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

Louros, Nikolaos; Orlando, Gabriele; Vleeschouwer, Matthias De; Rousseau, Frédéric; Schymkowitz, Joost

doi:10.1038/s41467-020-17207-3

Cited by 70 publications

(79 citation statements)

References 73 publications

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“…To quantify the impact of b-sheet propensity and hydrophobicity in defining the APRs, we computed Receiver-Operator Curves (ROCs). The analysis shows that the experimentally validated APRs can indeed be identified within these sequences with accuracy close to current state-of-art predictors 15 from either b-sheet propensity (Fig. 3G, AUC = 0.87, MCC = 0.65) or hydrophobicity ( Fig.…”

Section: Amyloids Possess Segments Of High Structural Stability and Rsupporting

confidence: 69%

A structural analysis of amyloid polymorphism in disease: clues for selective vulnerability?

Kant

Louros

Schymkowitz

et al. 2021

Preprint

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The increasing amount of amyloid structures offers an opportunity to investigate the general principles determining amyloid stability and polymorphism in disease. We find that amyloid stability is dominated by about 30% of residues localized in few segments interspersed with regions that are often structurally frustrated in the cross-β conformation. These stable segments correspond to known aggregation-nucleating regions and constitute a cross-β structural framework that is shared among polymorphs. Alternative tertiary packing of these segments within the protofibril results in conformationally different but energetically similar polymorphs. This combination of a conserved structural framework along the axis and energetic ambiguity across the axis results in polymorphic plasticity that explains a number of fundamental amyloid properties, including fibril defects and brittleness but also the polymorphic instability of amyloids in simple aqueous buffers. Together these findings suggest a structural model for in vivo polymorphic bias and selective cellular vulnerability whereby (1) polymorphic bias is induced by particular templating interactions in susceptible cells, (2) once formed specific polymorphs are entropically primed to selectively bind similar targets in neighbouring cells, (3) conservation of polymorphic bias during pathological spreading implies the continued presence of similar templating interactions in successive susceptible cells and (4) absence of templating interactions relaxes polymorphic bias possibly allowing for the modification of cellular susceptibilities during disease progression by novel templating interactions.

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Section: Amyloids Possess Segments Of High Structural Stability and Rsupporting

confidence: 69%

A structural analysis of amyloid polymorphism in disease: clues for selective vulnerability?

Kant

Louros

Schymkowitz

et al. 2021

Preprint

Self Cite

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“…In the future, it will be interesting to investigate the role of specific molecular partners, such as RNA, in regulating aggregation. Computational approaches such as the one presented in this study [39,40] will be key to achieve a more complete understanding of molecular evolution, and to increase our ability to manipulate proteins for specific purposes.…”

Section: Discussionmentioning

confidence: 99%

Aggregation is a context-dependent constraint on protein evolution

Monti

Armaos

Fantini

et al. 2021

Preprint

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Solubility is a requirement for many cellular processes. Loss of solubility and aggregation can lead to the partial or complete abrogation of protein function. Thus, understanding the relationship between protein evolution and aggregation is an important goal. Here, we analysed two deep mutational scanning experiments to investigate the role of protein aggregation in molecular evolution.In one data set, mutants of a protein involved in RNA biogenesis and processing, human TAR DNA binding protein 43 (TDP-43), were expressed inS. cerevisiae. In the other data set, mutants of a bacterial enzyme that controls resistance to penicillins and cephalosporins, TEM-1 beta-lactamase, were expressed inE. coli under the selective pressure of an antibiotic treatment. We found that aggregation differentiates the effects of mutations in the two different cellular contexts. Specifically, aggregation was found to be associated with increased cell fitness in the case of TDP-43 mutations, as it protects the host from aberrant interactions. By contrast, in the case of TEM-1 beta-lactamase mutations, aggregation is linked to a decreased cell fitness due to inactivation of protein function.Our study shows that aggregation is an important context-dependent constraint of molecular evolution and opens up new avenues to investigate the role of aggregation in different cellular contexts-

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“…ML approaches have assisted the target identification and characterization in AD, which is the initial phase of drug discovery. For example, Cordax 413 (https://cordax.switchlab.org) is a novel structure‐based amyloid core sequence prediction method that implements ML to detect aggregation‐prone regions in proteins as well as to predict the structural topology, orientation and overall architecture of the resulting amyloid core. As an aggregation predictor, it uses structural information on amyloid cores currently available in the protein databank and translates structural compatibility and interaction energies into sequence aggregation propensity using logistic regression.…”

Section: Ai/ml Applications In Cns Drug Discoverymentioning

confidence: 99%

Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

Vatansever

Schlessinger

Wacker

et al. 2020

Medicinal Research Reviews

217

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Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML‐driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML‐powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state‐of‐the‐art of AI/ML‐guided CNS drug discovery, focusing on blood–brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.

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

Cited by 70 publications

References 73 publications

A structural analysis of amyloid polymorphism in disease: clues for selective vulnerability?

A structural analysis of amyloid polymorphism in disease: clues for selective vulnerability?

Aggregation is a context-dependent constraint on protein evolution

Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

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