Advancing Anticancer Drug Discovery: Leveraging Metabolomics and Machine Learning for Mode of Action Prediction by Pattern Recognition.

A bottleneck in the development of new anti-cancer drugs is the recognition of their mode of action (MoA). Metabolomics combined with machine learning allowed to predict MoAs of novel anti-proliferative drug candidates, focusing on human prostate cancer cells (PC-3). As proof of concept, 38 drugs are studied with known effects on 16 key processes of cancer metabolism, profiling low molecular weight intermediates of the central carbon and cellular energy metabolism (CCEM) by LC-MS/MS. These metabolic patterns unveiled distinct MoAs, enabling accurate MoA predictions for novel agents by machine learning. The transferability of MoA predictions based on PC-3 cell treatments is validated with two other cancer cell models, i.e., breast cancer and Ewing's sarcoma, and show that correct MoA predictions for alternative cancer cells are possible, but still at some expense of prediction quality. Furthermore, metabolic profiles of treated cells yield insights into intracellular processes, exemplified for drugs inducing different types of mitochondrial dysfunction. Specifically, it is predicted that pentacyclic triterpenes inhibit oxidative phosphorylation and affect phospholipid biosynthesis, as confirmed by respiration parameters, lipidomics, and molecular docking. Using biochemical insights from individual drug treatments, this approach offers new opportunities, including the optimization of combinatorial drug applications.

Evolutionary Probability and Stacked Regressions Enable Data-Driven Protein Engineering with Minimized Experimental Effort.

Protein engineering through directed evolution and (semi)rational approaches is routinely applied to optimize protein properties for a broad range of applications in industry and academia. The multitude of possible variants, combined with limited screening throughput, hampers efficient protein engineering. Data-driven strategies have emerged as a powerful tool to model the protein fitness landscape that can be explored in silico, significantly accelerating protein engineering campaigns. However, such methods require a certain amount of data, which often cannot be provided, to generate a reliable model of the fitness landscape. Here, we introduce MERGE, a method that combines direct coupling analysis (DCA) and machine learning (ML). MERGE enables data-driven protein engineering when only limited data are available for training, typically ranging from 50 to 500 labeled sequences. Our method demonstrates remarkable performance in predicting a protein's fitness value and rank based on its sequence across diverse proteins and properties. Notably, MERGE outperforms state-of-the-art methods when only small data sets are available for modeling, requiring fewer computational resources, and proving particularly promising for protein engineers who have access to limited amounts of data.

Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides.

With their diverse biological activities, peptides are promising candidates for therapeutic applications, showing antimicrobial, antitumour and hormonal signalling capabilities. Despite their advantages, therapeutic peptides face challenges such as short half-life, limited oral bioavailability and susceptibility to plasma degradation. The rise of computational tools and artificial intelligence (AI) in peptide research has spurred the development of advanced methodologies and databases that are pivotal in the exploration of these complex macromolecules. This perspective delves into integrating AI in peptide development, encompassing classifier methods, predictive systems and the avant-garde design facilitated by deep-generative models like generative adversarial networks and variational autoencoders. There are still challenges, such as the need for processing optimization and careful validation of predictive models. This work outlines traditional strategies for machine learning model construction and training techniques and proposes a comprehensive AI-assisted peptide design and validation pipeline. The evolving landscape of peptide design using AI is emphasized, showcasing the practicality of these methods in expediting the development and discovery of novel peptides within the context of peptide-based drug discovery.

Sesquiterpene Cyclase BcBOT2 Promotes the Unprecedented Wagner-Meerwein Rearrangement of the Methoxy Group.

Presilphiperfolan-8ß-ol synthase (BcBOT2), a substrate-promiscuous sesquiterpene cyclase (STC) of fungal origin, is capable of converting two new farnesyl pyrophosphate (FPP) derivatives modified at C7 of farnesyl pyrophosphate (FPP) bearing either a hydroxymethyl group or a methoxymethyl group. These substrates were chosen based on a computationally generated model. Biotransformations yielded five new oxygenated terpenoids. Remarkably, the formation of one of these tricyclic products can only be explained by a cationically induced migration of the methoxy group, presumably via a Meerwein-salt intermediate, unprecedented in synthetic chemistry and biosynthesis. The results show the great principle and general potential of terpene cyclases for mechanistic studies of unusual cation chemistry and for the creation of new terpene skeletons.

Combinatorial biosynthesis in yeast leads to over 200 diterpenoids.

Diterpenoids form a diverse group of natural products, many of which are or could become pharmaceuticals or industrial chemicals. The modular character of diterpene biosynthesis and the promiscuity of the enzymes involved make combinatorial biosynthesis a promising approach to generate libraries of diverse diterpenoids. Here, we report on the combinatorial assembly in yeast of ten diterpene synthases producing (+)-copalyl diphosphate-derived backbones and four cytochrome P450 oxygenases (CYPs) in diverse combinations. This resulted in the production of over 200 diterpenoids. Based on literature and chemical database searches, 162 of these compounds can be considered new-to-Nature. The CYPs accepted most substrates they were given but remained regioselective with few exceptions. Our results provide the basis for the systematic exploration of the diterpenoid chemical space in yeast using sequence databases.

Diversification of a Novel α-Galactosyl Ceramide Hotspot Boosts the Adjuvant Properties in Parenteral and Mucosal Vaccines.

The development of potent adjuvants is an important step for improving the performance of subunit vaccines. CD1d agonists, such as the prototypical α-galactosyl ceramide (α-GalCer), are of special interest due to their ability to activate iNKT cells and trigger rapid dendritic cell maturation and B-cell activation. Herein, we introduce a novel derivatization hotspot at the α-GalCer skeleton, namely the N-substituent at the amide bond. The multicomponent diversification of this previously unexplored glycolipid chemotype space permitted the introduction of a variety of extra functionalities that can either potentiate the adjuvant properties or serve as handles for further conjugation to antigens toward the development of self-adjuvanting vaccines. This strategy led to the discovery of compounds eliciting enhanced antigen-specific T cell stimulation and a higher antibody response when delivered by either the parenteral or the mucosal route, as compared to a known potent CD1d agonist. Notably, various functionalized α-GalCer analogues showed a more potent adjuvant effect after intranasal immunization than a PEGylated α-GalCer analogue previously optimized for this purpose. Ultimately, this work could open multiple avenues of opportunity for the use of mucosal vaccines against microbial infections.

Chemoenzymatic Synthesis of a New Germacrene Derivative Named Germacrene F.

The new farnesyl pyrophosphate (FPP) derivative with a shifted olefinic double bond from C6-C7 to C7-C8 is accepted and converted by the sesquiterpene cyclases protoilludene synthase (Omp7) as well as viridiflorene synthase (Tps32). In both cases, a so far unknown germacrene derivative was found to be formed, which we name "germacrene F". Both cases are examples in which a modification around the central olefinic double bond in FPP leads to a change in the mode of initial cyclization (from 1→11 to 1→10). For Omp7 a rationale for this behaviour was found by carrying out molecular docking studies. Temperature-dependent NMR experiments, accompanied by NOE studies, show that germacrene F adopts a preferred mirror-symmetric conformation with both methyl groups oriented in the same directions in the cyclodecane ring.

Machine Learning-Assisted Engineering of Light, Oxygen, Voltage Photoreceptor Adduct Lifetime.

Naturally occurring and engineered flavin-binding, blue-light-sensing, light, oxygen, voltage (LOV) photoreceptor domains have been used widely to design fluorescent reporters, optogenetic tools, and photosensitizers for the visualization and control of biological processes. In addition, natural LOV photoreceptors with engineered properties were recently employed for optimizing plant biomass production in the framework of a plant-based bioeconomy. Here, the understanding and fine-tuning of LOV photoreceptor (kinetic) properties is instrumental for application. In response to blue-light illumination, LOV domains undergo a cascade of photophysical and photochemical events that yield a transient covalent FMN-cysteine adduct, allowing for signaling. The rate-limiting step of the LOV photocycle is the dark-recovery process, which involves adduct scission and can take between seconds and days. Rational engineering of LOV domains with fine-tuned dark recovery has been challenging due to the lack of a mechanistic model, the long time scale of the process, which hampers atomistic simulations, and a gigantic protein sequence space covering known mutations (combinatorial challenge). To address these issues, we used machine learning (ML) trained on scarce literature data and iteratively generated and implemented experimental data to design LOV variants with faster and slower dark recovery. Over the three prediction-validation cycles, LOV domain variants were successfully predicted, whose adduct-state lifetimes spanned 7 orders of magnitude, yielding optimized tools for synthetic (opto)biology. In summary, our results demonstrate ML as a viable method to guide the design of proteins even with limited experimental data and when no mechanistic model of the underlying physical principles is available.

Allosteric crosstalk in modular proteins: Function fine-tuning and drug design.

Modular proteins are regulatory proteins that carry out more than one function. These proteins upregulate or downregulate a biochemical cascade to establish homeostasis in cells. To switch the function or alter the efficiency (based on cellular needs), these proteins require different facilitators that bind to a site different from the catalytic (active/orthosteric) site, aka 'allosteric site', and fine-tune their function. These facilitators (or effectors) are allosteric modulators. In this Review, we have discussed the allostery, characterized them based on their mechanisms, and discussed how allostery plays an important role in the activity modulation and function fine-tuning of proteins. Recently there is an emergence in the discovery of allosteric drugs. We have also emphasized the role, significance, and future of allostery in therapeutic applications.

The impact of Akkermansia muciniphila and its extracellular vesicles in the regulation of serotonergic gene expression in a small intestine of mice.

OBJECTIVES: A better understanding of host-microbe interactions as a cross-talk between the gastrointestinal (GI) tract and the gut microbiota can help treat and prevent GI disorders by improving the maintenance of GI homeostasis. The gut microbiota can affect signaling molecules, such as serotonin, which regulates endocrine systems through the GI tract. Moreover, studying the effects of gut microbiota in the small intestine on the human GI tract health is pivotal. METHODS: Male C57BL/6J mice (n = 30, 10 mice per group) were orally gavaged with 200 µL of PBS (control group); mice in group II were orally gavaged with 109 colony-forming units (CFU)/200 µL of viable A. muciniphila, suspended in PBS (A. muciniphila group); and mice in group III were orally gavaged with 10 µg of protein/200 µL of EVs (A. muciniphila-EV group) once daily for four weeks. The gene expression of serotonin system-related genes (Slc6a4, Tph1, Mao, Htr3, Htr4, and Htr7) was examined by quantitative real-time PCR (qPCR) method. RESULTS: Based on the results, A. muciniphila significantly affected the mRNA expression of genes related to the serotonin system (Tph1, Mao, Htr3B, and Htr7) in the duodenum and (Htr3B, Htr4 and Htr7) in the ileum of mice (P < 0.05). Moreover, A. muciniphila-derived EVs affected the expression of major genes related to the serotonin system (Tph1, slc6a4a, Mao, Htr3B, Htr4, and Htr7) in the duodenum and ileum of mice (P < 0.05). CONCLUSIONS: The present findings may pave the way for further investigation of the effects of strain-specific probiotics on the serotonergic system, which is currently in its infancy.

A step forward to the optimized HlyA type 1 secretion system through directed evolution.

Secretion of proteins into the extracellular space has great advantages for the production of recombinant proteins. Type 1 secretion systems (T1SS) are attractive candidates to be optimized for biotechnological applications, as they have a relatively simple architecture compared to other classes of secretion systems. A paradigm of T1SS is the hemolysin A type 1 secretion system (HlyA T1SS) from Escherichia coli harboring only three membrane proteins, which makes the plasmid-based expression of the system easy. Although for decades the HlyA T1SS has been successfully applied for secretion of a long list of heterologous proteins from different origins as well as peptides, but its utility at commercial scales is still limited mainly due to low secretion titers of the system. To address this drawback, we engineered the inner membrane complex of the system, consisting of HlyB and HlyD proteins, following KnowVolution strategy. The applied KnowVolution campaign in this study provided a novel HlyB variant containing four substitutions (T36L/F216W/S290C/V421I) with up to 2.5-fold improved secretion for two hydrolases, a lipase and a cutinase. KEY POINTS: • An improvement in protein secretion via the use of T1SS • Reaching almost 400 mg/L of soluble lipase into the supernatant • A step forward to making E. coli cells more competitive for applying as a secretion host.

Purpurascenines A-C, Azepino-Indole Alkaloids from Cortinarius purpurascens: Isolation, Biosynthesis, and Activity Studies on the 5-HT2A Receptor.

Three previously undescribed azepino-indole alkaloids, named purpurascenines A-C (1-3), together with the new-to-nature 7-hydroxytryptophan (4) as well as two known compounds, adenosine (5) and riboflavin (6), were isolated from fruiting bodies of Cortinarius purpurascens Fr. (Cortinariaceae). The structures of 1-3 were elucidated based on spectroscopic analyses and ECD calculations. Furthermore, the biosynthesis of purpurascenine A (1) was investigated by in vivo experiments using 13C-labeled sodium pyruvate, alanine, and sodium acetate incubated with fruiting bodies of C. purpurascens. The incorporation of 13C into 1 was analyzed using 1D NMR and HRESIMS methods. With [3-13C]-pyruvate, a dramatic enrichment of 13C was observed, and hence a biosynthetic route via a direct Pictet-Spengler reaction between α-keto acids and 7-hydroxytryptophan (4) is suggested for the biosynthesis of purpurascenines A-C (1-3). Compound 1 exhibits no antiproliferative or cytotoxic effects against human prostate (PC-3), colorectal (HCT-116), and breast (MCF-7) cancer cells. An in silico docking study confirmed the hypothesis that purpurascenine A (1) could bind to the 5-HT2A serotonin receptor's active site. A new functional 5-HT2A receptor activation assay showed no functional agonistic but some antagonistic effects of 1 against the 5-HT-dependent 5-HT2A activation and likely antagonistic effects on putative constitutive activity of the 5-HT2A receptor.

Lipase-Mediated Conversion of Protecting Group Silyl Ethers: An Unspecific Side Reaction.

Silyl ether protecting groups are important tools in organic synthesis, ensuring selective reactions of hydroxyl functional groups. Enantiospecific formation or cleavage could simultaneously enable the resolution of racemic mixtures and thus significantly increase the efficiency of complex synthetic pathways. Based on reports that lipases, which today are already particularly important tools in chemical synthesis, can catalyze the enantiospecific turnover of trimethylsilanol (TMS)-protected alcohols, the goal of this study was to determine the conditions under which such a catalysis occurs. Through detailed experimental and mechanistic investigation, we demonstrated that although lipases mediate the turnover of TMS-protected alcohols, this occurs independently of the known catalytic triad, as this is unable to stabilize a tetrahedral intermediate. The reaction is essentially non-specific and therefore most likely completely independent of the active site. This rules out lipases as catalysts for the resolution of racemic mixtures of alcohols through protection or deprotection with silyl groups.

Modulating Substrate Specificity of Rhizobium sp. Histamine Dehydrogenase through Protein Engineering for Food Quality Applications.

Histamine is a biogenic amine found in fish-derived and fermented food products with physiological relevance since its concentration is proportional to food spoilage and health risk for sensitive consumers. There are various analytical methods for histamine quantification from food samples; however, a simple and quick enzymatic detection and quantification method is highly desirable. Histamine dehydrogenase (HDH) is a candidate for enzymatic histamine detection; however, other biogenic amines can change its activity or produce false positive results with an observed substrate inhibition at higher concentrations. In this work, we studied the effect of site saturation mutagenesis in Rhizobium sp. Histamine Dehydrogenase (Rsp HDH) in nine amino acid positions selected through structural alignment analysis, substrate docking, and proximity to the proposed histamine-binding site. The resulting libraries were screened for histamine and agmatine activity. Variants from two libraries (positions 72 and 110) showed improved histamine/agmatine activity ratio, decreased substrate inhibition, and maintained thermal resistance. In addition, activity characterization of the identified Phe72Thr and Asn110Val HDH variants showed a clear substrate inhibition curve for histamine and modified kinetic parameters. The observed maximum velocity (Vmax) increased for variant Phe72Thr at the cost of an increased value for the Michaelis-Menten constant (Km) for histamine. The increased Km value, decreased substrate inhibition, and biogenic amine interference observed for variant Phe72Thr support a tradeoff between substrate affinity and substrate inhibition in the catalytic mechanism of HDHs. Considering this tradeoff for future enzyme engineering of HDH could lead to breakthroughs in performance increases and understanding of this enzyme class.

Identification of the First Sulfobetaine Hydrogel-Binding Peptides via Phage Display Assay.

Using the M13 phage display, a series of 7- and 12-mer peptides which interact with new sulfobetaine hydrogels are identified. Two peptides each from the 7- and 12-mer peptide libraries bind to the new sulfobetaine hydrogels with high affinity compared to the wild-type phage lacking a dedicated hydrogel binding peptide. This is the first report of peptides binding to zwitterionic sulfobetaine hydrogels and the study therefore opens up the pathway toward new phage or peptide/hydrogel hybrids with high application potential.

The molecular basis and enzyme engineering strategies for improvement of coupling efficiency in cytochrome P450s.

Cytochrome P450s are heme-thiolate enzymes that have been broadly applied in pharmaceutical and biosynthesis because of their efficient oxidation at inert carbons. Extensive engineering campaigns are applied to P450s to explore new non-natural substrates and reactions; however, achieving high coupling efficiency is one of the main challenges. The undesirable uncoupling reactions result in the extra consumption of expensive cofactor NAD(P)H, and lead to the accumulation of reactive oxygen species and the inactivation of enzymes and organisms. Using protein engineering methods, these limitations can be overcome by engineering and fine-tuning P450s. A systemic perspective of the enzyme structure and the catalytic mechanism is essential for P450 engineering campaigns for higher coupling efficiency. This review provide an overview on factors contributing to uncoupling and protein engineering approaches to minimize uncoupling and thereby generating efficient and robust P450s for industrials use. Contributing uncoupling factors are classified into three main groups: i) substrate binding pocket; ii) ligand access tunnel(s); and iii) electron transfer pathway(s). Finally, we draw future directions for combinations of effective state-of-the-art technologies and available software/online tools for P450s engineering campaigns.

Heterocyclic and Alkyne Terpenoids by Terpene Synthase-Mediated Biotransformation of Non-Natural Prenyl Diphosphates: Access to New Fragrances and Probes.

Two terpene cyclases were used as biocatalytic tool, namely, limonene synthase from Cannabis sativa (CLS) and 5-epi-aristolochene synthase (TEAS) from Nicotiana tabacum. They showed significant substrate flexibility towards non-natural prenyl diphosphates to form novel terpenoids, including core oxa- and thia-heterocycles and alkyne-modified terpenoids. We elucidated the structures of five novel monoterpene-analogues and a known sesquiterpene-analogue. These results reflected the terpene synthases' ability and promiscuity to broaden the pool of terpenoids with structurally complex analogues. Docking studies highlight an on-off conversion of the unnatural substrates.

Recombination of Single Beneficial Substitutions Obtained from Protein Engineering by Computer-Assisted Recombination (CompassR).

A large number of beneficial substitutions can be obtained from a successful directed enzyme evolution campaign and/or (semi)rational design. It is expected that the recombination of some beneficial substitutions leads to a much higher degree of performance through synergistic effect. However, systematic recombination studies show that poorly performing variants are often obtained after recombination of three to four individual beneficial substitutions and this limits protein engineers to exploit nature's potential in generating better performing enzymes. Computer-assisted Recombination (CompassR) strategy allows the recombination of identified beneficial substitutions in an effective and efficient manner in order to generate active enzymes with improved performance. Here, we describe in detail the CompassR procedure with an example of recombining four substitutions and discuss some important practical issues that should be considered (such as the selection of protein structures, number of FoldX runs, evaluation of calculations) for application of the CompassR rule. The core part of this protocol (system setup, ΔΔGfold calculation, and CompassR application) is transferable to other enzymes and any recombination of single beneficial substitutions.

Combinatorial InVitroFlow-assisted mutagenesis (CombIMut) yields a 41-fold improved CelA2 cellulase.

The combination of diversity generation methods and ultrahigh-throughput screening (uHTS) technologies is key to efficiently explore nature's sequence space and elucidate structure-function relationships of enzymes. Beneficial substitutions often cluster in a few regions and simultaneous amino acid substitutions at multiple positions (e.g., by OmniChange) will likely lead to further improved enzyme variants. An extensive screening effort is required to identify such variants, as the simultaneous randomization of four codons can easily yield over 105 potential enzyme variants. The combination of flow cytometer-based uHTS with cell-free compartmentalization technology using (w/o/w) double emulsions (InVitroFlow), provides analysis capabilities of up to 107 events per hour, thus enabling efficient screening. InVitroFlow is an elegant solution since diversity loss through a transformation of host cells is omitted and emulsion compartments provide a genotype-phenotype linkage through a fluorescence readout. In this study, a multisite saturation mutagenesis and an OmniChange library with four simultaneously saturated positions in the active site of CelA2 cellulase were screened using InVitroFlow. Screening of over 36 million events, yielded a significantly improved cellulase variant CelA2-M3 (H288F/H524Q) with an 8-fold increase in specific activity compared to the parent CelA2-H288F (83.9 U/mg) and a 41-fold increased specific activity (674.5 U/mg) compared to wildtype CelA2 (16.6 U/mg) for the substrate 4-MUC (4-methylumbelliferyl-ß d-cellobioside).

In Silico and Experimental ADAM17 Kinetic Modeling as Basis for Future Screening System for Modulators.

Understanding the mechanisms of modulators' action on enzymes is crucial for optimizing and designing pharmaceutical substances. The acute inflammatory response, in particular, is regulated mainly by a disintegrin and metalloproteinase (ADAM) 17. ADAM17 processes several disease mediators such as TNFα and APP, releasing their soluble ectodomains (shedding). A malfunction of this process leads to a disturbed inflammatory response. Chemical protease inhibitors such as TAPI-1 were used in the past to inhibit ADAM17 proteolytic activity. However, due to ADAM17's broad expression and activity profile, the development of active-site-directed ADAM17 inhibitor was discontinued. New 'exosite' (secondary substrate binding site) inhibitors with substrate selectivity raised the hope of a substrate-selective modulation as a promising approach for inflammatory disease therapy. This work aimed to develop a high-throughput screen for potential ADAM17 modulators as therapeutic drugs. By combining experimental and in silico methods (structural modeling and docking), we modeled the kinetics of ADAM17 inhibitor. The results explain ADAM17 inhibition mechanisms and give a methodology for studying selective inhibition towards the design of pharmaceutical substances with higher selectivity.