Bioactive Conformation Research Articles

Dynamic Docking of Macrocycles in Bound and Unbound Protein Structures with DynaDock.

Macrocycles are interesting molecules with unique features due to their conformationally constrained yet flexible ring structure. This characteristic poses a difficult challenge for computational modeling studies since they rely on accurate structural descriptions. In particular, molecular docking calculations suffer from the lack of ring flexibility during pose generation, which is often compensated by using pregenerated ligand conformer ensembles. Moreover, receptor structures are mainly treated rigidly, which limits the use of many docking tools. In this study, we optimized our previous molecular dynamics-based sampling and docking pipeline specifically designed for the accurate prediction of macrocyclic compounds. We developed a dihedral classification procedure for in-depth conformational analysis of the macrocyclic rings and extracted structural ensembles that were subsequently docked in both bound and unbound protein structures employing a fully flexible approach. Our results suggest that including a ring conformer close to the bound state in the starting ensemble increases the chance of successful docking. The bioactive conformations of a diverse set of ligands could be predicted with high and decent accuracy in bound and unbound protein structures, respectively, due to the incorporation of full molecular flexibility in our approach. The remaining unsuccessful docking calculations were mainly caused by large flexible substituents that bind to surface-exposed binding sites, rather than the macrocyclic ring per se and could be further improved by explicit molecular dynamics simulations of the docked complex.

Controlling oncogenic KRAS signaling pathways with a Palladium-responsive peptide

RAS oncoproteins are molecular switches associated with critical signaling pathways that regulate cell proliferation and differentiation. Mutations in the RAS family, mainly in the KRAS isoform, are responsible for some of the deadliest cancers, which has made this protein a major target in biomedical research. Here we demonstrate that a designed bis-histidine peptide derived from the αH helix of the cofactor SOS1 binds to KRAS with high affinity upon coordination to Pd(II). NMR spectroscopy and MD studies demonstrate that Pd(II) has a nucleating effect that facilitates the access to the bioactive α-helical conformation. The binding can be suppressed by an external metal chelator and recovered again by the addition of more Pd(II), making this system the first switchable KRAS binder, and demonstrates that folding-upon-binding mechanisms can operate in metal-nucleated peptides. In vitro experiments show that the metallopeptide can efficiently internalize into living cells and inhibit the MAPK kinase cascade.

Discovery of new chemotypes of dual 5-HT2A/D2 receptor antagonists with a strategy of drug design methodologies.

Aim: Through the application of structure- and ligand-based methods, the authors aimed to create an integrative approach to developing a computational protocol for the rational drug design of potent dual 5-HT2A/D2 receptor antagonists without off-target activities on H1 receptors. Materials & methods: Molecular dynamics and virtual docking methods were used to identify key interactions of the structurally diverse antagonists in the binding sites of the studied targets, and to generate their bioactive conformations for further 3D-quantitative structure-activity relationship modeling. Results & conclusion: Toward the goal of finding multi-potent drugs with a more effective and safer profile, the obtained results led to the design of a new set of dual antagonists and opened a new perspective on the therapy for complex brain diseases.

Dual therapeutic approach to modulate Glycogen Synthase kinase −3 beta (GSK-3Β) and inhibitor of nuclear factor kappa kinase-beta (IKK-β) receptors by in silico designing of inhibitors

Cancer malignancies require the application of advanced strategies leading to the development of novel theranostic. Quite often drugs target a variety of receptors in the cell signaling cascades that could be explored to combat aggressive tumors. Herein, two receptors that are over-expressed during the diagnosis of breast cancer are used as the primary drug targets, inclusively Glycogen Synthase kinase −3 beta (GSK-3Β) and Inhibitor of nuclear factor kappa kinase-beta (IKK-β). Dual-targeting inhibitors pave the way for a challenging pathway in the treatment of aberrant tumor progression. The present study involves the observation of similarities in the structure of the receptors, along with the designing of novel therapeutics that act on them by molecular docking followed by a pharmacokinetic screening approach. A 3D QSAR modeling study is performed to approach the functionality of the bioactive conformer molecules. Additionally, Molecular Dynamic Simulation parameters are used for the validation of the drug complexes. Already available inhibitors are used as reference compounds and a library of analogs generated for these compounds from the PubChem database has been used for in silico designing of novel inhibitors. Molecular Docking and ADME analysis narrowed down the vast library of compounds to two specific classes of chemical compounds. Molecular Dynamic simulation studies used for the selection of the novel moieties showed significant superiority in their stability studies and binding trajectories resulted in two novel molecules A6 and B3 that could inhibit the kinase receptors. The current work involves computational designing of therapeutics targeting two major oncogenic proteins.

A Combination of Pharmacophore-Based Virtual Screening, Structure-Based Lead Optimization, and DFT Study for the Identification of S. epidermidis TcaR Inhibitors.

The transcriptional regulator (TcaR) enzyme plays an important role in biofilm formation. Prevention of TcaR-DNA complex formation leads to inhibit the biofilm formation is likely to reveal therapeutic ways for the treatment of bacterial infections. To identify the novel ligands for TcaR and to provide a new idea for drug design, two efficient drug design methods, such as pharmacophore modeling and structure-based drug design, were used for virtual screening of database and lead optimization, respectively. Gemifloxacin (FDA-approved drug) was considered to generate the pharmacophore model for virtual screening of the ZINC database, and five hits, namely ZINC77906236, ZINC09550296, ZINC77906466, ZINC09751390, and ZINC01269201, were identified as novel inhibitors of TcaR with better binding energies. Using structure-based drug design, a set of 7a–7p inhibitors of S. epidermidis were considered, and Mol34 was identified with good binding energy and high fitness score with improved pharmacological properties. The active site residues ARG110, ASN20, HIS42, ASN45, ALA38, VAL63, VAL68, ALA24, VAL43, ILE57, and ARG71 are playing a promising role in inhibition process. In addition, we performed DFT simulations of final hits to understand the electronic properties and their significant role in driving the inhibitor to adopt apposite bioactive conformations in the active site. Conclusively, the newly identified and designed hits from both the methods are promising inhibitors of TcaR, which can hinder biofilm formation.

Discovery of 1′-(1-phenylcyclopropane-carbonyl)-3H-spiro[isobenzofuran-1,3′-pyrrolidin]-3-one as a novel steroid mimetic scaffold for the potent and tissue-specific inhibition of 11β-HSD1 using a scaffold-hopping approach

11β-hydroxysteroid dehydrogenase 1 (11β-HSD1) has been identified as the primary enzyme responsible for the activation of hepatic cortisone to cortisol in specific peripheral tissues resulting in the concomitant antagonism of insulin action within these tissues. Dysregulation of 11β-HSD1, particularly in adipose tissues, has been associated with metabolic syndrome and type 2 diabetes mellitus. Therefore, inhibition of 11β-HSD1 with a small nonsteroidal molecule is therapeutically desirable. Implementation of a scaffold-hopping approach revealed a three-point pharmacophore for 11β-HSD1 that was utilized to design a steroid mimetic scaffold. Reiterative optimization provided valuable insight into the bioactive conformation of our novel scaffold and led to the discovery of INCB13739. Clinical evaluation of INCB13739 confirmed for the first time that tissue-specific inhibition of 11β-HSD1 in patients with type 2 diabetes mellitus was efficacious in controlling glucose levels and reducing cardiovascular risk factors.

The Shapes of Sulfonamides: A Rotational Spectroscopy Study.

Benzenesulfonamides are a class of molecules of extreme interest in the biochemical field because many of them are active against a variety of diseases. In this work, the pharmacophoric group benzensulfonamide, its derivatives para-toluensulfonamide and ortho-toluensulfonamide, and the bioactive molecule sulfanilamide, were investigated using rotational spectroscopy to determine their conformations and the influence of different substituents on their structures. For all species, the hyperfine structure due to the 14N atom was analyzed, and this provided crucial information for the unambiguous identification of the observed conformation of all molecules. In addition, for ortho-toluensulfonamide, the vibration–rotation hyperfine structure related to the methyl torsion was analyzed, and the methyl group rotation barrier was determined. For benzensulfonamide, partial rS and r0 structures were established from the experimental rotational constants of the parent and two deuterated isotopic species. In all compounds except ortho-toluensulfonamide, the amino group of the sulfonamide group lies perpendicular to the benzene plane with the aminic hydrogens eclipsing the oxygen atoms. In ortho-toluensulfonamide, where weak attractive interactions occur between the nitrogen lone pair and the methyl hydrogen atoms, the amino group lies in a gauche orientation, retaining the eclipsed configuration with respect to the SO2 frame. A comparison of the geometrical arrangements found in the PDB database allowed us to understand that the bioactive conformations are different from those found in isolated conditions. The conformations within the receptor are reached with an energy cost, which is balanced by the interactions established in the receptor.

Attenuating the Streptococcus pneumoniae Competence Regulon Using Urea-Bridged Cyclic Dominant-Negative Competence-Stimulating Peptide Analogs.

Streptococcus pneumoniae (pneumococcus) is a prevalent human pathogen that utilizes the competence regulon quorum sensing circuitry to acquire antibiotic resistance and initiate its attack on the human host. Therefore, targeting the competence regulon can be applied as an anti-infective approach with minimal pressure for resistance development. Herein, we report the construction of a library of urea-bridged cyclic dominant-negative competence-stimulating peptide (dnCSP) derivatives and their evaluation as competitive inhibitors of the competence regulon. Our results reveal the first pneumococcus dual-action CSPs that inhibit the group 1 pneumococcus competence regulon while activating the group 2 pneumococcus competence regulon. Structural analysis indicates that the urea-bridge cyclization stabilizes the bioactive α-helix conformation, while in vivo studies using a mouse model of infection exhibit that the lead dual-action dnCSP, CSP1-E1A-cyc(Dab6Dab10), attenuates group 1-mediated mortality without significantly reducing the bacterial burden. Overall, our results pave the way for developing novel therapeutics against this notorious pathogen.

Water Mapping and Scoring Approaches to Predict the Role of Hydration Sites in the Binding Affinity of PAK1 Inhibitors

Aim: This study aims to develop and establish a computational model that can identify potent molecules for p21-activating kinase 1 (PAK1). Background: PAK1 is a well-established drug target that has been explored for various therapeutic interventions. Control of this protein requires an indispensable inhibitor to curb the structural changes and subsequent activation of signalling effectors responsible for the progression of diseases, such as cancer, inflammatory, viral, and neurological disorders. Objective: To establish a computational model that could identify active molecules which will further provide a platform for developing potential PAK1 inhibitors. Method: A congeneric series of 27 compounds was considered for this study with Ki (nm) covering a minimum of 3 log range. The compounds were developed based on a previously reported Group-I PAK inhibitor, namely G-5555. The 27 compounds were subjected to the SP and XP mode of docking, to understand the binding mode, its conformation and interaction patterns. To understand the relevance of biological activity from computational approaches, the compounds were scored against generated water maps to obtain WM/MM ΔG binding energy. Moreover, molecular dynamics analysis was performed for the highly active compound, to understand the conformational variability and complex’s stability. We then evaluate the predictable binding pose obtained from the docking studies. Result: From the SP and XP modes of docking, the common interaction pattern with the amino acid residues Arg299 (cation-π), Glu345 (Aromatic hydrogen bond), hinge region Leu347, salt bridges Asp393 and Asp407 was observed, among the congeneric compounds. The interaction pattern was compared with the co-crystal inhibitor FRAX597 of the PAK1 crystal structure (PDB id: 4EQC). The correlation with different docking parameters in the SP and XP modes was insignificant and thereby revealed that the SP and XP’s scoring functions could not predict the active compounds. This was due to the limitations in the docking methodology that neglected the receptor flexibility and desolvation parameters. Hence, to recognise the desolvation and explicit solvent effects, as well as to study the Structure-Activity Relationships (SARs) extensively, WaterMap (WM) calculations were performed on the congeneric compounds. Based on displaceable unfavourable hydration sites (HS) and their associated thermodynamic properties, the WM calculations facilitated to understand the significance of correlation in the folds of activity of highly (19 and 17), moderate (16 and 21) and less active (26 and 25) compounds. Furthermore, the scoring function from WaterMap, namely WM/MM, led to a significant R2 value of 0.72, due to a coupled conjunction with MM treatment and displaced unfavourable waters at the binding site. To check the “optimal binding conformation”, molecular dynamics simulation was carried out with the highly active compound 19 to explain the binding mode, stability, interactions, solvent accessible area, etc., which could support the predicted conformation with bioactive conformation. Conclusion: This study determined the best scoring function, established SARs and predicted active molecules through a computational model. This will contribute towards development of the most potent PAK1 inhibitors.

On the Value of Using 3D Shape and Electrostatic Similarities in Deep Generative Methods.

Multiparameter optimization, the heart of drug design, is still an open challenge. Thus, improved methods for automated compound design with multiple controlled properties are desired. Here, we present a significant extension to our previously described fragment-based reinforcement learning method (DeepFMPO) for the generation of novel molecules with optimal properties. As before, the generative process outputs optimized molecules similar to the input structures, now with the improved feature of replacing parts of these molecules with fragments of similar three-dimensional (3D) shape and electrostatics. We developed and benchmarked a new python package, ESP-Sim, for the comparison of the electrostatic potential and the molecular shape, allowing the calculation of high-quality partial charges (e.g., RESP with B3LYP/6-31G**) obtained using the quantum chemistry program Psi4. By performing comparisons of 3D fragments, we can simulate 3D properties while overcoming the notoriously difficult step of accurately describing bioactive conformations. The new improved generative (DeepFMPO v3D) method is demonstrated with a scaffold-hopping exercise identifying CDK2 bioisosteres. The code is open-source and freely available.

Intramolecular Interactions Enhance the Potency of Gallinamide A Analogues against Trypanosoma cruzi.

Gallinamide A, a metabolite of the marine cyanobacterium Schizothrix sp., selectively inhibits cathepsin L-like cysteine proteases. We evaluated the potency of gallinamide A and 23 synthetic analogues against intracellular Trypanosoma cruzi amastigotes and the cysteine protease, cruzain. We determined the co-crystal structures of cruzain with gallinamide A and two synthetic analogues at ∼2 Å. SAR data revealed that the N-terminal end of gallinamide A is loosely bound and weakly contributes in drug-target interactions. At the C-terminus, the intramolecular π-π stacking interactions between the aromatic substituents at P1' and P1 restrict the bioactive conformation of the inhibitors, thus minimizing the entropic loss associated with target binding. Molecular dynamics simulations showed that in the absence of an aromatic group at P1, the substituent at P1' interacts with tryptophan-184. The P1-P1' interactions had no effect on anti-cruzain activity, whereas anti-T. cruzi potency increased by ∼fivefold, likely due to an increase in solubility/permeability of the analogues.

Antioxidant and cytotoxic activities of selected salicylidene imines: experimental and computational study.

Selected salicylidene imines were evaluated for their antioxidant and cytotoxic potentials. Several of them exerted potent scavenging capacity towards ABTS radical and hydrogen peroxide. The insight into the preferable antioxidative mechanism was reached employing density functional theory. In the absence of free radicals, the SPLET mechanism is dominant in polar surroundings, while HAT is prevailing in a non-polar environment. The results obtained for the reactions of the most active compounds with some medically relevant radicals pointed out competition between HAT and SPLET mechanisms. The assessment of their cytotoxic properties revealed inhibition of ER-a human breast adenocarcinoma cells or estrogen-independent prostate cancer cells. Molecular docking study with the cyclooxygenase (COX) 2 enzyme was performed to examine the most probable bioactive conformations and possible interactions between the tested derivatives and COX-2 binding pocket.

Exploring the enzyme-catalyzed synthesis of isotope labeled cyclopropanes.

Cyclopropanes are commonly employed structural moieties in drug design since their incorporation is often associated with increased target affinity, improved metabolic stability, and increased rigidity to access bioactive conformations. Robust chemical cyclopropanation procedures have been developed which proceed with high yield and broad substrate scope, and have been applied to labeled substrates. Recently, engineered enzymes have been shown to perform cyclopropanations with remarkable diastereoselectivity and enantioselectivity, but this biocatalytic approach has not been applied to labeled substrates to date. In this study, the use of enzyme catalysis for the synthesis of labeled cyclopropanes was investigated. Two readily available enzymes, a modified CYP450 enzyme and a modified Aeropyrum pernix protoglobin, were investigated for the cyclopropanation of a variety of substituted styrenes. For this biocatalytic transformation, the enzymes required the use of ethyl diazoacetate. Due to the highly energetic nature of this molecule, alternatives were investigated. The final optimized cyclopropanation was successfully demonstrated using n‐hexyl diazoacetate, resulting in moderate to high enantiomeric excess. The optimized procedure was used to generate labeled cyclopropanes from 13C‐glycine, forming all four labeled stereoisomers of phosphodiesterase type‐IV inhibitor, MK0952. These reactions provide a convenient and effective biocatalytic route to stereoselective 13C‐labeled cyclopropanes and serve as a proof‐of‐concept for generating stereoselective labeled cyclopropanes.

Trajectory data for the publication "Investigating the bioactive conformation of Angiotensin II using Markov State Modeling revisited with web-scale clustering"

Trajectory data for the publication "Investigating the bioactive conformation of Angiotensin II using Markov State Modeling revisited with web-scale clustering"

Synthesis of 8-aminomorphans with high KOR affinity

2-Azabicyclo[3.3.1]nonanes (morphans) with a (3,4-dichlorophenyl)acetyl group at 2-position and a pyrrolidino moiety at 8-position were designed as conformationally restricted analogs of piperidine-based KOR agonists. The synthesis started with 4-oxopiperidine-2-carboxylic acid comprising 13 reaction steps. At first the ketone 10 was transformed into diester 7 bearing a propionate side chain. Dieckmann condensation of diester 7 to afford bicyclic enolester 14 and subsequent Krapcho deethoxycarbonylation represent the key steps of the synthesis. The enantiomeric pyrrolidines (1S,5R,8R)-5a and (1R,5S,8S)-5a were separated by chiral HPLC. The eutomer (1S,5R,8R)-5a showed high KOR affinity (Ki = 18 nM) and selectivity over MOR, DOR and σ2 receptors. It was concluded that the dihedral angle of the KOR pharmacophore N(pyrrolididine)-C-C-N(acyl) of (1S,5R,8R)-5a (68°) is close to the bioactive conformation of the flexible KOR agonist 3.

An open-source molecular builder and free energy preparation workflow

Automated free energy calculations for the prediction of binding free energies of congeneric series of ligands to a protein target are growing in popularity, but building reliable initial binding poses for the ligands is challenging. Here, we introduce the open-source FEgrow workflow for building user-defined congeneric series of ligands in protein binding pockets for input to free energy calculations. For a given ligand core and receptor structure, FEgrow enumerates and optimises the bioactive conformations of the grown functional group(s), making use of hybrid machine learning/molecular mechanics potential energy functions where possible. Low energy structures are optionally scored using the gnina convolutional neural network scoring function, and output for more rigorous protein–ligand binding free energy predictions. We illustrate use of the workflow by building and scoring binding poses for ten congeneric series of ligands bound to targets from a standard, high quality dataset of protein–ligand complexes. Furthermore, we build a set of 13 inhibitors of the SARS-CoV-2 main protease from the literature, and use free energy calculations to retrospectively compute their relative binding free energies. FEgrow is freely available at https://github.com/cole-group/FEgrow, along with a tutorial.

QSAR Modeling Based on Conformation Ensembles Using a Multi-Instance Learning Approach

Modern QSAR approaches have wide practical applications in drug discovery for designing potentially bioactive molecules. If such models are based on the use of 2D descriptors, important information contained in the spatial structures of molecules is lost. The major problem in constructing models using 3D descriptors is the choice of a putative bioactive conformation, which affects the predictive performance. The multi-instance (MI) learning approach considering multiple conformations in model training could be a reasonable solution to the above problem. In this study, we implemented several multi-instance algorithms, both conventional and based on deep learning, and investigated their performance. We compared the performance of MI-QSAR models with those based on the classical single-instance QSAR (SI-QSAR) approach in which each molecule is encoded by either 2D descriptors computed for the corresponding molecular graph or 3D descriptors issued for a single lowest energy conformation. The calculations were carried out on 175 data sets extracted from the ChEMBL23 database. It is demonstrated that (i) MI-QSAR outperforms SI-QSAR in numerous cases and (ii) MI algorithms can automatically identify plausible bioactive conformations.

Computational studies of molecular pre-organization through macrocyclization: Conformational distribution analysis of closely related non-macrocyclic and macrocyclic analogs

Macrocycles form an important compound class in medicinal chemistry due to their interesting structural and biological properties. To help design macrocycles, it is important to understand how the conformational preferences are affected upon macrocyclization of a lead compound. To address this, we collected a unique data set of protein–ligand complexes containing “non-macrocyclic” (“linear”) ligands matched with macrocyclic analogs binding to the same protein in a similar pose. Out of the 39 co-crystallized ligands considered, 10 were linear and 29 were macrocyclic. To enable a more general analysis, 128 additional ligands from the publications associated with these protein data bank entries were added to the data set. Using in total 167 collected ligands, we investigated if the conformers in the macrocyclic conformational ensembles were more similar to the bioactive conformation in comparison to the conformers of their linear counterparts. Unexpectedly, in most cases the macrocycle conformational ensemble distributions were not very different from those of the linear compounds. Thus, care should be taken when designing macrocycles with the aim to focus their conformational preference towards the bioactive conformation. We also set out to investigate potential conformational flexibility differences between the two compound classes, computational energy window settings and evaluate a literature metric for approximating the conformational focusing on the bioactive conformation.

Conformational Characterization of the Co-Activator Binding Site Revealed the Mechanism to Achieve the Bioactive State of FXR.

FXR bioactive states are responsible for the regulation of metabolic pathways, which are modulated by agonists and co-activators. The synergy between agonist binding and ‘co-activator’ recruitment is highly conformationally driven. The characterization of conformational dynamics is essential for mechanistic and therapeutic understanding. To shed light on the conformational ensembles, dynamics, and structural determinants that govern the activation process of FXR, molecular dynamic (MD) simulation is employed. Atomic insights into the ligand binding domain (LBD) of FXR revealed significant differences in inter/intra molecular bonding patterns, leading to structural anomalies in different systems of FXR. The sole presence of an agonist or ‘co-activator’ fails to achieve the essential bioactive conformation of FXR. However, the presence of both establishes the bioactive conformation of FXR as they modulate the internal wiring of key residues that coordinate allosteric structural transitions and their activity. We provide a precise description of critical residue positioning during conformational changes that elucidate the synergy between its binding partners to achieve an FXR activation state. Our study offers insights into the associated modulation occurring in FXR at bound and unbound forms. Thereafter, we also identified hot-spots that are critical to arrest the activation mechanism of FXR that would be helpful for the rational design of its agonists.

Pyrotinib in HER2 heterogeneously mutated or amplified advanced non-small cell lung cancer patients: a retrospective real-world study (PEARL)

Human epidermal growth factor receptor 2 (HER2) amplification or activating mutations are found in 1.6%–4% of non-small cell lung cancer (NSCLC). Pyrotinib has been reported to have better potency in NSCLC patients with HER2 exon 20 insertion (ex20ins) mutations; however, more clinical evidence is urgently needed to guide pyrotinib-based therapy in NSCLC with HER2 amplification or heterogeneous mutations. We retrospectively analyzed advanced NSCLC patients with HER2 amplification or mutations who were treated with pyrotinib-based therapy between September 25, 2018 and October 30, 2020 in our hospital. Molecular dynamics simulation was used to explore the bioactive conformation and binding mechanisms of pan-ErbB tyrosine kinase inhibitors (TKIs) including pyrotinib for different HER2 ex20ins variants. In this study, 79 eligible patients were included with 70 ex20ins variants, 6 missense mutations and 3 primary HER2 amplifications identified. A775_G776insYVMA insertion was the most common observed subtype. The median progression-free survival (mPFS) was 5.8 (95% CI: 4.1–7.4) months. Use of pyrotinib-based therapy in first-/second-line settings showed a significantly better prognosis than that observed in third-line settings or above (mPFS: 9.1 vs. 4.4 months; P = 0.0003). Compared with HER2 amplification and exon 20 non-YVMA insertion variants, patients with HER2 missense mutations had a visible mPFS benefit (12.2 vs. 6.8 vs. 5.2 months). Computational docking simulations revealed that pyrotinib failed to interact with the specific insertion variant P780_Y781insGSP. These results indicated that pyrotinib-based therapy exhibited good anti-tumor activity and acceptable safety profile in HER2-altered advanced NSCLC.