Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks.

Characterization of protein complexes, i.e. sets of proteins assembling into a single larger physical entity, is important, as such assemblies play many essential roles in cells such as gene regulation. From networks of protein-protein interactions, potential protein complexes can be identified computationally through the application of community detection methods, which flag groups of entities interacting with each other in certain patterns. Most community detection algorithms tend to be unsupervised and assume that communities are dense network subgraphs, which is not always true, as protein complexes can exhibit diverse network topologies. The few existing supervised machine learning methods are serial and can potentially be improved in terms of accuracy and scalability by using better-suited machine learning models and parallel algorithms. Here, we present Super.Complex, a distributed, supervised AutoML-based pipeline for overlapping community detection in weighted networks. We also propose three new evaluation measures for the outstanding issue of comparing sets of learned and known communities satisfactorily. Super.Complex learns a community fitness function from known communities using an AutoML method and applies this fitness function to detect new communities. A heuristic local search algorithm finds maximally scoring communities, and a parallel implementation can be run on a computer cluster for scaling to large networks. On a yeast protein-interaction network, Super.Complex outperforms 6 other supervised and 4 unsupervised methods. Application of Super.Complex to a human protein-interaction network with ~8k nodes and ~60k edges yields 1,028 protein complexes, with 234 complexes linked to SARS-CoV-2, the COVID-19 virus, with 111 uncharacterized proteins present in 103 learned complexes. Super.Complex is generalizable with the ability to improve results by incorporating domain-specific features. Learned community characteristics can also be transferred from existing applications to detect communities in a new application with no known communities. Code and interactive visualizations of learned human protein complexes are freely available at: https://sites.google.com/view/supercomplex/super-complex-v3-0.

Recommendations and discussion points on immunogenicity, biomarkers, automation/technology and protein-MS from the 2021 European Bioanalysis Forum Focus Workshops.

During the first half of 2021, and due to the SARS-CoV-2 pandemic preventing in-person meetings, the European Bioanalysis Forum organized four workshops as live interactive online meetings. The themes discussed at the workshops were carefully selected to match the cyberspace dynamics of the meeting format. The first workshop was a training day on challenges related to immunogenicity. The second one focused on biomarkers and continued the important discussion on integrating the principles of Context of Use (CoU) in biomarker research. The third workshop was dedicated to technology, that is, cutting-edge development in cell-based and ligand-binding assays and automation strategies. The fourth was on progress and the continued scientific and regulatory challenges related to peptide and protein analysis with MS. In all four workshops, the European Bioanalysis Forum included a mixture of scientific and regulatory themes, while reminding the audience of important strategic aspects and our responsibility toward the patient.

Biosynthesis of Self-Assembled Proteinaceous Nanoparticles for Vaccination.

Recent years have seen enormous advances in nanovaccines for both prophylactic and therapeutic applications, but most of these technologies employ chemical or hybrid semi-biosynthetic production methods. Thus, production of nanovaccines has to date failed to exploit biology-only processes like complex sequential post-translational biochemical modifications and scalability, limiting the realization of the initial promise for offering major performance advantages and improved therapeutic outcomes over conventional vaccines. A Nano-B5 platform for in vivo production of fully protein-based, self-assembling, stable nanovaccines bearing diverse antigens including peptides and polysaccharides is presented here. Combined with the self-assembly capacities of pentamer domains from the bacterial AB5 toxin and unnatural trimer peptides, diverse nanovaccine structures can be produced in common Escherichia coli strains and in attenuated pathogenic strains. Notably, the chassis of these nanovaccines functions as an immunostimulant. After showing excellent lymph node targeting and immunoresponse elicitation and safety performance in both mouse and monkey models, the strong prophylactic effects of these nanovaccines against infection, as well as their efficient therapeutic effects against tumors are further demonstrated. Thus, the Nano-B5 platform can efficiently combine diverse modular components and antigen cargos to efficiently generate a potentially very large diversity of nanovaccine structures using many bacterial species.

The lncRNA Snhg1-Vps13D vesicle trafficking system promotes memory CD8 T cell establishment via regulating the dual effects of IL-7 signaling.

The efficient induction and long-term persistence of pathogen-specific memory CD8 T cells are pivotal to rapidly curb the reinfection. Recent studies indicated that long-noncoding RNAs expression is highly cell- and stage-specific during T cell development and differentiation, suggesting their potential roles in T cell programs. However, the key lncRNAs playing crucial roles in memory CD8 T cell establishment remain to be clarified. Through CD8 T cell subsets profiling of lncRNAs, this study found a key lncRNA-Snhg1 with the conserved naivehi-effectorlo-memoryhi expression pattern in CD8 T cells of both mice and human, that can promote memory formation while impeding effector CD8 in acute viral infection. Further, Snhg1 was found interacting with the conserved vesicle trafficking protein Vps13D to promote IL-7Rα membrane location specifically. With the deep mechanism probing, the results show Snhg1-Vps13D regulated IL-7 signaling with its dual effects in memory CD8 generation, which not just because of the sustaining role of STAT5-BCL-2 axis for memory survival, but more through the STAT3-TCF1-Blimp1 axis for transcriptional launch program of memory differentiation. Moreover, we performed further study with finding a similar high-low-high expression pattern of human SNHG1/VPS13D/IL7R/TCF7 in CD8 T cell subsets from PBMC samples of the convalescent COVID-19 patients. The central role of Snhg1-Vps13D-IL-7R-TCF1 axis in memory CD8 establishment makes it a potential target for improving the vaccination effects to control the ongoing pandemic.

Immunomodulatory Therapeutic Proteins in COVID-19: Current Clinical Development and Clinical Pharmacology Considerations.

The coronavirus disease 2019 (COVID-19) pandemic caused by infection with SARS-CoV-2 has led to more than 600 000 deaths worldwide. Patients with severe disease often experience acute respiratory distress characterized by upregulation of multiple cytokines. Immunomodulatory biological therapies are being evaluated in clinical trials for the management of the systemic inflammatory response and pulmonary complications in patients with advanced stages of COVID-19. In this review, we summarize the clinical pharmacology considerations in the development of immunomodulatory therapeutic proteins for mitigating the heightened inflammatory response identified in COVID-19.