Is EC class predictable from reaction mechanism?

We thank the Scottish Universities Life Sciences Alliance (SULSA) and the Scottish Overseas Research Student Awards Scheme of the Scottish Funding Council (SFC) for financial support.Background: We investigate the relationships between the EC (Enzyme Commission) class, the associated chemical reaction, and the reaction mechanism by building predictive models using Support Vector Machine (SVM), Random Forest (RF) and k-Nearest Neighbours (kNN). We consider two ways of encoding the reaction mechanism in descriptors, and also three approaches that encode only the overall chemical reaction. Both cross-validation and also an external test set are used. Results: The three descriptor sets encoding overall chemical transformation perform better than the two descriptions of mechanism. SVM and RF models perform comparably well; kNN is less successful. Oxidoreductases and hydrolases are relatively well predicted by all types of descriptor; isomerases are well predicted by overall reaction descriptors but not by mechanistic ones. Conclusions: Our results suggest that pairs of similar enzyme reactions tend to proceed by different mechanisms. Oxidoreductases, hydrolases, and to some extent isomerases and ligases, have clear chemical signatures, making them easier to predict than transferases and lyases. We find evidence that isomerases as a class are notably mechanistically diverse and that their one shared property, of substrate and product being isomers, can arise in various unrelated ways. The performance of the different machine learning algorithms is in line with many cheminformatics applications, with SVM and RF being roughly equally effective. kNN is less successful, given the role that non-local information plays in successful classification. We note also that, despite a lack of clarity in the literature, EC number prediction is not a single problem; the challenge of predicting protein function from available sequence data is quite different from assigning an EC classification from a cheminformatics representation of a reaction.Publisher PDFPeer reviewe

Integration of bioinformatics analysis and experimental biocatalysis for a comprehensive approach to the synthesis of renewable polyesters

La crescente domanda di poliesteri funzionalizzabili ha accresciuto l\u2019interesse nello sviluppo di nuove strade per la sintesi biocatalizzata di polimeri, dove gli enzimi sono in grado di rispondere alla sfida di combinare condizioni di reazione sostenibili dal punto di vista ambientale con l\u2019alta selettivit\ue0 ed efficienza della catalisi. Gli enzimi sono un\u2019attrattiva sostenibile ai catalizzatori tossici usati nelle policondensazione, come quelli metallici, stagno in particolare. L\u2019obiettivo di questa tesi \ue8 quello di integrare approcci sperimentali e bioinformatici per lo studio di nuovi biocatalizzatori per le policondensazioni. Una valida alternativa \ue8 infatti rappresentata dagli enzimi, i quali consentono riciclabilit\ue0, assenza di contaminazione del prodotto grazie all\u2019immobilizzazione della proteina. Attualmente, i supporti per l\u2019immobilizzazione sono di natura non rinnovabile (metacrilici e stirenici). In questa tesi (Capitolo 3) si \ue8 esplorata la possibilit\ue0 di utilizzare la lolla di riso \u2013 un economico materiale di scarto lignocellulosico - a questo proposito. Verr\ue0 proposto un confronto tra metodi chemoenzimatici per la funzionalizzazione della lolla, con l\u2019obiettivo di ottenere un supporto di immobilizzazione rinnovabile capace di rispondere alle sfide della green chemistry. Il metodo enzimatico utilizza un sistema laccasi-mediatore con l\u2019inserimento di un linker diamminico. Questo approccio consente di evitare l\u2019utilizzo del periodato di sodio, che \ue8 responsabile di importanti alterazioni nella struttura morfologica della lolla, come dimostrato da microscopia SEM. Candida antarctica Lipasi B e due asparaginasi sono state immobilizzate e testate. La lipasi immobilizzata \ue8 stata utilizzata per sintetizzare un poliestere con l\u2019acido itaconico. Mentre le lipasi sono la pi\uf9 comune scelta per le reazioni di policondensazione, il nostro gruppo si \ue8 concentrato sullo studio di nuove serin idrolasi da utilizzare in questo campo, nello specifico, le cutinasi. Questa classe di enzimi \ue8 gi\ue0 stata utilizzata per catalizzare la sintesi efficiente di poliesteri con monomeri biobased, lavorando in condizioni sostenibili dal punto di vista ambientale. Uno studio bioinformatico approfondito delle cutinasi verr\ue0 proposto nel Capitolo 4 utilizzando i descrittori GRID-based di BioGPS. Il software ha consentito di proiettare una selezione di cutinasi su un modello UPCA (Unsupervised Pattern Cognition Analysis) precedentemente studiato da questo gruppo di ricerca, confermando che l\u2019ambiente chimico-fisico pre-organizzato di Cutinasi 1 da Thermobifida cellulosilytica \ue8 molto simile a quello di Candida antarctica Lipasi B ed \ue8 in grado di offrire ulteriori vantaggi in termini di tipologie di substrato accettati grazie al suo sito attivo molto superficiale. BioGPS \ue8 stato usato anche per generare il \u201ccataloforo\u201d di differenti sottoclassi di serin idrolasi, permettendo di estrarre le caratteristiche minime proprie di ciascuna di esse. Utilizzando il \u201ccataloforo\u201d e studi di dinamica molecolare \ue8 stato possibile chiarire le ragioni alla base delle caratteristiche vantaggiose delle cutinasi nella sintesi di poliesteri.The rising demand for advanced polyesters, displaying new functional properties, has boosted the development of new biocatalysed routes for polymer synthesis, where enzymes concretely respond to the challenge of combining benign conditions with high selectivity and efficient catalysis. Enzymes are attractive sustainable alternatives to toxic catalysts used in polycondensation, such as metal catalysts and tin in particular. Moreover, they enable the synthesis of functional polyesters that are otherwise not easily accessible by using traditional chemical routes. The aim of the present thesis is to integrate experimental and bioinformatics approaches in order to study new biocatalysts to be used in polycondensations. A valid alternative to metal catalysts is represented by enzymes. Biocatalyst recyclability and avoidance of product contamination are usually obtained via enzyme immobilization on solid carriers. Nowadays, non-renewable petrochemical-based supports are used for this purpose, namely methacrylic and styrenic resins. In this thesis (Chapter 3), rice husk - a waste product of rice milling available worldwide at a negligible price - has been explored as an innovative and fully renewable lignocellulosic carrier endowed with morphological complexity and chemical versatility that makes it prone to multiple and benign chemo-enzymatic modifications. A comparison of chemical and enzymatic methods for the functionalization of rice husk has been carried out, enabling the development of a renewable immobilization carrier suitable for responding to the looming challenge of green chemistry. The enzymatic method relies on laccase oxidation using laccase from Trametes spec. and TEMPO-radical mediator, followed by the insertion of a diamine spacer. As compared to the classical cellulose oxidation performed via sodium periodate, the enzymatic method offers the advantage of preserving the morphology of rice husk, as demonstrated by SEM microscopy. Laccase oxidation also assures benign operative conditions. Candida antarctica Lipase B, and two commercially available formulations of asparaginase, were immobilized and tested. In the first case, the lipase was successfully applied in the polycondensation of the biobased monomer dimethyl itaconate whereas the immobilized asparaginases were applied in the hydrolysis of asparagine, a precursor of the toxic acrylamide in food. In addition, lignin removal via alkaline hydrogen peroxide bleaching has been tested as a method for increasing the specific activity of the immobilized formulation. While lipases being the most common alternative for polycondensation reactions, our research group focused on the study of a novel class of serine hydrolases to be used in these kind of reactions, namely the cutinases. The cutinase class proved to catalyse the efficient polycondensation of biobased monomers working in mild conditions in terms of pressure and temperature. A thorough bioinformatics study was carried out based on GRID-based BioGPS descriptors (Chapter 4). BioGPS allowed to project a selection of cutinases on a Unsupervised Pattern Cognition Analysis (UPCA) model previously published by this research group, confirming that the pre-organized physicochemical environment in the active site of Cutinase 1 from Thermobifida cellulosilytica is very similar to the one of Candida antarctica Lipase B, while offering increased capabilities in terms of the size of the substrate accepted, thanks to a superficial and wide active site. The said software was used also to generate the \u201ccatalophor\u201d of different serine hydrolase subfamilies, enabling to extract the structural features that distinguish the various sub-families of serine hydrolases. Exploiting the \u201ccatalophor\u201d tool and molecular dynamics studies it was possible to shed light on the particular behaviour that makes cutinases an advantageous biocatalyst to be used in polycondensation reactions

Criteria for Engineering Cutinases: Bioinformatics Analysis of Catalophores

Cutinases are bacterial and fungal enzymes that catalyze the hydrolysis of natural cutin, a three-dimensional inter-esterified polyester with epoxy-hydroxy fatty acids with chain lengths between 16 and 18 carbon atoms. Due to their ability to accept long chain substrates, cutinases are also effective in catalyzing in vitro both the degradation and synthesis of several synthetic polyesters and polyamides. Here, we present a bioinformatics study that intends to correlate the structural features of cutinases with their catalytic properties to provide rational basis for their effective exploitation, particularly in polymer synthesis and biodegradation. The bioinformatics study used the BioGPS method (Global Positioning System in Biological Space) that computed molecular descriptors based on Molecular Interaction Fields (MIFs) described in the GRID force field. The information was used to generate catalophores, spatial representations of the ability of each enzymatic active site to establish hydrophobic and electrostatic interactions. These tools were exploited for comparing cutinases to other serine-hydrolases enzymes, namely lipases, esterases, amidases and proteases, and for highlighting differences and similarities that might guide rational engineering strategies. Structural features of cutinases with their catalytic properties were correlated. The \u201ccatalophore\u201d of cutinases indicate shared features with lipases and esterases

Quantitative and evolutionary global analysis of enzyme reaction mechanisms

The most widely used classification system describing enzyme-catalysed reactions is the Enzyme Commission (EC) number. Understanding enzyme function is important for both fundamental scientific and pharmaceutical reasons. The EC classification is essentially unrelated to the reaction mechanism. In this work we address two important questions related to enzyme function diversity. First, to investigate the relationship between the reaction mechanisms as described in the MACiE (Mechanism, Annotation, and Classification in Enzymes) database and the main top-level class of the EC classification. Second, how well these enzymes biocatalysis are adapted in nature. In this thesis, we have retrieved 335 enzyme reactions from the MACiE database. We consider two ways of encoding the reaction mechanism in descriptors, and three approaches that encode only the overall chemical reaction. To proceed through my work, we first develop a basic model to cluster the enzymatic reactions. Global study of enzyme reaction mechanism may provide important insights for better understanding of the diversity of chemical reactions of enzymes. Clustering analysis in such research is very common practice. Clustering algorithms suffer from various issues, such as requiring determination of the input parameters and stopping criteria, and very often a need to specify the number of clusters in advance. Using several well known metrics, we tried to optimize the clustering outputs for each of the algorithms, with equivocal results that suggested the existence of between two and over a hundred clusters. This motivated us to design and implement our algorithm, PFClust (Parameter-Free Clustering), where no prior information is required to determine the number of cluster. The analysis highlights the structure of the enzyme overall and mechanistic reaction. This suggests that mechanistic similarity can influence approaches for function prediction and automatic annotation of newly discovered protein and gene sequences. We then develop and evaluate the method for enzyme function prediction using machine learning methods. Our results suggest that pairs of similar enzyme reactions tend to proceed by different mechanisms. The machine learning method needs only chemoinformatics descriptors as an input and is applicable for regression analysis. The last phase of this work is to test the evolution of chemical mechanisms mapped onto ancestral enzymes. This domain occurrence and abundance in modern proteins has showed that the / architecture is probably the oldest fold design. These observations have important implications for the origins of biochemistry and for exploring structure-function relationships. Over half of the known mechanisms are introduced before architectural diversification over the evolutionary time. The other halves of the mechanisms are invented gradually over the evolutionary timeline just after organismal diversification. Moreover, many common mechanisms includes fundamental building blocks of enzyme chemistry were found to be associated with the ancestral fold

Predicting drug metabolism: experiment and/or computation?

Drug metabolism can produce metabolites with physicochemical and pharmacological properties that differ substantially from those of the parent drug, and consequently has important implications for both drug safety and efficacy. To reduce the risk of costly clinical-stage attrition due to the metabolic characteristics of drug candidates, there is a need for efficient and reliable ways to predict drug metabolism in vitro, in silico and in vivo. In this Perspective, we provide an overview of the state of the art of experimental and computational approaches for investigating drug metabolism. We highlight the scope and limitations of these methods, and indicate strategies to harvest the synergies that result from combining measurement and prediction of drug metabolism.This is the accepted manuscript of a paper published in Nature Reviews Drug Discovery (Kirchmair J, Göller AH, Lang D, Kunze J, Testa B, Wilson ID, Glen RC, Schneider G, Nature Reviews Drug Discovery, 2015, 14, 387–404, doi:10.1038/nrd4581). The final version is available at http://dx.doi.org/10.1038/nrd458

The Eighth Central European Conference "Chemistry towards Biology": snapshot

The Eighth Central European Conference "Chemistry towards Biology" was held in Brno, Czech Republic, on 28 August – 1 September 2016The Eighth Central European Conference "Chemistry towards Biology" was held in Brno, Czech Republic, on 28 August-1 September 2016 to bring together experts in biology, chemistry and design of bioactive compounds; promote the exchange of scientific results, methods and ideas; and encourage cooperation between researchers from all over the world. The topics of the conference covered "Chemistry towards Biology", meaning that the event welcomed chemists working on biology-related problems, biologists using chemical methods, and students and other researchers of the respective areas that fall within the common scope of chemistry and biology. The authors of this manuscript are plenary speakers and other participants of the symposium and members of their research teams. The following summary highlights the major points/topics of the meeting

Protein function and inhibitor prediction by statistical learning approach

Ph.DDOCTOR OF PHILOSOPH

Flavor challenges in extruded plant-based meat alternatives: A review

Demand for plant-based meat alternatives has increased in recent years due to concerns about health, ethics, the environment, and animal welfare. Nevertheless, the market share of plant-based meat alternatives must increase significantly if they are to support sustainable food production and consumption. Flavor is an important limiting factor of the acceptability and marketability of plant-based meat alternatives. Undesirable chemosensory perceptions, such as a beany flavor, bitter taste, and astringency, are often associated with plant proteins and products that use them. This study reviewed 276 articles to answer the following five research questions: (1) What are the volatile and nonvolatile compounds responsible for off-flavors? (2) What are the mechanisms by which these flavor compounds are generated? (3) What is the influence of thermal extrusion cooking (the primary structuring technique to transform plant proteins into fibrous products that resemble meat in texture) on the flavor characteristics of plant proteins? (4) What techniques are used in measuring the flavor properties of plant-based proteins and products? (5) What strategies can be used to reduce off-flavors and improve the sensory appeal of plant-based meat alternatives? This article comprehensively discusses, for the first time, the flavor issues of plant-based meat alternatives and the technologies available to improve flavor and, ultimately, acceptability.Peer reviewe

A Metaphysics for the Classification of Chemical Reactions in Practice

This thesis investigates the classification of chemical reactions in practice. It is motivated by the lack of discussion in the natural kind literature on the classification of reactions and other non-entity like things. I appeal to the discipline of chemoinformatics and study a new approach to the classification of reactions which is aimed specifically at meeting the needs of chemists in industry. I show that this methodology consists of three stages; the identification of a type of reaction of interest, the identification of a quantitative structural activity reaction and the importing of this data into a neural network. The output of classification is a reaction landscape which represents the similarity relations that hold between the different reactions. My aim is to outline a metaphysics that is descriptively fit for purpose with respect to my case study. I argue that such a metaphysics must be descriptively accurate, capture appropriate similarity relations and promote explanatory unification. I evaluate the entities and activities ontology proposed by Machamer, Darden and Craver, an ontology consisting on entities and dispositional properties and causal dispositionalism, against my criteria. I argue that none of these accounts are descriptively fit for purpose and that commitment to an ontological category of processes is required alongside commitment to entities and dispositional properties. I suggest that the types of reactions revealed in classification fall in the category of processes. From my analysis of reaction classification throughout the course of my thesis, I generate a list of characteristics associated with reactions and use this to provide an account of the metaphysics underlying the category of processes. My proposal focuses on the relationship between potentiality and actuality in a given chemical reaction