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.

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.

Fungal gasdermin-like proteins are controlled by proteolytic cleavage.

Gasdermins are a family of pore-forming proteins controlling an inflammatory cell death reaction in the mammalian immune system. The pore-forming ability of the gasdermin proteins is released by proteolytic cleavage with the removal of their inhibitory C-terminal domain. Recently, gasdermin-like proteins have been discovered in fungi and characterized as cell death-inducing toxins in the context of conspecific non-self-discrimination (allorecognition). Although functional analogies have been established between mammalian and fungal gasdermins, the molecular pathways regulating gasdermin activity in fungi remain largely unknown. Here, we characterize a gasdermin-based cell death reaction controlled by the het-Q allorecognition genes in the filamentous fungus Podospora anserina We show that the cytotoxic activity of the HET-Q1 gasdermin is controlled by proteolysis. HET-Q1 loses a ∼5-kDa C-terminal fragment during the cell death reaction in the presence of a subtilisin-like serine protease termed HET-Q2. Mutational analyses and successful reconstitution of the cell death reaction in heterologous hosts (Saccharomyces cerevisiae and human 293T cells) suggest that HET-Q2 directly cleaves HET-Q1 to induce cell death. By analyzing the genomic landscape of het-Q1 homologs in fungi, we uncovered that the vast majority of the gasdermin genes are clustered with protease-encoding genes. These HET-Q2-like proteins carry either subtilisin-like or caspase-related proteases, which, in some cases, correspond to the N-terminal effector domain of nucleotide-binding and oligomerization-like receptor proteins. This study thus reveals the proteolytic regulation of gasdermins in fungi and establishes evolutionary parallels between fungal and mammalian gasdermin-dependent cell death pathways.

Searching for universal model of amyloid signaling motifs using probabilistic context-free grammars.

BACKGROUND: Amyloid signaling motifs are a class of protein motifs which share basic structural and functional features despite the lack of clear sequence homology. They are hard to detect in large sequence databases either with the alignment-based profile methods (due to short length and diversity) or with generic amyloid- and prion-finding tools (due to insufficient discriminative power). We propose to address the challenge with a machine learning grammatical model capable of generalizing over diverse collections of unaligned yet related motifs. RESULTS: First, we introduce and test improvements to our probabilistic context-free grammar framework for protein sequences that allow for inferring more sophisticated models achieving high sensitivity at low false positive rates. Then, we infer universal grammars for a collection of recently identified bacterial amyloid signaling motifs and demonstrate that the method is capable of generalizing by successfully searching for related motifs in fungi. The results are compared to available alternative methods. Finally, we conduct spectroscopy and staining analyses of selected peptides to verify their structural and functional relationship. CONCLUSIONS: While the profile HMMs remain the method of choice for modeling homologous sets of sequences, PCFGs seem more suitable for building meta-family descriptors and extrapolating beyond the seed sample.

Identification of NLR-associated Amyloid Signaling Motifs in Bacterial Genomes.

In filamentous fungi, amyloid signaling sequences allow Nod-like receptors (NLRs) to activate downstream cell-death inducing proteins with HeLo and HeLo-like (HELL) domains and amyloid RHIM and RHIM-related motifs control immune defense pathways in mammals and flies. Herein, we show bioinformatically that analogous amyloid signaling motifs exist in bacteria. These short motifs are found at the N terminus of NLRs and at the C terminus of proteins with a domain we term BELL. The corresponding NLR and BELL proteins are encoded by adjacent genes. We identify 10 families of such bacterial amyloid signaling sequences (BASS), one of which (BASS3) is homologous to RHIM and a fungal amyloid motif termed PP. BASS motifs occur nearly exclusively in bacteria forming multicellular structures (mainly in Actinobacteria and Cyanobacteria). We analyze experimentally a subset of seven of these motifs (from the most common BASS1 family and the RHIM-related BASS3 family) and find that these sequences form fibrils in vitro. Using a fungal in vivo model, we show that all tested BASS-motifs form prions and that the NLR-side motifs seed prion-formation of the corresponding BELL-side motif. We find that BASS3 motifs show partial prion cross-seeding with mammalian RHIM and fungal PP-motifs and that proline mutations on key positions of the BASS3 core motif, conserved in RHIM and PP-motifs, abolish prion formation. This work expands the paradigm of prion amyloid signaling to multicellular prokaryotes and suggests a long-term evolutionary conservation of these motifs from bacteria, to fungi and animals.

Estimating probabilistic context-free grammars for proteins using contact map constraints.

Interactions between amino acids that are close in the spatial structure, but not necessarily in the sequence, play important structural and functional roles in proteins. These non-local interactions ought to be taken into account when modeling collections of proteins. Yet the most popular representations of sets of related protein sequences remain the profile Hidden Markov Models. By modeling independently the distributions of the conserved columns from an underlying multiple sequence alignment of the proteins, these models are unable to capture dependencies between the protein residues. Non-local interactions can be represented by using more expressive grammatical models. However, learning such grammars is difficult. In this work, we propose to use information on protein contacts to facilitate the training of probabilistic context-free grammars representing families of protein sequences. We develop the theory behind the introduction of contact constraints in maximum-likelihood and contrastive estimation schemes and implement it in a machine learning framework for protein grammars. The proposed framework is tested on samples of protein motifs in comparison with learning without contact constraints. The evaluation shows high fidelity of grammatical descriptors to protein structures and improved precision in recognizing sequences. Finally, we present an example of using our method in a practical setting and demonstrate its potential beyond the current state of the art by creating a grammatical model of a meta-family of protein motifs. We conclude that the current piece of research is a significant step towards more flexible and accurate modeling of collections of protein sequences. The software package is made available to the community.

The first MICCAI challenge on PET tumor segmentation.

INTRODUCTION: Automatic functional volume segmentation in PET images is a challenge that has been addressed using a large array of methods. A major limitation for the field has been the lack of a benchmark dataset that would allow direct comparison of the results in the various publications. In the present work, we describe a comparison of recent methods on a large dataset following recommendations by the American Association of Physicists in Medicine (AAPM) task group (TG) 211, which was carried out within a MICCAI (Medical Image Computing and Computer Assisted Intervention) challenge. MATERIALS AND METHODS: Organization and funding was provided by France Life Imaging (FLI). A dataset of 176 images combining simulated, phantom and clinical images was assembled. A website allowed the participants to register and download training data (n = 19). Challengers then submitted encapsulated pipelines on an online platform that autonomously ran the algorithms on the testing data (n = 157) and evaluated the results. The methods were ranked according to the arithmetic mean of sensitivity and positive predictive value. RESULTS: Sixteen teams registered but only four provided manuscripts and pipeline(s) for a total of 10 methods. In addition, results using two thresholds and the Fuzzy Locally Adaptive Bayesian (FLAB) were generated. All competing methods except one performed with median accuracy above 0.8. The method with the highest score was the convolutional neural network-based segmentation, which significantly outperformed 9 out of 12 of the other methods, but not the improved K-Means, Gaussian Model Mixture and Fuzzy C-Means methods. CONCLUSION: The most rigorous comparative study of PET segmentation algorithms to date was carried out using a dataset that is the largest used in such studies so far. The hierarchy amongst the methods in terms of accuracy did not depend strongly on the subset of datasets or the metrics (or combination of metrics). All the methods submitted by the challengers except one demonstrated good performance with median accuracy scores above 0.8.

Quantiprot - a Python package for quantitative analysis of protein sequences.

BACKGROUND: The field of protein sequence analysis is dominated by tools rooted in substitution matrices and alignments. A complementary approach is provided by methods of quantitative characterization. A major advantage of the approach is that quantitative properties defines a multidimensional solution space, where sequences can be related to each other and differences can be meaningfully interpreted. RESULTS: Quantiprot is a software package in Python, which provides a simple and consistent interface to multiple methods for quantitative characterization of protein sequences. The package can be used to calculate dozens of characteristics directly from sequences or using physico-chemical properties of amino acids. Besides basic measures, Quantiprot performs quantitative analysis of recurrence and determinism in the sequence, calculates distribution of n-grams and computes the Zipf's law coefficient. CONCLUSIONS: We propose three main fields of application of the Quantiprot package. First, quantitative characteristics can be used in alignment-free similarity searches, and in clustering of large and/or divergent sequence sets. Second, a feature space defined by quantitative properties can be used in comparative studies of protein families and organisms. Third, the feature space can be used for evaluating generative models, where large number of sequences generated by the model can be compared to actually observed sequences.

Overlapping Podospora anserina Transcriptional Responses to Bacterial and Fungal Non Self Indicate a Multilayered Innate Immune Response.

Recognition and response to non self is essential to development and survival of all organisms. It can occur between individuals of the same species or between different organisms. Fungi are established models for conspecific non self recognition in the form of vegetative incompatibility (VI), a genetically controlled process initiating a programmed cell death (PCD) leading to the rejection of a fusion cell between genetically different isolates of the same species. In Podospora anserina VI is controlled by members of the hnwd gene family encoding for proteins analogous to NOD Like Receptors (NLR) immune receptors in eukaryotes. It was hypothesized that the hnwd controlled VI reaction was derived from the fungal innate immune response. Here we analyze the P. anserina transcriptional responses to two bacterial species, Serratia fonticola to which P. anserina survives and S. marcescens to which P. anserina succumbs, and compare these to the transcriptional response induced under VI conditions. Transcriptional responses to both bacteria largely overlap, however the number of genes regulated and magnitude of regulation is more important when P. anserina survives. Transcriptional responses to bacteria also overlap with the VI reaction for both up or down regulated gene sets. Genes up regulated tend to be clustered in the genome, and display limited phylogenetic distribution. In all three responses we observed genes related to autophagy to be up-regulated. Autophagy contributes to the fungal survival in all three conditions. Genes encoding for secondary metabolites and histidine kinase signaling are also up regulated in all three conditions. Transcriptional responses also display differences. Genes involved in response to oxidative stress, or encoding small secreted proteins are essentially expressed in response to bacteria, while genes encoding NLR proteins are expressed during VI. Most functions encoded in response to bacteria favor survival of the fungus while most functions up regulated during VI would lead to cell death. These differences are discussed in the frame of a multilayered response to non self in fungi.

Fast assessment of structural models of ion channels based on their predicted current-voltage characteristics.

Computational prediction of protein structures is a difficult task, which involves fast and accurate evaluation of candidate model structures. We propose to enhance single-model quality assessment with a functionality evaluation phase for proteins whose quantitative functional characteristics are known. In particular, this idea can be applied to evaluation of structural models of ion channels, whose main function - conducting ions - can be quantitatively measured with the patch-clamp technique providing the current-voltage characteristics. The study was performed on a set of KcsA channel models obtained from complete and incomplete contact maps. A fast continuous electrodiffusion model was used for calculating the current-voltage characteristics of structural models. We found that the computed charge selectivity and total current were sensitive to structural and electrostatic quality of models. In practical terms, we show that evaluating predicted conductance values is an appropriate method to eliminate models with an occluded pore or with multiple erroneously created pores. Moreover, filtering models on the basis of their predicted charge selectivity results in a substantial enrichment of the candidate set in highly accurate models. Tests on three other ion channels indicate that, in addition to being a proof of the concept, our function-oriented single-model quality assessment method can be directly applied to evaluation of structural models of some classes of protein channels. Finally, our work raises an important question whether a computational validation of functionality should be included in the evaluation process of structural models, whenever possible.

Theme and variations: evolutionary diversification of the HET-s functional amyloid motif.

In mammals and fungi, Nod-like receptors (NLR) activate downstream cell death execution proteins by a prion-like mechanism. In Podospora anserina, the NWD2 NLR activates the HET-S Helo-domain pore-forming protein by converting its prion-forming domain into a characteristic ß-solenoid amyloid fold. The amyloid forming region of HET-S/s comprises two repetitions of a 21 amino acid motif. Herein, we systematically analyze the sequences of C-terminal regions of fungal HeLo and HeLo-like domain proteins to identify HET-s-related amyloid motifs (HRAM). We now identify four novel HRAM subfamilies in addition to the canonical HET-S/s subfamily. These novel motifs share the pseudo-repeat structure of HET-S/s and a specific pattern of distribution of hydrophobic and polar residues. Sequence co-variance analyses predict parallel in-register ß-stacking of the two repeats and residue-residue interactions compatible with the ß-solenoid fold. As described for HET-S, most genes encoding the HeLo proteins are adjacent to genes encoding NLRs also displaying HRAMs. The motifs of the NLRs are similar to those of their cognate HeLo-domain protein, indicating concerted evolution between repeats. This study shows that HET-s-related amyloid motifs are more common than anticipated and that they have diversified into discrete subfamilies that apparently share a common overall fold.

Diversity and variability of NOD-like receptors in fungi.

Nucleotide-binding oligomerization domain (NOD)-like receptors (NLRs) are intracellular receptors that control innate immunity and other biotic interactions in animals and plants. NLRs have been characterized in plant and animal lineages, but in fungi, this gene family has not been systematically described. There is however previous indications of the involvement of NLR-like genes in nonself recognition and programmed cell death in fungi. We have analyzed 198 fungal genomes for the presence of NLRs and have annotated a total of 5,616 NLR candidates. We describe their phylogenetic distribution, domain organization, and evolution. Fungal NLRs are characterized by a great diversity of domain organizations, suggesting frequently occurring combinatorial assortments of different effector, NOD and repeat domains. The repeat domains are of the WD, ANK, and TPR type; no LRR motifs were found. As previously documented for WD-repeat domains of fungal NLRs, TPR, and ANK repeats evolve under positive selection and show highly conserved repeats and repeat length polymorphism, suggesting the possibility of concerted evolution of these repeats. We identify novel effector domains not previously found associated with NLRs, whereas others are related to effector domains of plant or animals NLRs. In particular, we show that the HET domain found in fungal NLRs may be related to Toll/interleukin-1 receptor domains found in animal and plant immune receptors. This description of fungal NLR repertoires reveals both similarities and differences with plant and animals NLR collections, highlights the importance of domain reassortment and repeat evolution and provides a novel entry point to explore the evolution of NLRs in eukaryotes.

Probabilistic grammatical model for helix-helix contact site classification.

BACKGROUND: Hidden Markov Models power many state-of-the-art tools in the field of protein bioinformatics. While excelling in their tasks, these methods of protein analysis do not convey directly information on medium- and long-range residue-residue interactions. This requires an expressive power of at least context-free grammars. However, application of more powerful grammar formalisms to protein analysis has been surprisingly limited. RESULTS: In this work, we present a probabilistic grammatical framework for problem-specific protein languages and apply it to classification of transmembrane helix-helix pairs configurations. The core of the model consists of a probabilistic context-free grammar, automatically inferred by a genetic algorithm from only a generic set of expert-based rules and positive training samples. The model was applied to produce sequence based descriptors of four classes of transmembrane helix-helix contact site configurations. The highest performance of the classifiers reached AUCROC of 0.70. The analysis of grammar parse trees revealed the ability of representing structural features of helix-helix contact sites. CONCLUSIONS: We demonstrated that our probabilistic context-free framework for analysis of protein sequences outperforms the state of the art in the task of helix-helix contact site classification. However, this is achieved without necessarily requiring modeling long range dependencies between interacting residues. A significant feature of our approach is that grammar rules and parse trees are human-readable. Thus they could provide biologically meaningful information for molecular biologists.

Optimization of 3D Poisson-Nernst-Planck model for fast evaluation of diverse protein channels.

We show the accuracy and applicability of our fast algorithmic implementation of a three-dimensional Poisson-Nernst-Planck (3D-PNP) flow model for characterizing different protein channels. Due to its high computational efficiency, our model can predict the full current-voltage characteristics of a channel within minutes, based on the experimental 3D structure of the channel or its computational model structure. Compared with other methods, such as Brownian dynamics, which currently needs a few weeks of the computational time, or even much more demanding molecular dynamics modeling, 3D-PNP is the only available method for a function-based evaluation of very numerous tentative structural channel models. Flow model tests of our algorithm and its optimal parametrization are provided for five native channels whose experimental structures are available in the protein data bank (PDB) in an open conductive state, and whose experimental current-voltage characteristics have been published. The channels represent very different geometric and structural properties, which makes it the widest test to date of the accuracy of 3D-PNP on real channels. We test whether the channel conductance, rectification, and charge selectivity obtained from the flow model, could be sufficiently sensitive to single-point mutations, related to unsignificant changes in the channel structure. Our results show that the classical 3D-PNP model, under proper parametrization, is able to achieve a qualitative agreement with experimental data for a majority of the tested characteristics and channels, including channels with narrow and irregular conductivity pores. We propose that although the standard PNP model cannot provide insight into complex physical phenomena due to its intrinsic limitations, its semiquantitative agreement is achievable for rectification and selectivity at a level sufficient for the bioinformatical purpose of selecting the best structural models with a great advantage of a very short computational time.

A stochastic context free grammar based framework for analysis of protein sequences.

BACKGROUND: In the last decade, there have been many applications of formal language theory in bioinformatics such as RNA structure prediction and detection of patterns in DNA. However, in the field of proteomics, the size of the protein alphabet and the complexity of relationship between amino acids have mainly limited the application of formal language theory to the production of grammars whose expressive power is not higher than stochastic regular grammars. However, these grammars, like other state of the art methods, cannot cover any higher-order dependencies such as nested and crossing relationships that are common in proteins. In order to overcome some of these limitations, we propose a Stochastic Context Free Grammar based framework for the analysis of protein sequences where grammars are induced using a genetic algorithm. RESULTS: This framework was implemented in a system aiming at the production of binding site descriptors. These descriptors not only allow detection of protein regions that are involved in these sites, but also provide insight in their structure. Grammars were induced using quantitative properties of amino acids to deal with the size of the protein alphabet. Moreover, we imposed some structural constraints on grammars to reduce the extent of the rule search space. Finally, grammars based on different properties were combined to convey as much information as possible. Evaluation was performed on sites of various sizes and complexity described either by PROSITE patterns, domain profiles or a set of patterns. Results show the produced binding site descriptors are human-readable and, hence, highlight biologically meaningful features. Moreover, they achieve good accuracy in both annotation and detection. In addition, findings suggest that, unlike current state-of-the-art methods, our system may be particularly suited to deal with patterns shared by non-homologous proteins. CONCLUSION: A new Stochastic Context Free Grammar based framework has been introduced allowing the production of binding site descriptors for analysis of protein sequences. Experiments have shown that not only is this new approach valid, but produces human-readable descriptors for binding sites which have been beyond the capability of current machine learning techniques.

Ion flux through membrane channels--an enhanced algorithm for the Poisson-Nernst-Planck model.

A novel algorithmic scheme for numerical solution of the 3D Poisson-Nernst-Planck model is proposed. The algorithmic improvements are universal and independent of the detailed physical model. They include three major steps: an adjustable gradient-based step value, an adjustable relaxation coefficient, and an optimized segmentation of the modeled space. The enhanced algorithm significantly accelerates the speed of computation and reduces the computational demands. The theoretical model was tested on a regular artificial channel and validated on a real protein channel-alpha-hemolysin, proving its efficiency.