Protein promiscuity and its implications for biotechnology

Binding Promiscuity of Therapeutic Factor VIII

The binding promiscuity of proteins defines their ability to indiscriminately bind multiple unrelated molecules. Binding promiscuity is implicated, at least in part, in the off-target reactivity, nonspecific biodistribution, immunogenicity, and/or short half-life of potentially efficacious protein drugs, thus affecting their clinical use. In this review, we discuss the current evidence for the binding promiscuity of factor VIII (FVIII), a protein used for the treatment of hemophilia A, which displays poor pharmacokinetics, and elevated immunogenicity. We summarize the different canonical and noncanonical interactions that FVIII may establish in the circulation and that could be responsible for its therapeutic liabilities. We also provide information suggesting that the FVIII light chain, and especially its C1 and C2 domains, could play an important role in the binding promiscuity. We believe that the knowledge accumulated over years of FVIII usage could be exploited for the development of strategies to predict protein binding promiscuity and therefore anticipate drug efficacy and toxicity. This would open a mutational space to reduce the binding promiscuity of emerging protein drugs while conserving their therapeutic potency.

Predicting therapeutic and side effects from drug binding affinities to human proteome structures

Evaluation of the binding affinities of drugs to proteins is a crucial process for identifying drug pharmacological actions, but it requires three dimensional structures of proteins. Herein, we propose novel computational methods to predict the therapeutic indications and side effects of drug candidate compounds from the binding affinities to human protein structures on a proteome-wide scale. Large-scale docking simulations were performed for 7,582 drugs with 19,135 protein structures revealed by AlphaFold (including experimentally unresolved proteins), and machine learning models on the proteome-wide binding affinity score (PBAS) profiles were constructed. We demonstrated the usefulness of the method for predicting the therapeutic indications for 559 diseases and side effects for 285 toxicities. The method enabled to predict drug indications for which the related protein structures had not been experimentally determined and to successfully extract proteins eliciting the side effects. The proposed method will be useful in various applications in drug discovery.

Testing the predictive power of reverse screening to infer drug targets, with the help of machine learning

Estimating protein targets of compounds based on the similarity principle-similar molecules are likely to show comparable bioactivity-is a long-standing strategy in drug research. Having previously quantified this principle, we present here a large-scale evaluation of its predictive power for inferring macromolecular targets by reverse screening an unprecedented vast external test set of more than 300,000 active small molecules against another bioactivity set of more than 500,000 compounds. We show that machine-learning can predict the correct targets, with the highest probability among 2069 proteins, for more than 51% of the external molecules. The strong enrichment thus obtained demonstrates its usefulness in supporting phenotypic screens, polypharmacology, or repurposing. Moreover, we quantified the impact of the bioactivity knowledge available for proteins in terms of number and diversity of actives. Finally, we advise that developers of such approaches follow an application-oriented benchmarking strategy and use large, high-quality, non-overlapping datasets as provided here.

BioProtIS: Streamlining protein-ligand interaction pipeline for analysis in genomic and transcriptomic exploration

The identification of protein-ligand interactions plays a pivotal role in elucidating biological processes and discovering potential bioproducts. Harnessing the capabilities of computational methods in drug discovery, we introduce an innovative Inverted Virtual Screening (IVS) pipeline. This pipeline Integrated molecular dynamics and docking analyses to ensure that protein structures are not only energetically favorable but also representative of stable conformations. The primary objective of this pipeline is to automate and streamline the analysis of protein-ligand interactions at both genomic and transcriptomic scales. In the contemporary post-genomic era, high-throughput computational screening for bioproducts, biological systems, and therapeutic drugs has become a cornerstone practice. This approach offers the promise of cost-effectiveness, time efficiency, and optimization of laboratory work. Nevertheless, a notable deficiency persists in the availability of efficient pipelines capable of automating the virtual screening process, seamlessly integrating input and output, and leveraging the full potential of open-source tools. To bridge this critical gap, we have developed a versatile pipeline known as BioProtIS. This tool seamlessly integrates a suite of state-of-the-art tools, including Modeller, AlphaFold, Gromacs, FPOCKET, and AutoDock Vina, thus facilitating the streamlined docking of ligands with an expansive repertoire of proteins sourced from genomes and transcriptomes, and substrates. To assess the pipeline's performance, we employed the transcriptomes of Cereus jamacaru (a cactus species) and Aspisoma lineatum (firefly), along with the genome of Homo sapiens. This integration not only improves the accuracy of ligand-protein interactions by minimizing replicability deviations but also optimizes the discovery process by enabling the simultaneous evaluation of multiple substrates. Furthermore, our pipeline accommodates distinct testing scenarios, such as blind docking or site-specific targeting, which are invaluable in applications ranging from drug repositioning to the exploration of new allosteric binding sites and toxicity assessments. BioProtIS has been designed with modularity at its core. This inherent flexibility empowers users to make custom modifications directly within the source code, tailoring the pipeline to their specific research needs. Moreover, it lays the foundation for seamless integration of diverse docking algorithms in future iterations, promising ongoing advancements in the field of computational biology. This pipeline is available for free distribution and can be download at: https://github.com/BBMDO/BioProtIS.

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.

Redox Out of the Box: Catalytic Versatility Across NAD(P)H-Dependent Oxidoreductases

The asymmetric reduction of double bonds using NAD(P)H-dependent oxidoreductases has proven to be an efficient tool for the synthesis of important chiral molecules in research and on industrial scale. These enzymes are commercially available in screening kits for the reduction of C=O (ketones), C=C (activated alkenes), or C=N bonds (imines). Recent reports, however, indicate that the ability to accommodate multiple reductase activities on distinct C=X bonds occurs in different enzyme classes, either natively or after mutagenesis. This challenges the common perception of highly selective oxidoreductases for one type of electrophilic substrate. Consideration of this underexplored potential in enzyme screenings and protein engineering campaigns may contribute to the identification of complementary biocatalytic processes for the synthesis of chiral compounds. This review will contribute to a global understanding of the promiscuous behavior of NAD(P)H-dependent oxidoreductases on C=X bond reduction and inspire future discoveries with respect to unconventional biocatalytic routes in asymmetric synthesis.

Exploring the Specificity of Nanozymes

Nanozymes, nanomaterials exhibiting enzyme-like activities, have emerged as a prominent interdisciplinary field over the past decade. To date, over 1200 different nanomaterials have been identified as nanozymes, covering four catalytic categories: oxidoreductases, hydrolases, isomerases, and lyases. Catalytic activity and specificity are two pivotal benchmarks for evaluating enzymatic performance. Despite substantial progress being made in quantifying and optimizing the catalytic activity of nanozymes, there is still a lack of in-depth research on the catalytic specificity of nanozymes, preventing the formation of consensual knowledge and impeding a more refined and systematic classification of nanozymes. Recently, debates have emerged regarding whether nanozymes could possess catalytic specificity similar to that of enzymes. This Perspective discusses the specificity of nanozymes by referring to the catalytic specificity of enzymes, highlights the specificity gap between nanozymes and enzymes, and concludes by offering our perspective on future research on the specificity of nanozymes.

Precision enzyme discovery through targeted mining of metagenomic data

Metagenomics has opened new avenues for exploring the genetic potential of uncultured microorganisms, which may serve as promising sources of enzymes and natural products for industrial applications. Identifying enzymes with improved catalytic properties from the vast amount of available metagenomic data poses a significant challenge that demands the development of novel computational and functional screening tools. The catalytic properties of all enzymes are primarily dictated by their structures, which are predominantly determined by their amino acid sequences. However, this aspect has not been fully considered in the enzyme bioprospecting processes. With the accumulating number of available enzyme sequences and the increasing demand for discovering novel biocatalysts, structural and functional modeling can be employed to identify potential enzymes with novel catalytic properties. Recent efforts to discover new polysaccharide-degrading enzymes from rumen metagenome data using homology-based searches and machine learning-based models have shown significant promise. Here, we will explore various computational approaches that can be employed to screen and shortlist metagenome-derived enzymes as potential biocatalyst candidates, in conjunction with the wet lab analytical methods traditionally used for enzyme characterization.

Cheminformatics Analysis of the Multitarget Structure-Activity Landscape of Environmental Chemicals Binding to Human Endocrine Receptors

In human exposome, environmental chemicals can target and disrupt different endocrine axes, ultimately leading to several endocrine disorders. Such chemicals, termed endocrine disrupting chemicals, can promiscuously bind to different endocrine receptors and lead to varying biological end points. Thus, understanding the complexity of molecule-receptor binding of environmental chemicals can aid in the development of robust toxicity predictors. Toward this, the ToxCast project has generated the largest resource on the chemical-receptor activity data for environmental chemicals that were screened across various endocrine receptors. However, the heterogeneity in the multitarget structure-activity landscape of such chemicals is not yet explored. In this study, we systematically curated the chemicals targeting eight human endocrine receptors, their activity values, and biological end points from the ToxCast chemical library. We employed dual-activity difference and triple-activity difference maps to identify single-, dual-, and triple-target cliffs across different target combinations. We annotated the identified activity cliffs through the matched molecular pair (MMP)-based approach and observed that a small fraction of activity cliffs form MMPs. Further, we structurally classified the activity cliffs and observed that R-group cliffs form the highest fraction among the cliffs identified in various target combinations. Finally, we leveraged the mechanism of action (MOA) annotations to analyze structure-mechanism relationships and identified strong MOA-cliffs and weak MOA-cliffs, for each of the eight endocrine receptors. Overall, insights from this first study analyzing the structure-activity landscape of environmental chemicals targeting multiple human endocrine receptors will likely contribute toward the development of better toxicity prediction models for characterizing the human chemical exposome.

Direct observation of negative cooperativity in a detoxification enzyme at the atomic level by Electron Paramagnetic Resonance spectroscopy and simulation

The catalytic activity of human glutathione S-transferase A1-1 (hGSTA1-1), a homodimeric detoxification enzyme, is dependent on the conformational dynamics of a key C-terminal helix α9 in each monomer. However, the structural details of how the two monomers interact upon binding of substrates is not well understood and the structure of the ligand-free state of the hGSTA1-1 homodimer has not been resolved. Here, we used a combination of electron paramagnetic resonance (EPR) distance measurements and weighted ensemble (WE) simulations to characterize the conformational ensemble of the ligand-free state at the atomic level. EPR measurements reveal a broad distance distribution between a pair of Cu(II) labels in the ligand-free state that gradually shifts and narrows as a function of increasing ligand concentration. These shifts suggest changes in the relative positioning of the two α9 helices upon ligand binding. WE simulations generated unbiased pathways for the seconds-timescale transition between alternate states of the enzyme, leading to the generation of atomically detailed structures of the ligand-free state. Notably, the simulations provide direct observations of negative cooperativity between the monomers of hGSTA1-1, which involve the mutually exclusive docking of α9 in each monomer as a lid over the active site. We identify key interactions between residues that lead to this negative cooperativity. Negative cooperativity may be essential for interaction of hGSTA1-1 with a wide variety of toxic substrates and their subsequent neutralization. More broadly, this work demonstrates the power of integrating EPR distances with WE rare-events sampling strategy to gain mechanistic information on protein function at the atomic level.

Reduction in PLANT DEFENSIN 1 expression in Arabidopsis thaliana results in increased resistance to pathogens and zinc toxicity

Ectopic expression of defensins in plants correlates with their increased capacity to withstand abiotic and biotic stresses. This applies to Arabidopsis thaliana, where some of the seven members of the PLANT DEFENSIN 1 family (AtPDF1) are recognised to improve plant responses to necrotrophic pathogens and increase seedling tolerance to excess zinc (Zn). However, few studies have explored the effects of decreased endogenous defensin expression on these stress responses. Here, we carried out an extensive physiological and biochemical comparative characterization of (i) novel artificial microRNA (amiRNA) lines silenced for the five most similar AtPDF1s, and (ii) a double null mutant for the two most distant AtPDF1s. Silencing of five AtPDF1 genes was specifically associated with increased aboveground dry mass production in mature plants under excess Zn conditions, and with increased plant tolerance to different pathogens - a fungus, an oomycete and a bacterium, while the double mutant behaved similarly to the wild type. These unexpected results challenge the current paradigm describing the role of PDFs in plant stress responses. Additional roles of endogenous plant defensins are discussed, opening new perspectives for their functions.

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.

Catalytic site flexibility facilitates the substrate and catalytic promiscuity of Vibrio dual lipase/transferase

Although enzyme catalysis is typified by high specificity, enzymes can catalyze various substrates (substrate promiscuity) and/or different reaction types (catalytic promiscuity) using a single active site. This interesting phenomenon is widely distributed in enzyme catalysis, with both fundamental and applied importance. To date, the mechanistic understanding of enzyme promiscuity is very limited. Herein, we report the structural mechanism underlying the substrate and catalytic promiscuity of Vibrio dual lipase/transferase (VDLT). Crystal structures of the VDLT from Vibrio alginolyticus (ValDLT) and its fatty acid complexes were solved, revealing prominent structural flexibility. In particular, the "Ser-His-Asp" catalytic triad machinery of ValDLT contains an intrinsically flexible oxyanion hole. Analysis of ligand-bound structures and mutagenesis showed that the flexible oxyanion hole and other binding residues can undergo distinct conformational changes to facilitate substrate and catalytic promiscuity. Our study reveals a previously unknown flexible form of the famous catalytic triad machinery and proposes a "catalytic site tuning" mechanism to expand the mechanistic paradigm of enzyme promiscuity.

Self-assembly of a multimeric genomic hydrogel via multi-primed chain reaction of dual single-stranded circular plasmids for cell-free protein production

Recent technical advances in cell-free protein synthesis (CFPS) offer several advantages over cell-based expression systems, including the application of cellular machinery, such as transcription and translation, in the test tube. Inspired by the advantages of CFPS, we have fabricated a multimeric genomic DNA hydrogel (mGD-gel) via rolling circle chain amplification (RCCA) using dual single-stranded circular plasmids with multiple primers. The mGD-gel exhibited significantly enhanced protein yield. In addition, mGD-gel can be reused at least five times, and the shape of the mGD-gel can be easily manipulated without losing the feasibility of protein expression. The mGD-gel platform based on the self-assembly of multimeric genomic DNA strands (mGD strands) has the potential to be used in CFPS systems for a variety of biotechnological applications.

Nanodisc-embedded cytochrome P450 P3A4 binds diverse ligands by distributing conformational dynamics to its flexible elements

Cytochrome P450 3A4 (CYP3A4) metabolizes a wide range of drugs and toxins. Interactions of CYP3A4 with ligands are difficult to predict due to promiscuity and conformational flexibility. To better understand CYP3A4 conformational responses to ligands we use hydrogen deuterium exchange mass spectrometry (HDX-MS) to investigate the effect of ligands on nanodisc-embedded CYP3A4. For a subset of CYP3A4-ligand complexes, differences in the low-frequency modes derived by principal component analyses of molecular dynamics trajectories mirrored the HDX-MS results. The effects of ligands are distributed to flexible elements of CYP3A4 between stretches of secondary structure. The largest effects occur in the F- and G-helices, where most ligands increase the flexibility of the F-helix and connecting loops and decrease the flexibility of the C-term of the G-helix. Most ligands affect the E-F-G, CD and HI regions of the protein. Ligand-dependent differences are observed in the A"-A' loop, BC region, E-helix, K-β1 region, proximal loop, and C-term loop. Correlated HDX responses were observed in the CD region and the C-term of the G-helix that were most pronounced for Type II ligands. Collectively, the HDX and molecular dynamics results suggest that CYP3A4 accommodates diverse binding partners by propagating local backbone fluctuations from the binding site onto the flexible regions of the enzyme via long-range interactions that are differentially modulated by ligands. In contrast to the paradigm wherein ligands decrease protein dynamics at their binding site, a wide range of ligands modestly increase CYP3A4 dynamics throughout the protein including effects remote from the active site.

Conversion of Similar Xenochemicals to Dissimilar Products: Exploiting Competing Reactions in Whole-Cell Catalysis

Many enzymes have latent activities that can be used in the conversion of non-natural reactants for novel organic conversions. A classic example is the conversion of benzaldehyde to a phenylacetyl carbinol, a precursor for ephedrine manufacture. It is often tacitly assumed that purified enzymes are more promising catalysts than whole cells, despite the lower cost and easier maintenance of the latter. Competing substrates inside the cell have been known to elicit currently hard-to-predict selectivities that are not easily measured inside the living cell. We employ NMR spectroscopic assays to rationally combine isomers for selective reactions in commercial S. cerevisiae. This approach uses internal competition between alternative pathways of aldehyde clearance in yeast, leading to altered selectivities compared to catalysis with the purified enzyme. In this manner, 4-fluorobenzyl alcohol and 2-fluorophenylacetyl carbinol can be formed with selectivities in the order of 90%. Modification of the cellular redox state can be used to tune product composition further. Hyperpolarized NMR shows that the cellular reaction and pathway usage are affected by the xenochemical. Overall, we find that the rational construction of ternary or more complex substrate mixtures can be used for in-cell NMR spectroscopy to optimize the upgrading of similar xenochemicals to dissimilar products with cheap whole-cell catalysts.

Promiscuity in drug discovery on the verge of the structural revolution: recent advances and future chances

INTRODUCTION

Promiscuity denotes the ability of ligands and targets to specifically interact with multiple binding partners. Despite negative aspects like side effects, promiscuity is receiving increasing attention in drug discovery as it can enhance drug efficacy and provides a molecular basis for drug repositioning. The three-dimensional structure of ligand-target complexes delivers exclusive insights into the molecular mechanisms of promiscuity and structure-based methods enable the identification of promiscuous interactions. With the recent breakthrough in protein structure prediction, novel possibilities open up to reveal unknown connections in ligand-target interaction networks.

AREAS COVERED

This review highlights the significance of structure in the identification and characterization of promiscuity and evaluates the potential of protein structure prediction to advance our knowledge of drug-target interaction networks. It discusses the definition and relevance of promiscuity in drug discovery and explores different approaches to detecting promiscuous ligands and targets.

EXPERT OPINION

Examination of structural data is essential for understanding and quantifying promiscuity. The recent advancements in structure prediction have resulted in an abundance of targets that are well-suited for structure-based methods like docking. In silico approaches may eventually completely transform our understanding of drug-target networks by complementing the millions of predicted protein structures with billions of predicted drug-target interactions.

A general model to predict small molecule substrates of enzymes based on machine and deep learning

For most proteins annotated as enzymes, it is unknown which primary and/or secondary reactions they catalyze. Experimental characterizations of potential substrates are time-consuming and costly. Machine learning predictions could provide an efficient alternative, but are hampered by a lack of information regarding enzyme non-substrates, as available training data comprises mainly positive examples. Here, we present ESP, a general machine-learning model for the prediction of enzyme-substrate pairs with an accuracy of over 91% on independent and diverse test data. ESP can be applied successfully across widely different enzymes and a broad range of metabolites included in the training data, outperforming models designed for individual, well-studied enzyme families. ESP represents enzymes through a modified transformer model, and is trained on data augmented with randomly sampled small molecules assigned as non-substrates. By facilitating easy in silico testing of potential substrates, the ESP web server may support both basic and applied science.

Genome-wide analysis of the MADS-box gene family involved in salt and waterlogging tolerance in barley (Hordeum vulgare L.)

MADS-box transcription factors are crucial members of regulatory networks underlying multiple developmental pathways and abiotic stress regulatory networks in plants. Studies on stress resistance-related functions of MADS-box genes are very limited in barley. To gain insight into this gene family and elucidate their roles in salt and waterlogging stress resistance, we performed genome-wide identification, characterization and expression analysis of MADS-box genes in barley. A whole-genome survey of barley revealed 83 MADS-box genes, which were categorized into type I (Mα, Mβ and Mγ) and type II (AP1, SEP1, AGL12, STK, AGL16, SVP and MIKC*) lineages based on phylogeny, protein motif structure. Twenty conserved motifs were determined and each HvMADS contained one to six motifs. We also found tandem repeat duplication was the driven force for HvMADS gene family expansion. Additionally, the co-expression regulatory network of 10 and 14 HvMADS genes was predicted in response to salt and waterlogging stress, and we proposed HvMADS11,13 and 35 as candidate genes for further exploration of the functions in abiotic stress. The extensive annotations and transcriptome profiling reported in this study ultimately provides the basis for MADS functional characterization in genetic engineering of barley and other gramineous crops.

Moonlighting enzymes: when cellular context defines specificity

It is not often realized that the absolute protein specificity is an exception rather than a rule. Two major kinds of protein multi-specificities are promiscuity and moonlighting. This review discusses the idea of enzyme specificity and then focusses on moonlighting. Some important examples of protein moonlighting, such as crystallins, ceruloplasmin, metallothioniens, macrophage migration inhibitory factor, and enzymes of carbohydrate metabolism are discussed. How protein plasticity and intrinsic disorder enable the removing the distinction between enzymes and other biologically active proteins are outlined. Finally, information on important roles of moonlighting in human diseases is updated.

Applications of promiscuity of FDA-approved kinase inhibitors in drug repositioning and toxicity

Promiscuity of therapeutics has important implications in treatment and toxicity. So far, a comprehensive understanding of promiscuity related to kinase inhibitors is lacking and such an analysis may offer potential opportunities for drug repurposing. In the present study, profiling of inhibitor-specific kinases based on the available biochemical IC50s was performed, fold-change of IC50 values for additional targets were calculated by taking the primary target as the reference kinase, and finally the promiscuity degree (PD) for FDA-approved kinase inhibitors was calculated. Surprisingly, class II inhibitors showed more PD than that of the class I inhibitors. We further identified cancer types and sub-types in which additional kinase targets or off-targets of inhibitors were overexpressed for potential drug repurposing. In addition, the expression of these kinases in normal human tissues were also profiled to predict toxicity following drug repositioning. Taken together, the study offers opportunities for cancer treatment in a kinase-specific manner.

Rational Construction of a Novel Bioluminescent Substrate for Sensing the Tumor-Associated Hydrolase Notum

Notum, one of the key serine hydrolases in mammals, hydrolyzes the palmitoleoyl moieties of many important proteins and modulates multiple signaling pathways including Wnt/β-catenin signaling. Notum is tightly associated with multiple human diseases, but the reliable and practical tools for sensing Notum activities in complex biological systems are rarely reported. Herein, an efficient strategy was used to rationally construct a specific bioluminescent substrate for Notum. Following computer-aided molecular design and experimental verification, octanoyl luciferin (OL) was selected as the optimum substrate for human Notum, with excellent specificity, high detection sensitivity and high signal-to-noise ratio. Under physiological conditions, OL was readily hydrolyzed by Notum or Notum-containing biological specimens to release d-luciferin that could be easily detected by various fluorescence devices in the presence of luciferase. The applicability of OL for real-time sensing native Notum was examined in living cells, extracellular matrix, and tissue preparations. OL was also used for constructing a high-throughput assay for screening of Notum inhibitors, while a natural compound (bergapten) was newly identified as a potent Notum inhibitor. Collectively, this study devises a reliable and easy-to-use tool for sensing Notum activities in biological systems, which will strongly facilitate hNotum-associated fundamental studies, disease diagnosis, and drug discovery.

Profiling disease-selective drug targets: From proteomics to ligandomics

Despite advancements in omics technologies, including proteomics and transcriptomics, identification of therapeutic targets remains challenging. Ligandomics recently emerged as a unique technology of functional proteomics for global profiling of cell-binding protein ligands. When applied to diseased versus healthy vasculatures, comparative ligandomics systematically maps novel disease-restricted ligands that allow selective targeting of pathological but not physiological pathways, providing high efficacy with intrinsic safety. In this review, we discuss the potential of cellular ligands as therapeutic targets and summarize the development of ligandomics. We further compare the advantages and limitations of different omics technologies for drug target discovery and discuss target selection criteria to improve drug R&D success rates.

A Cold-Active Flavin-Dependent Monooxygenase from Janthinobacterium svalbardensis Unlocks Applications of Baeyer–Villiger Monooxygenases at Low Temperature

Cold-active enzymes maintain a large part of their optimal activity at low temperatures. Therefore, they can be used to avoid side reactions and preserve heat-sensitive compounds. Baeyer-Villiger monooxygenases (BVMO) utilize molecular oxygen as a co-substrate to catalyze reactions widely employed for steroid, agrochemical, antibiotic, and pheromone production. Oxygen has been described as the rate-limiting factor for some BVMO applications, thereby hindering their efficient utilization. Considering that oxygen solubility in water increases by 40% when the temperature is decreased from 30 to 10 °C, we set out to identify and characterize a cold-active BVMO. Using genome mining in the Antarctic organism Janthinobacterium svalbardensis, a cold-active type II flavin-dependent monooxygenase (FMO) was discovered. The enzyme shows promiscuity toward NADH and NADPH and high activity between 5 and 25 °C. The enzyme catalyzes the monooxygenation and sulfoxidation of a wide range of ketones and thioesters. The high enantioselectivity in the oxidation of norcamphor (eeS = 56%, eeP > 99%, E > 200) demonstrates that the generally higher flexibility observed in the active sites of cold-active enzymes, which compensates for the lower motion at cold temperatures, does not necessarily reduce the selectivity of these enzymes. To gain a better understanding of the unique mechanistic features of type II FMOs, we determined the structure of the dimeric enzyme at 2.5 Å resolution. While the unusual N-terminal domain has been related to the catalytic properties of type II FMOs, the structure shows a SnoaL-like N-terminal domain that is not interacting directly with the active site. The active site of the enzyme is accessible only through a tunnel, with Tyr-458, Asp-217, and His-216 as catalytic residues, a combination not observed before in FMOs and BVMOs.

Non-complementary strand commutation as a fundamental alternative for information processing by DNA and gene regulation

The discovery of the DNA double helix has revolutionized our understanding of data processing in living systems, with the complementarity of the two DNA strands providing a reliable mechanism for the storage of hereditary information. Here I reveal the 'strand commutation' phenomenon-a fundamentally different mechanism of information storage and processing by DNA/RNA based on the reversible low-affinity interactions of essentially non-complementary nucleic acids. I demonstrate this mechanism by constructing a memory circuit, a 5-min square-root circuit for 4-bit inputs comprising only nine processing ssDNAs, simulating a 572-input AND gate (surpassing the bitness of current electronic computers), and elementary algebra systems with continuously changing variables. Most importantly, I show potential pathways of gene regulation with strands of maximum non-complementarity to the gene sequence that may be key to the reduction of off-target therapeutic effects. This Article uncovers the information-processing power of the low-affinity interactions that may underlie major processes in an organism-from short-term memory to cancer, ageing and evolution.

Docking-Based Virtual Screening Enables Prioritizing Protein Kinase Inhibitors With In Vitro Phenotypic Activity Against Schistosoma mansoni

Schistosomiasis is a parasitic neglected disease with praziquantel (PZQ) utilized as the main drug for treatment, despite its low effectiveness against early stages of the worm. To aid in the search for new drugs to tackle schistosomiasis, computer-aided drug design has been proved a helpful tool to enhance the search and initial identification of schistosomicidal compounds, allowing fast and cost-efficient progress in drug discovery. The combination of high-throughput in silico data followed by in vitro phenotypic screening assays allows the assessment of a vast library of compounds with the potential to inhibit a single or even several biological targets in a more time- and cost-saving manner. Here, we describe the molecular docking for in silico screening of predicted homology models of five protein kinases (JNK, p38, ERK1, ERK2, and FES) of Schistosoma mansoni against approximately 85,000 molecules from the Managed Chemical Compounds Collection (MCCC) of the University of Nottingham (UK). We selected 169 molecules predicted to bind to SmERK1, SmERK2, SmFES, SmJNK, and/or Smp38 for in vitro screening assays using schistosomula and adult worms. In total, 89 (52.6%) molecules were considered active in at least one of the assays. This approach shows a much higher efficiency when compared to using only traditional high-throughput in vitro screening assays, where initial positive hits are retrieved from testing thousands of molecules. Additionally, when we focused on compound promiscuity over selectivity, we were able to efficiently detect active compounds that are predicted to target all kinases at the same time. This approach reinforces the concept of polypharmacology aiming for “one drug-multiple targets”. Moreover, at least 17 active compounds presented satisfactory drug-like properties score when compared to PZQ, which allows for optimization before further in vivo screening assays. In conclusion, our data support the use of computer-aided drug design methodologies in conjunction with high-throughput screening approach.

Combined MD and QM/MM Calculations Reveal Allostery-Driven Promiscuity in Dipeptide Epimerases of Enolase Family

The adaptability of the active site to amplify the secondary function is supposed to be the fundamental cause of the promiscuity and the evolution of new functions in enzymes. In most cases, mutations occur close to the active site and/or in the catalytic site to change the active site plasticity to accommodate the non-native substrate. In the present study, using MD simulations and hybrid QM/MM calculations, we have shown a way to enhance the promiscuity, i. e., the allostery-driven promiscuity. Using a case study of the AEE enzyme where the capping loop recognizes the substrate, herein, we show that a single site mutation (D321G) far from the capping loop can induce a large conformational change in the capping loop to recognize different substrates for different functions. The QM/MM calculations for the WT and mutated enzyme provide a first validation of the mechanism of 1,1-proton transfer and dehydration by the AEE enzyme. Since AEE epimerase possesses a highly conserved TIM-barrel fold, we believe that our study provides a crucial lead to understanding the mechanism of emergence of secondary function which can be useful to repurpose ancient enzymes for modern usage.

Characterization of a novel β-alanine biosynthetic pathway consisting of promiscuous metabolic enzymes

Bacteria adapt to utilize the nutrients available in their environment through a sophisticated metabolic system composed of highly specialized enzymes. Although these enzymes can metabolize molecules other than those for which they evolved, their efficiency toward promiscuous substrates is considered too low to be of physiological relevance. Herein, we investigated the possibility that these promiscuous enzymes are actually efficient enough at metabolizing secondary substrates to modify the phenotype of the cell. For example, in the bacterium Acinetobacter baylyi ADP1 (ADP1), panD (coding for l-aspartate decarboxylase) encodes the only protein known to catalyze the synthesis of β-alanine, an obligate intermediate in CoA synthesis. However, we show that the ADP1 ΔpanD mutant could also form this molecule through an unknown metabolic pathway arising from promiscuous enzymes and grow as efficiently as the wildtype strain. Using metabolomic analyses, we identified 1,3-diaminopropane and 3-aminopropanal as intermediates in this novel pathway. We also conducted activity screening and enzyme kinetics to elucidate candidate enzymes involved in this pathway, including 2,4-diaminobutyrate aminotransferase (Dat) and 2,4-diaminobutyrate decarboxylase (Ddc) and validated this pathway in vivo by analyzing the phenotype of mutant bacterial strains. Finally, we experimentally demonstrate that this novel metabolic route is not restricted to ADP1. We propose that the occurrence of conserved genes in hundreds of genomes across many phyla suggests that this previously undescribed pathway is widespread in prokaryotes.

Computational design of an amidase by combining the best electrostatic features of two promiscuous hydrolases

While there has been emerging interest in designing new enzymes to solve practical challenges, computer-based options to redesign catalytically active proteins are rather limited. Here, a rational QM/MM molecular dynamics strategy based on combining the best electrostatic properties of enzymes with activity in a common reaction is presented. The computational protocol has been applied to the re-design of the protein scaffold of an existing promiscuous esterase from Bacillus subtilis Bs2 to enhance its secondary amidase activity. After the alignment of Bs2 with a non-homologous amidase Candida antarctica lipase B (CALB) within rotation quaternions, a relevant spatial aspartate residue of the latter was transferred to the former as a means to favor the electrostatics of transition state formation, where a clear separation of charges takes place. Deep computational insights, however, revealed a significant conformational change caused by the amino acid replacement, provoking a shift in the pK a of the inserted aspartate and counteracting the anticipated catalytic effect. This prediction was experimentally confirmed with a 1.3-fold increase in activity. The good agreement between theoretical and experimental results, as well as the linear correlation between the electrostatic properties and the activation energy barriers, suggest that the presented computational-based investigation can transform in an enzyme engineering approach.

In silico drug repositioning based on integrated drug targets and canonical correlation analysis

Background

Besides binding to proteins, the most recent advances in pharmacogenomics indicate drugs can regulate the expression of non-coding RNAs (ncRNAs). The polypharmacological feature in drugs enables us to find new uses for existing drugs (namely drug repositioning). However, current computational methods for drug repositioning mainly consider proteins as drug targets. Meanwhile, these methods identify only statistical relationships between drugs and diseases. They provide little information about how drug-disease associations are formed at the molecular target level.

Methods

Herein, we first comprehensively collect proteins and two categories of ncRNAs as drug targets from public databases to construct drug–target interactions. Experimentally confirmed drug-disease associations are downloaded from an established database. A canonical correlation analysis (CCA) based method is then applied to the two datasets to extract correlated sets of targets and diseases. The correlated sets are regarded as canonical components, and they are used to investigate drug’s mechanism of actions. We finally develop a strategy to predict novel drug-disease associations for drug repositioning by combining all the extracted correlated sets.

Results

We receive 400 canonical components which correlate targets with diseases in our study. We select 4 components for analysis and find some top-ranking diseases in an extracted set might be treated by drugs interfacing with the top-ranking targets in the same set. Experimental results from 10-fold cross-validations show integrating different categories of target information results in better prediction performance than only using proteins or ncRNAs as targets. When compared with 3 state-of-the-art approaches, our method receives the highest AUC value 0.8576. We use our method to predict new indications for 789 drugs and confirm 24 predictions in the top 1 predictions.

Conclusions

To the best of our knowledge, this is the first computational effort which combines both proteins and ncRNAs as drug targets for drug repositioning. Our study provides a biologically relevant interpretation regarding the forming of drug-disease associations, which is useful for guiding future biomedical tests.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01203-1.

The Emergence of New Catalytic Abilities in an Endoxylanase from Family GH10 by Removing an Intrinsically Disordered Region

Endoxylanases belonging to family 10 of the glycoside hydrolases (GH10) are versatile in the use of different substrates. Thus, an understanding of the molecular mechanisms underlying substrate specificities could be very useful in the engineering of GH10 endoxylanases for biotechnological purposes. Herein, we analyzed XynA, an endoxylanase that contains a (β/α)₈-barrel domain and an intrinsically disordered region (IDR) of 29 amino acids at its amino end. Enzyme activity assays revealed that the elimination of the IDR resulted in a mutant enzyme (XynAΔ29) in which two new activities emerged: the ability to release xylose from xylan, and the ability to hydrolyze p-nitrophenyl-β-d-xylopyranoside (pNPXyl), a substrate that wild-type enzyme cannot hydrolyze. Circular dichroism and tryptophan fluorescence quenching by acrylamide showed changes in secondary structure and increased flexibility of XynAΔ29. Molecular dynamics simulations revealed that the emergence of the pNPXyl-hydrolyzing activity correlated with a dynamic behavior not previously observed in GH10 endoxylanases: a hinge-bending motion of two symmetric regions within the (β/α)₈-barrel domain, whose hinge point is the active cleft. The hinge-bending motion is more intense in XynAΔ29 than in XynA and promotes the formation of a wider active site that allows the accommodation and hydrolysis of pNPXyl. Our results open new avenues for the study of the relationship between IDRs, dynamics and activity of endoxylanases, and other enzymes containing (β/α)₈-barrel domain.

Toward improved terpenoids biosynthesis: strategies to enhance the capabilities of cell factories

Terpenoids form the most diversified class of natural products, which have gained application in the pharmaceutical, food, transportation, and fine and bulk chemical industries. Extraction from naturally occurring sources does not meet industrial demands, whereas chemical synthesis is often associated with poor enantio-selectivity, harsh working conditions, and environmental pollutions. Microbial cell factories come as a suitable replacement. However, designing efficient microbial platforms for isoprenoid synthesis is often a challenging task. This has to do with the cytotoxic effects of pathway intermediates and some end products, instability of expressed pathways, as well as high enzyme promiscuity. Also, the low enzymatic activity of some terpene synthases and prenyltransferases, and the lack of an efficient throughput system to screen improved high-performing strains are bottlenecks in strain development. Metabolic engineering and synthetic biology seek to overcome these issues through the provision of effective synthetic tools. This review sought to provide an in-depth description of novel strategies for improving cell factory performance. We focused on improving transcriptional and translational efficiencies through static and dynamic regulatory elements, enzyme engineering and high-throughput screening strategies, cellular function enhancement through chromosomal integration, metabolite tolerance, and modularization of pathways.

Secreted Phospholipases A2 - not just Enzymes: Revisited

Secreted phospholipases A₂ (sPLA₂s) participate in a very broad spectrum of biological processes through their enzymatic activity and as ligands for membrane and soluble receptors. The physiological roles of sPLA₂s as enzymes have been very well described, while their functions as ligands are still poorly known. Since the last overview of sPLA₂-binding proteins (sPLA₂-BPs) 10 years ago, several important discoveries have occurred in this area. New and more sensitive analytical tools have enabled the discovery of additional sPLA₂-BPs, which are presented and critically discussed here. The structural diversity of sPLA₂-BPs reveals sPLA₂s as very promiscuous proteins, and we offer some structural explanations for this nature that makes these proteins evolutionarily highly advantageous. Three areas of physiological engagement of sPLA₂-BPs have appeared most clearly: cellular transport and signalling, and regulation of the enzymatic activity of sPLA₂s. Due to the multifunctionality of sPLA₂s, they appear to be exceptional pharmacological targets. We reveal the potential to exploit interactions of sPLA₂s with other proteins in medical terms, for the development of original diagnostic and therapeutic procedures. We conclude this survey by suggesting the priority questions that need to be answered.

Molecular insights into the unusually promiscuous and catalytically versatile Fe(II)/α-ketoglutarate-dependent oxygenase SptF

Non-heme iron and α-ketoglutarate-dependent (Fe/αKG) oxygenases catalyze various oxidative biotransformations. Due to their catalytic flexibility and high efficiency, Fe/αKG oxygenases have attracted keen attention for their application as biocatalysts. Here, we report the biochemical and structural characterizations of the unusually promiscuous and catalytically versatile Fe/αKG oxygenase SptF, involved in the biosynthesis of fungal meroterpenoid emervaridones. The in vitro analysis revealed that SptF catalyzes several continuous oxidation reactions, including hydroxylation, desaturation, epoxidation, and skeletal rearrangement. SptF exhibits extremely broad substrate specificity toward various meroterpenoids, and efficiently produced unique cyclopropane-ring-fused 5/3/5/5/6/6 and 5/3/6/6/6 scaffolds from terretonins. Moreover, SptF also hydroxylates steroids, including androsterone, testosterone, and progesterone, with different regiospecificities. Crystallographic and structure-based mutagenesis studies of SptF revealed the molecular basis of the enzyme reactions, and suggested that the malleability of the loop region contributes to the remarkable substrate promiscuity. SptF exhibits great potential as a promising biocatalyst for oxidation reactions.

Fine-tuning spermidine binding modes in the putrescine binding protein PotF

A profound understanding of the molecular interactions between receptors and ligands is important throughout diverse research, such as protein design, drug discovery, or neuroscience. What determines specificity and how do proteins discriminate against similar ligands? In this study, we analyzed factors that determine binding in two homologs belonging to the well-known superfamily of periplasmic binding proteins, PotF and PotD. Building on a previously designed construct, modes of polyamine binding were swapped. This change of specificity was approached by analyzing local differences in the binding pocket as well as overall conformational changes in the protein. Throughout the study, protein variants were generated and characterized structurally and thermodynamically, leading to a specificity swap and improvement in affinity. This dataset not only enriches our knowledge applicable to rational protein design but also our results can further lay groundwork for engineering of specific biosensors as well as help to explain the adaptability of pathogenic bacteria.

Stabilization of enzymes via immobilization: Multipoint covalent attachment and other stabilization strategies

The use of enzymes in industrial processes requires the improvement of their features in many instances. Enzyme immobilization, a requirement to facilitate the recovery and reuse of these water-soluble catalysts, is one of the tools that researchers may utilize to improve many of their properties. This review is focused on how enzyme immobilization may improve enzyme stability. Starting from the stabilization effects that an enzyme may experience by the mere fact of being inside a solid particle, we detail other possibilities to stabilize enzymes: generation of favorable enzyme environments, prevention of enzyme subunit dissociation in multimeric enzymes, generation of more stable enzyme conformations, or enzyme rigidification via multipoint covalent attachment. In this last point, we will discuss the features of an "ideal" immobilization protocol to maximize the intensity of the enzyme-support interactions. The most interesting active groups in the support (glutaraldehyde, epoxide, glyoxyl and vinyl sulfone) will be also presented, discussing their main properties and uses. Some instances in which the number of enzyme-support bonds is not directly related to a higher stabilization will be also presented. Finally, the possibility of coupling site-directed mutagenesis or chemical modification to get a more intense multipoint covalent immobilization will be discussed.

Utility, promise, and limitations of liquid chromatography-mass spectrometry-based therapeutic drug monitoring in precision medicine

Therapeutic drug monitoring (TDM) is typically referred to as the measurement of the concentration of drugs in patient blood. Although in the past, TDM was restricted to drugs with a narrow therapeutic range in order to avoid drug toxicity, TDM has recently become a major tool for precision medicine being applied to many more drugs. Through compensating for interindividual differences in a drug's pharmacokinetics, improved dosing of individual patients based on TDM ensures maximum drug effectiveness while minimizing side effects. This is especially relevant for individuals that present a particularly high intervariability in pharmacokinetics, such as newborns, or for critically/severely ill patients. In this article, we will review the applications for and limitations of TDM, discuss for which patients TDM is most beneficial and why, examine which techniques are being used for TDM, and demonstrate how mass spectrometry is increasingly becoming a reliable and convenient alternative for the TDM of different classes of drugs. We will also highlight the advances, challenges, and limitations of the existing repertoire of TDM methods and discuss future opportunities for TDM-based precision medicine.

Hidden protein functions and what they may teach us

Bottom-up synthetic biology is a new research field with the goal of constructing living systems from a minimal number of functional components. The key challenges are, first, to identify a necessary canon of functions for a system to be considered alive, and second, to reconstitute these respective modules in vitro. When using proteins as obvious candidates, it appears that not only some of their described physiological functions fail to unfold outside the cellular context, but that completely new and unexpected functions are being observed. We put these insights in the context of other recent findings on protein functionality and discuss their potential role in the emergence and evolution of life.

Novel perspectives of environmental proteomics

The connection among genome expression, proteome alteration, metabolism regulation and phenotype change under environmental stresses is very vague. It is a tough task for the traditional research approaches to reveal the related scientific mechanisms of the above connection at molecular and systematic levels. Proteomics approach is an insightful tool for revealing the biological functions, metabolic networks and functional protein interaction networks of cells and organisms under stresses at the systematic level. The purpose of this review is to provide an insightful guideline on how to set up a proteomic investigation for revealing biomolecule mechanisms, protein biomarkers and metabolism networks related to stress response, pollutant recognition, transport and biodegradation, and providing an insightful high-throughput approach for screening functional enzymes and effective microbes based on bioinformatics and functional verification method. Furthermore, the toxicity evaluation of pollutants and byproducts by proteomics approaches provides a scientific insight for early diagnosis of ecological risk and determination of the effectiveness of pollutant treatment techniques.

The Transporter-Mediated Cellular Uptake and Efflux of Pharmaceutical Drugs and Biotechnology Products: How and Why Phospholipid Bilayer Transport Is Negligible in Real Biomembranes

Over the years, my colleagues and I have come to realise that the likelihood of pharmaceutical drugs being able to diffuse through whatever unhindered phospholipid bilayer may exist in intact biological membranes in vivo is vanishingly low. This is because (i) most real biomembranes are mostly protein, not lipid, (ii) unlike purely lipid bilayers that can form transient aqueous channels, the high concentrations of proteins serve to stop such activity, (iii) natural evolution long ago selected against transport methods that just let any undesirable products enter a cell, (iv) transporters have now been identified for all kinds of molecules (even water) that were once thought not to require them, (v) many experiments show a massive variation in the uptake of drugs between different cells, tissues, and organisms, that cannot be explained if lipid bilayer transport is significant or if efflux were the only differentiator, and (vi) many experiments that manipulate the expression level of individual transporters as an independent variable demonstrate their role in drug and nutrient uptake (including in cytotoxicity or adverse drug reactions). This makes such transporters valuable both as a means of targeting drugs (not least anti-infectives) to selected cells or tissues and also as drug targets. The same considerations apply to the exploitation of substrate uptake and product efflux transporters in biotechnology. We are also beginning to recognise that transporters are more promiscuous, and antiporter activity is much more widespread, than had been realised, and that such processes are adaptive (i.e., were selected by natural evolution). The purpose of the present review is to summarise the above, and to rehearse and update readers on recent developments. These developments lead us to retain and indeed to strengthen our contention that for transmembrane pharmaceutical drug transport "phospholipid bilayer transport is negligible".

An Overview of 7α- and 7β-Hydroxysteroid Dehydrogenases: Structure, Specificity and Practical Application

7α-Hydroxysteroid dehydrogenase and 7β-hydroxysteroid dehydrogenase are key enzymes involved in bile acid metabolism. They catalyze the epimerization of a hydroxyl group through 7-keto bile acid intermediates. Basic research of the two enzymes has focused on exploring new enzymes and the structure-function relationship. The application research focused on the in vitro biosynthesis of bile acid drugs and the exploration and improvement of their catalytic ability based on molecular engineering. This article summarized the primary and advanced structural characteristics, specificities, biochemical properties, and applications of the two enzymes. The emphasis is also given to obtaining of novel 7α-hydroxysteroid dehydrogenase and 7β-hydroxysteroid dehydrogenase that are thermally stable and active in the presence of organic solvents, high substrate concentration, and extreme pH values. To achieve these goals, enzyme redesigning based on protein engineering and genomics may be the most useful approaches.

F/G Region Rigidity is Inversely Correlated to Substrate Promiscuity of Human CYP Isoforms Involved in Metabolism

Of 57 human cytochrome P450 (CYP) enzymes, 12 metabolize 90% of xenobiotics. To our knowledge, no study has addressed the relation between enzyme dynamics and substrate promiscuity for more than three CYPs. Here, we show by constraint dilution simulations with the Constraint Network Analysis for the 12 isoforms that structural rigidity of the F/G region is significantly inversely correlated to the enzymes' substrate promiscuity. This highlights the functional importance of structural dynamics of the substrate tunnel.

Enzyme Promiscuity Prediction Using Hierarchy-Informed Multi-Label Classification

MOTIVATION

As experimental efforts are costly and time consuming, computational characterization of enzyme capabilities is an attractive alternative. We present and evaluate several machine-learning models to predict which of 983 distinct enzymes, as defined via the Enzyme Commission (EC) numbers, are likely to interact with a given query molecule. Our data consists of enzyme-substrate interactions from the BRENDA database. Some interactions are attributed to natural selection and involve the enzyme's natural substrates. The majority of the interactions however involve non-natural substrates, thus reflecting promiscuous enzymatic activities.

RESULTS

We frame this "enzyme promiscuity prediction" problem as a multi-label classification task. We maximally utilize inhibitor and unlabelled data to train prediction models that can take advantage of known hierarchical relationships between enzyme classes. We report that a hierarchical multi-label neural network, EPP-HMCNF, is the best model for solving this problem, outperforming k-nearest neighbours similarity-based and other machine learning models. We show that inhibitor information during training consistently improves predictive power, particularly for EPP-HMCNF. We also show that all promiscuity prediction models perform worse under a realistic data split when compared to a random data split, and when evaluating performance on non-natural substrates compared to natural substrates.

AVAILABILITY AND IMPLEMENTATION

We provide Python code for EPP-HMCNF and other models in a repository termed EPP (Enzyme Promiscuity Prediction) at https://github.com/hassounlab/EPP.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

[Historical construction of the concept of the enzyme and approaches in biology textbooks]

The use of the history and philosophy of science in teaching and learning is commonly neglected, linear, and/or out of context in textbooks. This article investigates whether this also occurs with the concept of enzymes. A brief review of the literature establishes the theoretical foundation to investigate how the concept of enzymes is presented in nine textbooks, following three different lines of analysis. A general lack of interconnection was seen in biochemistry topics, with enzymes usually only presented via the "lock-and-key" model, which does not best represent their complexity. Furthermore, conceptual limitations resulting from a lack of historical contextualization (partial or complete) were also observed.

Nonspecific Binding-Fundamental Concepts and Consequences for Biosensing Applications

Nature achieves differentiation of specific and nonspecific binding in molecular interactions through precise control of biomolecules in space and time. Artificial systems such as biosensors that rely on distinguishing specific molecular binding events in a sea of nonspecific interactions have struggled to overcome this issue. Despite the numerous technological advancements in biosensor technologies, nonspecific binding has remained a critical bottleneck due to the lack of a fundamental understanding of the phenomenon. To date, the identity, cause, and influence of nonspecific binding remain topics of debate within the scientific community. In this review, we discuss the evolution of the concept of nonspecific binding over the past five decades based upon the thermodynamic, intermolecular, and structural perspectives to provide classification frameworks for biomolecular interactions. Further, we introduce various theoretical models that predict the expected behavior of biosensors in physiologically relevant environments to calculate the theoretical detection limit and to optimize sensor performance. We conclude by discussing existing practical approaches to tackle the nonspecific binding challenge in vitro for biosensing platforms and how we can both address and harness nonspecific interactions for in vivo systems.