Receptor pharmacophore ensemble (REPHARMBLE): a probabilistic pharmacophore modeling approach using multiple protein-ligand complexes

Identification of novel covalent JAK3 inhibitors through consensus scoring virtual screening: integration of common feature pharmacophore and covalent docking

Accumulated research strongly indicates that Janus kinase 3 (JAK3) is intricately involved in the initiation and advancement of a diverse range of human diseases, underscoring JAK3 as a promising target for therapeutic intervention. However, JAK3 shows significant homology with other JAK family isoforms, posing substantial challenges in the development of JAK3 inhibitors. To address these limitations, one strategy is to design selective covalent JAK3 inhibitors. Therefore, this study introduces a virtual screening approach that combines common feature pharmacophore modeling, covalent docking, and consensus scoring to identify novel inhibitors for JAK3. First, common feature pharmacophore models were constructed based on a selection of representative covalent JAK3 inhibitors. The optimal qualitative pharmacophore model proved highly effective in distinguishing active and inactive compounds. Second, 14 crystal structures of the JAK3-covalent inhibitor complex were chosen for the covalent docking studies. Following validation of the screening performance, 5TTU was identified as the most suitable candidate for screening potential JAK3 inhibitors due to its higher predictive accuracy. Finally, a virtual screening protocol based on consensus scoring was conducted, integrating pharmacophore mapping and covalent docking. This approach resulted in the discovery of multiple compounds with notable potential as effective JAK3 inhibitors. We hope that the developed virtual screening strategy will provide valuable guidance in the discovery of novel covalent JAK3 inhibitors.

Identification of new potent NLRP3 inhibitors by multi-level in-silico approaches

Nod-like receptor protein 3 (NLRP-3), is an intracellular sensor that is involved in inflammasome activation, and the aberrant expression of NLRP3 is responsible for diabetes mellitus, its complications, and many other inflammatory diseases. NLRP3 is considered a promising drug target for novel drug design. Here, a pharmacophore model was generated from the most potent inhibitor, and its validation was performed by the Gunner-Henry scoring method. The validated pharmacophore was used to screen selected compounds databases. As a result, 646 compounds were mapped on the pharmacophore model. After applying Lipinski's rule of five, 391 hits were obtained. All the hits were docked into the binding pocket of target protein. Based on docking scores and interactions with binding site residues, six compounds were selected potential hits. To check the stability of these compounds, 100 ns molecular dynamic (MD) simulations were performed. The RMSD, RMSF, DCCM and hydrogen bond analysis showed that all the six compounds formed stable complex with NLRP3. The binding free energy with the MM-PBSA approach suggested that electrostatic force, and van der Waals interactions, played a significant role in the binding pattern of these compounds. Thus, the outcomes of the current study could provide insights into the identification of new potential NLRP3 inflammasome inhibitors against diabetes and its related disorders.

Integration of the Butina algorithm and ensemble learning strategies for the advancement of a pharmacophore ligand-based model: an in silico investigation of apelin agonists

Introduction: 3D pharmacophore models describe the ligand's chemical interactions in their bioactive conformation. They offer a simple but sophisticated approach to decipher the chemically encoded ligand information, making them a valuable tool in drug design. Methods: Our research summarized the key studies for applying 3D pharmacophore models in virtual screening for 6,944 compounds of APJ receptor agonists. Recent advances in clustering algorithms and ensemble methods have enabled classical pharmacophore modeling to evolve into more flexible and knowledge-driven techniques. Butina clustering categorizes molecules based on their structural similarity (indicated by the Tanimoto coefficient) to create a structurally diverse training dataset. The learning method combines various individual pharmacophore models into a set of pharmacophore models for pharmacophore space optimization in virtual screening. Results: This approach was evaluated on Apelin datasets and afforded good screening performance, as proven by Receiver Operating Characteristic (AUC score of 0.994 ± 0.007), enrichment factor of (EF1% of 50.07 ± 0.211), Güner-Henry score of 0.956 ± 0.015, and F-measure of 0.911 ± 0.031. Discussion: Although one of the high-scoring models achieved statistically superior results in each dataset (AUC of 0.82; an EF1% of 19.466; GH of 0.131 and F1-score of 0.071), the ensemble learning method including voting and stacking method balanced the shortcomings of each model and passed with close performance measures.

Discovery of potent microtubule-destabilizing agents targeting for colchicine site by virtual screening, biological evaluation, and molecular dynamics simulation

Microtubule has been considered as attractive therapeutic target for various cancers. Although numerous of chemically diverse compounds targeting to colchicine site have been reported, none of them was approved by Food and Drug Administration. In this investigation, the virtual screening methods, including pharmacophore model, molecular docking, and interaction molecular fingerprints similarity, were applied to discover novel microtubule-destabilizing agents from database with 324,474 compounds. 22 compounds with novel scaffolds were identified as microtubule-destabilizing agents, and then submitted to the biological evaluation. Among these 22 hits, hit4 with novel scaffold represents the best anti-proliferative activity with IC₅₀ ranging from 4.51 to 14.81 μM on four cancer cell lines. The in vitro assays reveal that hit4 can effectively inhibit tubulin assembly, and disrupt the microtubule network in MCF-7 cell at a concentration-dependent manner. Finally, the molecular dynamics simulation analysis exhibits that hit4 can stably bind to colchicine site, interact with key residues, and induce αT5 and βT7 regions changes. The values of ΔG_bind for the tubulin-colchicine and tubulin-hit4 are -172.9±10.5 and -166.0±12.6 kJ·mol^-1, respectively. The above results indicate that the hit4 is a novel microtubule destabilizing agent targeting to colchicine-binding site, which could be developed as a promising tubulin polymerization inhibitor with higher activity for cancer therapy.

Targeting Streptomyces-Derived Streptenol Derivatives against Gynecological Cancer Target PIK3CA: An In Silico Approach

Streptomyces is amongst the most amenable genera for biotechnological applications, and it is extensively used as a scaffold for drug development. One of the most effective therapeutic applications in the treatment of cancer is targeted therapy. Small molecule therapy is one of them, and it has gotten a lot of attention recently. Streptomyces derived compounds namely streptenols A, C, and F-I and streptazolin were subjected for ADMET property assessment. Our computational studies based on molecular docking effectively displayed the synergistic effect of streptomyces-derived compounds on the gynecological cancer target PIK3CA. These compounds were observed with the highest docking scores as well as promising intermolecular interaction stability throughout the molecular dynamic simulation. Molecular docking and molecular dynamic modeling techniques were utilized to investigate the binding mode stability of drugs using a pharmacophore scaffold, as well as physicochemical and pharmacokinetic aspects linked to alpelisib. With a root mean square fluctuation of the protein backbone of less than 0.7 nm, they demonstrated a steady binding mode in the target binding pocket. They have also prompted hydrogen bonding throughout the simulations, implying that the chemicals have firmly occupied the active site. A comprehensive study showed that streptenol D, streptenol E, streptenol C, streptenol G, streptenol F, and streptenol B can be considered as lead compounds for PIK3CA-based inhibitor design. To warrant the treatment efficacy against cancer, comprehensive computational research based on proposed chemicals must be assessed through in vitro studies.

PharmRF: A machine-learning scoring function to identify the best protein-ligand complexes for structure-based pharmacophore screening with high enrichments

Structure-based pharmacophore models are often developed by selecting a single protein-ligand complex with good resolution and better binding affinity data which prevents the analysis of other structures having a similar potential to act as better templates. PharmRF is a pharmacophore-based scoring function for selecting the best crystal structures with the potential to attain high enrichment rates in pharmacophore-based virtual screening prospectively. The PharmRF scoring function is trained and tested on the PDBbind v2018 protein-ligand complex dataset and employs a random forest regressor to correlate protein pocket descriptors and ligand pharmacophoric elements with binding affinity. PharmRF score represents the calculated binding affinity which identifies high-affinity ligands by thorough pruning of all the PDB entries available for a particular protein of interest with a high PharmRF score. Ligands with high PharmRF scores can provide a better basis for structure-based pharmacophore enumerations with a better enrichment rate. Evaluated on 10 protein-ligand systems of the DUD-E dataset, PharmRF achieved superior performance (average success rate: 77.61%, median success rate: 87.16%) than Vina docking score (75.47%, 79.39%). PharmRF was further evaluated using the CASF-2016 benchmark set yielding a moderate correlation of 0.591 with experimental binding affinity, similar in performance to 25 scoring functions tested on this dataset. Independent assessment of PharmRF on 8 protein-ligand systems of LIT-PCBA dataset exhibited average and median success rates of 57.55% and 74.72% with 4 targets attaining success rate > 90%. The PharmRF scoring model, scripts, and related resources can be accessed at https://github.com/Prasanth-Kumar87/PharmRF.

Ligand-based G Protein Coupled Receptor pharmacophore modeling: Assessing the role of ligand function in model development

Integral membrane proteins in the G Protein-Coupled Receptor (GPCR) class are attractive drug development targets. However, computational methods applicable to ligand discovery for many GPCR targets are restricted by limited numbers of known ligands. Pharmacophore models can be developed using variously sized training sets and applied in database mining to prioritize candidate ligands for subsequent validation. This in silico study assessed the impact of key pharmacophore modeling decisions that arise when known ligand numbers for a target of interest are low. GPCR included in this study are the adrenergic alpha-1A, 1D and 2A, adrenergic beta 2 and 3, kappa, delta and mu opioid, serotonin 1A and 2A, and the muscarinic 1 and 2 receptors, all of which have rich ligand data sets suitable to assess the performance of protocols intended for application to GPCR with limited ligand data availability. Impact of ligand function, potency and structural diversity in training set selection was assessed to define when pharmacophore modeling targeting GPCR with limited known ligands becomes viable. Pharmacophore elements and pharmacophore model selection criteria were also assessed. Pharmacophore model assessment was based on percent pharmacophore model generation failure, as well as Güner-Henry enrichment and goodness-of-hit scores. Three of seven pharmacophore element schemes evaluated in MOE 2018.0101, Unified, PCHD, and CHD, showed substantially lower failure rates and higher enrichment scores than the others. Enrichment and GH scores were used to compare construction protocol for pharmacophore models of varying purposes- such as function specific versus nonspecific ligand identification. Notably, pharmacophore models constructed from ligands of mixed functions (agonists and antagonists) were capable of enriching hitlists with active compounds, and therefore can be used when available sets of known ligands are limited in number.

Improving Docking Power for Short Peptides Using Random Forest

In recent years, therapeutic peptides have gained a lot interest as demonstrated by the 60 peptides approved as drugs in major markets and 150+ peptides currently in clinical trials. However, while small molecule docking is routinely used in rational drug design efforts, docking peptides has proven challenging partly because docking scoring functions, developed and calibrated for small molecules, perform poorly for these molecules. Here, we present random forest classifiers trained to discriminate correctly docked peptides. We show that, for a testing set of 47 protein-peptide complexes, structurally dissimilar from the training set and previously used to benchmark AutoDock Vina's ability to dock short peptides, these random forest classifiers improve docking power from ∼25% for AutoDock scoring functions to an average of ∼70%. These results pave the way for peptide-docking success rates comparable to those of small molecule docking. To develop these classifiers, we compiled the ProptPep37_2021 data set, a curated, high-quality set of 322 crystallographic protein-peptides complexes annotated with structural similarity information. The data set also provides a collection of high-quality putative poses with a range of deviations from the crystallographic pose, providing correct and incorrect poses (i.e., decoys) of the peptide for each entry. The ProptPep37_2021 data set as well as the classifiers presented here are freely available.

Flexi-pharma: a molecule-ranking strategy for virtual screening using pharmacophores from ligand-free conformational ensembles

Computer-aided strategies are useful for reducing the costs and increasing the success-rate in drug discovery. Among these strategies, methods based on pharmacophores (an ensemble of electronic and steric features representing the target active site) are efficient to implement over large compound libraries. However, traditional pharmacophore-based methods require knowledge of active compounds or ligand-receptor structures, and only few ones account for target flexibility. Here, we developed a pharmacophore-based virtual screening protocol, Flexi-pharma, that overcomes these limitations. The protocol uses molecular dynamics (MD) simulations to explore receptor flexibility, and performs a pharmacophore-based virtual screening over a set of MD conformations without requiring prior knowledge about known ligands or ligand-receptor structures for building the pharmacophores. The results from the different receptor conformations are combined using a "voting" approach, where a vote is given to each molecule that matches at least one pharmacophore from each MD conformation. Contrarily to other approaches that reduce the pharmacophore ensemble to some representative models and score according to the matching models or molecule conformers, the Flexi-pharma approach takes directly into account the receptor flexibility by scoring in regards to the receptor conformations. We tested the method over twenty systems, finding an enrichment of the dataset for 19 of them. Flexi-pharma is computationally efficient allowing for the screening of thousands of compounds in minutes on a single CPU core. Moreover, the ranking of molecules by vote is a general strategy that can be applied with any pharmacophore-filtering program.

COVID-19 Coronavirus spike protein analysis for synthetic vaccines, a peptidomimetic antagonist, and therapeutic drugs, and analysis of a proposed achilles' heel conserved region to minimize probability of escape mutations and drug resistance

This paper continues a recent study of the spike protein sequence of the COVID-19 virus (SARS-CoV-2). It is also in part an introductory review to relevant computational techniques for tackling viral threats, using COVID-19 as an example. Q-UEL tools for facilitating access to knowledge and bioinformatics tools were again used for efficiency, but the focus in this paper is even more on the virus. Subsequence KRSFIEDLLFNKV of the S2′ spike glycoprotein proteolytic cleavage site continues to appear important. Here it is shown to be recognizable in the common cold coronaviruses, avian coronaviruses and possibly as traces in the nidoviruses of reptiles and fish. Its function or functions thus seem important to the coronaviruses. It might represent SARS-CoV-2 Achilles’ heel, less likely to acquire resistance by mutation, as has happened in some early SARS vaccine studies discussed in the previous paper. Preliminary conformational analysis of the receptor (ACE2) binding site of the spike protein is carried out suggesting that while it is somewhat conserved, it appears to be more variable than KRSFIEDLLFNKV. However compounds like emodin that inhibit SARS entry, apparently by binding ACE2, might also have functions at several different human protein binding sites. The enzyme 11β-hydroxysteroid dehydrogenase type 1 is again argued to be a convenient model pharmacophore perhaps representing an ensemble of targets, and it is noted that it occurs both in lung and alimentary tract. Perhaps it benefits the virus to block an inflammatory response by inhibiting the dehydrogenase, but a fairly complex web involves several possible targets.

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This paper “drills down” into the studies of the author's previous COVID-19 paper.

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Designing vaccine and drugs must seek to avoid escape mutations.

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Subsequence KRSFIEDLLFNKV seems recognizable across many coronaviruses.

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The ACE2 binding domain is a target, but shows variation.

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A steroid dehydrogenase is argued to remain an interesting model pharmacophore.

Pharmacophore-Based Virtual Screening Toward the Discovery of Novel Anti-echinococcal Compounds

Echinococcosis is a serious helminthic zoonosis with a great impact on human health and livestock husbandry. However, the clinically used drugs (benzimidazoles) have a low cure rate, so alternative drugs are urgently needed. Currently, drug screenings for echinococcosis are mainly phenotype-based, and the efficiency of identifying active compounds is very low. With a pharmacophore model generated from the structures of active amino alcohols, we performed a virtual screening to discover novel compounds with anti-echinococcal activity. Sixty-two compounds from the virtual screening were tested on Echinococcus multilocularis protoscoleces, and 10 of these compounds were found to be active. After further evaluation of their cytotoxicity, S6 was selected along with two active amino alcohols for in vivo pharmacodynamic and pharmacokinetic studies. At the two tested doses (50 and 25 mg/kg), S6 inhibited the growth of E. multilocularis in mice (14.43 and 9.53%), but no significant difference between the treatment groups and control group was observed. Treatment with BTB4 and HT3 was shown to be ineffective. During the 28 days of treatment, the death of mice in the mebendazole, HT3, and BTB4 groups indicated their toxicity. The plasma concentration of S6 administered by both methods was very low, with the C_max being only 1 ng/ml after oral administration and below the detection limit after intramuscular administration. In addition, the plasma concentrations of BTB4 and HT3 in vitro did not reach high enough levels to kill the parasites. The toxicities of these two amino alcohols indicated that they are not suitable for further development as anti-echinococcal drugs. However, further attempts should be made to increase the bioavailability of S6 and modify its structure. In this study, we demonstrate that pharmacophore-based virtual screenings with high drug identification efficiency could be used to find novel drugs for treating echinococcosis.

Cardiotonic steroids as potential Na+/K+-ATPase inhibitors - a computational study

Cardiotonic steroids (CTS) are steroidal drugs, processed from the seeds and dried leaves of the genus Digitalis as well as from the skin and parotid gland of amphibians. The most commonly known CTS are ouabain, digoxin, digoxigenin and bufalin. CTS can be used for safer medication of congestive heart failure and other related conditions due to promising pharmacological and medicinal properties. Ouabain isolated from plants is widely utilized in in vitro studies to specifically block the sodium potassium (Na⁺/K⁺-ATPase) pump. For checking, whether ouabain derivatives are robust inhibitors of Na⁺/K⁺-ATPase pump, molecular docking simulation was performed between ouabain and its derivatives using YASARA software. The docking energy falls within the range of 8.470 kcal/mol to 7.234 kcal/mol, in which digoxigenin was found to be the potential ligand with the best docking energy of 8.470 kcal/mol. Furthermore, pharmacophore modeling was applied to decipher the electronic features of CTS. Molecular dynamics simulation was also employed to determine the conformational properties of Na⁺/K⁺-ATPase-ouabain and Na⁺/K⁺-ATPase-digoxigenin complexes with the plausible structural integrity through conformational ensembles for 100 ns which promoted digoxigenin as the most promising CTS for treating conditions of congestive heart failure patients.