Novel 2-(hydrazinocarbonyl)-3-phenyl-1H-indole-5-sulfonamide based thiosemicarbazides as potent and selective inhibitors of tumor-associated human carbonic anhydrase IX and XII: Synthesis, cytotoxicity, and molecular modelling studies

In the pursuit of discovering new selective carbonic anhydrase (CA, EC 4.2.1.1) inhibitors, a small collection of novel thiosemicarbazides (5a-5t) were designed and synthesized starting from 2-(hydrazinocarbonyl)-3-phenyl-1H-indole-5-sulfonamide which was evaluated as a potent inhibitor of different CA isoforms in a previous study. The newly synthesized compounds were examined against four human carbonic anhydrases (hCA), namely transmembrane tumor-related hCA IX/XII and cytosolic widespread off-targets hCA I/II. In enzyme inhibition assays, all nineteen compounds display up to ∼340-fold selectivity for hCA IX/XII over off-target isoforms hCA I/II. Four compounds have enzyme inhibition values (Ki) lower than 10 nM against tumor-associated isoforms hCA IX/XII including two compounds in the subnanomolar range (5r and 5s; hCA XII; Ki: 0.69 and 0.87 nM). The potential binding interactions of the most potent compounds against hCA IX and XII, compounds 5s and 5r, respectively, were investigated using ensemble docking and molecular dynamics studies. Cell viability assays using human colorectal adenocarcinoma cell line HT-29 and healthy skin fibroblasts CCD-86Sk show that compound 5e selectively inhibits HT-29 cancer cell proliferation (IC50: 53.32 ± 7.74 µM for HT-29; IC50: 74.64 ± 14.15 µM for CCD-986Sk). Finally, Western blot assays show that compounds 5e and 5r significantly reduce the expression of hCA XII in HT-29 cells. Moreover, 5e shows better cytotoxic activity in hypoxia compared to normoxic conditions. Altogether, the newly designed compounds show stronger inhibition of the tumor-associated hCA IX and XII isoforms and several tested compounds show selective cytotoxicity as well as downregulation of hCA XII expression.

Ensemble-based virtual screening of African natural products to target human thymidylate synthase

Human thymidylate synthase (hTS) is a validated drug target for chemotherapy. A virtual screening experiment was used to prioritize a list of compounds from African Natural Products Databases docked against the orthosteric binding pocket of hTS. Consensus scores of binding affinities from ensemble-based virtual screening, hydrated docking and MM-PBSA calculations ranked compounds NEA4433 and NEA4434 as the best candidates owing to binding affinity scores in the picomolar order, their excellent ADMET profiles and the good stability of the protein-ligand complexes formed. The current study demonstrates the role of water in small molecule binding to hTS in mediating protein-ligand interactions. Similarly, the robust ensemble docking (relaxed scheme complex) ranked NEA4433 and NEA4434 as the best candidates. Furthermore, the best candidates prioritized were shown to strongly interact with the same residues that interacted with hTS substrate and cofactor.

Repurposing of drug molecules from FDA database against Hepatitis C virus E2 protein using ensemble docking approach

Hepatitis C virus, a member of the Flaviviridae family and genus Hepacivirus, is an enveloped, positively single stranded RNA virus. Its surface consists of a heterodimer of E1 and E2 proteins which play a crucial role in receptor binding and membrane fusion. In this study we have used in silico virtual screening by utilizing ensemble docking on the approved drugs. These drugs can bind with high efficiency to the 36 prominent conformations of the CD81 binding site clustered from a total of 3 µs MD simulation data on the E2 protein. We started with 9213 compounds from the FDA list of drugs and progressively came down to 5 compounds which have been seen to bind with very high efficiency to not only all the conformations but also the two predicted druggable pockets that encompass the CD81 binding site. MM/PBSA binding energy calculations also point to the highly stable interaction of the compounds to the E2 protein. This study may in future broaden the arsenal of therapeutics for use against HCV infection and lead to more effective care against the virus.

Targeting human thymidylate synthase: Ensemble-based virtual screening for drug repositioning and the role of water

A drug repositioning computational approach was carried to search inhibitors for human thymidylate synthase. An ensemble-based virtual screening of FDA-approved drugs showed the drugs Imatinib, Lumacaftor and Naldemedine to be likely candidates for repurposing. The role of water in the drug-receptor interactions was revealed by the application of an extended AutoDock scoring function that included the water forcefield. The binding affinity scores when hydrated ligands were docked were improved in the drugs considered. Further binding free energy calculations based on the Molecular Mechanics Poisson-Boltzmann Surface Area method revealed that Imatinib, Lumacaftor and Naldemedine scored -130.7 ± 28.1, -210.6 ± 29.9 and -238.0 ± 25.4 kJ/mol, respectively, showing good binding affinity for the candidates considered. Overall, the analysis of the molecular dynamics trajectory of the receptor-drug complexes revealed stable structures for Imatinib, Lumacaftor and Naldemedine, for the entire simulation time.

Benchmarking ensemble docking methods in D3R Grand Challenge 4

The discovery of new drugs is a time consuming and expensive process. Methods such as virtual screening, which can filter out ineffective compounds from drug libraries prior to expensive experimental study, have become popular research topics. As the computational drug discovery community has grown, in order to benchmark the various advances in methodology, organizations such as the Drug Design Data Resource have begun hosting blinded grand challenges seeking to identify the best methods for ligand pose-prediction, ligand affinity ranking, and free energy calculations. Such open challenges offer a unique opportunity for researchers to partner with junior students (e.g., high school and undergraduate) to validate basic yet fundamental hypotheses considered to be uninteresting to domain experts. Here, we, a group of high school-aged students and their mentors, present the results of our participation in Grand Challenge 4 where we predicted ligand affinity rankings for the Cathepsin S protease, an important protein target for autoimmune diseases. To investigate the effect of incorporating receptor dynamics on ligand affinity rankings, we employed the Relaxed Complex Scheme, a molecular docking method paired with molecular dynamics-generated receptor conformations. We found that Cathepsin S is a difficult target for molecular docking and we explore some advanced methods such as distance-restrained docking to try to improve the correlation with experiments. This project has exemplified the capabilities of high school students when supported with a rigorous curriculum, and demonstrates the value of community-driven competitions for beginners in computational drug discovery.

Assessing the impact of substrate-level enzyme regulations limiting ethanol titer in Clostridium thermocellum using a core kinetic model

Clostridium thermocellum is a promising candidate for consolidated bioprocessing because it can directly ferment cellulose to ethanol. Despite significant efforts, achieved yields and titers fall below industrially relevant targets. This implies that there still exist unknown enzymatic, regulatory, and/or possibly thermodynamic bottlenecks that can throttle back metabolic flow. By (i) elucidating internal metabolic fluxes in wild-type C. thermocellum grown on cellobiose via ¹³C-metabolic flux analysis (¹³C-MFA), (ii) parameterizing a core kinetic model, and (iii) subsequently deploying an ensemble-docking workflow for discovering substrate-level regulations, this paper aims to reveal some of these factors and expand our knowledgebase governing C. thermocellum metabolism. Generated ¹³C labeling data were used with ¹³C-MFA to generate a wild-type flux distribution for the metabolic network. Notably, flux elucidation through MFA alluded to serine generation via the mercaptopyruvate pathway. Using the elucidated flux distributions in conjunction with batch fermentation process yield data for various mutant strains, we constructed a kinetic model of C. thermocellum core metabolism (i.e. k-ctherm138). Subsequently, we used the parameterized kinetic model to explore the effect of removing substrate-level regulations on ethanol yield and titer. Upon exploring all possible simultaneous (up to four) regulation removals we identified combinations that lead to many-fold model predicted improvement in ethanol titer. In addition, by coupling a systematic method for identifying putative competitive inhibitory mechanisms using K-FIT kinetic parameterization with the ensemble-docking workflow, we flagged 67 putative substrate-level inhibition mechanisms across central carbon metabolism supported by both kinetic formalism and docking analysis.

Insights into the binding of morin to human γD-crystallin

Crystallin aggregation in the eye lens is one of the leading causes of cataract formation. The increase in the human γD-crystallin (HγDC) aggregation propensity has been associated with the oligomerization of its partially folded and fully unfolded structure. A recent study demonstrated that the binding of flavonoid morin (MOR) to HγDC inhibits the fibrillation of this protein. In this work, we carry out an exhaustive search for the possible binding site of MOR on HγDC by combining an ensemble docking approach with the Wrap 'N' Shake protocol. In agreement with previous results, we found a potential MOR-binding site in the cleft formed between the N-terminal and C-terminal domains of HγDC. MOR preference for the cleft residues was observed even with the interface-opened intermediate conformers of HγDC. In addition, metadynamics simulations were carried out to corroborate the stabilizing activity of MOR on HγDC structure and to identify the structural regions implicated during the unfolding inhibition. Overall, this study provides relevant insights into the identification of new HγDC aggregation inhibitors.

DINC-COVID: A webserver for ensemble docking with flexible SARS-CoV-2 proteins

An unprecedented research effort has been undertaken in response to the ongoing COVID-19 pandemic. This has included the determination of hundreds of crystallographic structures of SARS-CoV-2 proteins, and numerous virtual screening projects searching large compound libraries for potential drug inhibitors. Unfortunately, these initiatives have had very limited success in producing effective inhibitors against SARS-CoV-2 proteins. A reason might be an often overlooked factor in these computational efforts: receptor flexibility. To address this issue we have implemented a computational tool for ensemble docking with SARS-CoV-2 proteins. We have extracted representative ensembles of protein conformations from the Protein Data Bank and from in silico molecular dynamics simulations. Twelve pre-computed ensembles of SARS-CoV-2 protein conformations have now been made available for ensemble docking via a user-friendly webserver called DINC-COVID (dinc-covid.kavrakilab.org). We have validated DINC-COVID using data on tested inhibitors of two SARS-CoV-2 proteins, obtaining good correlations between docking-derived binding energies and experimentally-determined binding affinities. Some of the best results have been obtained on a dataset of large ligands resolved via room temperature crystallography, and therefore capturing alternative receptor conformations. In addition, we have shown that the ensembles available in DINC-COVID capture different ranges of receptor flexibility, and that this diversity is useful in finding alternative binding modes of ligands. Overall, our work highlights the importance of accounting for receptor flexibility in docking studies, and provides a platform for the identification of new inhibitors against SARS-CoV-2 proteins.

Computational evidence of new putative allosteric sites in the acetylcholinesterase receptor

Acetylcholinesterase (AChE), with a rigid structure and buried active site at the end of a deep narrow gorge, is interesting enough to solve the paradox between high catalytic activity and unavailability of the active site in treatment of Alzheimer's disease (AD). In this way, the blind docking process is performed on an ensemble of AChE structures created with molecular dynamics (MD) simulations to survey the whole space of AChE to find multiple access pathways to the active site and ranking them based on their affinity scores. Our results show that there are other allosteric binding sites in the protein structure whose inhibition, can affect protein function by disrupting the release of the Acetylcholine (AC) degradation products. In this study, inhibitory activities of Hybride14 and two natural compounds (Papaverine and Palmatine) were evaluated for all possible allosteric sites via docking method. The results confirmed the non-competitive inhibition mechanism. The best binding mode for these inhibitors and efficacy of hydrogen bonds and hydrophobic interactions on inhibitory activities of ligands were also disclosed. Furthermore, our studies provide significant molecular insight for AChE inhibition that could aid in the development of new drugs for AD's treatment.

Targeting multiple conformations of SARS-CoV2 Papain-Like Protease for drug repositioning: An in-silico study

Papain-Like Protease (PLpro) is a key protein for SARS-CoV-2 viral replication which is the cause of the emerging COVID-19 pandemic. Targeting PLpro can suppress viral replication and provide treatment options for COVID-19. Due to the dynamic nature of its binding site loop, PLpro multiple conformations were generated through a long-range 1 micro-second molecular dynamics (MD) simulation. Clustering the MD trajectory enabled us to extract representative structures for the conformational space generated. Adding to the MD representative structures, X-ray structures were involved in an ensemble docking approach to screen the FDA approved drugs for a drug repositioning endeavor. Guided by our recent benchmarking study of SARS-CoV-2 PLpro, FRED docking software was selected for such a virtual screening task. The results highlighted potential consensus binders to many of the MD clusters as well as the newly introduced X-ray structure of PLpro complexed with a small molecule. For instance, three drugs Benserazide, Dobutamine and Masoprocol showed a superior consensus enrichment against the PLpro conformations. Further MD simulations for these drugs complexed with PLpro suggested the superior stability and binding of dobutamine and masoprocol inside the binding site compared to Benserazide. Generally, this approach can facilitate identifying drugs for repositioning via targeting multiple conformations of a crucial target for the rapidly emerging COVID-19 pandemic.

Retrospective ensemble docking of allosteric modulators in an adenosine G-protein-coupled receptor

BACKGROUND

Ensemble docking has proven useful in drug discovery and development. It increases the hit rate by incorporating receptor flexibility into molecular docking as demonstrated on important drug targets including G-protein-coupled receptors (GPCRs). Adenosine A₁ receptor (A₁AR) is a key GPCR that has been targeted for treating cardiac ischemia-reperfusion injuries, neuropathic pain and renal diseases. Development of allosteric modulators, compounds binding to distinct and less conserved GPCR target sites compared with agonists and antagonists, has attracted increasing interest for designing selective drugs of the A₁AR. Despite significant advances, more effective approaches are needed to discover potent and selective allosteric modulators of the A₁AR.

METHODS

Ensemble docking that integrates Gaussian accelerated molecular dynamic (GaMD) simulations and molecular docking using Autodock has been implemented for retrospective docking of known positive allosteric modulators (PAMs) in the A₁AR.

RESULTS

Ensemble docking outperforms docking of the receptor cryo-EM structure. The calculated docking enrichment factors (EFs) and the area under the receiver operating characteristic curves (AUC) are significantly increased.

CONCLUSIONS

Receptor ensembles generated from GaMD simulations are able to increase the success rate of discovering PAMs of A₁AR. It is important to account for receptor flexibility through GaMD simulations and flexible docking.

GENERAL SIGNIFICANCE

Ensemble docking is a promising approach for drug discovery targeting flexible receptors.

Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4

The D3R Grand Challenge 4 provided a brilliant opportunity to test macrocyclic docking protocols on a diverse high-quality experimental data. We participated in both pose and affinity prediction exercises. Overall, we aimed to use an automated structure-based docking pipeline built around a set of tools developed in our team. This exercise again demonstrated a crucial importance of the correct local ligand geometry for the overall success of docking. Starting from the second part of the pose prediction stage, we developed a stable pipeline for sampling macrocycle conformers. This resulted in the subangstrom average precision of our pose predictions. In the affinity prediction exercise we obtained average results. However, we could improve these when using docking poses submitted by the best predictors. Our docking tools including the Convex-PL scoring function are available at https://team.inria.fr/nano-d/software/.

Elucidation of endocrine-disrupting polychlorinated biphenyls binding potency with steroidogenic genes: Integration of in silico methods and ensemble docking approaches

A myriad of polychlorinated biphenyls (PCBs) may have potential to reproductive axis and endocrine disruptions. PCBs mostly breach the cholesterol biotransformation in mitochondria through interfering with steroidogenic genes and lead to adverse consequences in steroidogenesis; however, studies are scanty. In this examination, the combinations of quantitative structure-activity relationship (QSAR) modeling and ensemble docking approaches was performed to envisage structural properties of PCBs that influence the developmental toxicity, estrogen receptor-mediated impacts, and to provide a better comprehension of binding levels between PCBs, steroidogenic acute regulatory protein (StAR) and cholesterol side-chain cleavage enzyme (CYP11A1). Prognostic QSAR models were illustrated with good robustness and predictive ability. Models provide extensive data on applicability domain and similarities between PCBs and training set compounds was used to implement for clustering and classification of toxic PCBs by employing Self-Organizing Maps. Docking and interaction profiles interpretations provided various insights into the structural features of PCBs that influence the cholesterol binding to StAR and CYP11A1 domains. The non-polar, atomic π - stacking and halogen bonds of PCBs with novel hotspots residues of StAR and CYP11A1 was indicated subtle conformational changes that barrier to cholesterol binding and/or locks to cholesterol ejection from Ω1-loop of StAR, and inhibits cholesterol to pregnenolone biosynthesis by CYP11A1; thus, these are probably revealed as block-cluster mechanisms. These outcomes are potential to be useful to predict developmental toxicity, endocrine disruption potencies and anti-steroidogenic activities of other environmental pollutants and provided sorted pinpoints for further evaluation of interaction mechanisms of PCBs with other sterodogenic genes.

In silico insights into prediction and analysis of potential novel pyrrolopyridine analogs against human MAPKAPK-2: a new SAR-based hierarchical clustering approach

In the present study, we have focused on to elucidate potential bioactive pyrrolopyridine (PYP23) analogs against human mitogen-activated protein kinase-activated protein kinase-2 (MK-2). Here, in silico methods and computational systems biology tools were used as rational strategies to predict novel PYP23 analogs against the MK-2. Initially, crystal structure (PDB-ID: 2P3G) consists steriochemical conflicts were rectified by structure-optimization approaches using the Modeller program, and a new optimized-high resolution model was generated. The stereochemical qualities of the predicted MK-2 model were judged; these showed that the model was reliable for docking assessments. SAR-based bioactivity analysis showed that among the 197 datasets only 15 candidates contained bioactivity data and were accepted as probable MK-2 inhibitors. Virtual screening and docking strategies of dataset compounds against the ligand-binding domain of MK-2 recognized 13 composites containing high binding affinity than known compounds. Furthermore, the comparative structure clustering, in silico toxicogenomics and QSAR-based anticancer properties prediction approaches were successful in the recognition of five best potential compounds such as 60118340, 60118338, 60117736, 60118473 and 60118322, which have great anticancer and drug-likeness with non-toxicity class indices. Leu⁷⁰, Glu¹³⁹, Leu¹⁴¹, Glu¹⁴⁵, Glu¹⁹⁰, Thr²⁰⁶ and Asp²⁰⁷ were found to be novel hotspot residues prominently involved in H-bonds framing with ligands. Interestingly, they have shown better molecular similarity with known bioactive PYP inhibitors. Thus, predicted five compounds can useful as possible chemotherapeutic agents for MK-2 and show similar molecular actions like known PYP inhibitors. Overall, these streamlined new methods may have great potential to reveal possible ligands toward other molecular targets and biomarkers.

Computational protein design and protein-ligand interaction studies for the improvement of organophosphorus degrading potential of Deinococcus radiodurans

The organophosphorus hydrolase enzyme is involved in the catalyzing reaction that involve hydrolysis of organophosphate toxic compounds. An enzyme from Deinococcus radiodurans reported as homologous to phosphotriesterase and show activity against organophosphate. In the past activity of this enzyme is low and efforts made to improve the activity by experimental mutation study. However only very few organophosphates tested against very few catalytic site mutations. In order to improve the catalytic power of the organophosphorus hydrolase enzyme, we carried out systematic functional hotspot based protein engineering strategy. The mutants tested against 46 know organophosphate compounds using molecular docking study. Finally, we carried out an extensive molecular docking study to predict the binding of 46 organophosphate compounds to wild-type protein and mutant organophosphorus hydrolase enzyme. At the end we are able to improve the degrading potential of organophosphorus hydrolase enzyme against organophosphate toxic compounds. This preliminary study and the outcome would be useful guide for the experimental scientist involved in the bioremediation of toxic organophosphate compounds.

Integration of in silico approaches to determination of endocrine-disrupting perfluorinated chemicals binding potency with steroidogenic acute regulatory protein

A myriad of perfluorinated compounds (PFCs) have the ability to interfere with steroidogenic acute regulatory (StAR) protein. Consequently, PFCs breaches cholesterol biotransformation in mitochondria and cause fatal consequences in steroidogenesis, however, these were poorly characterized. In the present study, we have evaluated toxic potencies, nuclear mediated probabilities and interaction profiles with StAR of PFCs using computational system biology tools. Toxicity endpoints revealed that PFCs contain high carcinogenicity, developmental toxicity, skin sensitization effects with low mutagenic activity. Consensus qualitative nuclear receptor agonist models show higher probability rates towards ER and PPAR-γ receptor than AR and AhR models were observed. To poise the subtle fluctuations of actual predictions, balanced accuracy and MCC were computed, and they signify perfect correlation ranges in all models. Screening studies resulting protein-ligand interaction profiles showed that residues Asn148, Asn150, Glu169, Ala171, Arg182, Phe184, Arg188, Trp241, Thr263 and Phe267 were identified as novel hotspots, participated in halogen bonds, H-bonds, atomic π-stacking, π-cation interactions and salt-bridges formation. Thus, the additional bonds contribute conformer stability that holds the protein structure at flexible state, so that PFCs acts as a barrier to cholesterol binding. From docking outcomes, representation space was created, that specifies high and medium StAR binders were occupied in toxic endpoints space with active concern. PFCs restrain molecular features and mitochondrial membrane disruption functions were revealed by efficient toxicogenomics studies. These data indicate toxicity and StAR protein binding levels of PFCs, sorted pinpoints could be useful in a promising way to know the other environmental pollutants and health risks.

Analysis of the Glutamate Agonist LY404,039 Binding to Nonstatic Dopamine Receptor D2 Dimer Structures and Consensus Docking

Dopamine receptor D2 (D2R) plays an important role in the human central nervous system and is a focal target of antipsychotic agents. The D2^HighR and D2^LowR dimeric models previously developed by our group are used to investigate the prediction of binding affinity of the LY404,039 ligand and its binding mechanism within the catalytic domain. The computational data obtained using molecular dynamics simulations fit well with the experimental results. The calculated binding affinities of LY404,039 using MM/PBSA for the D2^HighR and D2^LowR targets were -12.04 and -9.11 kcal/mol, respectively. The experimental results suggest that LY404,039 binds to D2^HighR and D2^LowR with binding affinities (K_i) of 8.2 and 1640 nM, respectively. The high binding affinity of LY404,039 in terms of binding to [³H]domperidone was inhibited by the presence of a guanine nucleotide, indicating an agonist action of the drug at D2^HighR. The interaction analysis demonstrated that while Asp114 was among the most critical amino acids for D2^HighR binding, residues Ser193 and Ser197 were significantly more important within the binding cavity of D2^LowR. Molecular modeling analyses are extended to ensemble docking as well as structure-based pharmacophore model (E-pharmacophore) development using the bioactive conformation of LY404,039 at the binding pocket as a template and screening of small-molecule databases with derived pharmacophore models.

Identification of potent inhibitors of DNA methyltransferase 1 (DNMT1) through a pharmacophore-based virtual screening approach

DNA methylation is an epigenetic change that results in the addition of a methyl group at the carbon-5 position of cytosine residues. DNA methyltransferase (DNMT) inhibitors can suppress tumour growth and have significant therapeutic value. However, the established inhibitors are limited in their application due to their substantial cytotoxicity. Additionally, the standard drugs for DNMT inhibition are non-selective cytosine analogues with considerable cytotoxic side-effects. In the present study, we have designed a workflow by integrating various ligand-based and structure-based approaches to discover new agents active against DNMT1. We have derived a pharmacophore model with the help of available DNMT1 inhibitors. Utilising this model, we performed the virtual screening of Maybridge chemical library and the identified hits were then subsequently filtered based on the Naïve Bayesian classification model. The molecules that have returned from this classification model were subjected to ensemble based docking. We have selected 10 molecules for the biological assay by inspecting the interactions portrayed by these molecules. Three out of the ten tested compounds have shown DNMT1 inhibitory activity. These compounds were also found to demonstrate potential inhibition of cellular proliferation in human breast cancer MDA-MB-231 cells. In the present study, we have utilized a multi-step virtual screening protocol to identify inhibitors of DNMT1, which offers a starting point to develop more potent DNMT1 inhibitors as anti-cancer agents.

Application of the ATTRACT Coarse-Grained Docking and Atomistic Refinement for Predicting Peptide-Protein Interactions

Peptide-protein interactions are abundant in the cell and form an important part of the interactome. Large-scale modeling of peptide-protein complexes requires a fully blind approach; i.e., simultaneously predicting the peptide-binding site and the peptide conformation to high accuracy. Here, we present one of the first fully blind peptide-protein docking protocols, pepATTRACT. It combines a coarse-grained ensemble docking search of the entire protein surface with two stages of atomistic flexible refinement. pepATTRACT yields high-quality predictions for 70 % of the cases when tested on a large benchmark of peptide-protein complexes. This performance in fully blind mode is similar to state-of-the-art local docking approaches that use information on the location of the binding site. Limiting the search to the peptide-binding region, the resulting pepATTRACT-local approach further improves the performance. Docking scripts for pepATTRACT and pepATTRACT-local can be generated via a web interface at www.attract.ph.tum.de/peptide.html . Here, we explain how to set up a docking run with the pepATTRACT web interface and demonstrate its usage by an application on binding of disordered regions from tumor suppressor p53 to a partner protein.

Information-Driven, Ensemble Flexible Peptide Docking Using HADDOCK

Modeling protein-peptide interactions remains a significant challenge for docking programs due to the inherent highly flexible nature of peptides, which often adopt different conformations whether in their free or bound forms. We present here a protocol consisting of a hybrid approach, combining the most frequently found peptide conformations in complexes with representative conformations taken from molecular dynamics simulations of the free peptide. This approach intends to broaden the range of conformations sampled during docking. The resulting ensemble of conformations is used as a starting point for information-driven flexible docking with HADDOCK. We demonstrate the performance of this protocol on six cases of increasing difficulty, taken from a protein-peptide benchmark set. In each case, we use knowledge of the binding site on the receptor to drive the docking process. In the majority of cases where MD conformations are added to the starting ensemble for docking, we observe an improvement in the quality of the resulting models.

Ensemble docking-based virtual screening yields novel spirocyclic JAK1 inhibitors

Small molecule inhibition of Janus kinases (JAKs) has been demonstrated as a viable strategy for the treatment of various inflammatory conditions and continues to emerge in cancer-related indications. In this study, a large supplier database was screened to identify novel chemistry starting points for JAK1. The docking-based screening was followed up by testing ten hit compounds experimentally, out of which five have displayed single-digit micromolar and submicromolar IC₅₀ values on JAK1. Thus, the study was concluded with the discovery of five novel JAK inhibitors from a tiny screening deck with a remarkable hitrate of 50%. The results have highlighted spirocyclic pyrrolopyrimidines with submicromolar JAK1 IC₅₀ values and a preference for JAK1 over JAK2 as potential starting points in developing a novel class of JAK1 inhibitors.

Weak self-association of cytochrome c peroxidase molecules observed by paramagnetic NMR

There is growing experimental evidence that many proteins exhibit a tendency for (ultra)weak homo- or hetero- oligomerization interactions. With the development of paramagnetic relaxation enhancement NMR spectroscopy it has become possible to characterize weak complexes experimentally and even detect complexes with affinities in the 1-25 mM range. We present evidence for a weak complex between cytochrome c peroxidase (CcP) molecules. In a previous study, we attached nitroxide based spin labels at three positions on CcP with the intent of observing intramolecular PRE effects. However, several intermolecular PRE effects were also observed suggesting a weak self-association between CcP molecules. The CcP-CcP complex was characterized using paramagnetic NMR and protein docking. The interaction occurs between the surface that is also part of the stereo-specific binding site for its physiological partner, cytochrome c (Cc), and several small, positively charged patches on the "back" of CcP. The CcP-CcP complex is not a stereo-specific complex. It is a dynamic ensemble of orientations, characteristic of an encounter state. The contact areas resemble those observed for CcP molecules in crystals. The CcP-CcP complex formation competes with that of the CcP-Cc complex. However, the affinity for Cc is much larger and thus it is expected that, under physiological conditions, auto-inhibition will be limited. A weak self-association between cytochrome c peroxidase molecules was characterized using paramagnetic NMR.

Ensembling and filtering: an effective and rapid in silico multitarget drug-design strategy to identify RIPK1 and RIPK3 inhibitors

Necroptosis, a programmed necrosis pathway, is witnessed in diverse human diseases and is primarily regulated by receptor-interacting serine/threonine protein kinase 1 (RIPK1) and RIPK3. Ablation or inhibition of these individual proteins, or both, has been shown to be protective in various in vitro and in vivo disease models involving necroptosis. In this study, we propose an effective and rapid virtual screening strategy to identify multitarget inhibitors of both RIPK1 and RIPK3. It involves ensemble pharmacophore-based screening (EPS) of a compound database, post-EPS filtration (PEPSF) of the ligand hits, and multiple dockings. Structurally diverse inhibitors were identified through ensemble pharmacophore features, and the speed of this process was enhanced by filtering out the compounds containing cross-features. The stability of these inhibitors with both of the proteins was verified by means of molecular dynamics (MD) simulation. Graphical Abstract A generalized workflow employed in this study. Subsequent utilization of EPS and PEPSF might lead to reduced computational time and load.

Pharmacophore Modeling, Ensemble Docking, Virtual Screening, and Biological Evaluation on Glycogen Synthase Kinase-3β

Glycogen synthase kinase-3 (GSK-3) is a multifunctional serine/threonine protein kinase which is engaged in a variety of signaling pathways, regulating a wide range of cellular processes. GSK-3β, also known as tau protein kinase I (TPK-I), is one of the most important kinases implicated in the hyperphosphorylation of tau that leads to neurodegenerative diseases. Hence, GSK-3β has emerged as an important therapeutic target. To identify compounds that are structurally novel and diverse compared to previously reported ATP-competitive GSK-3β inhibitors, we performed virtual screening by implementing a mixed ligand/structure-based approach, which included pharmacophore modeling, diversity analysis, and ensemble docking. The sensitivities of different docking protocols to induced-fit effects were explored. An enrichment study was employed to verify the robustness of ensemble docking, using 13 X-ray structures of GSK-3β, compared to individual docking in terms of retrieving active compounds from a decoy dataset. A total of 24 structurally diverse compounds obtained from the virtual screening underwent biological validation. The bioassay results showed that 15 out of the 24 hit compounds are indeed GSK-3β inhibitors, and among them, one compound exhibiting sub-micromolar inhibitory activity is a reasonable starting point for further optimization.

Identification of Novel Histamine H4 Ligands by Virtual Screening on Molecular Dynamics Ensembles

We report the identification of novel histamine H4 receptor ligands by ensemble docking on homology model conformers derived from molecular dynamics simulations. Selected receptor models from the trajectories demonstrated superior virtual screening performance compared to the initial models. The ensemble of the best models was able to retrieve a diverse set of known H4 ligands. Prospective virtual screening against these models and subsequent in vitro experimental validation identified novel H4 ligands. Compound 3 showing highest affinity and ligand efficiency represents an interesting scaffold for further medicinal chemistry exploration.

Virtual fragment screening on GPCRs: a case study on dopamine D3 and histamine H4 receptors

Prospective structure based virtual fragment screening methodologies on two GPCR targets namely the dopamine D3 and the histamine H4 receptors with a library of 12,905 fragments were evaluated. Fragments were docked to the X-ray structure and the homology model of the D3 and H4 receptors, respectively. Representative receptor conformations for ensemble docking were obtained from molecular dynamics trajectories. In vitro confirmed hit rates ranged from 16% to 32%. Hits had high ligand efficiency (LE) values in the range of 0.31-0.74 and also acceptable lipophilic efficiency. The X-ray structure, the homology model and structural ensembles were all found suitable for docking based virtual screening of fragments against these GPCRs. However, there was little overlap among different hit sets and methodologies were thus complementary to each other.

Ensemble docking and molecular dynamics identify knoevenagel curcumin derivatives with potent anti-EGFR activity

Epidermal growth factor receptor tyrosine kinase (EGFR-TK) is an attractive target for cancer therapy. Despite a number of effective EGFR inhibitors that are constantly expanding and different methods being employed to obtain novel compounds, the search for newer EGFR inhibitors is still a major scientific challenge. In the present study, a molecular docking and molecular dynamics investigation has been carried out with an ensemble of EGFR-TK structures against a synthetically feasible library of curcumin analogs to discover potent EGFR inhibitors. To resolve protein flexibility issue we have utilized 5 EGFR wild type crystal structures during docking as this gives improved possibility of identifying an active compound as compared to using a single crystal structure. We then identified five curcumin analogs representing different scaffolds that can serve as lead molecules. Finally, the 5 ns molecular dynamics simulation shows that knoevenagel condensate of curcumin specifically C29 and C30 can be used as starting blocks for developing effective leads capable of inhibiting EGFR.