Monkeypox (Mpox) vs. Innate immune responses: Insights into evasion mechanisms and potential therapeutic strategies.

Orthopoxviruses, a group of zoonotic viral infections, have emerged as a significant health emergency and global concern, particularly exemplified by the re-emergence of monkeypox (Mpox). Effectively addressing these viral infections necessitates a comprehensive understanding of the intricate interplay between the viruses and the host's immune response. In this review, we aim to elucidate the multifaceted aspects of innate immunity in the context of orthopoxviruses, with a specific focus on monkeypox virus (MPXV). We provide an in-depth analysis of the roles of key innate immune cells, including natural killer (NK) cells, dendritic cells (DCs), and granulocytes, in the host defense against MPXV. Furthermore, we explore the interferon (IFN) response, highlighting the involvement of toll-like receptors (TLRs) and cytosolic DNA/RNA sensors in detecting and responding to the viral presence. This review also examines the complement system's contribution to the immune response and provides a detailed analysis of the immune evasion strategies employed by MPXV to evade host defenses. Additionally, we discuss current prevention and treatment strategies for Mpox, including pre-exposure (PrEP) and post-exposure (PoEP) prophylaxis, supportive treatments, antivirals, and vaccinia immune globulin (VIG).

Transferring chemical and energetic knowledge between molecular systems with machine learning.

Predicting structural and energetic properties of a molecular system is one of the fundamental tasks in molecular simulations, and it has applications in chemistry, biology, and medicine. In the past decade, the advent of machine learning algorithms had an impact on molecular simulations for various tasks, including property prediction of atomistic systems. In this paper, we propose a novel methodology for transferring knowledge obtained from simple molecular systems to a more complex one, endowed with a significantly larger number of atoms and degrees of freedom. In particular, we focus on the classification of high and low free-energy conformations. Our approach relies on utilizing (i) a novel hypergraph representation of molecules, encoding all relevant information for characterizing multi-atom interactions for a given conformation, and (ii) novel message passing and pooling layers for processing and making free-energy predictions on such hypergraph-structured data. Despite the complexity of the problem, our results show a remarkable Area Under the Curve of 0.92 for transfer learning from tri-alanine to the deca-alanine system. Moreover, we show that the same transfer learning approach can also be used in an unsupervised way to group chemically related secondary structures of deca-alanine in clusters having similar free-energy values. Our study represents a proof of concept that reliable transfer learning models for molecular systems can be designed, paving the way to unexplored routes in prediction of structural and energetic properties of biologically relevant systems.