The use of machine learning-based sequential virtual screening in the search of new ligands of 5-HT6 receptor

5-HT6 receptor takes part in learning and memory processes. For this reason, the use of ligands of this receptor in the treatment of neurodegenerative diseases such as Alzheimer's disease, depression or autism is being investigated. The development of machine learning (ML) and access to large compound databases allow for the increasing use of these methods in search of new drugs. The use of ML in pre-clinical tests allows for a reduction in time and costs of drug discovery. In this study, we used a sequential virtual screening approach in search of new structures with potential high affinity for the 5-HT6 receptor. Data from the ChEMBL database containing ligand binding affinities, measured as an inhibition constant (Ki), was used as the training dataset. Each step of the screening was based on machine learning models, the task of which was to classify compounds as potentially active and inactive. The first step included a ligand-based drug discovery (LBDD) approach, in which, using Klekota-Roth fingerprints and descriptors describing the chemical structure of the ligands, a classification model was developed to select a preliminary group of candidates from the Otava chemical compound database. In the second step, a structure-based drug discovery (SBDD) approach was used. For this purpose, compounds were docked to the homology model of the 5-HT6 receptor, developed using the AlphaFold algorithm and optimized by Induced-Fit Docking tool and molecular dynamics. Docking poses were scored by a trained Extra Trees classifier. Interactions of a reference ligand with 14 binding site residues were used as features for the trained model. The use of machine learning as a scoring function allowed to improve the virtual screening parameters compared to the Glide GScore scoring function. Based on the obtained model, it was also confirmed that the location of a ligand near the Ser5.43 and Phe5.38 residues is important for binding the compound to the receptor. The procedure has allowed to select 20 candidates with new chemical structures compared to known ligands. In addition, the obtained compounds had a relatively low basic pKa compared to known ligands and thus may be suspected to have a low affinity for hERG channels and good brain penetration

Molecular modeling of phosphodiesterase 4 and 7 inhibitors.

Choroby autoimmunologiczne są aktualnym i ważnym tematem poszukiwań leków ograniczających procesy zapalne. Jedną z ostatnio odkrytych możliwości takiego działania, wyrażonego zahamowaniem produkcji czynnika martwiczego nowotworu (TNF-α), jest zwiększenie stężenia cAMP w strukturach organizmu objętych stanem zapalnym, co można uczynić hamując aktywność enzymów go rozkładających - fosfodiesteraz 4 i 7. Nowa seria związków pochodnych 1,3–dimetylo-3,7-dihydropuryno-2,6-dionu, zsyntetyzowana w Zakładzie Chemii Leków UJ CM, wykazująca powyższe działanie jest przedmiotem badań zamieszczonych w niniejszej pracy magisterskiej. Tematem rozprawy jest określenie sposobu wiązania związków o dualnej aktywności w miejscach aktywnych poszczególnych fosfodiesteraz, analiza ich podstawowych właściwości fizykochemicznych i parametrów ADME. Dodatkowym celem jest wytypowanie nowych chemotypów kandydatów na dualne inhibitory PDE4/PDE7. Prace wykonywane są za pomocą technik obliczeniowych obejmujących modelowanie molekularne struktur enzymów, modelowanie farmakoforowe dualnych ligandów oraz wirtualny skrining.Ustalono, iż najważniejszymi elementami analizowanych struktur są: szkielet - metyloksantyny i podstawnik w pozycji 7, dzięki którym wiążą się w centrach enzymatycznych PDE4 i PDE7, wykazując charakterystyczne oddziaływania z odpowiednimi łańcuchami bocznymi aminokwasów, głównie Gln443/413, Phe446/416 oraz Phe414/384. Modele enzymów, użyte do badań, zostały przygotowane w oparciu o struktury krystaliczne celów biologicznych, przy pomocy skryptu Induced Fit Docking. Badane ligandy w większości spełniają wymogi dla dostępności biologicznej, jednakże wymagają optymalizacji w zakresie rozpuszczalności i stabilności metabolicznej. W wirtualnym skriningu bazy związków chemicznych wyselekcjonowano 4 nowe chemotypy potencjalnych dualnych inhibitorów, z których najlepiej rokującym jest pochodna aryloaminopirymidynowa kwasu octowego.Otrzymane wyniki poszerzyły wiedzę na temat budowy miejsca katalitycznego fosfodiesteraz typu 4 i 7 oraz struktury inhibitorów o dualnej aktywności, jak również nakreśliły możliwe kierunki ich dalszych modyfikacji.The research on drugs used in autoimmune disease’s treatment is an important worldwide topic. The recently discovered possibility for reducing inflammation expressed as decrease of TNF-α level is an increase of cAMP levels in inflammatory organisms. That can be achieved by inhibition of phosphodiesterase 4 and 7.The new series of 1,3-dimethyl-3,7-dihydropurin-2,6-dione derivatives synthesized in the Department of Medicinal Chemistry UJ CM exhibited the aforementioned activity and is the subject of presented in this master thesis research. The aim of these studies is to determine their binding mode in the phosphodiesterase active sites and analyze their basic physicochemical and ADME properties. The contributory target is to identify new chemotypes for dual PDE4/PDE7 inhibitors. The research is performed using computational techniques including - enzyme structures molecular modeling, dual ligands pharmacophore modeling and virtual screening.It was established that the most important fragments of the analyzed structures were: the methylxanthine scaffold and the position 7 substituent, which create interactions with amino acids Asn443/413, Phe446/416 and Phe414/384. The structural models were prepared based on the crystal structure of biological targets using Induced Fit Docking script. The tested ligands fulfil the basic requirements for bioavailability but require further optimization in terms of solubility and metabolic stability. In the virtual screening task 4 new potential dual inhibitors were selected - the best is arylaminepyrimidine acetic acid derivative.The obtained results have broadened knowledge about the structure of phosphodiesterase 4 and 7 catalytic sites and dual activity inhibitors as well as outlined possible directions for further modifications

Structural modeling of TRPA1 ion channel - determination of the binding site for antagonists

TRPA1 is a transmembrane cation channel, one of the most promising targets in the context of respiratory diseases. Its general structure has already been experimentally resolved, but the binding site of TRPA1 antagonists such as HC-030031, a model methylxanthine derivative, remains unknown. The present study aimed to determine the potential binding site of xanthine antagonists and to describe their binding mode, using a molecular modeling approach. This study represents the first attempt to bring together site-directed mutagenesis reports and the latest cryo-EM structure of an antagonist bound to TRPA1. Our research suggests that the core moiety of HC-030031 binds to a pocket formed by the TRP-like domain and the pre-S1, S4, S5 helices of one subunit. The structure, determined by cryo-EM, shows interactions of a core hypoxanthine moiety in the same area of the binding site, sharing the interaction of xanthine/hypoxanthine with Trp-711. Moreover, the predicted binding mode of HC-030031 assumes interaction with Asn-855, a residue demonstrated to be important for HC-030031 recognition in site-directed mutagenesis studies. Our model proved to be advantageous in a retrospective virtual screening benchmark; therefore, it will be useful in research on new TRPA1 antagonists among xanthine derivatives and their bioisosteres

The use of machine learning-based sequential virtual screening in the search of new ligands of 5-HT6 receptor

5-HT6 receptor takes part in learning and memory processes. For this reason, the use of ligands of this receptor in the treatment of neurodegenerative diseases such as Alzheimer's disease, depression or autism is being investigated. The development of machine learning (ML) and access to large compound databases allow for the increasing use of these methods in search of new drugs. The use of ML in pre-clinical tests allows for a reduction in time and costs of drug discovery. In this study, we used a sequential virtual screening approach in search of new structures with potential high affinity for the 5-HT6 receptor. Data from the ChEMBL database containing ligand binding affinities, measured as an inhibition constant (Ki), was used as the training dataset. Each step of the screening was based on machine learning models, the task of which was to classify compounds as potentially active and inactive. The first step included a ligand-based drug discovery (LBDD) approach, in which, using Klekota-Roth fingerprints and descriptors describing the chemical structure of the ligands, a classification model was developed to select a preliminary group of candidates from the Otava chemical compound database. In the second step, a structure-based drug discovery (SBDD) approach was used. For this purpose, compounds were docked to the homology model of the 5-HT6 receptor, developed using the AlphaFold algorithm and optimized by Induced-Fit Docking tool and molecular dynamics. Docking poses were scored by a trained Extra Trees classifier. Interactions of a reference ligand with 14 binding site residues were used as features for the trained model. The use of machine learning as a scoring function allowed to improve the virtual screening parameters compared to the Glide GScore scoring function. Based on the obtained model, it was also confirmed that the location of a ligand near the Ser5.43 and Phe5.38 residues is important for binding the compound to the receptor. The procedure has allowed to select 20 candidates with new chemical structures compared to known ligands. In addition, the obtained compounds had a relatively low basic pKa compared to known ligands and thus may be suspected to have a low affinity for hERG channels and good brain penetration

The selective 5-HT1A receptor biased agonists, F15599 and F13714, show antidepressant-like properties after a single administration in the mouse model of unpredictable chronic mild stress

RATIONALE: The prevalence of depression is ever-increasing throughout the population. However, available treatments are ineffective in around one-third of patients and there is a need for more effective and safer drugs. OBJECTIVES: The antidepressant-like and procognitive effects of the “biased agonists” F15599 (also known as NLX-101) which preferentially targets postsynaptic 5-HT(1A) receptors and F13714, which targets 5-HT(1A) autoreceptors, were investigated in mice. METHODS: Antidepressant-like properties of the compounds and their effect on cognitive functions were assessed using the forced swim test (FST) and the novel object recognition (NOR), respectively. Next, we induced a depressive-like state by an unpredictable chronic mild stress (UCMS) procedure to test the compounds’ activity in the depression model, followed by measures of sucrose preference, FST, and locomotor activity. Levels of phosphorylated cyclic AMP response element-binding protein (p-CREB) and phosphorylated extracellular signal-regulated kinase (p-ERK1/2) were also determined. RESULTS: F15599 reduced immobility time in the FST over a wider dose-range (2 to 16 mg/kg po) than F13714 (2 and 4 mg/kg po), suggesting accentuated antidepressant-like properties in mice. F15599 did not disrupt long-term memory consolidation in the NOR at any dose tested, while F13714 impaired memory formation, notably at higher doses (4–16 mg/kg). In UCMS mice, a single administration of F15599 and F13714 was sufficient to robustly normalize depressive-like behavior in the FST but did not rescue disrupted sucrose preference. Both F15599 and F13714 rescued cortical and hippocampal deficits in p-ERK1/2 levels of UCMS mice but did not influence the p-CREB levels. CONCLUSIONS: Our studies showed that 5-HT(1A) receptor biased agonists such as F13714 and especially F15599, due to its less pronounced side effects, might have potential as fast-acting antidepressants. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00213-021-05849-0