Conformational Dynamics of the Soluble and Membrane-Bound Forms of Interleukin-1 Receptor Type-1: Insights into Linker Flexibility and Domain Orientation.

Interleukin-1 receptor type 1 (IL-1R1) is a key player in inflammation and immune responses. This receptor regulates IL-1 activity in two forms: as a membrane-bound form and as a soluble ectodomain. The details and differences between the conformational dynamics of the membrane-bound and the soluble IL-1R1 ectodomains (ECDs) remain largely elusive. Here, we study and compare the structural dynamics of the soluble and membrane-bound IL-1R1-ECDs using molecular dynamics (MD) simulations, focusing on the flexible interdomain linker of the ECD, as well as the spatial rearrangements between the Ig-like domains of the ECD. To explore the membrane-bound conformations, a full-length IL-1R1 structural model was developed and subjected to classical equilibrium MD. Comparative analysis of multiple MD trajectories of the soluble and the membrane-bound IL-1R1-ECDs reveals that (i) as somewhat expected, the extent of the visited "open-to-closed" transitional states differs significantly between the soluble and membrane-bound forms; (ii) the soluble form presents open-closed transitions, sampling a wider rotational motion between the Ig-like domains of the ECD, visiting closed and "twisted" conformations in higher extent, whereas the membrane-bound form is characterized by more conformationally restricted states; (iii) interestingly, the backbone dihedral angles of residues Glu202, Glu203 and Asn204, located in the flexible linker, display the highest variations during the transition between discrete conformational states detected in IL-1R1, thus appearing to work as the "central wheel of a clock's movement". The simulations and analyses presented in this contribution offer a deeper insight into the structure and dynamics of IL-1R1, which may be explored in a drug discovery setting.

The Therapeutic Prospects of Targeting IL-1R1 for the Modulation of Neuroinflammation in Central Nervous System Disorders.

The interleukin-1 receptor type 1 (IL-1R1) holds pivotal roles in the immune system, as it is positioned at the "epicenter" of the inflammatory signaling networks. Increased levels of the cytokine IL-1 are a recognized feature of the immune response in the central nervous system (CNS) during injury and disease, i.e., neuroinflammation. Despite IL-1/IL-1R1 signaling within the CNS having been the subject of several studies, the roles of IL-1R1 in the CNS cellular milieu still cause controversy. Without much doubt, however, the persistent activation of the IL-1/IL-1R1 signaling pathway is intimately linked with the pathogenesis of a plethora of CNS disease states, ranging from Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS), all the way to schizophrenia and prion diseases. Importantly, a growing body of evidence is showing that blocking IL-1R1 signaling via pharmacological or genetic means in different experimental models of said CNS diseases leads to reduced neuroinflammation and delayed disease progression. The aim of this paper is to review the recent progress in the study of the biological roles of IL-1R1, as well as to highlight key aspects that render IL-1R1 a promising target for the development of novel disease-modifying treatments for multiple CNS indications.

An Innovative Sequence-to-Structure-Based Approach to Drug Resistance Interpretation and Prediction: The Use of Molecular Interaction Fields to Detect HIV-1 Protease Binding-Site Dissimilarities.

In silico methodologies have opened new avenues of research to understanding and predicting drug resistance, a pressing health issue that keeps rising at alarming pace. Sequence-based interpretation systems are routinely applied in clinical context in an attempt to predict mutation-based drug resistance and thus aid the choice of the most adequate antibiotic and antiviral therapy. An important limitation of approaches based on genotypic data exclusively is that mutations are not considered in the context of the three-dimensional (3D) structure of the target. Structure-based in silico methodologies are inherently more suitable to interpreting and predicting the impact of mutations on target-drug interactions, at the cost of higher computational and time demands when compared with sequence-based approaches. Herein, we present a fast, computationally inexpensive, sequence-to-structure-based approach to drug resistance prediction, which makes use of 3D protein structures encoded by input target sequences to draw binding-site comparisons with susceptible templates. Rather than performing atom-by-atom comparisons between input target and template structures, our workflow generates and compares Molecular Interaction Fields (MIFs) that map the areas of energetically favorable interactions between several chemical probe types and the target binding site. Quantitative, pairwise dissimilarity measurements between the target and the template binding sites are thus produced. The method is particularly suited to understanding changes to the 3D structure and the physicochemical environment introduced by mutations into the target binding site. Furthermore, the workflow relies exclusively on freeware, making it accessible to anyone. Using four datasets of known HIV-1 protease sequences as a case-study, we show that our approach is capable of correctly classifying resistant and susceptible sequences given as input. Guided by ROC curve analyses, we fined-tuned a dissimilarity threshold of classification that results in remarkable discriminatory performance (accuracy ≈ ROC AUC ≈ 0.99), illustrating the high potential of sequence-to-structure-, MIF-based approaches in the context of drug resistance prediction. We discuss the complementarity of the proposed methodology to existing prediction algorithms based on genotypic data. The present work represents a new step toward a more comprehensive and structurally-informed interpretation of the impact of genetic variability on the response to HIV-1 therapies.