Hop Leaves as an Alternative Source of Health-Active Compounds: Effect of Genotype and Drying Conditions

In hop cultivation, one-third of the crop is a valuable product (hop cones), and two-thirds is unexploited biomass, consisting mainly of leaves and stems, which, in a circular economy approach, can be recovered and, once stabilized, supplied to industrial sectors, such as cosmetics, pharmaceuticals and phytotherapy, with high added value. In this regard, this study aimed to investigate the effects of two different drying methods: oven drying (OD) at 45 °C and freeze-drying (FD), on the overall nutraceutical profile (i.e., total phenols, total flavans and total thiols), pigment content (i.e., carotenoids and chlorophylls) and the antioxidant potential of leaves from five different Humulus lupulus varieties grown in central Italy. Moreover, attenuated total reflectance infrared (ATR-FTIR) spectroscopy was applied to dried leaf powders to study the influence of both the variety and treatment on their molecular fingerprints. The spectral data were then analyzed by principal component analysis (PCA), which was able to group the samples mainly based on the applied treatment. Considering the overall phytochemical profile, FD appeared to be the most suitable drying method, while OD provided higher carotenoid retention, depending on the genotype considered. Finally, unsupervised chemometric tools (i.e., PCA and hierarchical clustering) revealed that the two main clusters contained subclusters based on the drying treatment applied; these subgroups were related to the susceptibility of the variety to the drying conditions studied.

fingeRNAt - a novel tool for high-throughput analysis of nucleic acid-ligand interactions

Computational methods play a pivotal role in drug discovery and are widely applied in virtual screening, structure optimization, and compound activity profiling. Over the last decades, almost all the attention in medicinal chemistry has been directed to protein-ligand binding, and computational tools have been created with this target in mind. With novel discoveries of functional RNAs and their possible applications, RNAs have gained considerable attention as potential drug targets. However, the availability of bioinformatics tools for nucleic acids is limited. Here, we introduce fingeRNAt - a software tool for detecting non-covalent interactions formed in complexes of nucleic acids with ligands. The program detects nine types of interactions: (i) hydrogen and (ii) halogen bonds, (iii) cation-anion, (iv) pi-cation, (v) pi-anion, (vi) pi-stacking, (vii) inorganic ion-mediated, (viii) water-mediated, and (ix) lipophilic interactions. However, the scope of detected interactions can be easily expanded using a simple plugin system. In addition, detected interactions can be visualized using the associated PyMOL plugin, which facilitates the analysis of medium-throughput molecular complexes. Interactions are also encoded and stored as a bioinformatics-friendly Structural Interaction Fingerprint (SIFt) - a binary string where the respective bit in the fingerprint is set to 1 if a particular interaction is present and to 0 otherwise. This output format, in turn, enables high-throughput analysis of interaction data using data analysis techniques. We present applications of fingeRNAt-generated interaction fingerprints for visual and computational analysis of RNA-ligand complexes, including analysis of interactions formed in experimentally determined RNA-small molecule ligand complexes deposited in the Protein Data Bank. We propose interaction-based similarity based on fingerprints as an alternative measure to RMSD to recapitulate complexes with similar interactions but different folding. We present an application of molecular fingerprints for the clustering of molecular complexes. This approach can be used to group ligands that form similar binding networks and thus have similar biological properties.

Machine-Learning-Enabled Virtual Screening for Inhibitors of Lysine-Specific Histone Demethylase 1

A machine learning approach has been applied to virtual screening for lysine specific demethylase 1 (LSD1) inhibitors. LSD1 is an important anti-cancer target. Machine learning models to predict activity were constructed using Morgan molecular fingerprints. The dataset, consisting of 931 molecules with LSD1 inhibition activity, was obtained from the ChEMBL database. An evaluation of several candidate algorithms on the main dataset revealed that the support vector regressor gave the best model, with a coefficient of determination (R2) of 0.703. Virtual screening, using this model, identified five predicted potent inhibitors from the ZINC database comprising more than 300,000 molecules. The virtual screening recovered a known inhibitor, RN1, as well as four compounds where activity against LSD1 had not previously been suggested. Thus, we performed a machine-learning-enabled virtual screening of LSD1 inhibitors using only the structural information of the molecules.

An open-source framework for fast-yet-accurate calculation of quantum mechanical features

We present the open-source framework kallisto that enables the efficient and robust calculation of quantum mechanical features for atoms and molecules. For a benchmark set of 49 experimental molecular polarizabilities, the predictive power of the presented method competes against second-order perturbation theory in a converged atomic-orbital basis set at a fraction of its computational costs. Robustness tests within a diverse validation set of more than 80,000 molecules show that the calculation of isotropic molecular polarizabilities has a low failure-rate of only 0.3 %. We present furthermore a generally applicable van der Waals radius model that is rooted on atomic static polarizabilites. Efficiency tests show that such radii can even be calculated for small- to medium-size proteins where the largest system (SARS-CoV-2 spike protein) has 42,539 atoms. Following the work of Domingo-Alemenara et al. [Domingo-Alemenara et al., Nat. Comm., 2019, 10, 5811], we present computational predictions for retention times for different chromatographic methods and describe how physicochemical features improve the predictive power of machine-learning models that otherwise only rely on two-dimensional features like molecular fingerprints. Additionally, we developed an internal benchmark set of experimental super-critical fluid chromatography retention times. For those methods, improvements of up to 17 % are obtained when combining molecular fingerprints with physicochemical descriptors. Shapley additive explanation values show furthermore that the physical nature of the applied features can be retained within the final machine-learning models. We generally recommend the kallisto framework as a robust, low-cost, and physically motivated featurizer for upcoming state-of-the-art machine-learning studies.

Prediction of small-molecule compound solubility in organic solvents by machine learning algorithms

Rapid solvent selection is of great significance in chemistry. However, solubility prediction remains a crucial challenge. This study aimed to develop machine learning models that can accurately predict compound solubility in organic solvents. A dataset containing 5081 experimental temperature and solubility data of compounds in organic solvents was extracted and standardized. Molecular fingerprints were selected to characterize structural features. lightGBM was compared with deep learning and traditional machine learning (PLS, Ridge regression, kNN, DT, ET, RF, SVM) to develop models for predicting solubility in organic solvents at different temperatures. Compared to other models, lightGBM exhibited significantly better overall generalization (logS ± 0.20). For unseen solutes, our model gave a prediction accuracy (logS ± 0.59) close to the expected noise level of experimental solubility data. lightGBM revealed the physicochemical relationship between solubility and structural features. Our method enables rapid solvent screening in chemistry and may be applied to solubility prediction in other solvents.

Advanced Omics and Radiobiological Tissue Archives: The Future in the Past

Archival formalin-fixed, paraffin-embedded (FFPE) tissues and their related diagnostic records are an invaluable source of biological information. The archival samples can be used for retrospective investigation of molecular fingerprints and biomarkers of diseases and susceptibility. Radiobiological archives were set up not only following clinical performance such as cancer diagnosis and therapy but also after accidental and occupational radiation exposure events where autopsies or cancer biopsies were sampled. These biobanks provide unique and often irreplaceable materials for the understanding of molecular mechanisms underlying radiation-related biological effects. In recent years, the application of rapidly evolving “omics” platforms, including transcriptomics, genomics, proteomics, metabolomics and sequencing, to FFPE tissues has gained increasing interest as an alternative to fresh/frozen tissue. However, omics profiling of FFPE samples remains a challenge mainly due to the condition and duration of tissue fixation and storage, and the extraction methods of biomolecules. Although biobanking has a long history in radiation research, the application of omics to profile FFPE samples available in radiobiological archives is still young. Application of the advanced omics technologies on archival materials provides a new opportunity to understand and quantify the biological effects of radiation exposure. These newly generated omics data can be well integrated into results obtained from earlier experimental and epidemiological analyses to shape a powerful strategy for modelling and evaluating radiation effects on health outcomes. This review aims to give an overview of the unique properties of radiation biobanks and their potential impact on radiation biology studies. Studies recently performed on FFPE samples from radiobiology archives using advanced omics are summarized. Furthermore, the compatibility of archived FFPE tissues for omics analysis and the major challenges that lie ahead are discussed.

Evolving Landscape of Long Non-coding RNAs in Cerebrospinal Fluid: A Key Role From Diagnosis to Therapy in Brain Tumors

Long non-coding RNAs (lncRNAs) are a type of non-coding RNAs that act as molecular fingerprints and modulators of many pathophysiological processes, particularly in cancer. Specifically, lncRNAs can be involved in the pathogenesis and progression of brain tumors, affecting stemness/differentiation, replication, invasion, survival, DNA damage response, and chromatin dynamics. Furthermore, the aberrations in the expressions of these transcripts can promote treatment resistance, leading to tumor recurrence. The development of next-generation sequencing technologies and the creation of lncRNA-specific microarrays have boosted the study of lncRNA etiology. Cerebrospinal fluid (CSF) directly mirrors the biological fluid of biochemical processes in the brain. It can be enriched for small molecules, peptides, or proteins released by the neurons of the central nervous system (CNS) or immune cells. Therefore, strategies that identify and target CSF lncRNAs may be attractive as early diagnostic and therapeutic options. In this review, we have reviewed the studies on CSF lncRNAs in the context of brain tumor pathogenesis and progression and discuss their potential as biomarkers and therapeutic targets.

Hybrid Graphene-Based Photonic-Plasmonic Biochemical Sensor with a Photonic and Acoustic Cavity Structure

In this study, we propose a biochemical sensor that features a photonic cavity integrated with graphene. The tunable hybrid plasmonic-photonic sensor can detect the molecular fingerprints of biochemicals with a small sample volume. The stacking sequence of the device is “ITO grating/graphene/TiO2/Au/Si substrate”, which composes a photonic band gap structure. A defect is created within the ITO gratings to form a resonant cavity. The plasmonic-photonic energy can be confined in the cavity to enhance the interaction between light and the analyte deposited in the cavity. The finite element simulation results indicated that the current sensor exhibits very high values in resonance shift and sensitivity. Moreover, the resonance spectrum with a broad resonance linewidth can identify the molecular vibration bands, which was exemplified by the fingerprint detections of protein and the chemical compound CBP. The sensor possesses an electrical tunability by including a graphene layer, which allowed us to tune the effective refractive index of the cavity to increase the sensor’s sensing performance. In addition, our device admits a phononic bandgap as well, which was exploited to sense the mechanical properties of two particular dried proteins based on the simplified elastic material model instead of using the more realistic viscoelastic model. The dual examinations of the optical and mechanical properties of analytes from a phoxonic sensor can improve the selectivity in analyte detections.