.