AR71, Histamine H3 Receptor Ligand-In Vitro and In Vivo Evaluation (Anti-Inflammatory Activity, Metabolic Stability, Toxicity, and Analgesic Action).

The future of therapy for neurodegenerative diseases (NDs) relies on new strategies targeting multiple pharmacological pathways. Our research led to obtaining the compound AR71 [(E)-3-(3,4,5-trimethoxyphenyl)-1-(4-(3-(piperidin-1-yl)propoxy)phenyl)prop-2-en-1-one], which has high affinity for human H3R (Ki = 24 nM) and selectivity towards histamine H1 and H4 receptors (Ki > 2500 nM), and showed anti-inflammatory activity in a model of lipopolysaccharide-induced inflammation in BV-2 cells. The presented tests confirmed its antagonist/inverse agonist activity profile and good metabolic stability while docking studies showed the binding mode to histamine H1, H3, and H4 receptors. In in vitro tests, cytotoxicity was evaluated at three cell lines (neuroblastoma, astrocytes, and human peripheral blood mononuclear cells), and a neuroprotective effect was observed in rotenone-induced toxicity. In vivo experiments in a mouse neuropathic pain model demonstrated the highest analgesic effects of AR71 at the dose of 20 mg/kg body weight. Additionally, AR71 showed antiproliferative activity in higher concentrations. These findings suggest the need for further evaluation of AR71's therapeutic potential in treating ND and CNS cancer using animal experimental models.

Docking Foundations: From Rigid to Flexible Docking.

Despite the development of methods for the experimental determination of protein structures, the dissonance between the number of known sequences and their solved structures is still enormous. This is particularly evident in protein-protein complexes. To fill this gap, diverse technologies have been developed to study protein-protein interactions (PPIs) in a cellular context including a range of biological and computational methods. The latter derive from techniques originally published and applied almost half a century ago and are based on interdisciplinary knowledge from the nexus of the fields of biology, chemistry, and physics about protein sequences, structures, and their folding. Protein-protein docking, the main protagonist of this chapter, is routinely treated as an integral part of protein research. Herein, we describe the basic foundations of the whole process in general terms, but step by step from protein representations through docking methods and evaluation of complexes to their final validation.

Machine Learning Methods in Protein-Protein Docking.

An exponential increase in the number of publications that address artificial intelligence (AI) usage in life sciences has been noticed in recent years, while new modeling techniques are constantly being reported. The potential of these methods is vast-from understanding fundamental cellular processes to discovering new drugs and breakthrough therapies. Computational studies of protein-protein interactions, crucial for understanding the operation of biological systems, are no exception in this field. However, despite the rapid development of technology and the progress in developing new approaches, many aspects remain challenging to solve, such as predicting conformational changes in proteins, or more "trivial" issues as high-quality data in huge quantities.Therefore, this chapter focuses on a short introduction to various AI approaches to study protein-protein interactions, followed by a description of the most up-to-date algorithms and programs used for this purpose. Yet, given the considerable pace of development in this hot area of computational science, at the time you read this chapter, the development of the algorithms described, or the emergence of new (and better) ones should come as no surprise.