Publications - Bioorganic Chemistry

Protein engineering through directed evolution and (semi)rational design has become a powerful approach for optimizing and enhancing proteins with desired properties. The integration of artificial intelligence methods has further accelerated protein engineering process by enabling the development of predictive models based on datadriven strategies. However, the lack of interpretability and transparency in these models limits their trustworthiness and applicability in real-world scenarios. Explainable Artificial Intelligence addresses these challenges by providing insights into the decision-making processes of machine learning models, enhancing their reliability and interpretability. Explainable strategies has been successfully applied in various biotechnology fields, including drug discovery, genomics, and medicine, yet its application in protein engineering remains underexplored. The incorporation of explainable strategies in protein engineering holds significant potential, as it can guide protein design by revealing how predictive models function, benefiting approaches such as machine learning-assisted directed evolution. This perspective work explores the principles and methodologies of explainable artificial intelligence, highlighting its relevance in biotechnology and its potential to enhance protein design. Additionally, three theoretical pipelines integrating predictive models with explainable strategies are proposed, focusing on their advantages, disadvantages, and technical requirements. Finally, the remaining challenges of explainable artificial intelligence in protein engineering and future directions for its development as a support tool for traditional protein engineering methodologies are discussed.

Hornstedtia scyphifera (J.Koenig) Steud. represents a lesser-known member of the ginger family (Zingiberaceae) that is used in Malaysia as spice and traditional medicine. The phytochemical investigation of leaves from this species utilizing diverse analytical methods has provided comprehensive insights into its chemical profile for the first time. Headspace gas chromatography-mass spectrometry (HS-GCMS) and GCMS analyses of essential oil and nonpolar extracts verified α-pinene, camphene, p-cymene, and camphor as main volatile compounds. Metabolite profiling of the crude extract by ultra-high-performance-liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) unveiled terpenoids, flavonoids and other phenolics as major compound classes. Isolation and follow-up structure elucidation, involving 1D and 2D NMR, HRMS, UV and CD analysis, yielded two new sesquiterpenoids, (1R,5S,6S,7R,10R)-mustak-14-oic acid (1) and (1R,6S,7S,10R)-6-hydroxy-anhuienosol (2), along with 24 known compounds (seven terpenoids, seven flavonoids, ten phenolics), 21 of these never reported for H. scyphifera. Additionally, the crude extract and fractions from the purification process were screened for antibacterial and antifungal activity. This is supplemented by an extensive literature research for described bioactivities of all isolated compounds. Our results support and explain previously detected antimicrobial, antifungal and neuroprotective effects of H. scyphifera extracts and provide evidence for its potential pharmacological importance.

A bottleneck in the development of new anti-cancer drugs is the recognition of their mode of action (MoA). We combined metabolomics and machine learning to predict MoAs of novel anti-proliferative drug candidates, focusing on human prostate cancer cells (PC-3). As proof of concept, we studied 38 drugs 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. We validate the transferability of MoA predictions from PC-3 to two other cancer cell models and show that correct predictions are still possible, but at the 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, we predict that pentacyclic triterpenes inhibit oxidative phosphorylation and affect phospholipid biosynthesis, as supported by respiration parameters, lipidomics, and molecular docking. Using biochemical insights from individual drug treatments, our approach offers new opportunities, including the optimization of combinatorial drug applications.

Two connected histopathological hallmarks of Alzheimer’s disease (AD) are chronic neuroinflammation and synaptic dysfunction. The accumulation of the most prevalent posttranslationally modified form of Aβ1–42, pyroglutamylated amyloid-β (Aβ3(pE)-42) in astrocytes is directly linked to glial activation and the release of proinflammatory cytokines that in turn contribute to early synaptic dysfunction in AD. At present the mechanisms of Aβ3(pE)-42 uptake to astrocytes are unknown and pharmacological interventions that interfere with this process are not available. Here we developed a simple screening assay to identify substances from a plant extract library that prevent astroglial Aβ3(pE)-42 uptake. We first show that this approach yields valid and reproducible results. Second, we show endocytosis of Aβ3(pE)-42 oligomers by astrocytes and that quercetin, a plant flavonol, is effective to specifically block astrocytic buildup of oligomeric Aβ3(pE)-42. Importantly, quercetin does not induce a general impairment of endocytosis. However, it efficiently protects against early synaptic dysfunction following exogenous Aβ3(pE)-42 application.

Introduction: Climate change poses various threats to marine life, particularly in shallow tropical waters. Objective: The impact of increased temperature and ultraviolet (UV) exposure on two photosymbiotic cnidarians, a common bubble-tip anemone and an upside-down jellyfish, was investigated. Methods: To illustrate the response of aquatic organisms, the metabolomes of unstressed Entacmaea quadricolor and Cassiopea andromeda were compared for detailed metabolite profiling. UHPLC-MS cou-pled with chemometrics and GNPS molecular networking was employed for sample classification and identification of markers unique to stress responses in each organism. Results: Several compounds with bioactive functions, including peptides and terpenoids, were reported for the first time in both organisms, viz. cyclic tetraglutamate, campestriene, and ceramide aminoethyl phosphonate (CEAP d18:2/16:0). Both anemone and jellyfish were subjected to either elevated UV-B light intensity up to 6.6 KJ m-2 or increased temperatures (28°C, 30°C, 32°C, and 34°C) over 4 days. Phospholipids, steroids, and ceramides emerged as chief markers of both types of stress, as revealed by the multivariate data analysis. Lysophosphatidylcholine (LPC 16:0), LPC (18:0/0:0), and echinoclasterol sulfate appeared as markers in both UV and thermal stress models of the anemone, whereas methyl/pro-pyl cholestane-hexa-ol were discriminatory in the UV stress model only. In the case of jellyfish, nonpolar glycosyl ceramide GlcCer (d14:1/28:6) served as a marker for UV stress, whereas polar peptides were ele-vated in the thermal stress model. Interestingly, both models of jellyfish share a phospholipid, lysophos-phatidylethanolamine (LPE 20:4), as a distinctive marker for stress, reported to be associated indirectly with the activity of innate immune response within other photosymbiotic Cnidaria such as corals and appears to be a fundamental stress response in marine organisms. Conclusion: This study presents several bioinformatic tools for the first time in two cnidarian organisms to provide not only a broader coverage of their metabolome but also broader insights into cnidarian bleaching in response to different stressors, i.e., heat and UV light, by comparing their effects in anemone versus jellyfish.

Two connected histopathological hallmarks of Alzheimer’s disease (AD) are chronic neuroinflammation and synaptic dysfunction. The accumulation of the most prevalent posttranslationally modified form of Aβ1–42, pyroglutamylated amyloid-β (Aβ3(pE)-42) in astrocytes is directly linked to glial activation and the release of proinflammatory cytokines that in turn contribute to early synaptic dysfunction in AD. At present, the mechanisms of Aβ3(pE)-42 uptake to astrocytes are unknown and pharmacological interventions that interfere with this process are not available. Here we developed a simple screening assay to identify substances from a plant extract library that prevent astroglial Aβ3(pE)-42 uptake. We first show that this approach yields valid and reproducible results. Second, we show endocytosis of Aβ3(pE)-42 oligomers by astrocytes and that quercetin, a plant flavonol, is effective to specifically block astrocytic buildup of oligomeric Aβ3(pE)-42. Importantly, quercetin does not induce a general impairment of endocytosis. However, it efficiently protects against early synaptic dysfunction following exogenous Aβ3(pE)-42 application.

Protein engineering using directed evolution and (semi)rational design has emerged as a powerful strategy for optimizing and enhancing enzymes or proteins with desired properties. Integrating artificial intelligence methods has further enhanced and accelerated protein engineering through predictive models developed in data-driven strategies. However, the lack of explainability and interpretability in these models poses challenges. Explainable Artificial Intelligence addresses the interpretability and explainability of machine learning models, providing transparency and insights into predictive processes. Nonetheless, there is a growing need to incorporate explainable techniques in predicting protein properties in machine learning-assisted protein engineering. This work explores incorporating explainable artificial intelligence in predicting protein properties, emphasizing its role in trustworthiness and interpretability. It assesses different machine learning approaches, introduces diverse explainable methodologies, and proposes strategies for seamless integration, improving trust-worthiness. Practical cases demonstrate the explainable model’s effectiveness in identifying DNA binding proteins and optimizing Green Fluorescent Protein brightness. The study highlights the utility of explainable artificial intelligence in advancing computationally assisted protein design, fostering confidence in model reliability.

Flavor is the main factor driving consumers acceptance of food products. However, tracking the biochemistry of flavor is a formidable challenge due to the complexity of food composition. Current methodologies for linking individual molecules to flavor in foods and beverages are expensive and time-consuming. Predictive models based on machine learning (ML) are emerging as an alternative to speed up this process. Nonetheless, the optimal approach to predict flavor features of molecules remains elusive. In this work we present FlavorMiner, an ML-based multilabel flavor predictor. FlavorMiner seamlessly integrates different combinations of algorithms and mathematical representations, augmented with class balance strategies to address the inherent class of the input dataset. Notably, Random Forest and K-Nearest Neighbors combined with Extended Connectivity Fingerprint and RDKit molecular descriptors consistently outperform other combinations in most cases. Resampling strategies surpass weight balance methods in mitigating bias associated with class imbalance. FlavorMiner exhibits remarkable accuracy, with an average ROC AUC score of 0.88. This algorithm was used to analyze cocoa metabolomics data, unveiling its profound potential to help extract valuable insights from intricate food metabolomics data. FlavorMiner can be used for flavor mining in any food product, drawing from a diverse training dataset that spans over 934 distinct food products.

Taleb coined the term “antifragility” to describe systems that benefit from stressors and volatility. While nature provides several examples of systems with antifragile behavior, manufacturing has so far only aimed to avoid or absorb stressors and volatility. This article surveys existing examples of antifragile system behavior in biology, biotechnology, software engineering, risk management, and manufacturing. From these examples, components of antifragile systems and principles to implement these components are derived and organized in a framework. The framework intends to serve as guidance for practitioners as well as starting point for future research on the design of antifragile systems in manufacturing.

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.