Methods of probing the interactions between small molecules and disordered proteins

It is generally recognized that a large fraction of the human proteome is made up of proteins that remain disordered in their native states. Despite the fact that such proteins play key biological roles and are involved in many major human diseases, they still represent challenging targets for drug discovery. A major bottleneck for the identification of compounds capable of interacting with these proteins and modulating their disease-promoting behaviour is the development of effective techniques to probe such interactions. The difficulties in carrying out binding measurements have resulted in a poor understanding of the mechanisms underlying these interactions. In order to facilitate further methodological advances, here we review the most commonly used techniques to probe three types of interactions involving small molecules: (1) those that disrupt functional interactions between disordered proteins; (2) those that inhibit the aberrant aggregation of disordered proteins, and (3) those that lead to binding disordered proteins in their monomeric states. In discussing these techniques, we also point out directions for future developments.Gabriella T. Heller is supported by the Gates Cambridge Trust Scholarship. Francesco A. Aprile is supported by a Senior Research Fellowship award from the Alzheimer’s Society, UK (grant number 317, AS-SF-16-003)

A Blind Ring Signature Based on the Short Integer Solution Problem

2020, Springer Nature Switzerland AG. A blind ring signature scheme is a combination of a ring signature and a blind signature, which allows not only any member of a group of signers to sign on a message on behalf of the group without revealing its identity but also the user who possesses the message to blind it before sending to the group to be signed. Blind ring signature schemes are essential components in e-commercial, e-voting etc. In this paper, we propose the first blind ring signature scheme based on lattices. More precisely, our proposed scheme is proven to be secure in random oracle model under the hardness of the short integer solution (SIS) problem

Latent structure pattern mining

Pattern mining methods for graph data have largely been restricted to ground features, such as frequent or correlated subgraphs. Kazius et al. have demonstrated the use of elaborate patterns in the biochemical domain, summarizing several ground features at once. Such patterns bear the potential to reveal latent information not present in any individual ground feature. However, those patterns were handcrafted by chemical experts. In this paper, we present a data-driven bottom-up method for pattern generation that takes advantage of the embedding relationships among individual ground features. The method works fully automatically and does not require data preprocessing (e.g., to introduce abstract node or edge labels). Controlling the process of generating ground features, it is possible to align them canonically and merge (stack) them, yielding a weighted edge graph. In a subsequent step, the subgraph features can further be reduced by singular value decomposition (SVD). Our experiments show that the resulting features enable substantial performance improvements on chemical datasets that have been problematic so far for graph mining approaches

Peptide-specific recognition of human cytomegalovirus strains controls adaptive natural killer cells

Natural killer (NK) cells are innate lymphocytes that lack antigen-specific rearranged receptors, a hallmark of adaptive lymphocytes. In some people infected with human cytomegalovirus (HCMV), an NK cell subset expressing the activating receptor NKG2C undergoes clonal-like expansion that partially resembles anti-viral adaptive responses. However, the viral ligand that drives the activation and differentiation of adaptive NKG2C+ NK cells has remained unclear. Here we found that adaptive NKG2C+ NK cells differentially recognized distinct HCMV strains encoding variable UL40 peptides that, in combination with pro-inflammatory signals, controlled the population expansion and differentiation of adaptive NKG2C+ NK cells. Thus, we propose that polymorphic HCMV peptides contribute to shaping of the heterogeneity of adaptive NKG2C+ NK cell populations among HCMV-seropositive people

SARS-CoV-2 Nsp13 encodes for an HLA-E-stabilizing peptide that abrogates inhibition of NKG2A-expressing NK cells

26sinoneNatural killer (NK) cells are innate immune cells that contribute to host defense against virus infections. NK cells respond to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in vitro and are activated in patients with acute coronavirus disease 2019 (COVID-19). However, by which mechanisms NK cells detect SARS-CoV-2-infected cells remains largely unknown. Here, we show that the Non-structural protein 13 of SARS-CoV-2 encodes for a peptide that is presented by human leukocyte antigen E (HLA-E). In contrast with self-peptides, the viral peptide prevents binding of HLA-E to the inhibitory receptor NKG2A, thereby rendering target cells susceptible to NK cell attack. In line with these observations, NKG2A-expressing NK cells are particularly activated in patients with COVID-19 and proficiently limit SARS-CoV-2 replication in infected lung epithelial cells in vitro. Thus, these data suggest that a viral peptide presented by HLA-E abrogates inhibition of NKG2A+ NK cells, resulting in missing self-recognition.noneHammer Q.; Dunst J.; Christ W.; Picarazzi F.; Wendorff M.; Momayyezi P.; Huhn O.; Netskar H.K.; Maleki K.T.; Garcia M.; Sekine T.; Sohlberg E.; Azzimato V.; Aouadi M.; Degenhardt F.; Franke A.; Spallotta F.; Mori M.; Michaelsson J.; Bjorkstrom N.K.; Ruckert T.; Romagnani C.; Horowitz A.; Klingstrom J.; Ljunggren H.-G.; Malmberg K.-J.Hammer, Q.; Dunst, J.; Christ, W.; Picarazzi, F.; Wendorff, M.; Momayyezi, P.; Huhn, O.; Netskar, H. K.; Maleki, K. T.; Garcia, M.; Sekine, T.; Sohlberg, E.; Azzimato, V.; Aouadi, M.; Degenhardt, F.; Franke, A.; Spallotta, F.; Mori, M.; Michaelsson, J.; Bjorkstrom, N. K.; Ruckert, T.; Romagnani, C.; Horowitz, A.; Klingstrom, J.; Ljunggren, H. -G.; Malmberg, K. -J