Photodynamic inactivation of E. coli with cationic imidazolyl-porphyrin photosensitizers and their synergic combination with antimicrobial cinnamaldehyde.

Bacterial infections are a global health concern, particularly due to the increasing resistance of bacteria to antibiotics. Multi-drug resistance (MDR) is a considerable challenge, and novel approaches are needed to treat bacterial infections. Photodynamic inactivation (PDI) of microorganisms is increasingly recognized as an effective method to inactivate a broad spectrum of bacteria and overcome resistance mechanisms. This study presents the synthesis of a new cationic 5,15-di-imidazolyl porphyrin derivative and the impact of n-octanol/water partition coefficient (logP) values of this class of photosensitizers on PDI efficacy of Escherichia coli. The derivative with logP = -0.5, IP-H-OH2+, achieved a remarkable 3 log CFU reduction of E. coli at 100 nM with only 1.36 J/cm2 light dose at 415 nm, twice as effective as the second-best porphyrin IP-H-Me2+, of logP = -1.35. We relate the rapid uptake of IP-H-OH2+ by E. coli to improved PDI and the very low uptake of a fluorinated derivative, IP-H-CF32+, logP ≈ 1, to its poor performance. Combination of PDI with cinnamaldehyde, a major component of the cinnamon plant known to alter bacteria cell membranes, offered synergic inactivation of E. coli (7 log CFU reduction), using 50 nM of IP-H-OH2+ and just 1.36 J/cm2 light dose. The success of combining PDI with this natural compound broadens the scope of therapies for MDR infections that do not add drug resistance. In vivo studies on a mouse model of wound infection showed the potential of cationic 5,15-di-imidazolyl porphyrins to treat clinically relevant infected wounds.

SimRNAweb v2.0: a web server for RNA folding simulations and 3D structure modeling, with optional restraints and enhanced analysis of folding trajectories.

Research on ribonucleic acid (RNA) structures and functions benefits from easy-to-use tools for computational prediction and analyses of RNA three-dimensional (3D) structure. The SimRNAweb server version 2.0 offers an enhanced, user-friendly platform for RNA 3D structure prediction and analysis of RNA folding trajectories based on the SimRNA method. SimRNA employs a coarse-grained model, Monte Carlo sampling and statistical potentials to explore RNA conformational space, optionally guided by spatial restraints. Recognized for its accuracy in RNA 3D structure prediction in RNA-Puzzles and CASP competitions, SimRNA is particularly useful for incorporating restraints based on experimental data. The new server version introduces performance optimizations and extends user control over simulations and the processing of results. It allows the application of various hard and soft restraints, accommodating alternative structures involving canonical and noncanonical base pairs and unpaired residues, while also integrating data from chemical probing methods. Enhanced features include an improved analysis of folding trajectories, offering advanced clustering options and multiple analyses of the generated trajectories. These updates provide comprehensive tools for detailed RNA structure analysis. SimRNAweb v2.0 significantly broadens the scope of RNA modeling, emphasizing flexibility and user-defined parameter control. The web server is available at https://genesilico.pl/SimRNAweb.

DEGRONOPEDIA: a web server for proteome-wide inspection of degrons.

E3 ubiquitin ligases recognize substrates through their short linear motifs termed degrons. While degron-signaling has been a subject of extensive study, resources for its systematic screening are limited. To bridge this gap, we developed DEGRONOPEDIA, a web server that searches for degrons and maps them to nearby residues that can undergo ubiquitination and disordered regions, which may act as protein unfolding seeds. Along with an evolutionary assessment of degron conservation, the server also reports on post-translational modifications and mutations that may modulate degron availability. Acknowledging the prevalence of degrons at protein termini, DEGRONOPEDIA incorporates machine learning to assess N-/C-terminal stability, supplemented by simulations of proteolysis to identify degrons in newly formed termini. An experimental validation of a predicted C-terminal destabilizing motif, coupled with the confirmation of a post-proteolytic degron in another case, exemplifies its practical application. DEGRONOPEDIA can be freely accessed at degronopedia.com.

Refining antimicrobial photodynamic therapy: effect of charge distribution and central metal ion in fluorinated porphyrins on effective control of planktonic and biofilm bacterial forms.

Antibiotic resistance represents a pressing global health challenge, now acknowledged as a critical concern within the framework of One Health. Photodynamic inactivation of microorganisms (PDI) offers an attractive, non-invasive approach known for its flexibility, independence from microbial resistance patterns, broad-spectrum efficacy, and minimal risk of inducing resistance. Various photosensitizers, including porphyrin derivatives have been explored for pathogen eradication. In this context, we present the synthesis, spectroscopic and photophysical characteristics as well as antimicrobial properties of a palladium(II)-porphyrin derivative (PdF2POH), along with its zinc(II)- and free-base counterparts (ZnF2POH and F2POH, respectively). Our findings reveal that the palladium(II)-porphyrin complex can be classified as an excellent generator of reactive oxygen species (ROS), encompassing both singlet oxygen (Φ△ = 0.93) and oxygen-centered radicals. The ability of photosensitizers to generate ROS was assessed using a variety of direct (luminescence measurements) and indirect techniques, including specific fluorescent probes both in solution and in microorganisms during the PDI procedure. We investigated the PDI efficacy of F2POH, ZnF2POH, and PdF2POH against both Gram-negative and Gram-positive bacteria. All tested compounds proved high activity against Gram-positive species, with PdF2POH exhibiting superior efficacy, leading to up to a 6-log reduction in S. aureus viability. Notably, PdF2POH-mediated PDI displayed remarkable effectiveness against S. aureus biofilm, a challenging target due to its complex structure and increased resistance to conventional treatments. Furthermore, our results show that PDI with PdF2POH is more selective for bacterial than for mammalian cells, particularly at lower light doses (up to 5 J/cm2 of blue light illumination). This enhanced efficacy of PdF2POH-mediated PDI as compared to ZnF2POH and F2POH can be attributed to more pronounced ROS generation by palladium derivative via both types of photochemical mechanisms (high yields of singlet oxygen generation as well as oxygen-centered radicals). Additionally, PDI proved effective in eliminating bacteria within S. aureus-infected human keratinocytes, inhibiting infection progression while preserving the viability and integrity of infected HaCaT cells. These findings underscore the potential of metalloporphyrins, particularly the Pd(II)-porphyrin complex, as promising photosensitizers for PDI in various bacterial infections, warranting further investigation in advanced infection models.

Conserved structures and dynamics in 5'-proximal regions of Betacoronavirus RNA genomes.

Betacoronaviruses are a genus within the Coronaviridae family of RNA viruses. They are capable of infecting vertebrates and causing epidemics as well as global pandemics in humans. Mitigating the threat posed by Betacoronaviruses requires an understanding of their molecular diversity. The development of novel antivirals hinges on understanding the key regulatory elements within the viral RNA genomes, in particular the 5'-proximal region, which is pivotal for viral protein synthesis. Using a combination of cryo-electron microscopy, atomic force microscopy, chemical probing, and computational modeling, we determined the structures of 5'-proximal regions in RNA genomes of Betacoronaviruses from four subgenera: OC43-CoV, SARS-CoV-2, MERS-CoV, and Rousettus bat-CoV. We obtained cryo-electron microscopy maps and determined atomic-resolution models for the stem-loop-5 (SL5) region at the translation start site and found that despite low sequence similarity and variable length of the helical elements it exhibits a remarkable structural conservation. Atomic force microscopy imaging revealed a common domain organization and a dynamic arrangement of structural elements connected with flexible linkers across all four Betacoronavirus subgenera. Together, these results reveal common features of a critical regulatory region shared between different Betacoronavirus RNA genomes, which may allow targeting of these RNAs by broad-spectrum antiviral therapeutics.

Discovery of a trefoil knot in the RydC RNA: Challenging previous notions of RNA topology.

Knots are very common in polymers, including DNA and protein molecules. Yet, no genuine knot has been identified in natural RNA molecules to date. Upon re-examining experimentally determined RNA 3D structures, we discovered a trefoil knot 31, the most basic non-trivial knot, in the RydC RNA. This knotted RNA is a member of a small family of short bacterial RNAs, whose secondary structure is characterized by an H-type pseudoknot. Molecular dynamics simulations suggest a folding pathway of the RydC RNA that starts with a native twisted loop. Based on sequence analyses and computational RNA 3D structure predictions, we postulate that this trefoil knot is a conserved feature of all RydC-related RNAs. The first discovery of a knot in a natural RNA molecule introduces a novel perspective on RNA 3D structure formation and on fundamental research on the relationship between function and spatial structure of biopolymers.

MODOMICS: a database of RNA modifications and related information. 2023 update.

The MODOMICS database was updated with recent data and now includes new data types related to RNA modifications. Changes to the database include an expanded modification catalog, encompassing both natural and synthetic residues identified in RNA structures. This addition aids in representing RNA sequences from the RCSB PDB database more effectively. To manage the increased number of modifications, adjustments to the nomenclature system were made. Updates in the RNA sequences section include the addition of new sequences and the reintroduction of sequence alignments for tRNAs and rRNAs. The protein section was updated and connected to structures from the RCSB PDB database and predictions by AlphaFold. MODOMICS now includes a data annotation system, with 'Evidence' and 'Estimated Reliability' features, offering clarity on data support and accuracy. This system is open to all MODOMICS entries, enhancing the accuracy of RNA modification data representation. MODOMICS is available at https://iimcb.genesilico.pl/modomics/.

Enhancing Visible-Light Photocatalysis with Pd(II) Porphyrin-Based TiO2 Hybrid Nanomaterials: Preparation, Characterization, ROS Generation, and Photocatalytic Activity.

Novel hybrid TiO2-based materials were obtained by adsorption of two different porphyrins on the surface of nanoparticles-commercially available 5,10,15,20-tetrakis(4-sulfonatophenyl)porphyrin (TPPS) and properly modified metalloporphyrin-5,10,15,20-tetrakis(2,6-difluoro-3-sulfophenyl)porphyrin palladium(II) (PdF2POH). The immobilization of porphyrins on the surface of TiO2 was possible due to the presence of sulfonyl groups. To further elevate the adsorption of porphyrin, an anchoring linker-4-hydroxybenzoic acid (PHBA)-was used. The synthesis of hybrid materials was proven by electronic absorption spectroscopy, dynamic light scattering (DLS), and photoelectrochemistry. Results prove the successful photosensitization of TiO2 to visible light by both porphyrins. However, the presence of the palladium ion in the modifier structure played a key role in strong adsorption, enhanced charge separation, and thus effective photosensitization. The incorporation of halogenated metalloporphyrins into TiO2 facilitates the enhancement of the comprehensive characteristics of the investigated materials and enables the evaluation of their performance under visible light. The effectiveness of reactive oxygen species (ROS) generation was also determined. Porphyrin-based materials with the addition of PHBA seemed to generate ROS more effectively than other composites. Interestingly, modifications influenced the generation of singlet oxygen for TPPS but not hydroxyl radical, in contrast to PdF2POH, where singlet oxygen generation was not influenced but hydroxyl radical generation was increased. Palladium (II) porphyrin-modified materials were characterized by higher photostability than TPPS-based nanostructures, as TPPS@PHBA-P25 materials showed the highest singlet oxygen generation and may be oxidized during light exposure. Photocatalytic activity tests with two model pollutants-methylene blue (MB) and the opioid drug tramadol (TRML)-confirmed the light dose-dependent degradation of those two compounds, especially PdF2POH@P25, which led to the virtually complete degradation of MB.

KRAS and MT-CO1 genes in colorectal cancer: a molecular investigation.

Colorectal cancer (CRC) is the third leading cause of cancer-related deaths worldwide. The tumor suppressor gene MT-CO1, and Kristen Rat Sarcoma Virus (KRAS), an oncogene are primarily responsible for controlling cell apoptosis, cell cycle arrest, and cell proliferation, and any irregularities in these genes could lead to cancer. This study aims to examine the expression of KRAS and MT-CO1 in CRC biopsy specimens and investigate their relationship with one another in CRC patients residing in the Erbil city of Kurdistan Region, Iraq. The study involved categorizing 42 sets of colorectal cancer tissues and their corresponding controls based on their types and patients' clinical characteristics. The expression of KRAS and MT-CO1 in the samples was assessed using Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR), with statistical significance set at p<0.05. The expression of KRAS was found to be significantly higher in CRC compared to the control (n=42, p=0.0001). On the other hand, the expression of MT-CO1 did not exhibit significant differences compared to the control group with a p-value of 0.12. Furthermore, the Chi-square and correlation analysis results depicted that MT-CO1 expression negatively correlates with KRAS expression (p= 0.0001, r= -0.047) in CRC tissues. In conclusion, the variation in the expression of KRAS and MT-CO1, and their correlations could potentially serve as a good indicator in the detection and prognosis of CRC, which might lead to better translational research on the same. However, for a better understanding of the underlying mechanisms, further analysis is required.

RNA tertiary structure prediction in CASP15 by the GeneSilico group: Folding simulations based on statistical potentials and spatial restraints.

Ribonucleic acid (RNA) molecules serve as master regulators of cells by encoding their biological function in the ribonucleotide sequence, particularly their ability to interact with other molecules. To understand how RNA molecules perform their biological tasks and to design new sequences with specific functions, it is of great benefit to be able to computationally predict how RNA folds and interacts in the cellular environment. Our workflow for computational modeling of the 3D structures of RNA and its interactions with other molecules uses a set of methods developed in our laboratory, including MeSSPredRNA for predicting canonical and non-canonical base pairs, PARNASSUS for detecting remote homology based on comparisons of sequences and secondary structures, ModeRNA for comparative modeling, the SimRNA family of programs for modeling RNA 3D structure and its complexes with other molecules, and QRNAS for model refinement. In this study, we present the results of testing this workflow in predicting RNA 3D structures in the CASP15 experiment. The overall high score of the computational models predicted by our group demonstrates the robustness of our workflow and its individual components in terms of predicting RNA 3D structures of acceptable quality that are close to the target structures. However, the variance in prediction quality is still quite high, and the results are still too far from the level of protein 3D structure predictions. This exercise led us to consider several improvements, especially to better predict and enforce stacking interactions and non-canonical base pairs.

RNA 3D structure modeling by fragment assembly with small-angle X-ray scattering restraints.

SUMMARY: Structure determination is a key step in the functional characterization of many non-coding RNA molecules. High-resolution RNA 3D structure determination efforts, however, are not keeping up with the pace of discovery of new non-coding RNA sequences. This increases the importance of computational approaches and low-resolution experimental data, such as from the small-angle X-ray scattering experiments. We present RNA Masonry, a computer program and a web service for a fully automated modeling of RNA 3D structures. It assemblies RNA fragments into geometrically plausible models that meet user-provided secondary structure constraints, restraints on tertiary contacts, and small-angle X-ray scattering data. We illustrate the method description with detailed benchmarks and its application to structural studies of viral RNAs with SAXS restraints. AVAILABILITY AND IMPLEMENTATION: The program web server is available at http://iimcb.genesilico.pl/rnamasonry. The source code is available at https://gitlab.com/gchojnowski/rnamasonry.

RNA target highlights in CASP15: Evaluation of predicted models by structure providers.

The first RNA category of the Critical Assessment of Techniques for Structure Prediction competition was only made possible because of the scientists who provided experimental structures to challenge the predictors. In this article, these scientists offer a unique and valuable analysis of both the successes and areas for improvement in the predicted models. All 10 RNA-only targets yielded predictions topologically similar to experimentally determined structures. For one target, experimentalists were able to phase their x-ray diffraction data by molecular replacement, showing a potential application of structure predictions for RNA structural biologists. Recommended areas for improvement include: enhancing the accuracy in local interaction predictions and increased consideration of the experimental conditions such as multimerization, structure determination method, and time along folding pathways. The prediction of RNA-protein complexes remains the most significant challenge. Finally, given the intrinsic flexibility of many RNAs, we propose the consideration of ensemble models.

A comprehensive survey of long-range tertiary interactions and motifs in non-coding RNA structures.

Understanding the 3D structure of RNA is key to understanding RNA function. RNA 3D structure is modular and can be seen as a composition of building blocks of various sizes called tertiary motifs. Currently, long-range motifs formed between distant loops and helical regions are largely less studied than the local motifs determined by the RNA secondary structure. We surveyed long-range tertiary interactions and motifs in a non-redundant set of non-coding RNA 3D structures. A new dataset of annotated LOng-RAnge RNA 3D modules (LORA) was built using an approach that does not rely on the automatic annotations of non-canonical interactions. An original algorithm, ARTEM, was developed for annotation-, sequence- and topology-independent superposition of two arbitrary RNA 3D modules. The proposed methods allowed us to identify and describe the most common long-range RNA tertiary motifs. Along with the prevalent canonical A-minor interactions, a large number of previously undescribed staple interactions were observed. The most frequent long-range motifs were found to belong to three main motif families: planar staples, tilted staples, and helical packing motifs.

Reduced Plasticity in Coupling Strength in the Aging SCN Clock as Revealed by Kuramoto Modeling.

The mammalian circadian clock is located in the suprachiasmatic nucleus (SCN) and consists of a network of coupled neurons, which are entrained to the environmental light-dark cycle. The phase coherence of the neurons is plastic and driven by the duration of daylight. With aging, the capacity to behaviorally adapt to seasonal changes in photoperiod reduces. The mechanisms underlying photoperiodic adaptation are largely unknown, but are important to unravel for the development of novel interventions to improve the quality of life of the elderly. We analyzed the phase coherence of single-cell PERIOD2::LUCIFERASE (PER2::LUC) expression rhythms in the SCN of young and old mice entrained to either long or short photoperiod. The phase coherence was used as input to a 2-community noisy Kuramoto model to estimate the coupling strength between and within neuronal subpopulations. The model revealed a correlation between coupling strength and photoperiod-induced changes in the phase relationship among neurons, suggesting a functional link. We found that the SCN of young mice adapts in coupling strength over a large range, with weak coupling in long photoperiod (LP) and strong coupling in short photoperiod (SP). In aged mice, we also found weak coupling in LP, but a reduced capacity to reach strong coupling in SP. The inability to respond with an increase in coupling strength suggests that manipulation of photoperiod is not a suitable strategy to enhance clock function with aging. We conclude that the inability of aged mice to reach strong coupling contributes to deficits in behavioral adaptation to seasonal changes in photoperiod.

Structural interaction fingerprints and machine learning for predicting and explaining binding of small molecule ligands to RNA.

Ribonucleic acids (RNAs) play crucial roles in living organisms and some of them, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, whereas others, e.g. bacterial riboswitches or viral RNA motifs are considered as potential therapeutic targets. Thus, the continuous discovery of new functional RNA increases the demand for developing compounds targeting them and for methods for analyzing RNA-small molecule interactions. We recently developed fingeRNAt-a software for detecting non-covalent bonds formed within complexes of nucleic acids with different types of ligands. The program detects several non-covalent interactions and encodes them as structural interaction fingerprint (SIFt). Here, we present the application of SIFts accompanied by machine learning methods for binding prediction of small molecules to RNA. We show that SIFt-based models outperform the classic, general-purpose scoring functions in virtual screening. We also employed Explainable Artificial Intelligence (XAI)-the SHapley Additive exPlanations, Local Interpretable Model-agnostic Explanations and other methods to help understand the decision-making process behind the predictive models. We conducted a case study in which we applied XAI on a predictive model of ligand binding to human immunodeficiency virus type 1 trans-activation response element RNA to distinguish between residues and interaction types important for binding. We also used XAI to indicate whether an interaction has a positive or negative effect on binding prediction and to quantify its impact. Our results obtained using all XAI methods were consistent with the literature data, demonstrating the utility and importance of XAI in medicinal chemistry and bioinformatics.

Intrinsic fluctuations of reinforcement learning promote cooperation.

In this work, we ask for and answer what makes classical temporal-difference reinforcement learning with [Formula: see text]-greedy strategies cooperative. Cooperating in social dilemma situations is vital for animals, humans, and machines. While evolutionary theory revealed a range of mechanisms promoting cooperation, the conditions under which agents learn to cooperate are contested. Here, we demonstrate which and how individual elements of the multi-agent learning setting lead to cooperation. We use the iterated Prisoner's dilemma with one-period memory as a testbed. Each of the two learning agents learns a strategy that conditions the following action choices on both agents' action choices of the last round. We find that next to a high caring for future rewards, a low exploration rate, and a small learning rate, it is primarily intrinsic stochastic fluctuations of the reinforcement learning process which double the final rate of cooperation to up to 80%. Thus, inherent noise is not a necessary evil of the iterative learning process. It is a critical asset for the learning of cooperation. However, we also point out the trade-off between a high likelihood of cooperative behavior and achieving this in a reasonable amount of time. Our findings are relevant for purposefully designing cooperative algorithms and regulating undesired collusive effects.

1D2DSimScore: A novel method for comparing contacts in biomacromolecules and their complexes.

The biologically relevant structures of proteins and nucleic acids and their complexes are dynamic. They include a combination of regions ranging from rigid structural segments to structural switches to regions that are almost always disordered, which interact with each other in various ways. Comparing conformational changes and variation in contacts between different conformational states is essential to understand the biological functions of proteins, nucleic acids, and their complexes. Here, we describe a new computational tool, 1D2DSimScore, for comparing contacts and contact interfaces in all kinds of macromolecules and macromolecular complexes, including proteins, nucleic acids, and other molecules. 1D2DSimScore can be used to compare structural features of macromolecular models between alternative structures obtained in a particular experiment or to score various predictions against a defined "ideal" reference structure. Comparisons at the level of contacts are particularly useful for flexible molecules, for which comparisons in 3D that require rigid-body superpositions are difficult, and in biological systems where the formation of specific inter-residue contacts is more relevant for the biological function than the maintenance of a specific global 3D structure. Similarity/dissimilarity scores calculated by 1D2DSimScore can be used to complement scores describing 3D structural similarity measures calculated by the existing tools.

Are there double knots in proteins? Prediction and in vitro verification based on TrmD-Tm1570 fusion from C. nitroreducens.

We have been aware of the existence of knotted proteins for over 30 years-but it is hard to predict what is the most complicated knot that can be formed in proteins. Here, we show new and the most complex knotted topologies recorded to date-double trefoil knots (31 #31). We found five domain arrangements (architectures) that result in a doubly knotted structure in almost a thousand proteins. The double knot topology is found in knotted membrane proteins from the CaCA family, that function as ion transporters, in the group of carbonic anhydrases that catalyze the hydration of carbon dioxide, and in the proteins from the SPOUT superfamily that gathers 31 knotted methyltransferases with the active site-forming knot. For each family, we predict the presence of a double knot using AlphaFold and RoseTTaFold structure prediction. In the case of the TrmD-Tm1570 protein, which is a member of SPOUT superfamily, we show that it folds in vitro and is biologically active. Our results show that this protein forms a homodimeric structure and retains the ability to modify tRNA, which is the function of the single-domain TrmD protein. However, how the protein folds and is degraded remains unknown.

Computational Pipeline for Reference-Free Comparative Analysis of RNA 3D Structures Applied to SARS-CoV-2 UTR Models.

RNA is a unique biomolecule that is involved in a variety of fundamental biological functions, all of which depend solely on its structure and dynamics. Since the experimental determination of crystal RNA structures is laborious, computational 3D structure prediction methods are experiencing an ongoing and thriving development. Such methods can lead to many models; thus, it is necessary to build comparisons and extract common structural motifs for further medical or biological studies. Here, we introduce a computational pipeline dedicated to reference-free high-throughput comparative analysis of 3D RNA structures. We show its application in the RNA-Puzzles challenge, in which five participating groups attempted to predict the three-dimensional structures of 5'- and 3'-untranslated regions (UTRs) of the SARS-CoV-2 genome. We report the results of this puzzle and discuss the structural motifs obtained from the analysis. All simulated models and tools incorporated into the pipeline are open to scientific and academic use.