EC-BLAST: a tool to automatically search and compare enzyme reactions.

We present EC-BLAST (http://www.ebi.ac.uk/thornton-srv/software/rbl/), an algorithm and Web tool for quantitative similarity searches between enzyme reactions at three levels: bond change, reaction center and reaction structure similarity. It uses bond changes and reaction patterns for all known biochemical reactions derived from atom-atom mapping across each reaction. EC-BLAST has the potential to improve enzyme classification, identify previously uncharacterized or new biochemical transformations, improve the assignment of enzyme function to sequences, and assist in enzyme engineering

From spectrometric data to metabolic networks: an integrated view of cell metabolism

La biologia molecular ha avançat considerablement gràcies a importants progressos com la seqüenciació del ADN o la seva modificació per CRISPR. Tot i això, per entendre el metabolisme requerim estudiar els perfils metabòlics i les seves reaccions metabòliques. L™objectiu d™aquesta tesi és contribuir en aquest estudi del metabolism, el qual unifica dels camps de la proteòmica i la metabolòmica. Tradicionalment, l™anàlisi de dades òmiques es basa en el tractament independent de les diferents variables encara que està profundament establert que els mecanismes moleculars són controlats per la interacció de diferents molècules, i per tant seria més correcte tractar les dades de la mateixa manera. Avui dia, s™han descrit una gran quantitat de vies metabòliques, incluint els enzims responsables de les transformacions dels metabòlits que les formen, aquesta informació s™ha recopilat en bases de dades, que a la vegada poden ser utilitzades per a construir xarxes metabòliques. En aquesta tesi, s™han utilitzat xarxes metabòliques per a desenvolupar un algoritme que prediu metabòlits desregulats basant-se en el perfil d™expressió d™enzims gràcies a proteòmica quantitativa. Per a validar tals prediccions, és possible mesurar l™abundància d™aquests metabòlits, o el seu flux, o sigui la velocitat a la que s™han transformat, utilitzant experiments de marcatge amb isòtops estables, mesures completades mitjançant metabolòmica. Aqui, mostrem els productes del desenvolupament de dos mètodes per a l™anàlisi de dades de metabolòmica per a experiments amb isòtops estables: el primer per a la quantificació dirigida del flux en metabòlits del metabolisme central; i un segon, per la detecció no-dirigida de metabòlits marcats amb isòtops en altres vies metabòliques. Aquests mètodes han sigut provats en diferents estudis on han aportat resultats remarcables, revelant nous mecanismes moleculars en una complicació de la diabetes o en relació al metabolisme del càncer.La biología molecular ha avanzado considerablemente gracias a progresos como la secuenciación de ADN o su modificación por CRISPR. Sin embargo, para entender el metabolismo es indispensable estudiar los perfiles metabólicos y sus reacciones metabólicas. El objetivo de esta tesis es contribuir en el estudio del metabolismo, el cual implica los campos de la proteómica y la metabolómica. Tradicionalmente, el análisis de datos ómicas se basa en el tratamiento independiente de las diferentes variables aunque está profundamente aceptado que los mecanismos moleculares son controlados por la interacción de diferentes moléculas, y por lo tanto sería más correcto tratar los datos de esa manera. Hoy día, se han descrito una gran cantidad de vías metabólicas, incluyendo las enzimas responsables de las transformaciones de los metabolitos que las forman, esta información se ha recopilado en bases de datos, que a su vez pueden ser utilizadas para construir redes metabólicas . En esta tesis, se han utilizado redes metabólicas para desarrollar un algoritmo que predice metabolitos desregulados basándose en el perfil de expresión de enzimas por proteómica cuantitativa. Para validar tales predicciones, es posible medir la abundancia de estos metabolitos, o su flujo, o sea la velocidad a la que se han transformado, utilizando experimentos de marcado con isótopos estables, estas medidas se obtienen por metabolómica. Aquí, mostramos los productos del desarrollo de dos métodos para el análisis de datos de metabolómica para experimentos con isótopos estables: el primero para la cuantificación dirigida del flujo en metabolitos del metabolismo central; y un segundo, para la detección no-dirigida de metabolitos marcados con isótopos en otras vías metabólicas. Estos métodos han sido probados en diferentes estudios donde han aportado resultados interesantes, revelando nuevos mecanismos moleculares en una complicación de la diabetes o en relación al metabolismo del cáncer.Understanding the molecular basis of life has been in the spotlight of biochemistry research for more than a century already. Molecular biology has taken medicine forward thanks to technological breakthroughs like DNA sequencing and CRISPR editing. However, in order to understand metabolism we must rely on the study of metabolite profiles and metabolic reactions. The purpose of this thesis to contribute to this area, which unites the fields of proteomics and metabolomics. Traditionally, omics data analysis treats variables independently even if it is strongly settled that molecular mechanisms involve the interaction of diverse pathways, therefore data should be analyzed correspondingly. A vast amount of metabolic pathways have been described, together with enzymes that are responsible for metabolite transformations, this information has been assembled in databases that, in turn, can be used to build metabolic networks. In here, we use metabolic networks to predict metabolite dysregulation based on quantitative proteomics profiles. To validate the predictions, it is possible to measure the abundance of metabolites or their flux, namely the rate at which they are transformed, using stable isotope labelling experiments, both measurements can be performed by metabolomics. In this thesis, two different metabolomics-based stable isotope labelling approaches have been developed, one for the study of central carbon metabolites and one for the unbiased detection of deregulated fluxes in other metabolic pathways. These approaches have been tested on different datasets and have proven valuable to obtain remarkable results, unraveling molecular mechanisms in diabetes complications or novel metabolic hallmarks of cancer

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/

Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models.

Knowing the catalytic turnover numbers of enzymes is essential for understanding the growth rate, proteome composition, and physiology of organisms, but experimental data on enzyme turnover numbers is sparse and noisy. Here, we demonstrate that machine learning can successfully predict catalytic turnover numbers in Escherichia coli based on integrated data on enzyme biochemistry, protein structure, and network context. We identify a diverse set of features that are consistently predictive for both in vivo and in vitro enzyme turnover rates, revealing novel protein structural correlates of catalytic turnover. We use our predictions to parameterize two mechanistic genome-scale modelling frameworks for proteome-limited metabolism, leading to significantly higher accuracy in the prediction of quantitative proteome data than previous approaches. The presented machine learning models thus provide a valuable tool for understanding metabolism and the proteome at the genome scale, and elucidate structural, biochemical, and network properties that underlie enzyme kinetics

Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations

Computational Studies on Cellular Metabolism:From Biochemical Pathways to Complex Metabolic Networks

Biotechnology promises the biologically and ecologically sustainable production of commodity chemicals, biofuels, pharmaceuticals and other high-value products using industrial platform microorganisms. Metabolic engineering plays a key role in this process, providing the tools for targeted modifications of microbial metabolism to create efficient microbial cell factories that convert low value substrates to value-added chemicals. Engineering microbes for the bioproduction of chemicals has been practiced through three different approaches: (i) optimization of native pathways of a host organism; (ii) incorporation of heterologous pathways in an amenable organism; and finally (iii) design and introduction of synthetic pathways in an organism. So far, the progress that has been made in the biosynthesis of chemicals was mostly achieved using the first two approaches. Nevertheless, many novel biosynthetic pathways for the production of native and non-native compounds that have potential to provide near-theoretical yields and high specific production rates of chemicals remain yet to be discovered. Therefore, the third approach is crucial for the advancement of bio-based production of value-added chemicals. We need to fully comprehend and analyze the existing knowledge of metabolism in order to generate new hypotheses and design de novo pathways. In this thesis, through development and application of efficient computational methods, we took the research path to expand our understanding of cell metabolism with the aim to discover novel knowledge about metabolic networks. We analyze different aspects of metabolism through five distinct studies. In the first study, we begin with a holistic view of the enzymatic reactions across all the species, and we propose a computational approach for identifying all the theoretically possible enzymatic reactions based on the known biochemistry. We organize our results in a web-based database called âAtlas of biochemistryâ. In the second study, we focus on one of the most structurally diverse and ubiquitous constituents of metabolism, the lipid metabolism. Here we propose a computational framework for integrating lipid species with unknown metabolic/catabolic pathways into metabolic networks. In our next study, we investigate the full metabolic capacity of E. coli. We explore computationally all enzymatic potentials of this organism, and we introduce the âSuper E. coliâ, a new and advanced chassis for metabolic engineering studies. Our next contribution concentrates on the development of a new method for the atom-level description of metabolic networks. We demonstrate the significance of our approach through the reconstruction of atom-level map of the E. coli central metabolism. In the last study, we turn our focus on studying the thermodynamics of metabolism and we present our original approach for estimating the thermodynamic properties of an important class of metabolites. So far, the available thermodynamic properties either from experiments or the computational methods are estimated with respect to the standard conditions, which are different from typical biological conditions. Our workflow paves the way for reliable computing of thermochemical properties of biomolecules at biological conditions of temperature and pressure. Finally, in the conclusion chapter, we discuss the outlook of this work and the potential further applications of the computational methods that were developed in this thesis

Transformers and Large Language Models for Chemistry and Drug Discovery

Language modeling has seen impressive progress over the last years, mainly prompted by the invention of the Transformer architecture, sparking a revolution in many fields of machine learning, with breakthroughs in chemistry and biology. In this chapter, we explore how analogies between chemical and natural language have inspired the use of Transformers to tackle important bottlenecks in the drug discovery process, such as retrosynthetic planning and chemical space exploration. The revolution started with models able to perform particular tasks with a single type of data, like linearised molecular graphs, which then evolved to include other types of data, like spectra from analytical instruments, synthesis actions, and human language. A new trend leverages recent developments in large language models, giving rise to a wave of models capable of solving generic tasks in chemistry, all facilitated by the flexibility of natural language. As we continue to explore and harness these capabilities, we can look forward to a future where machine learning plays an even more integral role in accelerating scientific discovery

A treatment of stereochemistry in computer aided organic synthesis

This thesis describes the author’s contributions to a new stereochemical processing module constructed for the ARChem retrosynthesis program. The purpose of the module is to add the ability to perform enantioselective and diastereoselective retrosynthetic disconnections and generate appropriate precursor molecules. The module uses evidence based rules generated from a large database of literature reactions. Chapter 1 provides an introduction and critical review of the published body of work for computer aided synthesis design. The role of computer perception of key structural features (rings, functions groups etc.) and the construction and use of reaction transforms for generating precursors is discussed. Emphasis is also given to the application of strategies in retrosynthetic analysis. The availability of large reaction databases has enabled a new generation of retrosynthesis design programs to be developed that use automatically generated transforms assembled from published reactions. A brief description of the transform generation method employed by ARChem is given. Chapter 2 describes the algorithms devised by the author for handling the computer recognition and representation of the stereochemical features found in molecule and reaction scheme diagrams. The approach is generalised and uses flexible recognition patterns to transform information found in chemical diagrams into concise stereo descriptors for computer processing. An algorithm for efficiently comparing and classifying pairs of stereo descriptors is described. This algorithm is central for solving the stereochemical constraints in a variety of substructure matching problems addressed in chapter 3. The concise representation of reactions and transform rules as hyperstructure graphs is described. Chapter 3 is concerned with the efficient and reliable detection of stereochemical symmetry in both molecules, reactions and rules. A novel symmetry perception algorithm, based on a constraints satisfaction problem (CSP) solver, is described. The use of a CSP solver to implement an isomorph‐free matching algorithm for stereochemical substructure matching is detailed. The prime function of this algorithm is to seek out unique retron locations in target molecules and then to generate precursor molecules without duplications due to symmetry. Novel algorithms for classifying asymmetric, pseudo‐asymmetric and symmetric stereocentres; meso, centro, and C2 symmetric molecules; and the stereotopicity of trigonal (sp2) centres are described. Chapter 4 introduces and formalises the annotated structural language used to create both retrosynthetic rules and the patterns used for functional group recognition. A novel functional group recognition package is described along with its use to detect important electronic features such as electron‐withdrawing or donating groups and leaving groups. The functional groups and electronic features are used as constraints in retron rules to improve transform relevance. Chapter 5 details the approach taken to design detailed stereoselective and substrate controlled transforms from organised hierarchies of rules. The rules employ a rich set of constraints annotations that concisely describe the keying retrons. The application of the transforms for collating evidence based scoring parameters from published reaction examples is described. A survey of available reaction databases and the techniques for mining stereoselective reactions is demonstrated. A data mining tool was developed for finding the best reputable stereoselective reaction types for coding as transforms. For various reasons it was not possible during the research period to fully integrate this work with the ARChem program. Instead, Chapter 6 introduces a novel one‐step retrosynthesis module to test the developed transforms. The retrosynthesis algorithms use the organisation of the transform rule hierarchy to efficiently locate the best retron matches using all applicable stereoselective transforms. This module was tested using a small set of selected target molecules and the generated routes were ranked using a series of measured parameters including: stereocentre clearance and bond cleavage; example reputation; estimated stereoselectivity with reliability; and evidence of tolerated functional groups. In addition a method for detecting regioselectivity issues is presented. This work presents a number of algorithms using common set and graph theory operations and notations. Appendix A lists the set theory symbols and meanings. Appendix B summarises and defines the common graph theory terminology used throughout this thesis