Graph analysis of β2 adrenergic receptor structures: a "social network" of GPCR residues

Bow-tie architectures in biological and artificial neural networks: Implications for network evolution and assay design

Modern artificial neural networks (ANNs) have long been designed on foundations of mathematics as opposed to their original foundations of biomimicry. However, the structure and function of these modern ANNs are often analogous to real-life biological networks. We propose that the ubiquitous information-theoretic principles underlying the development of ANNs are similar to the principles guiding the macro-evolution of biological networks and that insights gained from one field can be applied to the other. We generate hypotheses on the bow-tie network structure of the Janus kinase - signal transducers and activators of transcription (JAK-STAT) pathway, additionally informed by the evolutionary considerations, and carry out ANN simulation experiments to demonstrate that an increase in the network's input and output complexity does not necessarily require a more complex intermediate layer. This observation should guide novel biomarker discovery-namely, to prioritize sections of the biological networks in which information is most compressed as opposed to biomarkers representing the periphery of the network.

A point mutation in the β-adrenergic-like octopamine receptor: possible association with amitraz resistance

BACKGROUND

Amitraz is a unique formamidine-class acaricide/insecticide that effectively controls ticks, mites, and insect pests. However, the recent emergence of amitraz-resistant cattle ticks is a serious problem that requires an urgent solution. A nonsynonymous single nucleotide polymorphism (A181T) leading to an amino acid substitution (I61F) in the β-adrenergic-like (β-AL) octopamine receptor (OAR) of amitraz-resistant southern cattle ticks (Rhipicephalus microplus) (RmβAOR) was proposed to be a cause of the amitraz resistance. However, it remains unclear whether this substitution exerts any functional effect on the action of amitraz. To make this clear, the functional role of this mutation was examined using an orthologous OAR (BmOAR2) from the silkworm (Bombyx mori).

RESULTS

Both amitraz and its metabolite N² -(2,4-dimethylphenyl)-N¹ -methyformamidine (DPMF) elevated intracellular cyclic AMP levels as orthosteric OAR agonists in HEK-293 cells stably expressing BmOAR2. The I45F mutant of BmOAR2 (equivalent to I61F in RmβAOR) was generated and tested for its sensitivity to amitraz and DPMF. The assay result showed that the I45F mutation reduces the potency of DPMF to a level similar to that of the endogenous agonist (R)-OA in wild-type BmOAR2.

CONCLUSION

The amino acid substitution found in the first transmembrane segment of RmβAOR most likely causes target-site insensitivity to DPMF, which might contribute to the resistance of R. microplus to amitraz. This needs to be further confirmed using RmβAOR. © 2020 Society of Chemical Industry.

Computational characterization of the binding mode between oncoprotein Ets-1 and DNA-repair enzymes

The Ets-1 oncoprotein is a transcription factor that promotes target gene expression in specific biological processes. Typically, Ets-1 activity is low in healthy cells, but elevated levels of expression have been found in cancerous cells, specifically related to tumor progression. Like the vast majority of the cellular effectors, Ets-1 does not act alone but in association with partners. Given the important role that is attributed to Ets-1 in major human diseases, it is crucial to identify its partners and characterize their interactions. In this context, two DNA-repair enzymes, PARP-1 and DNA-PK, have been identified recently as interaction partners of Ets-1. We here identify their binding mode by means of protein docking. The results identify the interacting surface between Ets-1 and the two DNA-repair enzymes centered on the α-helix H1 of the ETS domain, leaving α-helix H3 available to bind DNA. The models highlight a hydrophobic patch on Ets-1 at the center of the interaction interface that includes three tryptophans (Trp338, Trp356, and Trp361). We rationalize the binding mode using a series of computational analyses, including alanine scanning, molecular dynamics simulation, and residue centrality analysis. Our study constitutes a first but important step in the characterization, at the molecular level, of the interaction between an oncoprotein and DNA-repair enzymes.

Unifying view of mechanical and functional hotspots across class A GPCRs

G protein-coupled receptors (GPCRs) are the largest superfamily of signaling proteins. Their activation process is accompanied by conformational changes that have not yet been fully uncovered. Here, we carry out a novel comparative analysis of internal structural fluctuations across a variety of receptors from class A GPCRs, which currently has the richest structural coverage. We infer the local mechanical couplings underpinning the receptors’ functional dynamics and finally identify those amino acids whose virtual deletion causes a significant softening of the mechanical network. The relevance of these amino acids is demonstrated by their overlap with those known to be crucial for GPCR function, based on static structural criteria. The differences with the latter set allow us to identify those sites whose functional role is more clearly detected by considering dynamical and mechanical properties. Of these sites with a genuine mechanical/dynamical character, the top ranking is amino acid 7x52, a previously unexplored, and experimentally verifiable key site for GPCR conformational response to ligand binding.

The biological functionality of several receptors and enzymes depends on their capability to sustain large-scale structural fluctuations and adopt different conformational states in response to ligand binding. This is the case for G protein-coupled receptors (GPCRs), the largest superfamily of signaling proteins in mammals and a primary pharmaceutical target. To better understand the functional dynamics of GPCRs, we have analysed the inter-residue distance variations across the available structures for several receptors of the rhodopsin-like family (class A). We first reconstructed the network of mechanical, rigid-like couplings between nearby amino acids and then identified those acting as dynamical/mechanical hubs. These were the sites whose virtual removal led to a significant softening of the overall mechanical network. After validating the biological relevance of these sites by comparison against known key functional sites, we singled out those regions which emerge as prominent mechanical hubs and yet have an otherwise still unknown functional role. The most relevant of such novel putative functional sites, which could be probed by mutagenesis experiments, is at interface of two transmembrane helices and we expect it to be crucial for assisting GPCRs conformational response to agonist binding.

Network measures for chemical library design

In this overview, we examine recent developments in network approaches to drug design. A brief overview of networks is followed by a discussion of how chemical similarity networks and their properties address challenges in drug design. Multiple methods used to assess or enhance chemical diversity for early-stage drug discovery are discussed, as well as methods that can be used for drug repositioning and ligand polypharmacology.

G protein-coupled receptors: computer-aided ligand discovery and computational structural analyses in the 2010s

G protein-coupled receptors, or GPCRs, are a large superfamily of proteins found on the plasma membrane of cells. They are involved in most physiological and pathophysiological functions and constitute the target of the majority of marketed drugs. Although these receptors have been historically elusive to attempts of structural determination, GPCR crystallography is now in full blossom, opening the way to structure-based drug discovery and enabling homology modeling. This thematic issue of the journal In Silico Pharmacology, which illustrates how the expanding body of structural knowledge is fostering complex computational analyses of the structure-function relationships of the receptors and their interactions with their ligands, stems from the 31st Camerino-Cyprus-Noordwijkerhout Symposium held in Italy, in May 2013, at the University of Camerino. Specifically, it originates from a session of the symposium entitled “Structure-Based Discovery of Ligands of G Protein-Coupled Receptors: Finally a Reality”, and features a mix of research articles and reviews on the application of computational modeling to the analysis of the structure of GPCRs and the interactions of the receptors with their ligands.