Structural investigation of interactions between halogenated flavonoids and the lipid membrane along with their role as cytotoxic agents.

This study focuses on understanding the structural and molecular changes in lipid membranes under the influence of six halogenated flavonoid derivatives differing in the number and position of substitution of chlorine and bromine atoms (D1-D6). Utilizing various analytical techniques, including fluorometric methods, dynamic light scattering (DLS), attenuated Fourier transform infrared spectroscopy (ATR- FTIR), and FT-Raman spectroscopy, the research aims to elucidate the mechanisms underlying the interaction of flavonoids with cell membranes. Additionally, the study includes in silico analyses to explore the physicochemical properties of these compounds and their potential pharmaceutical applications, along with toxicity studies to assess their effects on cancer, normal, and red blood cells. Our study showed the ability of halogenated derivatives to interact mostly with the outer part of the membrane, especially in the lipid heads region however, some of them were able to penetrate deeper into the membrane and affect the fluidity of hydrocarbon chains. The potential to reduce cancer cell viability, the lack of toxicity towards erythrocytes, and the favourable physicochemical and pharmacokinetic properties suggest these halogenated flavonoids potential candidates for exploring their potential for medical use.

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.

Structural effects of charge destabilization and amino acid substitutions in amyloid fragments of CsgA.

The most studied functional amyloid is the CsgA, major curli subunit protein, which is produced by numerous strains of Enterobacteriaceae. Although CsgA sequences are highly conserved, they exhibit species diversity, which reflects the specific evolutionary and functional adaptability of the major curli subunit. Herein, we performed bioinformatics analyses to uncover the differences in the amyloidogenic properties of the R4 fragments in Escherichia coli and Salmonella enterica and proposed four mutants for more detailed studies: M1, M2, M3, and M4. The mutated sequences were characterized by various experimental techniques, such as circular dichroism, ATR-FTIR, FT-Raman, thioflavin T, transmission electron microscopy and confocal microscopy. Additionally, molecular dynamics simulations were performed to determine the role of buffer ions in the aggregation process. Our results demonstrated that the aggregation kinetics, fibril morphology, and overall structure of the peptide were significantly affected by the positions of charged amino acids within the repeat sequences of CsgA. Notably, substituting glycine with lysine resulted in the formation of distinctive spherically packed globular aggregates. The differences in morphology observed are attributed to the influence of phosphate ions, which disrupt the local electrostatic interaction network of the polypeptide chains. This study provides knowledge on the preferential formation of amyloid fibrils based on charge states within the polypeptide chain.

PACT - Prediction of amyloid cross-interaction by threading.

Amyloid proteins are often associated with the onset of diseases, including Alzheimer's, Parkinson's and many others. However, there is a wide class of functional amyloids that are involved in physiological functions, e.g., formation of microbial biofilms or storage of hormones. Recent studies showed that an amyloid fibril could affect the aggregation of another protein, even from a different species. This may result in amplification or attenuation of the aggregation process. Insight into amyloid cross-interactions may be crucial for better understanding of amyloid diseases and the potential influence of microbial amyloids on human proteins. However, due to the demanding nature of the needed experiments, knowledge of such interactions is still limited. Here, we present PACT (Prediction of Amyloid Cross-interaction by Threading) - the computational method for the prediction of amyloid cross-interactions. The method is based on modeling of a heterogeneous fibril formed by two amyloidogenic peptides. The resulting structure is assessed by the structural statistical potential that approximates its plausibility and energetic stability. PACT was developed and first evaluated mostly on data collected in the AmyloGraph database of interacting amyloids and achieved high values of Area Under ROC (AUC=0.88) and F1 (0.82). Then, we applied our method to study the interactions of CsgA - a bacterial biofilm protein that was not used in our in-reference datasets, which is expressed in several bacterial species that inhabit the human intestines - with two human proteins. The study included alpha-synuclein, a human protein that is involved in Parkinson's disease, and human islet amyloid polypeptide (hIAPP), which is involved in type 2 diabetes. In both cases, PACT predicted the appearance of cross-interactions. Importantly, the method indicated specific regions of the proteins, which were shown to play a central role in both interactions. We experimentally confirmed the novel results of the indicated CsgA fragments interacting with hIAPP based on the kinetic characteristics obtained with the ThT assay. PACT opens the possibility of high-throughput studies of amyloid interactions. Importantly, it can work with fairly long protein fragments, and as a purely physicochemical approach, it relies very little on scarce training data. The tool is available as a web server at https://pact.e-science.pl/pact/ . The local version can be downloaded from https://github.com/KubaWojciechowski/PACT .

Lysine deserts and cullin-RING ligase receptors: Navigating untrodden paths in proteostasis.

The ubiquitin-proteasome system (UPS) governs the degradation of proteins by ubiquitinating their lysine residues. Our study focuses on lysine deserts - regions in proteins conspicuously low in lysine residues - in averting ubiquitin-dependent proteolysis. We spotlight the prevalence of lysine deserts among bacteria leveraging the pupylation-dependent proteasomal degradation, and in the UPS of eukaryotes. To further scrutinize this phenomenon, we focused on human receptors VHL and SOCS1 to ascertain if lysine deserts could limit their ubiquitination within the cullin-RING ligase (CRL) complex. Our data indicate that the wild-type and lysine-free variants of VHL and SOCS1 maintain consistent turnover rates, unaltered by CRL-mediated ubiquitination, hinting at a protective mechanism facilitated by lysine deserts. Nonetheless, we noted their ubiquitination at non-lysine sites, alluding to alternative regulation by the UPS. Our research underscores the role of lysine deserts in limiting CRL-mediated ubiquitin tagging while promoting non-lysine ubiquitination, thereby advancing our understanding of proteostasis.

The role of tandem repeats in bacterial functional amyloids.

Repetitivity and modularity of proteins are two related notions incorporated into multiple evolutionary concepts. We discuss whether they may also be essential for functional amyloids. Amyloids are proteins that create very regular and usually highly insoluble fibrils, which are often associated with neurodegeneration. However, recent discoveries showed that amyloid structure of a protein could also be beneficial and desired, e.g., to promote cell adhesion. Functional amyloids are proteins which differ in their characteristics from pathological amyloids, so that the fibril formation could be more under control of an organism. We propose that repeats in the sequence could regulate the aggregation propensity of these proteins. The inclusion of multiple symmetric interactions, due to the presence of the repeats, could be supporting and strengthening the desirable structural properties of functional amyloids. Our results show that tandem repeats in bacterial functional amyloids have a distinct characteristic. The pattern of repeats supports the appropriate level of fibril formation and better controllability of fibril stability. The repeats tend to be more imperfect, which attenuates excessive aggregation propensity. Their desired structure and function are also reinforced by their amino acid profile. Although in the study we focused on bacterial functional amyloids, due to their importance in biofilm formation, we propose that similar mechanisms could be employed in other functional amyloids which are designed by evolution to aggregate in a desirable manner, but not necessarily in pathological amyloids.

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.

In silico analysis of the profilaggrin sequence indicates alterations in the stability, degradation route, and intracellular protein fate in filaggrin null mutation carriers.

Background: Loss of function mutation in FLG is the major genetic risk factor for atopic dermatitis (AD) and other allergic manifestations. Presently, little is known about the cellular turnover and stability of profilaggrin, the protein encoded by FLG. Since ubiquitination directly regulates the cellular fate of numerous proteins, their degradation and trafficking, this process could influence the concentration of filaggrin in the skin. Objective: To determine the elements mediating the interaction of profilaggrin with the ubiquitin-proteasome system (i.e., degron motifs and ubiquitination sites), the features responsible for its stability, and the effect of nonsense and frameshift mutations on profilaggrin turnover. Methods: The effect of inhibition of proteasome and deubiquitinases on the level and modifications of profilaggrin and processed products was assessed by immunoblotting. Wild-type profilaggrin sequence and its mutated variants were analysed in silico using the DEGRONOPEDIA and Clustal Omega tool. Results: Inhibition of proteasome and deubiquitinases stabilizes profilaggrin and its high molecular weight of presumably ubiquitinated derivatives. In silico analysis of the sequence determined that profilaggrin contains 18 known degron motifs as well as multiple canonical and non-canonical ubiquitination-prone residues. FLG mutations generate products with increased stability scores, altered usage of the ubiquitination marks, and the frequent appearance of novel degrons, including those promoting C-terminus-mediated degradation routes. Conclusion: The proteasome is involved in the turnover of profilaggrin, which contains multiple degrons and ubiquitination-prone residues. FLG mutations alter those key elements, affecting the degradation routes and the mutated products' stability.

The application of the hierarchical approach for the construction of foldameric peptide self-assembled nanostructures.

In this paper, we show that a hierarchical approach for the construction of nanofibrils based on α,ß-peptide foldamers is a rational method for the design of novel self-assembled nanomaterials based on peptides. Incorporation of a trans-(1S,2S)-2-aminocyclopentanecarboxylic acid residue into the outer positions of the model coiled-coil peptide led to the formation of helical foldamers, which was determined by circular dichroism (CD) and vibrational spectroscopy. The oligomerization state of the obtained peptides in water was established by analytical ultracentrifugation (AUC). The thioflavin T assay and Congo red methods showed that the obtained α,ß-peptides possess a strong tendency to aggregate, leading to the formation of self-assembled nanostructures, which were assessed by microscopic techniques. The location of the ß-amino acid in the heptad repeat of the coiled-coil structure proved to have an influence on the secondary structure of the obtained peptides and on the morphology of the self-assembled nanostructures.

AmyloGraph: a comprehensive database of amyloid-amyloid interactions.

Information about the impact of interactions between amyloid proteins on their fibrillization propensity is scattered among many experimental articles and presented in unstructured form. We manually curated information located in almost 200 publications (selected out of 562 initially considered), obtaining details of 883 experimentally studied interactions between 46 amyloid proteins or peptides. We also proposed a novel standardized terminology for the description of amyloid-amyloid interactions, which is included in our database, covering all currently known types of such a cross-talk, including inhibition of fibrillization, cross-seeding and other phenomena. The new approach allows for more specific studies on amyloids and their interactions, by providing very well-defined data. AmyloGraph, an online database presenting information on amyloid-amyloid interactions, is available at (http://AmyloGraph.com/). Its functionalities are also accessible as the R package (https://github.com/KotulskaLab/AmyloGraph). AmyloGraph is the only publicly available repository for experimentally determined amyloid-amyloid interactions.

A novel de novo FEM1C variant is linked to neurodevelopmental disorder with absent speech, pyramidal signs and limb ataxia.

The principal component of the protein homeostasis network is the ubiquitin-proteasome system. Ubiquitination is mediated by an enzymatic cascade involving, i.e. E3 ubiquitin ligases, many of which belong to the cullin-RING ligases family. Genetic defects in the ubiquitin-proteasome system components, including cullin-RING ligases, are known causes of neurodevelopmental disorders. Using exome sequencing to diagnose a pediatric patient with developmental delay, pyramidal signs and limb ataxia, we identified a de novo missense variant c.376G>C; p.(Asp126His) in the FEM1C gene encoding a cullin-RING ligase substrate receptor. This variant alters a conserved amino acid located within a highly constrained coding region and is predicted as pathogenic by most in silico tools. In addition, a de novo FEM1C mutation of the same residue p.(Asp126Val) was associated with an undiagnosed developmental disorder, and the relevant variant (FEM1CAsp126Ala) was found to be functionally compromised in vitro. Our computational analysis showed that FEM1CAsp126His hampers protein substrate binding. To further assess its pathogenicity, we used the nematode Caenorhabditis elegans. We found that the FEM-1Asp133His animals (expressing variant homologous to the FEM1C p.(Asp126Val)) had normal muscle architecture yet impaired mobility. Mutant worms were sensitive to the acetylcholinesterase inhibitor aldicarb but not levamisole (acetylcholine receptor agonist), showing that their disabled locomotion is caused by synaptic abnormalities and not muscle dysfunction. In conclusion, we provide the first evidence from an animal model suggesting that a mutation in the evolutionarily conserved FEM1C Asp126 position causes a neurodevelopmental disorder in humans.

Exploring a diverse world of effector domains and amyloid signaling motifs in fungal NLR proteins.

NLR proteins are intracellular receptors constituting a conserved component of the innate immune system of cellular organisms. In fungi, NLRs are characterized by high diversity of architectures and presence of amyloid signaling. Here, we explore the diverse world of effector and signaling domains of fungal NLRs using state-of-the-art bioinformatic methods including MMseqs2 for fast clustering, probabilistic context-free grammars for sequence analysis, and AlphaFold2 deep neural networks for structure prediction. In addition to substantially improving the overall annotation, especially in basidiomycetes, the study identifies novel domains and reveals the structural similarity of MLKL-related HeLo- and Goodbye-like domains forming the most abundant superfamily of fungal NLR effectors. Moreover, compared to previous studies, we found several times more amyloid motif instances, including novel families, and validated aggregating and prion-forming properties of the most abundant of them in vitro and in vivo. Also, through an extensive in silico search, the NLR-associated amyloid signaling was identified in basidiomycetes. The emerging picture highlights similarities and differences in the NLR architectures and amyloid signaling in ascomycetes, basidiomycetes and other branches of life.

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 fingerprint-based similarity as an alternative measure to RMSD to recapitulate complexes with similar interactions but different folding. We present an application of interaction 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. The fingeRNAt software is freely available at https://github.com/n-szulc/fingeRNAt.

A heterotypic assembly mechanism regulates CHIP E3 ligase activity.

CHIP (C-terminus of Hsc70-interacting protein) and its worm ortholog CHN-1 are E3 ubiquitin ligases that link the chaperone system with the ubiquitin-proteasome system (UPS). CHN-1 can cooperate with UFD-2, another E3 ligase, to accelerate ubiquitin chain formation; however, the basis for the high processivity of this E3s set has remained obscure. Here, we studied the molecular mechanism and function of the CHN-1-UFD-2 complex in Caenorhabditis elegans. Our data show that UFD-2 binding promotes the cooperation between CHN-1 and ubiquitin-conjugating E2 enzymes by stabilizing the CHN-1 U-box dimer. However, HSP70/HSP-1 chaperone outcompetes UFD-2 for CHN-1 binding, thereby promoting a shift to the autoinhibited CHN-1 state by acting on a conserved residue in its U-box domain. The interaction with UFD-2 enables CHN-1 to efficiently ubiquitylate and regulate S-adenosylhomocysteinase (AHCY-1), a key enzyme in the S-adenosylmethionine (SAM) regeneration cycle, which is essential for SAM-dependent methylation. Our results define the molecular mechanism underlying the synergistic cooperation of CHN-1 and UFD-2 in substrate ubiquitylation.

Challenges in Experimental Methods.

Experimental studies of amyloids encounter many challenges. There are many methods available for studying proteins, which can be applied to amyloids: from basic staining techniques, allowing visualization of fibers, to complex methods, e.g., AFM-IR used to their detailed biochemical and structural characterization in nanoscale. Which method is appropriate depends on the goal of an experiment: verification of aggregational properties of a peptide, distinguishing oligomers from mature fibers, or kinetic studies. Insolubility, rapid aggregation, and the need of using a high-purity peptide may be a limiting factor in studies involving amyloids. Moreover, the results obtained by various experimental methods often differ significantly, which may lead to misclassification of amyloid peptides. Due to ambiguity of experimental results, laborious and time-consuming analysis, bioinformatical methods become more widely used for amyloids.

Variability of Amyloid Propensity in Imperfect Repeats of CsgA Protein of Salmonella enterica and Escherichia coli.

CsgA is an aggregating protein from bacterial biofilms, representing a class of functional amyloids. Its amyloid propensity is defined by five fragments (R1-R5) of the sequence, representing non-perfect repeats. Gate-keeper amino acid residues, specific to each fragment, define the fragment's propensity for self-aggregation and aggregating characteristics of the whole protein. We study the self-aggregation and secondary structures of the repeat fragments of Salmonella enterica and Escherichia coli and comparatively analyze their potential effects on these proteins in a bacterial biofilm. Using bioinformatics predictors, ATR-FTIR and FT-Raman spectroscopy techniques, circular dichroism, and transmission electron microscopy, we confirmed self-aggregation of R1, R3, R5 fragments, as previously reported for Escherichia coli, however, with different temporal characteristics for each species. We also observed aggregation propensities of R4 fragment of Salmonella enterica that is different than that of Escherichia coli. Our studies showed that amyloid structures of CsgA repeats are more easily formed and more durable in Salmonella enterica than those in Escherichia coli.

Molecular dynamics simulations of the effects of lipid oxidation on the permeability of cell membranes.

The formation of transient pores in their membranes is a well-known mechanism of permeabilization of cells exposed to high-intensity electric pulses. However, the formation of such pores is not able to explain all aspects of the so-called electroporation phenomenon. In particular, the reasons for sustained permeability of cell membranes, persisting long after the pulses' application, remain elusive. The complete resealing of cell membranes takes indeed orders of magnitude longer than the time for electropore closure as reported from molecular dynamics (MD) investigations. Lipid peroxidation has been suggested as a possible mechanism to explain the sustainable permeability of cell membranes. However, theoretical investigations of membrane lesions containing excess amounts of hydroperoxides have shown that the conductivities of such lesions were not high enough to account for the experimental measurements. Here, expanding on these studies, we investigate quantitatively the permeability of cell membrane lesions that underwent secondary oxidation. MD simulations and free energy calculations of lipid bilayers show that such lesions provide a better model of post-pulse permeable and conductive electropermeabilized cells. These results are further discussed in the context of sonoporation and ferroptosis, respectively a procedure and a phenomenon, among others, in which, alike electroporation, substantial lipid oxidation might be triggered.

The Dose-Dependent Pleiotropic Effects of the UBB+1 Ubiquitin Mutant.

The proteolytic machinery activity diminishes with age, leading to abnormal accumulation of aberrant proteins; furthermore, a decline in protein degradation capacity is associated with multiple age-related proteinopathies. Cellular proteostasis can be maintained via the removal of ubiquitin (Ub)-tagged damaged and redundant proteins by the ubiquitin-proteasome system (UPS). However, during aging, central nervous system (CNS) cells begin to express a frameshift-mutated Ub, UBB+1. Its accumulation is a neuropathological hallmark of tauopathy, including Alzheimer's disease and polyglutamine diseases. Mechanistically, in cell-free and cell-based systems, an increase in the UBB+1 concentration disrupts proteasome processivity, leading to increased aggregation of toxic proteins. On the other hand, a low level of UBB+1 improves stress resistance and extends lifespan. Here we summarize recent findings regarding the impact of UBB+1 on Ub signaling and neurodegeneration. We also review the molecular basis of how UBB+1 affects UPS components as well as its dose-dependent switch between cytoprotective and cytotoxic roles.

Bioinformatics methods for identification of amyloidogenic peptides show robustness to misannotated training data.

Several disorders are related to amyloid aggregation of proteins, for example Alzheimer's or Parkinson's diseases. Amyloid proteins form fibrils of aggregated beta structures. This is preceded by formation of oligomers-the most cytotoxic species. Determining amyloidogenicity is tedious and costly. The most reliable identification of amyloids is obtained with high resolution microscopies, such as electron microscopy or atomic force microscopy (AFM). More frequently, less expensive and faster methods are used, especially infrared (IR) spectroscopy or Thioflavin T staining. Different experimental methods are not always concurrent, especially when amyloid peptides do not readily form fibrils but oligomers. This may lead to peptide misclassification and mislabeling. Several bioinformatics methods have been proposed for in-silico identification of amyloids, many of them based on machine learning. The effectiveness of these methods heavily depends on accurate annotation of the reference training data obtained from in-vitro experiments. We study how robust are bioinformatics methods to weak supervision, encountering imperfect training data. AmyloGram and three other amyloid predictors were applied. The results proved that a certain degree of misannotation in the reference data can be eliminated by the bioinformatics tools, even if they belonged to their training set. The computational results are supported by new experiments with IR and AFM methods.