Bridging molecular docking to molecular dynamics in exploring ligand-protein recognition process: An overview

Computational techniques have been applied in the drug discovery pipeline since the 1980s. Given the low computational resources of the time, the first molecular modeling strategies relied on a rigid view of the ligand-target binding process. During the years, the evolution of hardware technologies has gradually allowed simulating the dynamic nature of the binding event. In this work, we present an overview of the evolution of structure-based drug discovery techniques in the study of ligand-target recognition phenomenon, going from the static molecular docking toward enhanced molecular dynamics strategies

Exploring protein flexibility during docking to investigate ligand-target recognition

Ligand-protein binding models have experienced an evolution during time: from the lock-key model to induced-fit and conformational selection, the role of protein flexibility has become more and more relevant. Understanding binding mechanism is of great importance in drug-discovery, because it could help to rationalize the activity of known binders and to optimize them. The application of computational techniques to drug-discovery has been reported since the 1980s, with the advent computer-aided drug design. During the years several techniques have been developed to address the protein flexibility issue. The present work proposes a strategy to consider protein structure variability in molecular docking, through a ligand-based/structure-based integrated approach and through the development of a fully automatic cross-docking benchmark pipeline. Moreover, a full exploration of protein flexibility during the binding process is proposed through the Supervised Molecular Dynamics. The application of a tabu-like algorithm to classical molecular dynamics accelerates the binding process from the micro-millisecond to the nanosecond timescales. In the present work, an implementation of this algorithm has been performed to study peptide-protein recognition processes

Targeting Protein Kinase CK1\u3b4 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\u2005\u3b4, with an IC50 value of 16.1\u2005\u3bcm. This allows us to rewrite its possible molecular mechanism of action in the treatment of ALS. The inhibition of CK1\u3b4 catalytic activity indeed links the two main pathological hallmarks of ALS: transactive response DNA-binding protein of 43\u2005kDa (TDP-43) proteinopathy and glutamate excitotoxicity, exacerbated by the loss of expression of glial excitatory amino acid transporter-2 (EAAT2)

Thermal titration molecular dynamics (TTMD): shedding light on the stability of RNA-small molecule complexes

Ribonucleic acids are gradually becoming relevant players among putative drug targets, thanks to the increasing amount of structural data exploitable for the rational design of selective and potent binders that can modulate their activity. Mainly, this information allows employing different computational techniques for predicting how well would a ribonucleic-targeting agent fit within the active site of its target macromolecule. Due to some intrinsic peculiarities of complexes involving nucleic acids, such as structural plasticity, surface charge distribution, and solvent-mediated interactions, the application of routinely adopted methodologies like molecular docking is challenged by scoring inaccuracies, while more physically rigorous methods such as molecular dynamics require long simulation times which hamper their conformational sampling capabilities. In the present work, we present the first application of Thermal Titration Molecular Dynamics (TTMD), a recently developed method for the qualitative estimation of unbinding kinetics, to characterize RNA-ligand complexes. In this article, we explored its applicability as a post-docking refinement tool on RNA in complex with small molecules, highlighting the capability of this method to identify the native binding mode among a set of decoys across various pharmaceutically relevant test cases

The role of 5-arylalkylamino- and 5-piperazino- moieties on the 7-aminopyrazolo[4,3-d]pyrimidine core in affecting adenosine A1 and A2A receptor affinity and selectivity profiles

New 7-amino-2-phenylpyrazolo[4,3-d]pyrimidine derivatives, substituted at the 5-position with aryl(alkyl)amino- and 4-substituted-piperazin-1-yl- moieties, were synthesized with the aim of targeting human (h) adenosine A1 and/or A2A receptor subtypes. On the whole, the novel derivatives 1-24 shared scarce or no affinities for the off-target hA2B and hA3 ARs. The 5-(4-hydroxyphenethylamino)- derivative 12 showed both good affinity (Ki = 150 nM) and the best selectivity for the hA2A AR while the 5-benzylamino-substituted 5 displayed the best combined hA2A (Ki = 123 nM) and A1 AR affinity (Ki = 25 nM). The 5-phenethylamino moiety (compound 6) achieved nanomolar affinity (Ki = 11 nM) and good selectivity for the hA1 AR. The 5-(N4-substituted-piperazin-1-yl) derivatives 15-24 bind the hA1 AR subtype with affinities falling in the high nanomolar range. A structure-based molecular modeling study was conducted to rationalize the experimental binding data from a molecular point of view using both molecular docking studies and Interaction Energy Fingerprints (IEFs) analysis.[Formula: see text]

L-citrulline is protective in hyperoxic lung damage and improves matrix remodelling and alveolarization

Moderate hyperoxia alters alveolar and vascular lung morphogenesis. Nitric oxide (NO) and matrix metalloproteinases (MMP) have a crucial role in the homeostasis of the matrix and bronchoalveolar structure and may be regulated abnormally by exposure to hyperoxia. Disruption of vascular endothelial growth factor (VEGF)-NO signaling impairs vascular growth and contributes to hyperoxia-induced vascular disease in bronchopulmonary dysplasia (BPD). We hypothesize that L-citrulline, by raising the serum levels of L-arginine and enhancing endogenous NO synthesis, might attenuate hyperoxia-induced lung injury in an experimental model of BPD. Neonatal rats (1 day old) were exposed to 60% oxygen or room air for 14 days and administered L-citrulline or a vehicle (sham). Lung morphometry were performed; Serum was tested for arginine level; Matrix metalloproteinases2 (MMP2) gene expression, VEGF gene and protein expression and endothelial NO synthase (eNOS) protein expression were compared. Mean linear intercept was higher in the hyperoxia and sham groups when compared with the room air (RA) and L-citr+hyperoxia treated group (p<0.02). Secondary crests number was higher in L-citrulline treated and RA when compared to hyperoxia and sham group (p<0.02). L-Arginine level rose in the L-citrulline-treated group (p<0.05). L-citrulline did not affect MMP2 gene expression, but it regulated the MMP2 active protein, which rose in bronchoalveolar lavage fluid (p<0.05), presumably due to a post-transductional effect. Compared with RA controls, hyperoxia significantly decreased VEGF and eNOS protein expression. At the same time, an increased lung VEGF gene and protein expression (p<0.05) were also seen in the rats treated with L-citrulline. We conclude that: (i) hyperoxia decreases growth and disrupts VEGF-NO signaling of lung; (ii) the main effects of L-citrulline are an increased serum level of arginine, as a promoter and a substrate of the nitric oxide synthase; and (ii) a better alveolar growth and matrix control than in hyperoxia-induced lung damage seems promising

Slaughter weight rather than sex affects carcass cuts and tissue composition of Bisaro pigs

Carcass cuts and tissue composition were assessed in Bisaro pigs (n=64) from two sexes (31 gilts and 33 entire males) reared until three target slaughter body-weights (BW) means: 17 kg, 32 kg, and 79 kg. Dressing percentage and backfat thickness increased whereas carcass shrinkage decreased with increasing BW. Slaughter weight affected most of the carcass cut proportions, except shoulder and thoracic regions. Bone proportion decreased linearly with increasing slaughter BW, while intermuscular and subcutaneous adipose tissue depots increased concomitantly. Slaughter weight increased the subcutaneous adipose tissue proportion but this impaired intramuscular and intermuscular adipose tissues in the loin primal. The sex of the pigs minimally affected the carcass composition, as only the belly weight and the subcutaneous adipose tissue proportions were greater in gilts than in entire males. Light pigs regardless of sex are recommended to balance the trade-offs between carcass cuts and their non-edible compositional outcomes.Work included in the Portuguese PRODER research Project BISOPORC – Pork extensive production of Bísara breed, in two alternative systems: fattening on concentrate vs chesnut, Project PRODER SI I&DT Medida 4.1 “Cooperação para a Inovação”. The authors are grateful to Laboratory of Carcass and Meat Quality of Agriculture School of Polytechnic Institute of Bragança ‘Cantinho do Alfredo’. The authors are members of the MARCARNE network, funded by CYTED (ref. 116RT0503).info:eu-repo/semantics/publishedVersio

Exploring protein flexibility during docking to investigate ligand-target recognition

Ligand-protein binding models have experienced an evolution during time: from the lock-key model to induced-fit and conformational selection, the role of protein flexibility has become more and more relevant. Understanding binding mechanism is of great importance in drug-discovery, because it could help to rationalize the activity of known binders and to optimize them. The application of computational techniques to drug-discovery has been reported since the 1980s, with the advent computer-aided drug design. During the years several techniques have been developed to address the protein flexibility issue. The present work proposes a strategy to consider protein structure variability in molecular docking, through a ligand-based/structure-based integrated approach and through the development of a fully automatic cross-docking benchmark pipeline. Moreover, a full exploration of protein flexibility during the binding process is proposed through the Supervised Molecular Dynamics. The application of a tabu-like algorithm to classical molecular dynamics accelerates the binding process from the micro-millisecond to the nanosecond timescales. In the present work, an implementation of this algorithm has been performed to study peptide-protein recognition processes.I modelli di riconoscimento ligando-proteina si sono evoluti nel corso degli anni: dal modello chiave-serratura a quello di fit-indotto e selezione conformazionale, il ruolo della flessibilità proteica è diventato via via più importante. Capire il meccanismo di riconoscimento è di grande importanza nella progettazione di nuovi farmaci, perchè può dare la possibilità di razionalizzare l’attività di ligandi noti e di ottimizzarli. L’applicazione di tecniche computazionali alla scoperta di nuovi farmaci risale agli anni ‘80, con l’avvento del cosiddetto “Computer-Aided Drug Design”, o, tradotto, progettazione di farmaci aiutata dal computer. Negli anni sono state sviluppate molte tecniche che hanno affrontato il problema della flessibilità proteica. Questo lavoro propone una strategia per considerare la variabilità delle strutture proteiche nel docking, attraverso un approccio combinato ligand-based/structure-based e attraverso lo sviluppo di una procedura completamente automatizzata di docking incrociato. In aggiunta, viene proposta una piena esplorazione della flessibilità proteica durante il processo di legame attraverso la Dinamica Molecolare Supervisionata. L’applicazione di un algoritmo simil-tabu alla dinamica molecolare classica accelera il processo di riconoscimento dalla scala dei micro-millisecondi a quella dei nanosecondi. Nel presente lavoro è stata fatta un’implementazione di questa algoritmica per studiare il processo di riconoscimento peptide-proteina

Could Adenosine Recognize its Receptors with a Stoichiometry Other than 1 : 1?

One of the most largely accepted concepts in the G protein-coupled receptors (GPCRs) field is that the ligand, either agonist or antagonist, recognizes its receptor with a stoichiometry of 1 : 1. Recent experimental evidence, reporting ternary complexes formed by GPCR:orthosteric: allosteric ligands, has complicated the ligand-receptor 1 : 1 binding scenario. Molecular modeling simulations have been used to retrieve insights on the whole ligand-receptor recognition process, beyond information on the final bound state provided by experimental techniques. The simulation of adenosine binding pathways towards the A 2A adenosine receptor highlighted the presence of alternative binding sites (meta-binding sites) beside the canonical orthosteric one, mainly in proximity to the extracellular vestibule. In light of all these considerations, we investigated the possibility that a second molecule of adenosine engages its receptor when this is already in the holo form, generating a ternary complex with a stoichiometry of 2 : 1. Unexpectedly, supervised molecular dynamics (SuMD) simulations showed that the A 2A adenosine receptor could bind the second molecule of adenosine in one of the possible meta-binding sites as well as into its orthosteric site. The formation of this ternary complex, which favored the formation of the intracellular \u201cionic lock\u201d between R102 (3.50) and E228 (6.30), could putatively be framed in the context of a negative allosteric regulation

Combining self- and cross-docking as benchmark tools: the performance of DockBench in the D3R Grand Challenge 2

Abstract Molecular docking is a powerful tool in the field of computer-aided molecular design. In particular, it is the technique of choice for the prediction of a ligand pose within its target binding site. A multitude of docking methods is available nowadays, whose performance may vary depending on the data set. Therefore, some non-trivial choices should be made before starting a docking simulation. In the same framework, the selection of the target structure to use could be challenging, since the number of available experimental structures is increasing. Both issues have been explored within this work. The pose prediction of a pool of 36 compounds provided by D3R Grand Challenge 2 organizers was preceded by a pipeline to choose the best protein/docking-method couple for each blind ligand. An integrated benchmark approach including ligand shape comparison and cross-docking evaluations was implemented inside our DockBench software. The results are encouraging and show that bringing attention to the choice of the docking simulation fundamental components improves the results of the binding mode predictions