Fantastic [FeFe]-Hydrogenases and Where to Find Them

[FeFe]-hydrogenases are complex metalloenzymes, key to microbial energy metabolism in numerous organisms. During anaerobic metabolism, they dissipate excess reducing equivalents by using protons from water as terminal electron acceptors, leading to hydrogen production. This reaction is coupled to reoxidation of specific redox partners [ferredoxins, NAD(P)H or cytochrome c3], that can be used either individually or simultaneously (via flavin-based electron bifurcation). [FeFe]-hydrogenases also serve additional physiological functions such as H2 uptake (oxidation), H2 sensing, and CO2 fixation. This broad functional spectrum is enabled by a modular architecture and vast genetic diversity, which is not fully explored and understood. This Mini Review summarises recent advancements in identifying and characterising novel [FeFe]-hydrogenases, which has led to expanding our understanding of their multiple roles in metabolism and functional mechanisms. For example, while numerous well-known [FeFe]-hydrogenases are irreversibly damaged by oxygen, some newly discovered enzymes display intrinsic tolerance. These findings demonstrate that oxygen sensitivity varies between different [FeFe]-hydrogenases: in some cases, protection requires the presence of exogenous compounds such as carbon monoxide or sulphide, while in other cases it is a spontaneous built-in mechanism that relies on a reversible conformational change. Overall, it emerges that additional research is needed to characterise new [FeFe]-hydrogenases as this will reveal further details on the physiology and mechanisms of these enzymes that will enable potential impactful applications

[FeFe]-hydrogenases as biocatalysts in bio-hydrogen production

© 2016, Accademia Nazionale dei Lincei. [FeFe]-hydrogenases catalyse H2 production at exceptionally high turnover numbers (up to 104s−1). They are found in a variety of strict or facultative anaerobic microorganisms, such as bacteria of the genus Clostridium, Desulfovibrio, Thermotoga, and eukaryotes ranging from unicellular and coenobial green algae to anaerobic fungi, ciliates and trichomonads. Key to their activity is an organometallic centre, the H-cluster that cooperates tightly with the protein framework to reduce two protons into molecular hydrogen. The assembly of the catalytic site requires a specialised cellular mechanism based on the action of three other enzymes, called maturases: HydE, HydF and HydG. Recent advancements in the recombinant production of [FeFe]-hydrogenases have provided leaps forward in their exploitation in H2 production for clean energy storage. [FeFe]-hydrogenases have been used in several fermentative approaches where microorganisms are engineered to overexpress specific [FeFe]-hydrogenases to convert low-cost materials (e.g. wastes) into H2. [FeFe]-hydrogenases have also been proven to be excellent catalysts in different in vitro devices that can produce hydrogen directly from water, either via water electrolysis or via light-driven mechanisms, thus allowing the direct storage of solar energy into H2

Oxygen Stability in the New [FeFe]-Hydrogenase from Clostridium beijerinckii SM10 (CbA5H)

© 2016 American Chemical Society. The newly isolated Clostridium beijerinckii [FeFe]-hydrogenase CbA5H was characterized by Fourier transform infrared spectroscopy coupled to enzymatic activity assays. This showed for the first time that in this enzyme the oxygen-sensitive active state Hox can be simply and reversibly converted to the oxygen-stable inactive Hinact state. This suggests that oxygen sensitivity is not an intrinsic feature of the catalytic center of [FeFe]-hydrogenases (H-cluster), opening new challenging perspectives on the oxygen sensitivity mechanism as well as new possibilities for exploitation in industrial applications

PROTOtypical Logic Tensor Networks (PROTO-LTN) for Zero Shot Learning

Semantic image interpretation can vastly benefit from approaches that combine sub-symbolic distributed representation learning with the capability to reason at a higher level of abstraction. Logic Tensor Networks (LTNs) are a class of neuro-symbolic systems based on a differentiable, first-order logic grounded into a deep neural network. LTNs replace the classical concept of training set with a knowledge base of fuzzy logical axioms. By defining a set of differentiable operators to approximate the role of connectives, predicates, functions and quantifiers, a loss function is automatically specified so that LTNs can learn to satisfy the knowledge base. We focus here on the subsumption or isOfClass predicate, which is fundamental to encode most semantic image interpretation tasks. Unlike conventional LTNs, which rely on a separate predicate for each class (e.g., dog, cat), each with its own set of learnable weights, we propose a common isOfClass predicate, whose level of truth is a function of the distance between an object embedding and the corresponding class prototype. The PROTOtypical Logic Tensor Networks (PROTO-LTN) extend the current formulation by grounding abstract concepts as parametrized class prototypes in a high-dimensional embedding space, while reducing the number of parameters required to ground the knowledge base. We show how this architecture can be effectively trained in the few and zero-shot learning scenarios. Experiments on Generalized Zero Shot Learning benchmarks validate the proposed implementation as a competitive alternative to traditional embedding-based approaches. The proposed formulation opens up new opportunities in zero shot learning settings, as the LTN formalism allows to integrate background knowledge in the form of logical axioms to compensate for the lack of labelled examples

Atypical effect of temperature tuning on the insertion of the catalytic iron?sulfur center in a recombinant [FeFe]-hydrogenase

© 2015 The Protein Society. The expression of recombinant [FeFe]-hydrogenases is an important step for the production of large amount of these enzymes for their exploitation in biotechnology and for the characterization of the protein-metal cofactor interactions. The correct assembly of the organometallic catalytic site, named H-cluster, requires a dedicated set of maturases that must be coexpressed in the microbial hosts or used for in vitro assembly of the active enzymes. In this work, the effect of the post-induction temperature on the recombinant expression of CaHydA [FeFe]-hydrogenase in E. coli is investigated. The results show a peculiar behavior: the enzyme expression is maximum at lower temperatures (20C), while the specific activity of the purified CaHydA is higher at higher temperature (30C), as a consequence of improved protein folding and active site incorporation

Biocatalyst-artificial metalloenzyme cascade based on alcohol dehydrogenase

© The Royal Society of Chemistry. Chemo-enzymatic cascades of enzymes with transition metal catalysts can offer efficient synthetic strategies, but their development is challenging due to the incompatibility between proteins and transition metal complexes. Rhodium catalysts can be combined with alcohol dehydrogenases to regenerate nicotinamide cofactors using formate as the hydride donor. However, their use is limited, due to binding of the metals to residues on the enzyme surface, leading to mutual enzyme and catalyst inactivation. In this work, we replaced the zinc from Thermoanaerobacter brockii alcohol dehydrogenase (TbADH) with Rh(iii) catalysts possessing nitrogen donor ligands, by covalent conjugation to the active site cysteine, to create artificial metalloenzymes for NADP+ reduction. TbADH was used as protein scaffold for both alcohol synthesis and the recycling of the cofactor, by combination of the chemically modified species with the non-modified recombinant enzyme. Stability studies revealed that the incorporation of the catalysts into the TbADH pocket provided a shielding environment for the metal catalyst, resulting in increased stability of both the recycling catalyst and the ADH. The reduction of a representative ketone using this novel alcohol dehydrogenase-artificial formate dehydrogenase cascade yielded better conversions than in the presence of free metal catalyst

Rudere e natura: progetto per la valorizzazione dei resti del castello di Cantagallo.

Nella valle del Santerno, su un’altura che domina il paesaggio, si trovano i resti del castello di Cantagallo. Abbandonato a sè stesso più di quattro secoli fa, resta come una testimonianza di antichi tumulti, che il degrado e l’incuria stanno facendo lentamente scomparire. Le sue origini restano tutt’ora ignote a causa della carenza di documenti, andati ormai perduti, ma possiamo comunque affermare che ebbe per secoli signori propri e slegati dalle egemonie politiche di Castel del Rio, ad opera dell’allora influente famiglia degli Alidosi. Il castello era considerato come una rocha fortissima, e fu forse per tale motivo che divenne la tana di Ramazzotto de’ Ramazzotti nel 1523, a seguito di una durissima sconfitta. Vi abitò fino al 1534, quando fu costretto a rifugiarsi nell’Appenino Tosco- Romagnolo inseguito dai suoi sudditi, che aveva, per diversi anni, ferocemente sfruttato. Da allora il castello venne abbandonato e fu notato soltanto tre secoli più tardi dal pittore/scenografo Romolo Liverani, il quale ha lasciato le testimonianze più importanti, fonte di domande e ipotesi a cui si è cercato di trovare risposta. Nel corso del tempo si è venuto a formare un profondo legame tra il rudere e la natura circostante, così profondo da essere ormai inscindibile. Il castello non ha subìto alcun intervento di conservazione e risulta in avanzato stato di degrado. Inoltre nessuno, nel corso del tempo, ha mai dedicato uno studio specifico sul manufatto, lasciando il castello avvolto nel mistero e dimenticato dalla collettività. L’obiettivo di questo lavoro, dunque, è proprio quello di ottenere la prima restituzione grafica accurata del manufatto e proporre un progetto di conservazione e valorizzazione dei resti del castello di Cantagall

UninaStudents @ SardiStance: Stance Detection in Italian Tweets - Task A

This document describes a classification system for the SardiStance task at EVALITA 2020. The task consists in classifying the stance of the author of a series of tweets towards a specific discussion topic. The resulting system was specifically developed by the authors as final project for the Natural Language Processing class of the Master in Computer Science at University of Naples Federico II. The proposed system is based on an SVM classifier with a radial basis function as kernel making use of features like 2 char-grams, unigram hashtag and Afinn weight computed on automatic translated tweets. The results are promising in that the system performances are on average higher than that of the baseline proposed by the task organizers.Questo documento descrive un sistema di classificazione per il task SardiStance di EVALITA 2020. Il task consiste nel classificare la posizione dell’autore di una serie di tweets nei confronti di uno specifico topic di discussione. Il sistema risultante è stato specificamente sviluppato dagli autori come progetto finale per il corso di Elaborazione del Linguaggio Naturale nell’ambito del corso di laurea magistrale in Informatica presso l’università degli studi di Napoli Federico II. Il sistema qui proposto si basa su un classificatore SVM con una funzione radiale di base come kernel facendo uso di features come 2 char-grams, unigram hashtag e l’Afinn weight calcolato sui tweet tradotti in automatico. I risultati sono promettenti in quanto le performance sono in media superiori rispetto a quelle della baseline proposta dagli organizzatori del task