Machine Learning for Enzyme Promiscuity

With the discovery of an increasing number of catalytically promiscuous enzymes, which are capable of catalyzing multiple reactions, the traditional view of enzymes as highly specific proteins has been brought into question. The significant implications of protein promiscuity for the theory of enzyme evolution suggest that this inherent feature can be utilized as the seed for engineering new functions in biotechnology and synthetic biology as well as in drug design. Therefore, understanding protein promiscuity is becoming even more important as it provides new insights into the evolutionary process that has led to such vast functional diversity. While there have been numerous efforts devoted to recognizing the determinants of promiscuity, till date, this pertinent question regarding the distinctions between specialized enzymes and promiscuous enzymes has remained unanswered. As an in silico approach, in this thesis, we attempt to find a predictive model which can accurately classify unseen proteins into catalytically promiscuous and non-promiscuous. To this end, we exploit different representations and properties of proteins, and adopt different computational approaches accordingly. The role of proteins sequences as indicators of promiscuity is investigated by means of the BLAST algorithm as well as string kernels. Additionally, to validate the interplay between proteins' three-dimensional structures and their promiscuous behaviors, we employ a novel method which is modeling the topological details of proteins as graphs. Graph kernel functions are then applied to measure the structural similarities between the 3D structures of proteins. The classification is performed using SVM as a kernel-based method. The results indicate that proteins' sequences have limited bearings on promiscuity. Conversely, proteins' 3D structures can reliably predict whether a protein has promiscuous activities with an accuracy of 96%. Our best results are achieved using the Weisfeiler-Lehman subtree graph kernel and the secondary structure information of proteins

Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics.

Novel metabolites distinct from canonical pathways can be identified through the integration of three cheminformatics tools: BinVestigate, which queries the BinBase gas chromatography-mass spectrometry (GC-MS) metabolome database to match unknowns with biological metadata across over 110,000 samples; MS-DIAL 2.0, a software tool for chromatographic deconvolution of high-resolution GC-MS or liquid chromatography-mass spectrometry (LC-MS); and MS-FINDER 2.0, a structure-elucidation program that uses a combination of 14 metabolome databases in addition to an enzyme promiscuity library. We showcase our workflow by annotating N-methyl-uridine monophosphate (UMP), lysomonogalactosyl-monopalmitin, N-methylalanine, and two propofol derivatives

novoPathFinder: a webserver of designing novel-pathway with integrating GEM-model

To increase the number of value-added chemicals that can be produced by metabolic engineering and synthetic biology, constructing metabolic space with novel reactions/pathways is crucial. However, with the large number of reactions that existed in the metabolic space and complicated metabolisms within hosts, identifying novel pathways linking two molecules or heterologous pathways when engineering a host to produce a target molecule is an arduous task. Hence, we built a user-friendly web server, novoPathFinder, which has several features: (i) enumerate novel pathways between two specified molecules without considering hosts; (ii) construct heterologous pathways with known or putative reactions for producing target molecule within Escherichia coli or yeast without giving precursor; (iii) estimate novel pathways with considering several categories, including enzyme promiscuity, Synthetic Complex Score (SCScore) and LD50 of intermediates, overall stoichiometric conversions, pathway length, theoretical yields and thermodynamic feasibility. According to the results, novoPathFinder is more capable to recover experimentally validated pathways when comparing other rule-based web server tools. Besides, more efficient pathways with novel reactions could also be retrieved for further experimental exploration. novoPathFinder is available at http://design.rxnfinder.org/novopathfinder/

Prediction of degradation pathways of phenolic compounds in the human gut microbiota through enzyme promiscuity methods

The relevance of phenolic compounds in the human diet has increased in recent years, particularly due to their role as natural antioxidants and chemopreventive agents in different diseases. In the human body, phenolic compounds are mainly metabolized by the gut microbiota; however, their metabolism is not well represented in public databases and existing reconstructions. In a previous work, using different sources of knowledge, bioinformatic and modelling tools, we developed AGREDA, an extended metabolic network more amenable to analyze the interaction of the human gut microbiota with diet. Despite the substantial improvement achieved by AGREDA, it was not sufficient to represent the diverse metabolic space of phenolic compounds. In this article, we make use of an enzyme promiscuity approach to complete further the metabolism of phenolic compounds in the human gut microbiota. In particular, we apply RetroPath RL, a previously developed approach based on Monte Carlo Tree Search strategy reinforcement learning, in order to predict the degradation pathways of compounds present in Phenol-Explorer, the largest database of phenolic compounds in the literature. Reactions predicted by RetroPath RL were integrated with AGREDA, leading to a more complete version of the human gut microbiota metabolic network. We assess the impact of our improvements in the metabolic processing of various foods, finding previously undetected connections with output microbial metabolites. By means of untargeted metabolomics data, we present in vitro experimental validation for output microbial metabolites released in the fermentation of lentils with feces of children representing different clinical conditions

The Impact of Non-Enzymatic Reactions and Enzyme Promiscuity on Cellular Metabolism during (Oxidative) Stress Conditions.

Cellular metabolism assembles in a structurally highly conserved, but functionally dynamic system, known as the metabolic network. This network involves highly active, enzyme-catalyzed metabolic pathways that provide the building blocks for cell growth. In parallel, however, chemical reactivity of metabolites and unspecific enzyme function give rise to a number of side products that are not part of canonical metabolic pathways. It is increasingly acknowledged that these molecules are important for the evolution of metabolism, affect metabolic efficiency, and that they play a potential role in human disease-age-related disorders and cancer in particular. In this review we discuss the impact of oxidative and other cellular stressors on the formation of metabolic side products, which originate as a consequence of: (i) chemical reactivity or modification of regular metabolites; (ii) through modifications in substrate specificity of damaged enzymes; and (iii) through altered metabolic flux that protects cells in stress conditions. In particular, oxidative and heat stress conditions are causative of metabolite and enzymatic damage and thus promote the non-canonical metabolic activity of the cells through an increased repertoire of side products. On the basis of selected examples, we discuss the consequences of non-canonical metabolic reactivity on evolution, function and repair of the metabolic network.Work in the Ralser lab is funded from the Wellcome Trust (RG 093735/Z/10/Z), the ERC (Starting grant 260809). Markus A. Keller is supported by the Austrian Science Funds by an Erwin Schrödinger postdoctoral fellowship (FWF, J 3341). Markus Ralser is a Wellcome Trust Research Career Development and Wellcome-Beit Prize fellow.This is the final version of the article. It first appeared from MDPI via http://dx.doi.org/10.3390/biom503210

A pharmaceutical model for the molecular evolution of microbial natural products

Abstract Microbes are talented chemists with the ability to generate tremendously complex and diverse natural products which harbor potent biological activities. Natural products are produced using sets of specialized biosynthetic enzymes encoded by secondary metabolism pathways. Here, we present a two-step evolutionary model to explain the diversification of biosynthetic pathways that account for the proliferation of these molecules. We argue that the appearance of natural product families has been a slow and infrequent process. The first step led to the original emergence of bioactive molecules and different classes of natural products. However, much of the chemical diversity observed today has resulted from the endless modification of the ancestral biosynthetic pathways. The second step rapidly modulates the pre-existing biological activities to increase their potency and to adapt to changing environmental conditions. We highlight the importance of enzyme promiscuity in this process, as it facilitates both the incorporation of horizontally transferred genes into secondary metabolic pathways and the functional differentiation of proteins to catalyze novel chemistry. We provide examples where single point mutations or recombination events have been sufficient for new enzymatic activities to emerge. A unique feature in the evolution of microbial secondary metabolism is that gene duplication is not essential but offers opportunities to synthesize more complex metabolites. Microbial natural products are highly important for the pharmaceutical industry due to their unique bioactivities. Therefore, understanding the natural mechanisms leading to the formation of diverse metabolic pathways is vital for future attempts to utilize synthetic biology for the generation of novel molecules.Peer reviewe