Enzyme informatics

Computer-aided design and implementation of efficient biosynthetic pathways to produce high added-value products derived from tyrosine in Escherichia coli

Developing efficient bioprocesses requires selecting the best biosynthetic pathways, which can be challenging and time-consuming due to the vast amount of data available in databases and literature. The extension of the shikimate pathway for the biosynthesis of commercially attractive molecules often involves promiscuous enzymes or lacks well-established routes. To address these challenges, we developed a computational workflow integrating enumeration/retrosynthesis algorithms, a toolbox for pathway analysis, enzyme selection tools, and a gene discovery pipeline, supported by manual curation and literature review. Our focus has been on implementing biosynthetic pathways for tyrosine-derived compounds, specifically L-3,4-dihydroxyphenylalanine (L-DOPA) and dopamine, with significant applications in health and nutrition. We selected one pathway to produce L-DOPA and two different pathways for dopamine-one already described in the literature and a novel pathway. Our goal was either to identify the most suitable gene candidates for expression in Escherichia coli for the known pathways or to discover innovative pathways. Although not all implemented pathways resulted in the accumulation of target compounds, in our shake-flask experiments we achieved a maximum L-DOPA titer of 0.71 g/L and dopamine titers of 0.29 and 0.21 g/L for known and novel pathways, respectively. In the case of L-DOPA, we utilized, for the first time, a mutant version of tyrosinase from Ralstonia solanacearum. Production of dopamine via the known biosynthesis route was accomplished by coupling the L-DOPA pathway with the expression of DOPA decarboxylase from Pseudomonas putida, resulting in a unique biosynthetic pathway never reported in literature before. In the context of the novel pathway, dopamine was produced using tyramine as the intermediate compound. To achieve this, tyrosine was initially converted into tyramine by expressing TDC from Levilactobacillus brevis, which, in turn, was converted into dopamine through the action of the enzyme encoded by ppoMP from Mucuna pruriens. This marks the first time that an alternative biosynthetic pathway for dopamine has been validated in microbes. These findings underscore the effectiveness of our computational workflow in facilitating pathway enumeration and selection, offering the potential to uncover novel biosynthetic routes, thus paving the way for other target compounds of biotechnological interest.

Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges

Machine learning (ML), particularly deep learning (DL), has made rapid and substantial progress in synthetic biology in recent years. Biotechnological applications of biosystems, including pathways, enzymes, and whole cells, are being probed frequently with time. The intricacy and interconnectedness of biosystems make it challenging to design them with the desired properties. ML and DL have a synergy with synthetic biology. Synthetic biology can be employed to produce large data sets for training models (for instance, by utilizing DNA synthesis), and ML/DL models can be employed to inform design (for example, by generating new parts or advising unrivaled experiments to perform). This potential has recently been brought to light by research at the intersection of engineering biology and ML/DL through achievements like the design of novel biological components, best experimental design, automated analysis of microscopy data, protein structure prediction, and biomolecular implementations of ANNs (Artificial Neural Networks). I have divided this review into three sections. In the first section, I describe predictive potential and basics of ML along with myriad applications in synthetic biology, especially in engineering cells, activity of proteins, and metabolic pathways. In the second section, I describe fundamental DL architectures and their applications in synthetic biology. Finally, I describe different challenges causing hurdles in the progress of ML/DL and synthetic biology along with their solutions.

Enzymatic degradation of cellulose in soil: A review

Cellulose degradation is a critical process in soil ecosystems, playing a vital role in nutrient cycling and organic matter decomposition. Enzymatic degradation of cellulosic biomass is the most sustainable and green method of producing liquid biofuel. It has gained intensive research interest with future perspective as the majority of terrestrial lignocellulose biomass has a great potential to be used as a source of bioenergy. However, the recalcitrant nature of lignocellulose limits its use as a source of energy. Noteworthy enough, enzymatic conversion of cellulose biomass could be a leading future technology. Fungal enzymes play a central role in cellulose degradation. Our understanding of fungal cellulases has substantially redirected in the past few years with the discovery of a new class of enzymes and Cellulosome. Efforts have been made from time to time to develop an economically viable method of cellulose degradation. This review provides insights into the current state of knowledge regarding cellulose degradation in soil and identifies areas where further research is needed.

Synthetic Biology Meets Machine Learning

This chapter outlines the myriad applications of machine learning (ML) in synthetic biology, specifically in engineering cell and protein activity, and metabolic pathways. Though by no means comprehensive, the chapter highlights several prominent computational tools applied in the field and their potential use cases. The examples detailed reinforce how ML algorithms can enhance synthetic biology research by providing data-driven insights into the behavior of living systems, even without detailed knowledge of their underlying mechanisms. By doing so, ML promises to increase the efficiency of research projects by modeling hypotheses in silico that can then be tested through experiments. While challenges related to training dataset generation and computational costs remain, ongoing improvements in ML tools are paving the way for smarter and more streamlined synthetic biology workflows that can be readily employed to address grand challenges across manufacturing, medicine, engineering, agriculture, and beyond.

Development of Targeted Protein-Displaying Technology with a Novel Carbon Material

This study reports a new carbon material and its specific display of targeted protein. The properties of the carbon materials fabricated with carbon black MOGUL^® were analyzed. The carbon materials were spherical structures with 55.421 µm as a median value. The specific surface area, pore volume, average pore diameter, and total of the acidic functional group were 130 m²·g^-1, 0.55 cm³·g^-1, 17.2 nm, and 0.29 mEq·g^-1, respectively. The adsorption-desorption isoform of the carbon materials showed type IV of the hysteresis loop as defined by IUPAC, indicating non-uniform mesoporous structures (2-50 nm). The distribution of the log differential pore volume also indicated non-uniform porous structures because (i) the difference between the average pore size and the most frequent pore size was significant and (ii) the σ value was larger than the average value regarding the pore sizes. However, 10-90% of the integrated values of the log differential pore volume were 57.4% of the total integrated values, and the distribution was similar to the Gauss distribution model. Although the value of the total of the acidic functional group was 2.5-5.4 times lower than the values of the HPLC columns, the carbon materials require good scaffold quality rather than good HPLC quality. Therefore, the amounts could be enough for the scaffold of biotin hydrazide. To demonstrate the property of displaying the targeted proteins, carbon materials displaying biotin hydrazide by covalent bonding were prepared and avidin-labeled horse radish peroxidase (HRP) was bound to the biotin region. The carbon materials were porous structures, so the unspecific adsorption of HRP was estimated. Then, the maintenance ratios of HRP activities were analyzed in the repeated-use-with-wash processes after each evaluation, resulting in the activities of HRP on the carbon materials being treated with biotin hydrazide being significantly maintained compared to that of the ones without biotin hydrazide. The study revealed the properties of the carbon materials and indicated the display of HRP, suggesting that the carbon materials could be a new material for displaying targeted proteins.

Metabolic networks of the Nicotiana genus in the spotlight: content, progress and outlook

Manually curated metabolic databases residing at the Sol Genomics Network comprise two taxon-specific databases for the Solanaceae family, i.e. SolanaCyc and the genus Nicotiana, i.e. NicotianaCyc as well as six species-specific databases for Nicotiana tabacum TN90, N. tabacum K326, Nicotiana benthamiana, N. sylvestris, N. tomentosiformis and N. attenuata. New pathways were created through the extraction, examination and verification of related data from the literature and the aid of external database guided by an expert-led curation process. Here we describe the curation progress that has been achieved in these databases since the first release version 1.0 in 2016, the curation flow and the curation process using the example metabolic pathway for cholesterol in plants. The current content of our databases comprises 266 pathways and 36 superpathways in SolanaCyc and 143 pathways plus 21 superpathways in NicotianaCyc, manually curated and validated specifically for the Solanaceae family and Nicotiana genus, respectively. The curated data have been propagated to the respective Nicotiana-specific databases, which resulted in the enrichment and more accurate presentation of their metabolic networks. The quality and coverage in those databases have been compared with related external databases and discussed in terms of literature support and metabolic content.

Machine learning for metabolic engineering: A review

Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner in terms of data management, algorithm libraries, computational resources, and important non-technical issues. A variety of applications ranging from pathway construction and optimization, to genetic editing optimization, cell factory testing, and production scale-up are discussed. Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed. Finally, the future perspectives and most promising directions for this combination of disciplines are examined.

Are the Sublimation Thermodynamics of Organic Molecules Predictable?

We compare a range of computational methods for the prediction of sublimation thermodynamics (enthalpy, entropy, and free energy of sublimation). These include a model from theoretical chemistry that utilizes crystal lattice energy minimization (with the DMACRYS program) and quantitative structure property relationship (QSPR) models generated by both machine learning (random forest and support vector machines) and regression (partial least squares) methods. Using these methods we investigate the predictability of the enthalpy, entropy and free energy of sublimation, with consideration of whether such a method may be able to improve solubility prediction schemes. Previous work has suggested that the major source of error in solubility prediction schemes involving a thermodynamic cycle via the solid state is in the modeling of the free energy change away from the solid state. Yet contrary to this conclusion other work has found that the inclusion of terms such as the enthalpy of sublimation in QSPR methods does not improve the predictions of solubility. We suggest the use of theoretical chemistry terms, detailed explicitly in the Methods section, as descriptors for the prediction of the enthalpy and free energy of sublimation. A data set of 158 molecules with experimental sublimation thermodynamics values and some CSD refcodes has been collected from the literature and is provided with their original source references.

New light on bacterial carbonic anhydrases phylogeny based on the analysis of signal peptide sequences

Among protein families, carbonic anhydrases (CAs, EC 4.2.1.1) are metalloenzymes characterized by a common reaction mechanism in all life domains: the carbon dioxide hydration to bicarbonate and protons (CO2+H2O ⇔ HCO3(-)+H(+)). Six genetically distinct CA families are known to date, the α-, β-, γ-, δ-, ζ- and η-CAs. The last CA class was recently discovered analyzing the amino acid sequences of CAs from Plasmodia. Bacteria encode for enzymes belonging to the α-, β-, and γ-CA classes and recently, phylogenetic analysis revealed an interesting relationship regarding the evolution of bacterial CA classes. This result evidenced that the three bacterial CA classes, in spite of the high level of the structural similarity, are evolutionarily distinct, but we noted that the primary structure of some β-CAs identified in the genome of Gram-negative bacteria present a pre-sequence of 18 or more amino acid residues at the N-terminal part. These observations and subsequent phylogenetic data presented here prompted us to propose that the β-CAs found in Gram-negative bacteria with a periplasmic space and characterized by the presence of a signal peptide might have a periplasmic localization and a role similar to that described previously for the α-CAs.

Semisupervised Gaussian Process for Automated Enzyme Search

Synthetic biology is today harnessing the design of novel and greener biosynthesis routes for the production of added-value chemicals and natural products. The design of novel pathways often requires a detailed selection of enzyme sequences to import into the chassis at each of the reaction steps. To address such design requirements in an automated way, we present here a tool for exploring the space of enzymatic reactions. Given a reaction and an enzyme the tool provides a probability estimate that the enzyme catalyzes the reaction. Our tool first considers the similarity of a reaction to known biochemical reactions with respect to signatures around their reaction centers. Signatures are defined based on chemical transformation rules by using extended connectivity fingerprint descriptors. A semisupervised Gaussian process model associated with the similar known reactions then provides the probability estimate. The Gaussian process model uses information about both the reaction and the enzyme in providing the estimate. These estimates were validated experimentally by the application of the Gaussian process model to a newly identified metabolite in Escherichia coli in order to search for the enzymes catalyzing its associated reactions. Furthermore, we show with several pathway design examples how such ability to assign probability estimates to enzymatic reactions provides the potential to assist in bioengineering applications, providing experimental validation to our proposed approach. To the best of our knowledge, the proposed approach is the first application of Gaussian processes dealing with biological sequences and chemicals, the use of a semisupervised Gaussian process framework is also novel in the context of machine learning applied to bioinformatics. However, the ability of an enzyme to catalyze a reaction depends on the affinity between the substrates of the reaction and the enzyme. This affinity is generally quantified by the Michaelis constant KM. Therefore, we also demonstrate using Gaussian process regression to predict KM given a substrate-enzyme pair.

Computational Tools Applied to Enzyme Design − a review

The protein design toolbox has been greatly improved by the addition of enzyme computational simulations. Not only do they warrant a more ambitious and thorough exploration of sequence space, but a much higher number of variants and protein-ligand systems can be analyzed in silico compared to experimental engineering methods. Modern computational tools are being used to redesign and also for de novo generation of enzymes. These approaches are contingent on a deep understanding of the reaction mechanism and the enzyme’s three-dimensional structure coordinates, but the wealth of information produced by these analyses leads to greatly improved or even totally new types of catalysis.

One origin for metallo-β-lactamase activity, or two? An investigation assessing a diverse set of reconstructed ancestral sequences based on a sample of phylogenetic trees

Bacteria use metallo-β-lactamase enzymes to hydrolyse lactam rings found in many antibiotics, rendering them ineffective. Metallo-β-lactamase activity is thought to be polyphyletic, having arisen on more than one occasion within a single functionally diverse homologous superfamily. Since discovery of multiple origins of enzymatic activity conferring antibiotic resistance has broad implications for the continued clinical use of antibiotics, we test the hypothesis of polyphyly further; if lactamase function has arisen twice independently, the most recent common ancestor (MRCA) is not expected to possess lactam-hydrolysing activity. Two major problems present themselves. Firstly, even with a perfectly known phylogeny, ancestral sequence reconstruction is error prone. Secondly, the phylogeny is not known, and in fact reconstructing a single, unambiguous phylogeny for the superfamily has proven impossible. To obtain a more statistical view of the strength of evidence for or against MRCA lactamase function, we reconstructed a sample of 98 MRCAs of the metallo-β-lactamases, each based on a different tree in a bootstrap sample of reconstructed phylogenies. InterPro sequence signatures and homology modelling were then used to assess our sample of MRCAs for lactamase functionality. Only 5 % of these models conform to our criteria for metallo-β-lactamase functionality, suggesting that the ancestor was unlikely to have been a metallo-β-lactamase. On the other hand, given that ancestral proteins may have had metallo-β-lactamase functionality with variation in sequence and structural properties compared with extant enzymes, our criteria are conservative, estimating a lower bound of evidence for metallo-β-lactamase functionality but not an upper bound.