Computational Strategies to Identify New Drug Candidates against Neuroinflammation.

Increasing application of computational approaches in these last decades has deeply modified the process of discovery and commercialization of new therapeutic entities. This is especially true in the field of neuroinflammation, in which both the peculiar anatomical localization and the presence of the blood-brain barrier make it mandatory to finely tune the candidates' physicochemical properties from the early stages of the discovery pipeline. The aim of this review is, therefore, to provide a general overview of neuroinflammation to the readers, together with the most common computational strategies that can be exploited to discover and design small molecules controlling neuroinflammation, especially those based on the knowledge of the three-dimensional structure of the biological targets of therapeutic interest.

A Computational Workflow for the Identification of Novel Fragments Acting as Inhibitors of the Activity of Protein Kinase CK1δ.

Fragment-Based Drug Discovery (FBDD) has become, in recent years, a consolidated approach in the drug discovery process, leading to several drug candidates under investigation in clinical trials and some approved drugs. Among these successful applications of the FBDD approach, kinases represent a class of targets where this strategy has demonstrated its real potential with the approved kinase inhibitor Vemurafenib. In the Kinase family, protein kinase CK1 isoform δ (CK1δ) has become a promising target in the treatment of different neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis.

Shedding Light on the Molecular Recognition of Sub-Kilodalton Macrocyclic Peptides on Thrombin by Supervised Molecular Dynamics.

Macrocycles are attractive structures for drug development due to their favorable structural features, potential in binding to targets with flat featureless surfaces, and their ability to disrupt protein-protein interactions. Moreover, large novel highly diverse libraries of low-molecular-weight macrocycles with therapeutically favorable characteristics have been recently established. Considering the mentioned facts, having a validated, fast, and accurate computational protocol for studying the molecular recognition and binding mode of this interesting new class of macrocyclic peptides deemed to be helpful as well as insightful in the quest of accelerating drug discovery.

On the mechanism of tumor cell entry of aloe-emodin, a natural compound endowed with anticancer activity.

Aloe-emodin (1,8-dihydroxy-3-[hydroxymethyl]-anthraquinone), AE, is one of the active constituents of a number of plant species used in traditional medicine. We have previously identified, for the first time, AE as a new antitumor agent and shown that its selective in vitro and in vivo killing of neuroblastoma cells was promoted by a cell-specific drug uptake process. However, the molecular mechanism underlying the cell entry of AE has remained elusive as yet.

Inspecting the Mechanism of Fragment Hits Binding on SARS-CoV-2 M by Using Supervised Molecular Dynamics (SuMD) Simulations.

Computational approaches supporting the early characterization of fragment molecular recognition mechanism represent a valuable complement to more expansive and low-throughput experimental techniques. In this retrospective study, we have investigated the geometric accuracy with which high-throughput supervised molecular dynamics simulations (HT-SuMD) can anticipate the experimental bound state for a set of 23 fragments targeting the SARS-CoV-2 main protease. Despite the encouraging results herein reported, in line with those previously described for other MD-based posing approaches, a high number of incorrect binding modes still complicate HT-SuMD routine application.

Developing novel classes of protein kinase CK1δ inhibitors by fusing [1,2,4]triazole with different bicyclic heteroaromatic systems.

Protein kinase CK1δ expression and activity is involved in different pathological situations that include neuroinflammatory and neurodegenerative diseases. For this reason, protein kinase CK1δ has become a possible therapeutic target for these conditions. 5,6-fused bicyclic heteroaromatic systems that resemble adenine of ATP represent optimal scaffolds for the development of a new class of ATP competitive CK1δ inhibitors.

Targeting the coronavirus SARS-CoV-2: computational insights into the mechanism of action of the protease inhibitors lopinavir, ritonavir and nelfinavir.

Coronavirus SARS-CoV-2 is a recently discovered single-stranded RNA betacoronavirus, responsible for a severe respiratory disease known as coronavirus disease 2019, which is rapidly spreading. Chinese health authorities, as a response to the lack of an effective therapeutic strategy, started to investigate the use of lopinavir and ritonavir, previously optimized for the treatment and prevention of HIV/AIDS viral infection. Despite the clinical use of these two drugs, no information regarding their possible mechanism of action at the molecular level is still known for SARS-CoV-2.

HT-SuMD: making molecular dynamics simulations suitable for fragment-based screening. A comparative study with NMR.

Fragment-based lead discovery (FBLD) is one of the most efficient methods to develop new drugs. We present here a new computational protocol called High-Throughput Supervised Molecular Dynamics (HT-SuMD), which makes it possible to automatically screen up to thousands of fragments, representing therefore a new valuable resource to prioritise fragments in FBLD campaigns. The protocol was applied to Bcl-X, an oncological protein target involved in the regulation of apoptosis through protein-protein interactions.

Comparing Fragment Binding Poses Prediction Using HSP90 as a Key Study: When Bound Water Makes the Difference.

Fragment-Based Drug Discovery (FBDD) approaches have gained popularity not only in industry but also in academic research institutes. However, the computational prediction of the binding mode adopted by fragment-like molecules within a protein binding site is still a very challenging task. One of the most crucial aspects of fragment binding is related to the large amounts of bound waters in the targeted binding pocket.

The rise of molecular simulations in fragment-based drug design (FBDD): an overview.

Fragment-based drug discovery (FBDD) is an innovative approach, progressively more applied in the academic and industrial context, to enhance hit identification for previously considered undruggable biological targets. In particular, FBDD discovers low-molecular-weight (LMW) ligands (<300Da) able to bind to therapeutically relevant macromolecules in an affinity range from the micromolar (μM) to millimolar (mM). X-ray crystallography (XRC) and nuclear magnetic resonance (NMR) spectroscopy are commonly the methods of choice to obtain 3D information about the bound ligand-protein complex, but this can occasionally be problematic, mainly for early, low-affinity fragments.

Scaffold Repurposing of in-House Chemical Library toward the Identification of New Casein Kinase 1 δ Inhibitors.

Recent studies have highlighted the key role of Casein kinase 1 δ (CK1δ) in the development of several neurodegenerative pathologies, such as Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS). So far, CK1δ inhibitors are noncovalent ATP competitive ligands and no drugs are currently available for this molecular target, hence the interest in developing new CK1δ inhibitors. The study aims to identify new inhibitors able to bind the enzyme; by a dual approach in silico/in vitro, the virtual screening has been performed on an in-house chemical library, which was previously designed and synthesized for other targets.

New Insights into Key Determinants for Adenosine 1 Receptor Antagonists Selectivity Using Supervised Molecular Dynamics Simulations.

Adenosine receptors (ARs), like many otherGprotein-coupledreceptors (GPCRs), are targets of primary interest indrug design. However, one of the main limits for the development of drugs for this class of GPCRs is the complex selectivity profile usually displayed by ligands. Numerous efforts have been madefor clarifying the selectivity of ARs, leading to the development of many ligand-based models.

Exploring the RNA-Recognition Mechanism Using Supervised Molecular Dynamics (SuMD) Simulations: Toward a Rational Design for Ribonucleic-Targeting Molecules?

Although proteins have represented the molecular target of choice in the development of new drug candidates, the pharmaceutical importance of ribonucleic acids has gradually been growing. The increasing availability of structural information has brought to light the existence of peculiar three-dimensional RNA arrangements, which can, contrary to initial expectations, be recognized and selectively modulated through small chemical entities or peptides. The application of classical computational methodologies, such as molecular docking, for the rational development of RNA-binding candidates is, however, complicated by the peculiarities characterizing these macromolecules, such as the marked conformational flexibility, the singular charges distribution, and the relevant role of solvent molecules.

Deciphering the Molecular Recognition Mechanism of Multidrug Resistance NorA Efflux Pump Using a Supervised Molecular Dynamics Approach.

The use and misuse of antibiotics has resulted in critical conditions for drug-resistant bacteria emergency, accelerating the development of antimicrobial resistance (AMR). In this context, the co-administration of an antibiotic with a compound able to restore sufficient antibacterial activity may be a successful strategy. In particular, the identification of efflux pump inhibitors (EPIs) holds promise for new antibiotic resistance breakers (ARBs).

Revisiting the Allosteric Regulation of Sodium Cation on the Binding of Adenosine at the Human A Adenosine Receptor: Insights from Supervised Molecular Dynamics (SuMD) Simulations.

One of the most intriguing findings highlighted from G protein-coupled receptor (GPCR) crystallography is the presence, in many members of class A, of a partially hydrated sodium ion in the middle of the seven transmembrane helices (7TM) bundle. In particular, the human adenosine A receptor (A AR) is the first GPCR in which a monovalent sodium ion was crystallized in a distal site from the canonical orthosteric one, corroborating, from a structural point of view, its role as a negative allosteric modulator. However, the molecular mechanism by which the sodium ion influences the recognition of the A AR agonists is not yet fully understood.

Can We Still Trust Docking Results? An Extension of the Applicability of DockBench on PDBbind Database.

The number of entries in the Protein Data Bank (PDB) has doubled in the last decade, and it has increased tenfold in the last twenty years. The availability of an ever-growing number of structures is having a huge impact on the Structure-Based Drug Discovery (SBDD), allowing investigation of new targets and giving the possibility to have multiple structures of the same macromolecule in a complex with different ligands. Such a large resource often implies the choice of the most suitable complex for molecular docking calculation, and this task is complicated by the plethora of possible posing and scoring function algorithms available, which may influence the quality of the outcomes.

A Triazolotriazine-Based Dual GSK-3β/CK-1δ Ligand as a Potential Neuroprotective Agent Presenting Two Different Mechanisms of Enzymatic Inhibition.

Glycogen synthase kinase 3β (GSK-3β) and casein kinase 1δ (CK-1δ) are emerging targets for the treatment of neuroinflammatory disorders, including Parkinson's disease. An inhibitor able to target these two kinases was developed by docking-based design. Compound 12, 3-(7-amino-5-(cyclohexylamino)-[1,2,4]triazolo[1,5-a][1,3,5]triazin-2-yl)-2-cyanoacrylamide, showed combined inhibitory activity against GSK-3β and CK-1δ [IC (GSK-3β)=0.

Targeting Protein Kinase CK1δ with Riluzole: Could It Be One of the Possible Missing Bricks to Interpret Its Effect in the Treatment of ALS from a Molecular Point of View?

Riluzole, approved by the US Food and Drug Administration (FDA) in 1995, is the most widespread oral treatment for the fatal neurodegenerative disorder amyotrophic lateral sclerosis (ALS). The drug, whose mechanism of action is still obscure, mitigates progression of the illness, but unfortunately with only limited improvements. Herein we report the first demonstration, using a combination of computational and in vitro studies, that riluzole is an ATP-competitive inhibitor of the protein kinase CK1 isoform δ, with an IC value of 16.