Panoramic view of a superfamily of phosphatases through substrate profiling

7,8-Dihydroxyflavone is a direct inhibitor of human and murine pyridoxal phosphatase

Vitamin B6 deficiency has been linked to cognitive impairment in human brain disorders for decades. Still, the molecular mechanisms linking vitamin B6 to these pathologies remain poorly understood, and whether vitamin B6 supplementation improves cognition is unclear as well. Pyridoxal 5'-phosphate phosphatase (PDXP), an enzyme that controls levels of pyridoxal 5'-phosphate (PLP), the co-enzymatically active form of vitamin B6, may represent an alternative therapeutic entry point into vitamin B6-associated pathologies. However, pharmacological PDXP inhibitors to test this concept are lacking. We now identify a PDXP and age-dependent decline of PLP levels in the murine hippocampus that provides a rationale for the development of PDXP inhibitors. Using a combination of small-molecule screening, protein crystallography, and biolayer interferometry, we discover, visualize, and analyze 7,8-dihydroxyflavone (7,8-DHF) as a direct and potent PDXP inhibitor. 7,8-DHF binds and reversibly inhibits PDXP with low micromolar affinity and sub-micromolar potency. In mouse hippocampal neurons, 7,8-DHF increases PLP in a PDXP-dependent manner. These findings validate PDXP as a druggable target. Of note, 7,8-DHF is a well-studied molecule in brain disorder models, although its mechanism of action is actively debated. Our discovery of 7,8-DHF as a PDXP inhibitor offers novel mechanistic insights into the controversy surrounding 7,8-DHF-mediated effects in the brain.

Enhancing Machine-Learning Prediction of Enzyme Catalytic Temperature Optima through Amino Acid Conservation Analysis

Enzymes play a crucial role in various industrial production and pharmaceutical developments, serving as catalysts for numerous biochemical reactions. Determining the optimal catalytic temperature (Topt) of enzymes is crucial for optimizing reaction conditions, enhancing catalytic efficiency, and accelerating the industrial processes. However, due to the limited availability of experimentally determined Topt data and the insufficient accuracy of existing computational methods in predicting Topt, there is an urgent need for a computational approach to predict the Topt values of enzymes accurately. In this study, using phosphatase (EC 3.1.3.X) as an example, we constructed a machine learning model utilizing amino acid frequency and protein molecular weight information as features and employing the K-nearest neighbors regression algorithm to predict the Topt of enzymes. Usually, when conducting engineering for enzyme thermostability, researchers tend not to modify conserved amino acids. Therefore, we utilized this machine learning model to predict the Topt of phosphatase sequences after removing conserved amino acids. We found that the predictive model's mean coefficient of determination (R2) value increased from 0.599 to 0.755 compared to the model based on the complete sequences. Subsequently, experimental validation on 10 phosphatase enzymes with undetermined optimal catalytic temperatures shows that the predicted values of most phosphatase enzymes based on the sequence without conservative amino acids are closer to the experimental optimal catalytic temperature values. This study lays the foundation for the rapid selection of enzymes suitable for industrial conditions.

Descriptor-augmented machine learning for enzyme-chemical interaction predictions

Descriptors play a pivotal role in enzyme design for the greener synthesis of biochemicals, as they could characterize enzymes and chemicals from the physicochemical and evolutionary perspective. This study examined the effects of various descriptors on the performance of Random Forest model used for enzyme-chemical relationships prediction. We curated activity data of seven specific enzyme families from the literature and developed the pipeline for evaluation the machine learning model performance using 10-fold cross-validation. The influence of protein and chemical descriptors was assessed in three scenarios, which were predicting the activity of unknown relations between known enzymes and known chemicals (new relationship evaluation), predicting the activity of novel enzymes on known chemicals (new enzyme evaluation), and predicting the activity of new chemicals on known enzymes (new chemical evaluation). The results showed that protein descriptors significantly enhanced the classification performance of model on new enzyme evaluation in three out of the seven datasets with the greatest number of enzymes, whereas chemical descriptors appear no effect. A variety of sequence-based and structure-based protein descriptors were constructed, among which the esm-2 descriptor achieved the best results. Using enzyme families as labels showed that descriptors could cluster proteins well, which could explain the contributions of descriptors to the machine learning model. As a counterpart, in the new chemical evaluation, chemical descriptors made significant improvement in four out of the seven datasets, while protein descriptors appear no effect. We attempted to evaluate the generalization ability of the model by correlating the statistics of the datasets with the performance of the models. The results showed that datasets with higher sequence similarity were more likely to get better results in the new enzyme evaluation and datasets with more enzymes were more likely beneficial from the protein descriptor strategy. This work provides guidance for the development of machine learning models for specific enzyme families.

Benchmarking machine-readable vectors of chemical reactions on computed activation barriers

In recent years, there has been a surge of interest in predicting computed activation barriers, to enable the acceleration of the automated exploration of reaction networks. Consequently, various predictive approaches have emerged, ranging from graph-based models to methods based on the three-dimensional structure of reactants and products. In tandem, many representations have been developed to predict experimental targets, which may hold promise for barrier prediction as well. Here, we bring together all of these efforts and benchmark various methods (Morgan fingerprints, the DRFP, the CGR representation-based Chemprop, SLATMd, B2Rl2, EquiReact and language model BERT + RXNFP) for the prediction of computed activation barriers on three diverse datasets.

ALDELE: All-Purpose Deep Learning Toolkits for Predicting the Biocatalytic Activities of Enzymes

Rapidly predicting enzyme properties for catalyzing specific substrates is essential for identifying potential enzymes for industrial transformations. The demand for sustainable production of valuable industry chemicals utilizing biological resources raised a pressing need to speed up biocatalyst screening using machine learning techniques. In this research, we developed an all-purpose deep-learning-based multiple-toolkit (ALDELE) workflow for screening enzyme catalysts. ALDELE incorporates both structural and sequence representations of proteins, alongside representations of ligands by subgraphs and overall physicochemical properties. Comprehensive evaluation demonstrated that ALDELE can predict the catalytic activities of enzymes, and particularly, it identifies residue-based hotspots to guide enzyme engineering and generates substrate heat maps to explore the substrate scope for a given biocatalyst. Moreover, our models notably match empirical data, reinforcing the practicality and reliability of our approach through the alignment with confirmed mutation sites. ALDELE offers a facile and comprehensive solution by integrating different toolkits tailored for different purposes at affordable computational cost and therefore would be valuable to speed up the discovery of new functional enzymes for their exploitation by the industry.

Frustration can Limit the Adaptation of Promiscuous Enzymes Through Gene Duplication and Specialisation

Virtually all enzymes catalyse more than one reaction, a phenomenon known as enzyme promiscuity. It is unclear whether promiscuous enzymes are more often generalists that catalyse multiple reactions at similar rates or specialists that catalyse one reaction much more efficiently than other reactions. In addition, the factors that shape whether an enzyme evolves to be a generalist or a specialist are poorly understood. To address these questions, we follow a three-pronged approach. First, we examine the distribution of promiscuity in empirical enzymes reported in the BRENDA database. We find that the promiscuity distribution of empirical enzymes is bimodal. In other words, a large fraction of promiscuous enzymes are either generalists or specialists, with few intermediates. Second, we demonstrate that enzyme biophysics is not sufficient to explain this bimodal distribution. Third, we devise a constraint-based model of promiscuous enzymes undergoing duplication and facing selection pressures favouring subfunctionalization. The model posits the existence of constraints between the catalytic efficiencies of an enzyme for different reactions and is inspired by empirical case studies. The promiscuity distribution predicted by our constraint-based model is consistent with the empirical bimodal distribution. Our results suggest that subfunctionalization is possible and beneficial only in certain enzymes. Furthermore, the model predicts that conflicting constraints and selection pressures can cause promiscuous enzymes to enter a 'frustrated' state, in which competing interactions limit the specialisation of enzymes. We find that frustration can be both a driver and an inhibitor of enzyme evolution by duplication and subfunctionalization. In addition, our model predicts that frustration becomes more likely as enzymes catalyse more reactions, implying that natural selection may prefer catalytically simple enzymes. In sum, our results suggest that frustration may play an important role in enzyme evolution.

Improving enzyme functional annotation by integrating in vitro and in silico approaches: The example of histidinol phosphate phosphatases

Advances in sequencing technologies have led to a rapid growth of public protein sequence databases, whereby the fraction of proteins with experimentally verified function continuously decreases. This problem is currently addressed by automated functional annotations with computational tools, which however lack the accuracy of experimental approaches and are susceptible to error propagation. Here, we present an approach that combines the efficiency of functional annotation by in silico methods with the rigor of enzyme characterization in vitro. First, a thorough experimental analysis of a representative enzyme of a group of homologues is performed which includes a focused alanine scan of the active site to determine a fingerprint of function-determining residues. In a second step, this fingerprint is used in combination with a sequence similarity network to identify putative isofunctional enzymes among the homologues. Using this approach in a proof-of-principle study, homologues of the histidinol phosphate phosphatase (HolPase) from Pseudomonas aeruginosa, many of which were annotated as phosphoserine phosphatases, were predicted to be HolPases. This functional annotation of the homologues was verified by in vitro testing of several representatives and an analysis of the occurrence of annotated HolPases in the corresponding phylogenetic groups. Moreover, the application of the same approach to the homologues of the HolPase from the archaeon Nitrosopumilus maritimus, which is not related to the HolPase from P. aeruginosa and was newly discovered in the course of this work, led to the annotation of the putative HolPase from various archaeal species.

Determination of Phosphoglycolate Phosphatase Activity via a Coupled Reaction Using Recombinant Glycolate Oxidase

Phosphoglycolate phosphatase (PGLP) dephosphorylates 2-phosphoglycolate to glycolate that can be further metabolized to glyoxylate by glycolate oxidase (GOX) via an oxidative reaction that uses O2 and releases H2O2. The oxidation of o-dianisidine by H2O2 catalyzed by a peroxidase can be followed in real time by an absorbance change at 440 nm. Based on these reactions, a spectrophotometric method for measuring PGLP activity using a coupled reaction with recombinant Arabidopsis thaliana GOX is described. This protocol has been used successfully with either purified PGLP or total soluble proteins extracted from Arabidopsis rosette leaves.

Enzyme function and evolution through the lens of bioinformatics

Enzymes have been shaped by evolution over billions of years to catalyse the chemical reactions that support life on earth. Dispersed in the literature, or organised in online databases, knowledge about enzymes can be structured in distinct dimensions, either related to their quality as biological macromolecules, such as their sequence and structure, or related to their chemical functions, such as the catalytic site, kinetics, mechanism, and overall reaction. The evolution of enzymes can only be understood when each of these dimensions is considered. In addition, many of the properties of enzymes only make sense in the light of evolution. We start this review by outlining the main paradigms of enzyme evolution, including gene duplication and divergence, convergent evolution, and evolution by recombination of domains. In the second part, we overview the current collective knowledge about enzymes, as organised by different types of data and collected in several databases. We also highlight some increasingly powerful computational tools that can be used to close gaps in understanding, in particular for types of data that require laborious experimental protocols. We believe that recent advances in protein structure prediction will be a powerful catalyst for the prediction of binding, mechanism, and ultimately, chemical reactions. A comprehensive mapping of enzyme function and evolution may be attainable in the near future.

De novo artificial synthesis of hexoses from carbon dioxide

Developing artificial "CO2-sugar" platforms is meaningful for addressing challenges posed by land scarcity and climate change to the supply of dietary sugar. However, upcycling CO2 into complex polyoxygenated carbohydrates involves several major challenges, including achieving enantioselective and thermodynamically driven transformation and expanding product repertoires while reducing energy consumption. We present a versatile chemoenzymatic roadmap based on aldol condensation, iso/epimerization, and dephosphorylation reactions for asymmetric CO2 and H2 assembly into sugars with perfect stereocontrol. In particular, we developed a minimum ATP consumption and the shortest pathway for bottom-up biosynthesis of the fundamental precursor, fructose-6-phosphate, which is valuable for synthesizing structure-diverse sugars and derivatives. Engineering bottleneck-associated enzyme catalysts aided in the thermodynamically driven synthesis of several energy-dense and functional hexoses, such as glucose and D-allulose, featuring higher titer (63 mmol L-1) and CO2-product conversion rates (25 mmol C L-1 h-1) compared to established in vitro CO2-fixing pathways. This chemical-biological platform demonstrated greater carbon conversion yield than the conventional "CO2-bioresource-sugar" process and could be easily extended to precisely synthesize other high-order sugars from CO2.

Circulating Total Extracellular Vesicles Cargo Are Associated with Age-Related Oxidative Stress and Susceptibility to Cardiovascular Diseases: Exploratory Results from Microarray Data

Aging is a risk factor for many non-communicable diseases such as cardiovascular and neurodegenerative diseases. Extracellular vesicles and particles (EVP) carry microRNAs that may play a role in age-related diseases and may induce oxidative stress. We hypothesized that aging could impact EVP miRNA and impair redox homeostasis, contributing to chronic age-related diseases. Our aims were to investigate the microRNA profiles of circulating total EVPs from aged and young adult animals and to evaluate the pro- and antioxidant machinery in circulating total EVPs. Plasma from 3- and 21-month-old male Wistar rats were collected, and total EVPs were isolated. MicroRNA isolation and microarray expression analysis were performed on EVPs to determine the predicted regulation of targeted mRNAs. Thirty-one mature microRNAs in circulating EVPs were impacted by age and were predicted to target molecules in canonical pathways directly related to cardiovascular diseases and oxidative status. Circulating total EVPs from aged rats had significantly higher NADPH oxidase levels and myeloperoxidase activity, whereas catalase activity was significantly reduced in EVPs from aged animals. Our data shows that circulating total EVP cargo-specifically microRNAs and oxidative enzymes-are involved in redox imbalance in the aging process and can potentially drive cardiovascular aging and, consequently, cardiac disease.

Functional annotation of haloacid dehalogenase superfamily structural genomics proteins

Haloacid dehalogenases (HAD) are members of a large superfamily that includes many Structural Genomics proteins with poorly characterized functionality. This superfamily consists of multiple types of enzymes that can act as sugar phosphatases, haloacid dehalogenases, phosphonoacetaldehyde hydrolases, ATPases, or phosphate monoesterases. Here, we report on predicted functional annotations and experimental testing by direct biochemical assay for Structural Genomics proteins from the HAD superfamily. To characterize the functions of HAD superfamily members, nine representative HAD proteins and 21 structural genomics proteins are analyzed. Using techniques based on computed chemical and electrostatic properties of individual amino acids, the functions of five structural genomics proteins from the HAD superfamily are predicted and validated by biochemical assays. A dehalogenase-like hydrolase, RSc1362 (Uniprot Q8XZN3, PDB 3UMB) is predicted to be a dehalogenase and dehalogenase activity is confirmed experimentally. Four proteins predicted to be sugar phosphatases are characterized as follows: a sugar phosphatase from Thermophilus volcanium (Uniprot Q978Y6) with trehalose-6-phosphate phosphatase and fructose-6-phosphate phosphatase activity; haloacid dehalogenase-like hydrolase from Bacteroides thetaiotaomicron (Uniprot Q8A2F3; PDB 3NIW) with fructose-6-phosphate phosphatase and sucrose-6-phosphate phosphatase activity; putative phosphatase from Eubacterium rectale (Uniprot D0VWU2; PDB 3DAO) as a sucrose-6-phosphate phosphatase; and hypothetical protein from Geobacillus kaustophilus (Uniprot Q5L139; PDB 2PQ0) as a fructose-6-phosphate phosphatase. Most of these sugar phosphatases showed some substrate promiscuity.

Multiplexed Assessment of Promiscuous Non-Canonical Amino Acid Synthase Activity in a Pyridoxal Phosphate-Dependent Protein Family

Pyridoxal phosphate (PLP)-dependent enzymes afford access to a variety of non-canonical amino acids (ncAAs), which are premier buildings blocks for the construction of complex bioactive molecules. The vinylglycine ketimine (VGK) subfamily of PLP-dependent enzymes plays a critical role in sulfur metabolism and is home to a growing set of secondary metabolic enzymes that synthesize γ-substituted ncAAs. Identification of VGK enzymes for biocatalysis faces a distinct challenge because the subfamily contains both desirable synthases as well as lyases that break down ncAAs. Some enzymes have both activities, which may contribute to pervasive mis-annotation. To navigate this complex functional landscape, we used a substrate multiplexed screening approach to rapidly measure the substrate promiscuity of 40 homologs in the VGK subfamily. We found that enzymes involved in transsulfuration are less likely to have promiscuous activities and often possess undesirable lyase activity. Enzymes from direct sulfuration and secondary metabolism generally had a high degree of substrate promiscuity. From this cohort, we identified an exemplary γ-synthase from Caldicellulosiruptor hydrothermalis (CahyGS). This enzyme is thermostable and has high expression (~400 mg protein per L culture), enabling preparative scale synthesis of thioether containing ncAAs. When assayed with l-allylglycine, CahyGS catalyzes a stereoselective γ-addition reaction to afford access to a unique set of γ-methyl branched ncAAs. We determined high-resolution crystal structures of this enzyme that define an open-close transition associated with ligand binding and set the stage for future engineering within this enzyme subfamily.

Molecular determinants of protein evolvability

The plethora of biological functions that sustain life is rooted in the remarkable evolvability of proteins. An emerging view highlights the importance of a protein's initial state in dictating evolutionary success. A deeper comprehension of the mechanisms that govern the evolvability of these initial states can provide invaluable insights into protein evolution. In this review, we describe several molecular determinants of protein evolvability, unveiled by experimental evolution and ancestral sequence reconstruction studies. We further discuss how genetic variation and epistasis can promote or constrain functional innovation and suggest putative underlying mechanisms. By establishing a clear framework for these determinants, we provide potential indicators enabling the forecast of suitable evolutionary starting points and delineate molecular mechanisms in need of deeper exploration.

Contrastive learning in protein language space predicts interactions between drugs and protein targets

Sequence-based prediction of drug-target interactions has the potential to accelerate drug discovery by complementing experimental screens. Such computational prediction needs to be generalizable and scalable while remaining sensitive to subtle variations in the inputs. However, current computational techniques fail to simultaneously meet these goals, often sacrificing performance of one to achieve the others. We develop a deep learning model, ConPLex, successfully leveraging the advances in pretrained protein language models ("PLex") and employing a protein-anchored contrastive coembedding ("Con") to outperform state-of-the-art approaches. ConPLex achieves high accuracy, broad adaptivity to unseen data, and specificity against decoy compounds. It makes predictions of binding based on the distance between learned representations, enabling predictions at the scale of massive compound libraries and the human proteome. Experimental testing of 19 kinase-drug interaction predictions validated 12 interactions, including four with subnanomolar affinity, plus a strongly binding EPHB1 inhibitor (KD = 1.3 nM). Furthermore, ConPLex embeddings are interpretable, which enables us to visualize the drug-target embedding space and use embeddings to characterize the function of human cell-surface proteins. We anticipate that ConPLex will facilitate efficient drug discovery by making highly sensitive in silico drug screening feasible at the genome scale. ConPLex is available open source at https://ConPLex.csail.mit.edu.

In-depth analysis of biocatalysts by microfluidics: An emerging source of data for machine learning

Nowadays, the vastly increasing demand for novel biotechnological products is supported by the continuous development of biocatalytic applications which provide sustainable green alternatives to chemical processes. The success of a biocatalytic application is critically dependent on how quickly we can identify and characterize enzyme variants fitting the conditions of industrial processes. While miniaturization and parallelization have dramatically increased the throughput of next-generation sequencing systems, the subsequent characterization of the obtained candidates is still a limiting process in identifying the desired biocatalysts. Only a few commercial microfluidic systems for enzyme analysis are currently available, and the transformation of numerous published prototypes into commercial platforms is still to be streamlined. This review presents the state-of-the-art, recent trends, and perspectives in applying microfluidic tools in the functional and structural analysis of biocatalysts. We discuss the advantages and disadvantages of available technologies, their reproducibility and robustness, and readiness for routine laboratory use. We also highlight the unexplored potential of microfluidics to leverage the power of machine learning for biocatalyst development.

Demonstrating the utility of sugar-phosphate phosphatases in coupled enzyme assays: galactose-1-phosphate uridylyltransferase as proof-of-concept

During our biochemical characterization of select bacterial phosphatases belonging to the haloacid dehalogenase superfamily of hydrolases, we discovered a strong bias of Salmonella YidA for glucose-1-phosphate (Glc-1-P) over galactose-1-phosphate (Gal-1-P). We sought to exploit this ability of YidA to discriminate these two sugar-phosphate epimers in a simple coupled assay that could be a substitute for current cumbersome alternatives. To this end, we focused on Gal-1-P uridylyltransferase (GalT) that is defective in individuals with classical galactosemia, an inborn disorder. GalT catalyzes the conversion of Gal-1-P and UDP-glucose to Glc-1-P and UDP-galactose. When recombinant YidA was coupled to GalT, the final orthophosphate product (generated from selective hydrolysis of Glc-1-P by YidA) could be easily measured using the inexpensive malachite green reagent. When this new YidA-based colorimetric assay was benchmarked using a recombinant Duarte GalT variant, it yielded kcat/Km values that are ~2.5-fold higher than the standard coupled assay that employs phosphoglucomutase and glucose-6-phosphate dehydrogenase. Although the simpler design of our new GalT coupled assay might find appeal in diagnostics, a testable expectation, we spotlight the GalT example to showcase the untapped potential of sugar-phosphate phosphatases with distinctive substrate-recognition properties for measuring the activity of various metabolic enzymes (e.g. trehalose-6-phosphate synthase, N-acetyl-glucosamine-6-phosphate deacetylase, phosphofructokinase).

Synthesis of a Healthy Sweetener d-Tagatose from Starch Catalyzed by Semiartificial Cell Factories

d-Tagatose is one of the several healthy sweeteners that can be a substitute for sucrose and fructose in our daily life. Whole cell-catalyzed phosphorylation and dephosphorylation previously reported by our group afford a thermodynamic-driven strategy to achieve tagatose production directly from starch with high product yields. Nonetheless, the poor structural stability of cells and difficulty in biocatalyst recycling restrict its practical application. Herein, an efficient and stable semiartificial cell factory (SACF) was developed by constructing an organosilica network (OSN) artificial shell on the cells bearing five thermophilic enzymes to produce tagatose. The OSN artificial shell, the thickness of which can be regulated by changing the tetraethyl silicate concentration, exhibited tunable permeability and superior mechanical strength. In contrast with cells, SACFs showed a relative activity of 99.5% and an extended half-life from 33.3 to 57.8 h. Over 50% of initial activity was retained after 20 reuses. The SACFs can catalyze seven consecutive reactions with tagatose yields of over 40.7% in field applications.

HDF1, a novel flowering time regulator identified in a mutant suppressing sensitivity to red light reduced 1 early flowering

Arabidopsis SENSITIVITY TO RED LIGHT REDUCED 1 (SRR1) delays the transition from vegetative to reproductive development in noninductive conditions. A second-site suppressor screen for novel genes that overcome early flowering of srr1-1 identified a range of suppressor of srr1-1 mutants flowering later than srr1-1 in short photoperiods. Here, we focus on mutants flowering with leaf numbers intermediate between srr1-1 and Col. Ssm67 overcomes srr1-1 early flowering independently of day-length and ambient temperature. Full-genome sequencing and linkage mapping identified a causative SNP in a gene encoding a Haloacid dehalogenase superfamily protein, named HAD-FAMILY REGULATOR OF DEVELOPMENT AND FLOWERING 1 (HDF1). Both, ssm67 and hdf1-1 show increased levels of FLC, indicating that HDF1 is a novel regulator of this floral repressor. HDF1 regulates flowering largely independent of SRR1, as the effect is visible in srr1-1 and in Col, but full activity on FLC may require SRR1. Furthermore, srr1-1 has a delayed leaf initiation rate that is dependent on HDF1, suggesting that SRR1 and HDF1 act together in leaf initiation. Another mutant flowering intermediate between srr1-1 and wt, ssm15, was identified as a new allele of ARABIDOPSIS SUMO PROTEASE 1, previously implicated in the regulation of FLC stability.

How to Recruit a Promiscuous Enzyme to Serve a New Function

Promiscuous enzymes can be recruited to serve new functions when a genetic or environmental change makes catalysis of a novel reaction important for fitness or even survival. Subsequently, gene duplication and divergence can lead to evolution of an efficient and specialized new enzyme. Every organism likely has thousands of promiscuous enzyme activities that provide a vast reservoir of catalytic potential. However, much of this potential may not be accessible. We compiled kinetic parameters for promiscuous reactions catalyzed by 108 enzymes. The median value of k_cat/K_M is a very modest 31 M^-1 s^-1. Based upon the fluxes through metabolic pathways in E. coli, we estimate that many, if not most, promiscuous activities are too inefficient to impact fitness. However, mutations can elevate the level of an insufficient promiscuous activity by increasing enzyme expression, improving k_cat/K_M, or altering concentrations of the promiscuous and native substrates and allosteric regulators. Particularly in large bacterial populations, stochastic mutations may provide a viable pathway for recruitment of even inefficient promiscuous activities.

Experimental and computational analysis of the ancestry of an evolutionary young enzyme from histidine biosynthesis

The conservation of fold and chemistry of the enzymes associated with histidine biosynthesis suggests that this pathway evolved prior to the diversification of Bacteria, Archaea, and Eukaryotes. The only exception is the histidinol phosphate phosphatase (HolPase). So far, non-homologous HolPases that possess distinct folds and belong to three different protein superfamilies have been identified in various phylogenetic clades. However, their evolution has remained unknown to date. Here, we analyzed the evolutionary history of the HolPase from γ-Proteobacteria (HisB-N). It has been argued that HisB-N and its closest homologue d-glycero-d-manno-heptose-1,7-bisphosphate 7-phosphatase (GmhB) have emerged from the same promiscuous ancestral phosphatase. GmhB variants catalyze the hydrolysis of the anomeric d-glycero-d-manno-heptose-1,7-bisphosphate (αHBP or βHBP) with a strong preference for one anomer (αGmhB or βGmhB). We found that HisB-N from Escherichia coli shows promiscuous activity for βHBP but not αHBP, while βGmhB from Crassaminicella sp. shows promiscuous activity for HolP. Accordingly, a combined phylogenetic tree of αGmhBs, βGmhBs, and HisB-N sequences revealed that HisB-Ns form a compact subcluster derived from βGmhBs. Ancestral sequence reconstruction and in vitro analysis revealed a promiscuous HolPase activity in the resurrected enzymes prior to functional divergence of the successors. The following increase in catalytic efficiency of the HolP turnover is reflected in the shape and electrostatics of the active site predicted by AlphaFold. An analysis of the phylogenetic tree led to a revised evolutionary model that proposes the horizontal gene transfer of a promiscuous βGmhB from δ- to γ-Proteobacteria where it evolved to the modern HisB-N.

Functional Metagenomics Approach for the Discovery of Novel Genes Encoding Phosphatase Activity

Phosphate release from inorganic and organic phosphorus compounds can be enzymatically mediated. Phosphate-releasing enzymes, comprising acid and alkaline phosphatases, are recognized as useful biocatalysts in applications such as plant and animal nutrition, bioremediation, and diagnostic analysis. Here, we describe a functional metagenomics approach enabling rapid identification of genes encoding these enzymes. The target genes are detected based on small- and large-insert metagenomic libraries derived from diverse environments. This approach has the potential to unveil entirely new phosphatase families or subfamilies and members of known enzyme classes that hydrolyze phosphomonoester bonds such as phytases. Additionally, we provide a strategy for efficient heterologous expression of phosphatase genes.

Enzyme Activity Prediction of Sequence Variants on Novel Substrates using Improved Substrate Encodings and Convolutional Pooling

Protein engineering is currently being revolutionized by deep learning applications, especially through natural language processing (NLP) techniques. It has been shown that state-of-the-art self-supervised language models trained on entire protein databases capture hidden contextual and structural information in amino acid sequences and are capable of improving sequence-to-function predictions. Yet, recent studies have reported that current compound-protein modeling approaches perform poorly on learning interactions between enzymes and substrates of interest within one protein family. We attribute this to low-grade substrate encoding methods and over-compressed sequence representations received by downstream predictive models. In this study, we propose a new substrate-encoding based on Extended Connectivity Fingerprints (ECFPs) and a convolutional-pooling of the sequence embeddings. Through testing on an activity profiling dataset of haloalkanoate dehalogenase superfamily that measures activities of 218 phosphatases against 168 substrates, we show substantial improvements in predictive performances of compound-protein interaction modeling. In addition, we also test the workflow on three other datasets from the halogenase, kinase and aminotransferase families and show that our pipeline achieves good performance on these datasets as well. We further demonstrate the utility of this downstream model architecture by showing that it achieves good performance with six different protein embeddings, including ESM-1b (Rives et al., 2021), TAPE (Rao et al., 2019), ProtBert, ProtAlbert, ProtT5, and ProtXLNet (Elnaggar et al., 2021). This study provides a new workflow for activity prediction on novel substrates that can be used to engineer new enzymes for sustainability applications.

Adaptive evolvability through direct selection instead of indirect, second-order selection

Can evolvability itself be the product of adaptive evolution? To answer this question is challenging, because any DNA mutation that alters only evolvability is subject to indirect, "second order" selection on the future effects of this mutation. Such indirect selection is weaker than "first-order" selection on mutations that alter fitness, in the sense that it can operate only under restrictive conditions. Here I discuss a route to adaptive evolvability that overcomes this challenge. Specifically, a recent evolution experiment showed that some mutations can enhance both fitness and evolvability through a combination of direct and indirect selection. Unrelated evidence from gene duplication and the evolution of gene regulation suggests that mutations with such dual effects may not be rare. Through such mutations, evolvability may increase at least in part because it provides an adaptive advantage. These observations suggest a research program on the adaptive evolution of evolvability, which aims to identify such mutations and to disentangle their direct fitness effects from their indirect effects on evolvability. If evolvability is itself adaptive, Darwinian evolution may have created more than life's diversity. It may also have helped create the very conditions that made the success of Darwinian evolution possible.

Characterization of inositol lipid metabolism in gut-associated Bacteroidetes

Inositol lipids are ubiquitous in eukaryotes and have finely tuned roles in cellular signalling and membrane homoeostasis. In Bacteria, however, inositol lipid production is relatively rare. Recently, the prominent human gut bacterium Bacteroides thetaiotaomicron (BT) was reported to produce inositol lipids and sphingolipids, but the pathways remain ambiguous and their prevalence unclear. Here, using genomic and biochemical approaches, we investigated the gene cluster for inositol lipid synthesis in BT using a previously undescribed strain with inducible control of sphingolipid synthesis. We characterized the biosynthetic pathway from myo-inositol-phosphate (MIP) synthesis to phosphoinositol dihydroceramide, determined the crystal structure of the recombinant BT MIP synthase enzyme and identified the phosphatase responsible for the conversion of bacterially-derived phosphatidylinositol phosphate (PIP-DAG) to phosphatidylinositol (PI-DAG). In vitro, loss of inositol lipid production altered BT capsule expression and antimicrobial peptide resistance. In vivo, loss of inositol lipids decreased bacterial fitness in a gnotobiotic mouse model. We identified a second putative, previously undescribed pathway for bacterial PI-DAG synthesis without a PIP-DAG intermediate, common in Prevotella. Our results indicate that inositol sphingolipid production is widespread in host-associated Bacteroidetes and has implications for symbiosis.

The pathways responsible for inositol lipid production in human gut Bacteroides are characterized and these lipids are important for capsule expression and antimicrobial peptide resistance in vitro and colonization in vivo.

Engineering substrate specificity of HAD phosphatases and multienzyme systems development for the thermodynamic-driven manufacturing sugars

Naturally, haloacid dehalogenase superfamily phosphatases have been evolved with broad substrate promiscuity; however, strong specificity to a particular substrate is required for developing thermodynamically driven routes for manufacturing sugars. How to alter the intrinsic substrate promiscuity of phosphatases and fit the “one enzyme-one substrate” model remains a challenge. Herein, we report the structure-guided engineering of a phosphatase, and successfully provide variants with tailor-made preference for three widespread phosphorylated sugars, namely, glucose 6-phosphate, fructose 6-phosphate, and mannose 6-phosphate, while simultaneously enhancement in catalytic efficiency. A 12000-fold switch from unfavorite substrate to dedicated one is generated. Molecular dynamics simulations reveal the origin of improved activity and substrate specificity. Furthermore, we develop four coordinated multienzyme systems and accomplish the conversion of inexpensive sucrose and starch to fructose and mannose in excellent yield of 94–96%. This innovative sugar-biosynthesis strategy overcomes the reaction equilibrium of isomerization and provides the promise of high-yield manufacturing of other monosaccharides and polyols.

Haloacid dehalogenase-like phosphatases are widespread across all domains of life and play a crucial role in the regulation of levels of sugar phosphate metabolites in cells. The authors report on the structure-guided engineering of phosphatases for dedicated substrate specificity for the conversion of sucrose and starch into fructose and mannose.

Oxygenases as Powerful Weapons in the Microbial Degradation of Pesticides

Oxygenases, which catalyze the reductive activation of O₂ and incorporation of oxygen atoms into substrates, are widely distributed in aerobes. They function by switching the redox states of essential cofactors that include flavin, heme iron, Rieske non-heme iron, and Fe(II)/α-ketoglutarate. This review summarizes the catalytic features of flavin-dependent monooxygenases, heme iron-dependent cytochrome P450 monooxygenases, Rieske non-heme iron-dependent oxygenases, Fe(II)/α-ketoglutarate-dependent dioxygenases, and ring-cleavage dioxygenases, which are commonly involved in pesticide degradation. Heteroatom release (hydroxylation-coupled hetero group release), aromatic/heterocyclic ring hydroxylation to form ring-cleavage substrates, and ring cleavage are the main chemical fates of pesticides catalyzed by these oxygenases. The diversity of oxygenases, specificities for electron transport components, and potential applications of oxygenases are also discussed. This article summarizes our current understanding of the catalytic mechanisms of oxygenases and a framework for distinguishing the roles of oxygenases in pesticide degradation. Expected final online publication date for the Annual Review of Microbiology, Volume 76 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Proteome-wide landscape of solubility limits in a bacterial cell

Proteins are prone to aggregate when expressed above their solubility limits. Aggregation may occur rapidly, potentially as early as proteins emerge from the ribosome, or slowly, following synthesis. However, in vivo data on aggregation rates are scarce. Here, we classified the Escherichia coli proteome into rapidly and slowly aggregating proteins using an in vivo image-based screen coupled with machine learning. We find that the majority (70%) of cytosolic proteins that become insoluble upon overexpression have relatively low rates of aggregation and are unlikely to aggregate co-translationally. Remarkably, such proteins exhibit higher folding rates compared to rapidly aggregating proteins, potentially implying that they aggregate after reaching their folded states. Furthermore, we find that a substantial fraction (~ 35%) of the proteome remain soluble at concentrations much higher than those found naturally, indicating a large margin of safety to tolerate gene expression changes. We show that high disorder content and low surface stickiness are major determinants of high solubility and are favored in abundant bacterial proteins. Overall, our study provides a global view of aggregation rates and hence solubility limits of proteins in a bacterial cell.

Genomics-Based Reconstruction and Predictive Profiling of Amino Acid Biosynthesis in the Human Gut Microbiome

The human gut microbiota (HGM) have an impact on host health and disease. Amino acids are building blocks of proteins and peptides, also serving as precursors of many essential metabolites including nucleotides, cofactors, etc. Many HGM community members are unable to synthesize some amino acids (auxotrophs), while other members possess complete biosynthetic pathways for these nutrients (prototrophs). Metabolite exchange between auxotrophs and prototrophs affects microbial community structure. Previous studies of amino acid biosynthetic phenotypes were limited to model species or narrow taxonomic groups of bacteria. We analyzed over 2800 genomes representing 823 cultured HGM species with the aim to reconstruct biosynthetic pathways for proteinogenic amino acids. The genome context analysis of incomplete pathway variants allowed us to identify new potential enzyme variants in amino acid biosynthetic pathways. We further classified the studied organisms with respect to their pathway variants and inferred their prototrophic vs. auxotrophic phenotypes. A cross-species comparison was applied to assess the extent of conservation of the assigned phenotypes at distinct taxonomic levels. The obtained reference collection of binary metabolic phenotypes was used for predictive metabolic profiling of HGM samples from several large metagenomic datasets. The established approach for metabolic phenotype profiling will be useful for prediction of overall metabolic properties, interactions, and responses of HGM microbiomes as a function of dietary variations, dysbiosis and other perturbations.

The His23 and Lys79 pair determines the high catalytic efficiency of the inorganic pyrophosphatase of the haloacid dehalogenase superfamily

Haloacid dehalogenase (HAD) superfamily members are mainly phosphomonoesterases, while BT2127 from Bacteroides thetaiotaomicron of the HAD superfamily is identified as an inorganic pyrophosphatase. In this study, to explore the roles of the Lys79 and His23 pair in the hydrolysis reaction of inorganic pyrophosphate (PP_i) catalyzed by BT2127, a series of models were designed. Calculations were performed by using the density functional theory (DFT) method with the dispersion energy D3-B3LYP. The His23 and Lys79 pair plays a key role in the high catalytic efficiency of BT2127 with PPi. First, the His23 and Lys79 pair prompts Asp13 to easily provide a proton to the leaving group, which remarkably reduces the energy barrier of the phospho-transfer step; then, Lys79 provides a proton to the first leaving phosphate group via His23, produces a more electrically stabilized phosphate (H₃PO₄), makes this step exothermal, and further promotes the subsequent phospho-enzyme intermediate hydrolysis. The results suggest that the Lys79-His23 pair helps BT2127 reach high catalytic efficiency by strengthening the acid catalysis. Our study provides detailed chemical insights into the evolution of the inorganic pyrophosphatase function of BT2127 from the phosphomonoesterase of the HAD superfamily and the biomimetic enzyme design.

Machine learning modeling of family wide enzyme-substrate specificity screens

Biocatalysis is a promising approach to sustainably synthesize pharmaceuticals, complex natural products, and commodity chemicals at scale. However, the adoption of biocatalysis is limited by our ability to select enzymes that will catalyze their natural chemical transformation on non-natural substrates. While machine learning and in silico directed evolution are well-posed for this predictive modeling challenge, efforts to date have primarily aimed to increase activity against a single known substrate, rather than to identify enzymes capable of acting on new substrates of interest. To address this need, we curate 6 different high-quality enzyme family screens from the literature that each measure multiple enzymes against multiple substrates. We compare machine learning-based compound-protein interaction (CPI) modeling approaches from the literature used for predicting drug-target interactions. Surprisingly, comparing these interaction-based models against collections of independent (single task) enzyme-only or substrate-only models reveals that current CPI approaches are incapable of learning interactions between compounds and proteins in the current family level data regime. We further validate this observation by demonstrating that our no-interaction baseline can outperform CPI-based models from the literature used to guide the discovery of kinase inhibitors. Given the high performance of non-interaction based models, we introduce a new structure-based strategy for pooling residue representations across a protein sequence. Altogether, this work motivates a principled path forward in order to build and evaluate meaningful predictive models for biocatalysis and other drug discovery applications.

Predicting interactions between compounds and proteins represents a long-standing dream of drug discovery and protein engineering. Robust models of enzyme-substrate scope would dramatically advance our ability to design synthetic routes involving enzymatic catalysis. However, the lack of standardization between compound-protein interaction studies makes it difficult to evaluate the generalizability of such models. In this work we take a critical step forward by standardizing high-quality datasets measuring enzyme-substrate interactions, outlining rigorous evaluations, and proposing a new way to integrate structural information into protein representations. In testing previous modeling approaches, we highlight a surprising inability of existing models to effectively leverage compound-protein interactions to improve generalization, which challenges a perception in the literature. This establishes future opportunities for model development and integration of enzyme-substrate scope models into computer-aided synthesis planning software.

Identification of catabolic pathway for 1-deoxy-D-sorbitol in Bacillus licheniformis

1-Deoxy-D-sorbitol, the 1-deoxy analogue of D-sorbitol, has been detected in human urine as well as in natural herbs and spices. Although there are sporadic reports about 1-deoxy-D-sorbitol dehydrogenase, the complete catabolic pathway of 1-deoxy-D-sorbitol remains unsolved. Informed by the promiscuous activities of fructose-6-phosphate aldolase (FSA) which is involved in the sorbitol (glucitol) utilization (gut) operon and guided by the large scale bioinformatics analysis, we predicted and then experimentally verified the gut operon encoded by Bacillus licheniformis ATCC14580 is responsible for the catabolism of both D-sorbitol and 1-deoxy-D-sorbitol by in vitro activity assays of pathway enzymes, in vivo growth phenotypes, and transcriptomic studies. Moreover, the phylogenetic distribution analysis suggests that the D-sorbitol and 1-deoxy-D-sorbitol catabolic gene cluster is mostly conserved in members of Firmicutes phylum.

Prediction of Protein Sites and Physicochemical Properties Related to Functional Specificity

Specificity Determining Positions (SDPs) are protein sites responsible for functional specificity within a family of homologous proteins. These positions are extracted from a family’s multiple sequence alignment and complement the fully conserved positions as predictors of functional sites. SDP analysis is now routinely used for locating these specificity-related sites in families of proteins of biomedical or biotechnological interest with the aim of mutating them to switch specificities or design new ones. There are many different approaches for detecting these positions in multiple sequence alignments. Nevertheless, existing methods report the potential SDP positions but they do not provide any clue on the physicochemical basis behind the functional specificity, which has to be inferred a-posteriori by manually inspecting these positions in the alignment. In this work, a new methodology is presented that, concomitantly with the detection of the SDPs, automatically provides information on the amino-acid physicochemical properties more related to the change in specificity. This new method is applied to two different multiple sequence alignments of homologous of the well-studied RasH protein representing different cases of functional specificity and the results discussed in detail.

Machine Learning of Reaction Properties via Learned Representations of the Condensed Graph of Reaction

The estimation of chemical reaction properties such as activation energies, rates, or yields is a central topic of computational chemistry. In contrast to molecular properties, where machine learning approaches such as graph convolutional neural networks (GCNNs) have excelled for a wide variety of tasks, no general and transferable adaptations of GCNNs for reactions have been developed yet. We therefore combined a popular cheminformatics reaction representation, the so-called condensed graph of reaction (CGR), with a recent GCNN architecture to arrive at a versatile, robust, and compact deep learning model. The CGR is a superposition of the reactant and product graphs of a chemical reaction and thus an ideal input for graph-based machine learning approaches. The model learns to create a data-driven, task-dependent reaction embedding that does not rely on expert knowledge, similar to current molecular GCNNs. Our approach outperforms current state-of-the-art models in accuracy, is applicable even to imbalanced reactions, and possesses excellent predictive capabilities for diverse target properties, such as activation energies, reaction enthalpies, rate constants, yields, or reaction classes. We furthermore curated a large set of atom-mapped reactions along with their target properties, which can serve as benchmark data sets for future work. All data sets and the developed reaction GCNN model are available online, free of charge, and open source.

Discovery of Two Novel Oxidases Using a High-Throughput Activity Screen

Discovery of novel enzymes is a challenging task, yet a crucial one, due to their increasing relevance as chemical catalysts and biotechnological tools. In our work we present a high-throughput screening approach to discovering novel activities. A screen of 96 putative oxidases with 23 substrates led to the discovery of two new enzymes. The first enzyme, N-acetyl-D-hexosamine oxidase (EC 1.1.3.29) from Ralstonia solanacearum, is a vanillyl alcohol oxidase-like flavoprotein displaying the highest activity with N-acetylglucosamine and N-acetylgalactosamine. Before our discovery of the enzyme, its activity was an orphan one - experimentally characterized but lacking the link to amino acid sequence. The second enzyme, from an uncultured marine euryarchaeota, is a long-chain alcohol oxidase (LCAO, EC 1.1.3.20) active with a range of fatty alcohols, with 1-dodecanol being the preferred substrate. The enzyme displays no sequence similarity to previously characterised LCAOs, and thus is a completely novel representative of a protein with such activity.

Microbial 5'-nucleotidases: their characteristics, roles in cellular metabolism, and possible practical applications

5′-Nucleotidases (EC 3.1.3.5) are enzymes that catalyze the hydrolytic dephosphorylation of 5′-ribonucleotides and 5′-deoxyribonucleotides to their respective nucleosides and phosphate. Most 5′-nucleotidases have broad substrate specificity and are multifunctional enzymes capable of cleaving phosphorus from not only mononucleotide phosphate molecules but also a variety of other phosphorylated metabolites. 5′-Nucleotidases are widely distributed throughout all kingdoms of life and found in different cellular locations. The well-studied vertebrate 5′-nucleotidases play an important role in cellular metabolism. These enzymes are involved in purine and pyrimidine salvage pathways, nucleic acid repair, cell-to-cell communication, signal transduction, control of the ribo- and deoxyribonucleotide pools, etc. Although the first evidence of microbial 5′-nucleotidases was obtained almost 60 years ago, active studies of genetic control and the functions of microbial 5′-nucleotidases started relatively recently. The present review summarizes the current knowledge about microbial 5′-nucleotidases with a focus on their diversity, cellular localizations, molecular structures, mechanisms of catalysis, physiological roles, and activity regulation and approaches to identify new 5′-nucleotidases. The possible applications of these enzymes in biotechnology are also discussed.

Key points

• Microbial 5′-nucleotidases differ in molecular structure, hydrolytic mechanism, and cellular localization.

• 5′-Nucleotidases play important and multifaceted roles in microbial cells.

• Microbial 5′-nucleotidases have wide range of practical applications.

Experimental and computational investigation of enzyme functional annotations uncovers misannotation in the EC 1.1.3.15 enzyme class

Only a small fraction of genes deposited to databases have been experimentally characterised. The majority of proteins have their function assigned automatically, which can result in erroneous annotations. The reliability of current annotations in public databases is largely unknown; experimental attempts to validate the accuracy within individual enzyme classes are lacking. In this study we performed an overview of functional annotations to the BRENDA enzyme database. We first applied a high-throughput experimental platform to verify functional annotations to an enzyme class of S-2-hydroxyacid oxidases (EC 1.1.3.15). We chose 122 representative sequences of the class and screened them for their predicted function. Based on the experimental results, predicted domain architecture and similarity to previously characterised S-2-hydroxyacid oxidases, we inferred that at least 78% of sequences in the enzyme class are misannotated. We experimentally confirmed four alternative activities among the misannotated sequences and showed that misannotation in the enzyme class increased over time. Finally, we performed a computational analysis of annotations to all enzyme classes in the BRENDA database, and showed that nearly 18% of all sequences are annotated to an enzyme class while sharing no similarity or domain architecture to experimentally characterised representatives. We showed that even well-studied enzyme classes of industrial relevance are affected by the problem of functional misannotation.

Correct annotation of genomes is crucial for our understanding and utilization of functional gene diversity, yet the reliability of current protein annotations in public databases is largely unknown. In our work we validated annotations to an S-2-hydroxyacid oxidase enzyme class (EC 1.1.3.15) by assessing activity of 122 representative sequences in a high-throughput screening experiment. From this dataset we inferred that at least 78% of the sequences in the enzyme class are misannotated, and confirmed four alternative activities among the misannotated sequences. We showed that the misannotation is widespread throughout enzyme classes, affecting even well-studied classes of industrial relevance. Overall, our study highlights the value of experimental and computational validation of predicted functions within individual enzyme classes.

Protein residues determining interaction specificity in paralogous families

MOTIVATION

Predicting the residues controlling a protein's interaction specificity is important not only to better understand its interactions but also to design mutations aimed at fine-tuning or swapping them as well.

RESULTS

In this work, we present a methodology that combines sequence information (in the form of multiple sequence alignments) with interactome information to detect that kind of residues in paralogous families of proteins. The interactome is used to define pairwise similarities of interaction contexts for the proteins in the alignment. The method looks for alignment positions with patterns of amino-acid changes reflecting the similarities/differences in the interaction neighborhoods of the corresponding proteins. We tested this new methodology in a large set of human paralogous families with structurally characterized interactions, and discuss in detail the results for the RasH family. We show that this approach is a better predictor of interfacial residues than both, sequence conservation and an equivalent 'unsupervised' method that does not use interactome information.

AVAILABILITY AND IMPLEMENTATION

http://csbg.cnb.csic.es/pazos/Xdet/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Promiscuous Esterases Counterintuitively Are Less Flexible than Specific Ones

Understanding mechanisms of promiscuity is increasingly important from a fundamental and application point of view. As to enzyme structural dynamics, more promiscuous enzymes generally have been recognized to also be more flexible. However, examples for the opposite received much less attention. Here, we exploit comprehensive experimental information on the substrate promiscuity of 147 esterases tested against 96 esters together with computationally efficient rigidity analyses to understand the molecular origin of the observed promiscuity range. Unexpectedly, our data reveal that promiscuous esterases are significantly less flexible than specific ones, are significantly more thermostable, and have a significantly increased specific activity. These results may be reconciled with a model according to which structural flexibility in the case of specific esterases serves for conformational proofreading. Our results signify that an esterase sequence space can be screened by rigidity analyses for promiscuous esterases as starting points for further exploration in biotechnology and synthetic chemistry.

Insights into the functional divergence of the haloacid dehalogenase superfamily from phosphomonoesterase to inorganic pyrophosphatase

The evolution of enzyme catalytic structures and mechanisms has drawn increasing attention. In this study, we investigate the functional divergence from phosphomonoesterase to inorganic pyrophosphatase in the haloacid dehalogenase (HAD) superfamily. In this study, a series of models was constructed, and calculations were performed by using density functional theory with the B3LYP functional. The calculations suggest that in most HAD members, the active-site structure is unstable due to the binding of the substrate inorganic pyrophosphate (PPi), and reactions involving PPi cannot be catalyzed. In BT2127, which is a unique member of the HAD superfamily, the Mg²⁺-coordinating residues Asn172 and Glu47 play a role in stabilizing the active-site structure to adapt to the substrate PPi by providing much stronger coordination interactions with the Mg²⁺ ion. The calculation results suggest that Asn172 and Glu47 are crucial in the evolution of the inorganic pyrophosphatase activity in the HAD superfamily. Our study provides definitive chemical insight into the functional divergence of the HAD superfamily, and helps in understanding the evolution of enzyme catalytic structures and mechanisms.

Genome-wide analysis of haloacid dehalogenase genes reveals their function in phosphate starvation responses in rice

The HAD superfamily is named after the halogenated acid dehalogenase found in bacteria, which hydrolyses a diverse range of organic phosphate substrates. Although certain studies have shown the involvement of HAD genes in Pi starvation responses, systematic classification and bioinformatics analysis of the HAD superfamily in plants is lacking. In this study, 41 and 40 HAD genes were identified by genomic searching in rice and Arabidopsis, respectively. According to sequence similarity, these proteins are divided into three major groups and seven subgroups. Conserved motif analysis indicates that the majority of the identified HAD proteins contain phosphatase domains. A further structural analysis showed that HAD proteins have four conserved motifs and specified cap domains. Fewer HAD genes show collinearity relationships in both rice and Arabidopsis, which is consistent with the large variations in the HAD genes. Among the 41 HAD genes of rice, the promoters of 28 genes contain Pi-responsive cis-elements. Mining of transcriptome data and qRT-PCR results showed that at least the expression of 17 HAD genes was induced by Pi starvation in shoots or roots. These HAD proteins are predicted to be involved in intracellular or extracellular Po recycling under Pi stress conditions in plants.

Evolution of new enzymes by gene duplication and divergence

Thousands of new metabolic and regulatory enzymes have evolved by gene duplication and divergence since the dawn of life. New enzyme activities often originate from promiscuous secondary activities that have become important for fitness due to a change in the environment or a mutation. Mutations that make a promiscuous activity physiologically relevant can occur in the gene encoding the promiscuous enzyme itself, but can also occur elsewhere, resulting in increased expression of the enzyme or decreased competition between the native and novel substrates for the active site. If a newly useful activity is inefficient, gene duplication/amplification will set the stage for divergence of a new enzyme. Even a few mutations can increase the efficiency of a new activity by orders of magnitude. As efficiency increases, amplified gene arrays will shrink to provide two alleles, one encoding the original enzyme and one encoding the new enzyme. Ultimately, genomic rearrangements eliminate co-amplified genes and move newly evolved paralogs to a distant region of the genome.

The Ingenuity of Bacterial Genomes

The genomes of bacteria contain fewer genes and substantially less noncoding DNA than those of eukaryotes, and as a result, they have much less raw material to invent new traits. Yet, bacteria are vastly more taxonomically diverse, numerically abundant, and globally successful in colonizing new habitats compared to eukaryotes. Although bacterial genomes are generally considered to be optimized for efficient growth and rapid adaptation, nonadaptive processes have played a major role in shaping the size, contents, and compact organization of bacterial genomes and have allowed the establishment of deleterious traits that serve as the raw materials for genetic innovation.

Structure and catalytic regulation of Plasmodium falciparum IMP specific nucleotidase

Plasmodium falciparum (Pf) relies solely on the salvage pathway for its purine nucleotide requirements, making this pathway indispensable to the parasite. Purine nucleotide levels are regulated by anabolic processes and by nucleotidases that hydrolyse these metabolites into nucleosides. Certain apicomplexan parasites, including Pf, have an IMP-specific-nucleotidase 1 (ISN1). Here we show, by comprehensive substrate screening, that PfISN1 catalyzes the dephosphorylation of inosine monophosphate (IMP) and is allosterically activated by ATP. Crystal structures of tetrameric PfISN1 reveal complex rearrangements of domain organization tightly associated with catalysis. Immunofluorescence microscopy and expression of GFP-fused protein indicate cytosolic localization of PfISN1 and expression in asexual and gametocyte stages of the parasite. With earlier evidence on isn1 upregulation in female gametocytes, the structures reported in this study may contribute to initiate the design for possible transmission-blocking agents.

Plasmodium falciparum IMP-specific 5′-nucleotidase 1 (PfISN1) is of interest as a potential malaria drug target. Here, the authors report that IMP is a substrate, and ATP an allosteric activator, of PfISN1 and present PfISN1 crystal structures in the ligand-free state and bound to either IMP or ATP.

The physical basis and practical consequences of biological promiscuity

Proteins interact with metabolites, nucleic acids, and other proteins to orchestrate the myriad catalytic, structural and regulatory functions that support life from the simplest microbes to the most complex multicellular organisms. These molecular interactions are often exquisitely specific, but never perfectly so. Adventitious "promiscuous" interactions are ubiquitous due to the thousands of macromolecules and small molecules crowded together in cells. Such interactions may perturb protein function at the molecular level, but as long as they do not compromise organismal fitness, they will not be removed by natural selection. Although promiscuous interactions are physiologically irrelevant, they are important because they can provide a vast reservoir of potential functions that can provide the starting point for evolution of new functions, both in nature and in the laboratory.