MILAMP: Multiple Instance Prediction of Amyloid Proteins

Sequence-targeted Peptides Divert Functional Bacterial Amyloid Towards Destabilized Aggregates and Reduce Biofilm Formation

Functional bacterial amyloid provides structural stability in biofilm, making it a promising target for anti-biofilm therapeutics. Fibrils formed by CsgA, the major amyloid component in E. coli are extremely robust and can withstand very harsh conditions. Like other functional amyloids, CsgA contains relatively short aggregation-prone regions (APR) which drive amyloid formation. Here, we demonstrate the use of aggregation-modulating peptides to knock down CsgA protein into aggregates with low stability and altered morphology. Remarkably, these CsgA-peptides also modulate fibrillation of the unrelated functional amyloid protein FapC from Pseudomonas, possibly through recognition of FapC segments with structural and sequence similarity with CsgA. The peptides also reduce the level of biofilm formation in E. coli and P. aeruginosa, demonstrating the potential for selective amyloid targeting to combat bacterial biofilm.

Network Science and Group Fusion Similarity-Based Searching to Explore the Chemical Space of Antiparasitic Peptides

Antimicrobial peptides (AMPs) have appeared as promising compounds to treat a wide range of diseases. Their clinical potentialities reside in the wide range of mechanisms they can use for both killing microbes and modulating immune responses. However, the hugeness of the AMPs' chemical space (AMPCS), represented by more than 10⁶⁵ unique sequences, has represented a big challenge for the discovery of new promising therapeutic peptides and for the identification of common structural motifs. Here, we introduce network science and a similarity searching approach to discover new promising AMPs, specifically antiparasitic peptides (APPs). We exploited the network-based representation of APPs' chemical space (APPCS) to retrieve valuable information by using three network types: chemical space (CSN), half-space proximal (HSPN), and metadata (METN). Some centrality measures were applied to identify in each network the most important and nonredundant peptides. Then, these central peptides were considered as queries (Qs) in group fusion similarity-based searches against a comprehensive collection of known AMPs, stored in the graph database StarPepDB, to propose new potential APPs. The performance of the resulting multiquery similarity-based search models (mQSSMs) was evaluated in five benchmarking data sets of APP/non-APPs. The predictions performed by the best mQSSM showed a strong-to-very-strong performance since their external Matthews correlation coefficient (MCC) values ranged from 0.834 to 0.965. Outstanding MCC values (>0.85) were attained by the mQSSM with 219 Qs from both networks CSN and HSPN with 0.5 as similarity threshold in external data sets. Then, the performance of our best mQSSM was compared with the APPs prediction servers AMPDiscover and AMPFun. The proposed model showed its relevance by outperforming state-of-the-art machine learning models to predict APPs. After applying the best mQSSM and additional filters on the non-APP space from StarPepDB, 95 AMPs were repurposed as potential APP hits. Due to the high sequence diversity of these peptides, different computational approaches were applied to identify relevant motifs for searching and designing new APPs. Lastly, we identified 11 promising APP lead candidates by using our best mQSSMs together with diversity-based network analyses, and 24 web servers for activity/toxicity and drug-like properties. These results support that network-based similarity searches can be an effective and reliable strategy to identify APPs. The proposed models and pipeline are freely available through the StarPep toolbox software at http://mobiosd-hub.com/starpep.

Structure-based machine-guided mapping of amyloid sequence space reveals uncharted sequence clusters with higher solubilities

The amyloid conformation can be adopted by a variety of sequences, but the precise boundaries of amyloid sequence space are still unclear. The currently charted amyloid sequence space is strongly biased towards hydrophobic, beta-sheet prone sequences that form the core of globular proteins and by Q/N/Y rich yeast prions. Here, we took advantage of the increasing amount of high-resolution structural information on amyloid cores currently available in the protein databank to implement a machine learning approach, named Cordax (https://cordax.switchlab.org), that explores amyloid sequence beyond its current boundaries. Clustering by t-Distributed Stochastic Neighbour Embedding (t-SNE) shows how our approach resulted in an expansion away from hydrophobic amyloid sequences towards clusters of lower aliphatic content and higher charge, or regions of helical and disordered propensities. These clusters uncouple amyloid propensity from solubility representing sequence flavours compatible with surface-exposed patches in globular proteins, functional amyloids or sequences associated to liquid-liquid phase transitions.

An increasing number of amyloid structures are determined. Here, the authors present the structure-based amyloid core sequence prediction method Cordax that is based on machine learning and allows the detection of aggregation-prone regions with higher solubility, disorder and surface exposure, and furthermore predicts the structural topology, orientation and overall architecture of the resulting putative fibril core.

Novel Variations in Native Ethiopian Goat breeds PRNP Gene and Their Potential Effect on Prion Protein Stability

Scrapie is a lethal neurodegenerative disease of sheep and goats caused by the misfolding of the prion protein. Variants such as M142, D145, S146, H154, Q211, and K222 were experimentally found to increase resistance or extend scrapie incubation period in goats. We aimed to identify polymorphisms in the Afar and Arsi-Bale goat breeds of Ethiopia and computationally assess the effect of variants on prion protein stability. In the present study, four non-synonymous novel polymorphisms G67S, W68R, G69D, and R159H in the first octapeptide repeat and the highly conserved C-terminus globular domain of goat PrP were detected. The resistant genotype, S146, was detected in >50% of the present population. The current study population showed a genetic diversity in Ethiopian goat breeds. In the insilico analysis, the R68 variant was predicted to increase stability while S67, D69, and H159 decrease the stability of prion protein. The new variants in the octapeptide repeat motif were predicted to decrease amyloidogenicity but H159 increased the hotspot sequence amyloidogenic propensity. These novel variants could be the source of conformational flexibility that may trigger the gain or loss of function by prion protein. Further experimental study is required to depict the actual effects of variants on prion protein stability.

M. Brito R. Structure and Aggregation Mechanisms in Amyloids

The aggregation of a polypeptide chain into amyloid fibrils and their accumulation and deposition into insoluble plaques and intracellular inclusions is the hallmark of several misfolding diseases known as amyloidoses. Alzheimer′s, Parkinson′s and Huntington’s diseases are some of the approximately 50 amyloid diseases described to date. The identification and characterization of the molecular species critical for amyloid formation and disease development have been the focus of intense scrutiny. Methods such as X-ray and electron diffraction, solid-state nuclear magnetic resonance spectroscopy (ssNMR) and cryo-electron microscopy (cryo-EM) have been extensively used and they have contributed to shed a new light onto the structure of amyloid, revealing a multiplicity of polymorphic structures that generally fit the cross-β amyloid motif. The development of rational therapeutic approaches against these debilitating and increasingly frequent misfolding diseases requires a thorough understanding of the molecular mechanisms underlying the amyloid cascade. Here, we review the current knowledge on amyloid fibril formation for several proteins and peptides from a kinetic and thermodynamic point of view, the structure of the molecular species involved in the amyloidogenic process, and the origin of their cytotoxicity.