Biology subjects

Wojciechowski, J. W.

Publications and source records attributed to Wojciechowski, J. W..

4 recordsLinked to original sources

bioRxiv2024

Aggregating gut: on the link between neurodegeneration and bacterial functional amyloids

Amyloids are insoluble protein aggregates with a cross-beta structure, which are traditionally associated with neurodegeneration. Similar structures, named functional amyloids, expressed mostly by microorganisms, play important physiological roles, e.g. bacterial biofilm stabilization. Using a bioinformatics approach, we identify gut microbiome functional amyloids and analyze their potential impact on human health via the gut-brain axis. The results point to taxonomically diverse sources of functional amyloids and their frequent presence in the extracellular space. The retrieved interactions between gut microbiome functional amyloids and human proteins indicate their potential to trigger inflammation, affect transport and signaling processes. We also find a greater relative abundance of bacterial functional amyloids in patients diagnosed with Parkinsons disease and specifically a higher content of the curli amyloid protein, CsgA, in Alzheimers disease patients than in healthy controls. Our results provide a rationale for the tentative link between neurodegeneration and gut bacterial functional amyloids.

bioinformatics↗

bioRxiv2024

Non-standard proteins in the lenses of AlphaFold3 - case study of amyloids

While three-dimensional structures of globular and transmembrane forms are available for many amyloid proteins, structures of their amyloid forms are scarce in the PDB. Amyloids pose major challenges for both experimental structure determination and computational modelling. We evaluated the amyloid-modelling performance of the current top modelling software, AlphaFold 3 (AF3), using three datasets. Dataset 1 contains 153 proteins and peptides that are known to form fibrils, but their 3D structures have not been experimentally determined. Dataset 2 contains 56 non-aggregating/non-amyloid peptides. Dataset 3 contains seven proteins for which the three-dimensional fibrillar structure is known. Fibrillar structures were predicted for 34% of dataset 1, but unfortunately also for 54% of dataset 2. Fibrillar structures were successfully predicted for five out of seven proteins from dataset 3. Comparing AF3 with different methods, it outperformed Boltz, and predicted the structures of CsgA and -synuclein more correctly than RibbonFold, whereas the latter predicted A{beta}-42 better. The performance of AF3 in prediction of amyloid structures for our datasets seems hindered by low abundance of amyloid structures in the PDB and high prevalence of structure data for their non-fibrillar forms. AF3 tends to assign a higher quality score to globular oligomeric models than to fibrillar ones. A correct amyloid structure prediction is more likely to be obtained for shorter fragments. The amyloid modelling quality of AF3 seems underwhelming, but it can still provide hypotheses about amyloid structures in some cases. Our work also suggests the steps needed to achieve a better performance in the near future. Statement for a broader audienceAmyloid proteins can form stable, insoluble fibrils that are often related to a neurodegenerative disease. Knowledge of the three-dimensional structure of these fibrils is important, e.g. for a drug design. We evaluate the performance of AlphaFold 3 on the prediction of amyloid structures and observe that it struggles with these cases. The problems seem to arise mainly from the nature of the AlphaFold 3 training dataset and polymorphic nature of many amyloids. Although the results are underwhelming, AlphaFold 3 can sometimes provide valuable insights into amyloid protein structures, something that only a few years ago still seemed a very hard to reach goal.

bioinformatics↗

bioRxiv2022

PACT - Prediction of Amyloid Cross-interaction by Threading

Amyloids are protein aggregates usually associated with their contribution to several diseases e.g., Alzheimers and Parkinsons. However, they are also beneficially utilized by many organisms in physiological roles, such as microbial biofilm formation or hormone storage. Recent studies showed that an amyloid aggregate can affect aggregation of another protein. Such cross-interactions may be crucial for understanding the comorbidity of amyloid diseases or the influence of microbial amyloids on human amyloidogenic proteins. However, due to demanding experiments, understanding of interaction phenomena is still limited. Moreover, no dedicated computational method to predict potential amyloid interactions has been available until now. Here, we present PACT - a computational method for prediction of amyloid cross-interactions. The method is based on modeling a heterogenous fibril formed by two amyloidogenic peptides. The stability of the resulting structure is assessed using a statistical potential that approximates energetic stability of a model. Importantly, the method can work with long protein fragments and, as a purely physicochemical approach, it relies very little on training data. PACT was evaluated on data collected in the AmyloGraph database and it achieved high values of AUC (0.88) and F1 (0.82). The new method opens the possibility of high throughput studies of amyloid interactions. We used PACT to study interactions of CsgA, a bacterial biofilm protein from several bacterial species inhabiting human intestines, and human Alpha-synuclein protein which is involved in the onset of Parkinsons disease. We show that the method correctly predicted the interactions, performing experimental validation, and highlighted the importance of specific regions in both proteins. 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

bioinformatics↗

bioRxiv2022

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 multicellular 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 motifs, 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 is for the first time 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.

bioinformatics↗