Transglutaminase 2 (TG2) is a multifunctional protein with diverse catalytic activities and biological roles. Its best studied function is the Ca2+-dependent transamidase activity leading to formation of γ-glutamyl-e-lysine isopeptide crosslinks between proteins or γ-glutamyl-amine derivatives. TG2 has a poorly studied isopeptidase activity cleaving these bonds. We have developed and characterised TG2 mutants which are significantly deficient in transamidase activity while have normal or increased isopeptidase activity (W332F) and vice versa (W278F). The W332F mutation led to significant changes of both the Km and the Vmax kinetic parameters of the isopeptidase reaction of TG2 while its calcium and GTP sensitivity was similar to the wild type enzyme. The W278F mutation resulted in six times elevated amine incorporating transamidase activity demonstrating the regulatory significance of W278 and W332 in TG2 and that mutations can change opposed activities located at the same active site. The further application of our results in cellular systems may help to understand TG2 -driven physiological and pathological processes better and lead to novel therapeutic approaches where an increased amount of cross-linked proteins correlates with the manifestation of degenerative disorders.

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Isopeptidase activity of human transglutaminase 2: disconnection from transamidation and characterization by kinetic parameters

This is the third special issue focused on "Transglutaminases" that is now available on this journal and dedicated to one of the pioneers of these enzymes, John Edward Folk, who died December 2010 [see in this issue Beninati et al. 2012a]. The first edition, "Polyamines and Transglutaminases" was published in Amino Acids, vol 26, no. 4, 2004, with the contribution of two prestigious Guest Editors as Alberto Abbruzzese and Mauro Piacentini. This editorial initiative was followed by the second special issue published in occasion of the 50th years of the discovery of transglutaminase. Indeed, "Transglutaminase 2: 50th Anniversary of the Discovery" Amino Acids, vol 36, no. 4, 2009, was published with the valuable collaboration of Carlo Maria Bergamini and Mauro Piacentini (Beninati et al. 2009). To continue with this editorial tradition, on this occasion, an outstanding board of Guest Editors composed by Francesco Facchiano and Mauro Piacentini has also been invited to promote this initiative and recruit a selected panel of Authors, many of who participated in the first and second edition of the Gordon Conference on Transglutaminases: "Transglutaminases in Human Diseases Processes" chaired by Rickard L Eckert and Kapil Mehta on July 18-23, 2010, and by Kapil Mehta and Mauro Piacentini on July 15-20, 2012, held at Davidson College, NC, USA. In this Amino Acids special issue, the manuscripts were selected to reflect the progress and the future perspectives of transglutaminases.

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Transglutaminases: future perspectives.

Acridines interact with both nucleic acids and proteins. The targeting of these biopolymers is broadly applied in cancer therapy and gene delivery. Interestingly, due to direct interactions of acridines with various enzymes, acridines can be suitable drugs for treatment of neurodegenerative diseases, inflammation, immunological disorders, protozoal diseases, etc.General introduction to the field of acridines is provided including scope and limitations of the field. Some important structures, which are used as drugs, are highlighted. Acridines are heteroaromatic compounds (Figs. 1.1 and 2.1) that were applied for treatment of many diseases since the beginning of 20th century [1]. Namely, aminoacridines possessing higher solubility in physiological liquids were used as antiprotozoal and antibacterial drugs. The growth of transplanted tumor was suppressed by acriflavine (mixture of 3,6-diamino-10-methylacridinium chloride and 3,6-diaminoacridine) in the thirties [2]. In the past, anti-tumor activity of acridines was attributed to their interactions with nucleic acids; however, the more recent studies favor an explanation based on direct interaction of acridines with biologically important proteins [3–10]. According to search in RCSB PDB database conduced in August 2015, there were 48 structures containing acridine skeleton and 30 structures containing 1,2,3,4-tetrahydroacridine, respectively. From these 78 structures only 32 correspond to acridine binding to nucleic acids including one complex between amsacrine-nucleic acids and enzyme topoisomerase II. Thus, more than half of the structures represents direct binding of acridines to proteins. The development of human society has incorporated acridine drugs to daily life of millions of individuals worldwide [6, 10–28]. For selected acridine drugs see Fig. 1.1. Due to high toxicity, compounds 1–4 are applied only as local antiseptic agents. m-Amsacrine (6) is very active against malignant lymphomas and acute leukemia. Asulacrine (7), which combines the substructures of m-amsacrine (6) and DACA (5), has improved pharmacokinetic profile and broader scope of applications against leukemia, Lewis lung tumors, and many solid tumors. Nitracrine (8) serves for treatment of mammary and ovarian tumors. BRACO19 (9) is powerful binder of quadruplexes. Pt-ACRAMTU (10) is mimicking anticancer cisplatin; however, it has different mode of action. Quinacrine (11) can be viewed as a universal medicine for © Springer International Publishing AG 2017 J. Ježek et al., Biomedical Applications of Acridines, Progress in Drug Research 72, DOI 10.1007/978-3-319-63953-6_1 1

Biomedical Applications of Acridines

https://febs.onlinelibrary.wiley.com/doi/epdf/10.1002/2211-5463.12227

Calcium-dependent activation of transglutaminase 2 by nanosecond pulsed electric fields.

Human tissue transglutaminase (TG2) catalyses transamidation and deamidation reactions through a nucleophilic cysteine residue (CYS277). TG2 activity was found to increase in celiac disease, cystic fibrosis, neurodegenerative disorders and cancer. For this, TG2 has received much focus as a target for drug discovery and many inhibitors have been designed and tested. The most important of these have an electrophilic warhead that reacts covalently with CYS277 resulting in an irreversible inhibition of TG2. The work presented in this thesis aimed at the development of computational methods that could aid in the design and testing of potential TG2 inhibitors. 3-D models of TG2 active site were developed starting from published X-ray crystal structures by means of docking experiments with known irreversible inhibitors followed by molecular dynamics (MD) simulations. The models were validated by additional docking runs and MD simulations involving a larger set of compounds with a range of activities against TG2. The models performed reasonably well in the validation process and were, therefore, chosen as active site models of TG2. No straightforward correlation could be found to rank the compounds based on their activities. This was the rationale for the next stage of the work, where the mechanism of inhibition of TG2 by two classes of inhibitors was studied. The covalent-bond-forming events for the inhibitors bearing acrylamide warheads were followed by applying quantum mechanics/molecular mechanics (QM/MM) umbrella sampling MD simulations to the reaction. The produced activation energies correlated well with the biological activities for the inhibitors and a mechanism with an oxyanion intermediate was proposed. The mechanism of inhibition by compounds having sulfonium ion warheads was investigated using reaction path experiments, where a transition state was first identified and verified and was used as a starting point for the reaction path. The activation energies again produced a reasonable correlation with biological activity and an SN2 mechanism was suggested for this inhibition.On a different level, two allosteric inhibitors proposed in the literature were docked into an allosteric site in TG2 predicted by a collaborator from the University of Strathclyde, and docking complexes were subjected to accelerated MD (aMD) to inspect whether the binding would induce significant conformational changes in TG2. The binding of one inhibitor in the predicted site caused bending in TG2 structure that could be a starting event for complete TG2 inactivation. The other inhibitor seemed to produce a similar effect when bound to the original GDP binding site. An even more profound conformational change was reported due to the binding of GDP in its original binding site. aMD, for the simulation times used (400-1000 nanoseconds), was able to represent some large conformational changes in TG2 brought about by the binding of allosteric inhibitors. To sum up, the work presented in this thesis was successful in applying various computational approaches to the analysis of inhibition of TG2 with irreversible and allosteric inhibitors.

Computational investigation of small-molecule human tissue transglutaminase inhibitors

Transglutaminases are ubiquitous enzymes, which catalyze post-translational modifications of proteins. Recently, transglutaminases and tranglutaminase-catalyzed post-translational modification of proteins have been shown to be involved in the molecular mechanisms responsible for several human diseases. Transglutaminase activity has been hypothesized to be involved also in the pathogenetic mechanisms responsible for human neurodegenerative diseases. Neurodegenerative diseases, such as Alzheimer’s disease, Parkinson’s disease, supranuclear palsy, Huntington’s disease and other polyglutamine diseases, are characterized in part by aberrant cerebral transglutaminase activity and by increased cross-linked proteins in affected brains. In this review, we focus on the possible molecular mechanisms by which transglutaminase activity could be involved in the pathogenesis of neurodegenerative diseases, and on the possible therapeutic effects of selective transglutaminase inhibitors for the cure of patients with diseases characterized by aberrant transglutaminase activity.

Possible involvement of transglutaminase-catalyzed reactions in the physiopathology of neurodegenerative diseases.

This review covers the topics of cognitive dynamic systems and their definition following Simon Haykin’s work in the field as well as their application through cognitive radar, cognitive radio and cognitive control. Furthermore, the article presents the topic of cognitive IoT and discusses it under the lens of cognitive dynamic systems referencing research in the field. It also discusses the needs for interoperability between IoT architectures and the need to integrate cognitive radio with future IoT frameworks developments.

A Review of Cognitive Dynamic Systems and Cognitive IoT

Blockchain applications go far beyond cryptocurrency. As an essential blockchain tool, smart contracts are executable programs that establish an agreement between two parties. Millions of dollars of transactions attract hackers at a hastened pace, and cyber-attacks have caused large economic losses in the past. Due to this, the industry is seeking robust and effective methods to detect vulnerabilities in smart contracts to ultimately provide a remedy. The industry has been utilizing static analysis tools to reveal security gaps, which requires an understanding and insight over all possible execution paths to identify known contract vulnerabilities. Yet, the computational complexity increases as the path gets deeper. Recently, researchers have been proposing ML-driven intelligent techniques aiming to improve the efficiency and detection rate. Such solutions can provide quicker and more robust detection options than the traditionally used static analysis tools. As of this publication date, there is currently no published survey paper on smart contract vulnerability detection mechanisms using ML models. In order to set the ground for further development of ML-driven solutions, in this survey paper, we extensively reviewed and summarized a wide variety of ML-driven intelligent detection mechanism from the following databases: Google Scholar, Engineering Village, Springer, Web of Science, Academic Search Premier, and Scholars Portal Journal. In conclusion, we provided our insights on common traits, limitations and advancement of ML-driven solutions proposed for this field.

A survey on ethereum smart contract vulnerability detection using machine learning

In modern industrial settings, the quality of maintenance efforts directly influence equipment’s operational uptime and efficiency. Condition monitoring is a common process employed for predicting the health of a technical asset, whereby a predictive maintenance strategy can be adopted to minimize machine downtime and potential losses. Throughout the field, machine learning (ML) methods have become noteworthy for predicting failures before they occur, thereby preventing significant financial costs and providing a safer workplace environment. These benefits from predictive maintenance techniques, are particularly useful in the context of military equipment. Such equipment is often significantly expensive, and untimely machine failure could result in significant human endangerment. In this paper, a prognostic model (PROGNOS) is proposed to predict military equipment’s remaining useful life (RUL) based on their monitoring signals. The main considerations of PROGNOS are expectation maximization tuned Kalman Filter (EM-KF) for signal filtering, a recently introduced feature extraction algorithm (PCA-mRMR-VIF), and predictive LSTM model with an adaptive sliding window. The viability and performance of the proposed model were tested on a highly complex competition dataset: the NASA aircraft gas turbine engine degradation dataset, wherein readings from multiple sensor channels were recorded for degrading machines. According to testing results, we can confidently say that the proposed PROGNOS model was viable and robust overall, proving its general usefulness on all military equipment that emit signals.

PROGNOS: an automatic remaining useful life (RUL) prediction model for military systems using machine learning

Cognitive dynamic systems are a new field of physical systems inspired by several areas of study such as neuroscience, cognitive science, computer science, mathematics, physics and engineering. Building on Fuster’s paradigm, a system is considered cognitive when it is capable of five fundamental processes to human cognition: the perception-action cycle, memory, attention, intelligence and language. With these capabilities, a cognitive dynamic system can sense its environment, interact with it, and learn from it through continued interactions. The goal of this paper is to provide a thorough review of the cognitive dynamic system framework, along with its theory, applications, and its two special functions: cognitive control and cognitive risk control.

Alessandro Giuliano

Papers

Possible involvement of transglutaminase-catalyzed reactions in the physiopathology of neurodegenerative diseases.

A Review of Cognitive Dynamic Systems and Cognitive IoT

A survey on ethereum smart contract vulnerability detection using machine learning

PROGNOS: an automatic remaining useful life (RUL) prediction model for military systems using machine learning

A Review of Cognitive Dynamic Systems and Its Overarching Functions